Paper Digest: CIKM 2022 Highlights
Readers can search these papers by keywords using search console. To browse papers by author & read each author’s tech background review writen by our machine, here is a list of top CIKM-2022 authors. You may also like to explore our “Best Paper” Digest (CIKM), which lists the most influential CIKM papers since 1993. The ACM Conference on Information and Knowledge Management (CIKM) is an annual computer science research conference dedicated to information management and knowledge management. In 2022, it is to be held in Atlanta.
To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.
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TABLE 1: Paper Digest: CIKM 2022 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | AutoForecast: Automatic Time-Series Forecasting Model Selection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one. |
Mustafa Abdallah; Ryan Rossi; Kanak Mahadik; Sungchul Kim; Handong Zhao; Saurabh Bagchi; |
| 2 | On Smoothed Explanations: Quality and Robustness Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we initiate a very thorough evaluation of the quality and robustness of the explanations offered by smoothing approaches. |
Ahmad Ajalloeian; Seyed-Mohsen Moosavi-Dezfooli; Michalis Vlachos; Pascal Frossard; |
| 3 | Generative Adversarial Zero-Shot Learning for Cold-Start News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we utilize the generative adversarial zero-shot learning in building a framework, namely, GAZRec, which is able to address the CSP caused by purely new users or new news. |
Manal A. Alshehri; Xiangliang Zhang; |
| 4 | UnCommonSense: Informative Negative Knowledge About Everyday Concepts Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents the UNCOMMONSENSE framework for materializing informative negative commonsense statements. |
Hiba Arnaout; Simon Razniewski; Gerhard Weikum; Jeff Z. Pan; |
| 5 | KRAF: A Flexible Advertising Framework Using Knowledge Graph-Enriched Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present a Knowledge Graph-enriched Multi-Agent Reinforcement Learning Advertising Framework (KRAF). |
Jose A. Ayala-Romero; Péter Mernyei; Bichen Shi; Diego Mazón; |
| 6 | An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The reason is that the support set identification in these methods is implicit and thus cannot explicitly identify the low-complexity structure in practice, namely, they cannot discard useless coefficients of the associated features to achieve algorithmic acceleration via dimension reduction. To address this challenge, we propose a novel accelerated doubly stochastic gradient descent (ADSGD) method for sparsity regularized loss minimization problems, which can reduce the number of block iterations by eliminating inactive coefficients during the optimization process and eventually achieve faster explicit model identification and improve the algorithm efficiency. |
Runxue Bao; Bin Gu; Heng Huang; |
| 7 | CASA-Net: A Context-Aware Correlation Convolutional Network for Scale-Adaptive Crack Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Surface cracks in infrastructure are a key indicator of structural safety and degradation. Visual-based crack detection is a critical task for the enormous application demands of … |
Xin Bi; Shining Zhang; Yu Zhang; Lei Hu; Wei Zhang; Wenjing Niu; Ye Yuan; Guoren Wang; |
| 8 | Collaborative Image Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Such signals are commonly used for item recommendation, typically by deriving latent user and item representations from the data. In this work, we show that such collaborative information can be leveraged to improve the classification process of new images. |
Koby Bibas; Oren Sar Shalom; Dietmar Jannach; |
| 9 | Samba: Identifying Inappropriate Videos for Young Children on YouTube Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a fusion model, called Samba, which uses both metadata and video subtitles for content classification. |
Le Binh; Rajat Tandon; Chingis Oinar; Jeffrey Liu; Uma Durairaj; Jiani Guo; Spencer Zahabizadeh; Sanjana Ilango; Jeremy Tang; Fred Morstatter; Simon Woo; Jelena Mirkovic; |
| 10 | DocSemMap 2.0: Semantic Labeling Based on Textual Data Documentations Using Seq2Seq Context Learner Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we extend the current state of the art approach by uncovering existing shortcomings and presenting our own improvements. |
Andreas Burgdorf; Alexander Paulus; André Pomp; Tobias Meisen; |
| 11 | Memory Graph with Message Rehearsal for Multi-Turn Dialogue Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, with the accumulation of dialogue information, the deep semantic information is difficult to understand so that it needs a mechanism with the ability of reasoning and digesting information repeatedly, which is ignored by previous methods. In order to solve the above problems, we propose a Memory Graph with Message Rehearsal (MGMR) for dialogue generation based on the cognitive process of human memory. |
Xiaoyu Cai; Yao Fu; Hong Zhao; Weihao Jiang; Shiliang Pu; |
| 12 | Hard Negatives or False Negatives: Correcting Pooling Bias in Training Neural Ranking Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Therefore, in principle, we can formulate the false negative issue in training NRMs as learning from labeled datasets with pooling bias. To solve this problem, we propose a novel Coupled Estimation Technique (CET) that learns both a relevance model and a selection model simultaneously to correct the pooling bias for training NRMs. |
Yinqiong Cai; Jiafeng Guo; Yixing Fan; Qingyao Ai; Ruqing Zhang; Xueqi Cheng; |
| 13 | Imitation Learning to Outperform Demonstrators By Directly Extrapolating Demonstrations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Such methods can fail to control the distribution shift between demonstrations and the learned policy since the learned reward function may not generalize well on out-of-distribution samples and can mislead the agent to highly uncertain states, resulting in degenerated performance. To address this limitation, we propose a novel algorithm called Outperforming demonstrators by Directly Extrapolating Demonstrations(ODED). |
Yuanying Cai; Chuheng Zhang; Wei Shen; Xiaonan He; Xuyun Zhang; Longbo Huang; |
| 14 | Contrastive Cross-Domain Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships. In this paper, we propose C2DSR to tackle the above problems to capture precise user preferences. |
Jiangxia Cao; Xin Cong; Jiawei Sheng; Tingwen Liu; Bin Wang; |
| 15 | User Recommendation in Social Metaverse with VR Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we explore a scenario of socializing in metaverse with VR, which brings major advantages over conventional social media: 1) leverage flexible display of users’ 360-degree viewports to satisfy individual user interests, 2) ensure the user feelings of co-existence, 3) prevent view obstruction to help users find friends in crowds, and 4) support socializing with digital twins. |
Bing-Jyue Chen; De-Nian Yang; |
| 16 | Learning to Generalize in Heterogeneous Federated Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: More specifically, we propose a personalized <u>Fed</u>erated optimization framework with <u>M</u>eta <u>C</u>ritic (FedMC) that efficiently captures robust and generalizable domain-invariant knowledge across clients. |
Cen Chen; Tiandi Ye; Li Wang; Ming Gao; |
| 17 | Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS) Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: As a result, different inputs/tasks could have different assembled models. In this work, we take recommender system as an example and propose Modularized Adaptive Neural Architecture Search (MANAS) to demonstrate the above idea. |
Hanxiong Chen; Yunqi Li; He Zhu; Yongfeng Zhang; |
| 18 | Enhancing User Behavior Sequence Modeling By Generative Tasks for Session Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To help the encoding of the current user behavior sequence, we propose to use a decoder and the information of future sequences and a supplemental query. |
Haonan Chen; Zhicheng Dou; Yutao Zhu; Zhao Cao; Xiaohua Cheng; Ji-Rong Wen; |
| 19 | CorpusBrain: Pre-train A Generative Retrieval Model for Knowledge-Intensive Language Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Inspired by recent efforts in constructing model-based IR models, we propose to replace the traditional multi-step search pipeline with a novel single-step generative model, which can dramatically simplify the search process and be optimized in an end-to-end manner. |
Jiangui Chen; Ruqing Zhang; Jiafeng Guo; Yiqun Liu; Yixing Fan; Xueqi Cheng; |
| 20 | Towards Self-supervised Learning on Graphs with Heterophily Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a self-supervised representation learning paradigm on graphs with heterophily (namely HGRL) for improving the generalizability of node representations, where node representations are optimized without any label guidance. |
Jingfan Chen; Guanghui Zhu; Yifan Qi; Chunfeng Yuan; Yihua Huang; |
| 21 | Time Lag Aware Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the two challenges, we propose a novel model called Time Lag aware Sequential Recommendation (TLSRec), which integrates a hierarchical modeling of user preference and a time lag sensitive fine-grained fusion of the long-term and short-term preferences. |
Lihua Chen; Ning Yang; Philip S. Yu; |
| 22 | GCF-RD: A Graph-based Contrastive Framework for Semi-Supervised Learning on Relational Databases Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel graph-based contrastive framework for semi-supervised learning on relational databases, achieving promising predictive classification performance with only a handful of labeled data. |
Runjin Chen; Tong Li; Yanyan Shen; Luyu Qiu; Kaidi Li; Caleb Chen Cao; |
| 23 | Task Publication Time Recommendation in Spatial Crowdsourcing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose and study a novel SC framework, namely Task Assignment with Task Publication Time Recommendation. |
Xuanlei Chen; Yan Zhao; Kai Zheng; |
| 24 | Efficient Second-Order Optimization for Neural Networks with Kernel Machines Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: There have been some attempts to address this issue by approximation on the Hessian matrix, which unfortunately degrades the performance of the neural models. In order to tackle this issue, we propose Kernel Stochastic Gradient Descent (Kernel SGD) which solves the optimization problem in a space transformed by the Hessian matrix of the kernel machine. |
Yawen Chen; Yile Chen; Jian Chen; Zeyi Wen; Jin Huang; |
| 25 | ReLAX: Reinforcement Learning Agent Explainer for Arbitrary Predictive Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, existing CF generation methods either exploit the internals of specific models or depend on each sample’s neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets. This work aims to overcome these limitations and introduces ReLAX, a model-agnostic algorithm to generate optimal counterfactual explanations. |
Ziheng Chen; Fabrizio Silvestri; Jia Wang; He Zhu; Hongshik Ahn; Gabriele Tolomei; |
| 26 | Explainable Link Prediction in Knowledge Hypergraphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present HyperMLN, an n-ary, mixed, and explainable framework that interprets the path-reasoning process with first-order logic, which provides a knowledge-enhanced interpretable prediction framework, in which domain knowledge in the logic rules improves the performance of embedding models, while semantic information in the embedding space can optimize the weight of the logic rules in turn. |
Zirui Chen; Xin Wang; Chenxu Wang; Jianxin Li; |
| 27 | SpaDE: Improving Sparse Representations Using A Dual Document Encoder for First-stage Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Tackling the trade-off, we propose a novel uni-encoder ranking model, Sparse retriever using a Dual document Encoder (SpaDE), learning document representation via the dual encoder. |
Eunseong Choi; Sunkyung Lee; Minijn Choi; Hyeseon Ko; Young-In Song; Jongwuk Lee; |
| 28 | Finding Heterophilic Neighbors Via Confidence-based Subgraph Matching for Semi-supervised Node Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, they fail to generalize well under heterophilic setups, where neighbor nodes have different labels. To address this challenge, we employ a confidence ratio as a hyper-parameter, assuming that some of the edges are disassortative (heterophilic). |
Yoonhyuk Choi; Jiho Choi; Taewook Ko; Hyungho Byun; Chong-Kwon Kim; |
| 29 | Review-Based Domain Disentanglement Without Duplicate Users or Contexts for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite such progress, existing methods focus on domain-shareable information (overlapped users or same contexts) for a knowledge transfer, and they fail to generalize well without such requirements. To deal with these problems, we suggest utilizing review texts that are general to most e-commerce systems. |
Yoonhyuk Choi; Jiho Choi; Taewook Ko; Hyungho Byun; Chong-Kwon Kim; |
| 30 | An Empirical Study on How People Perceive AI-generated Music Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we evaluate human satisfaction with the state-of-the-art automatic symbolic music generation models using deep learning. |
Hyeshin Chu; Joohee Kim; Seongouk Kim; Hongkyu Lim; Hyunwook Lee; Seungmin Jin; Jongeun Lee; Taehwan Kim; Sungahn Ko; |
| 31 | AutoXAI: A Framework to Automatically Select The Most Adapted XAI Solution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, selecting the most relevant XAI solution among all this diversity is still a tedious task, especially if a user has specific needs and constraints. In this paper, we propose AutoXAI, a framework that recommends the best XAI solution and its hyperparameters according to specified XAI evaluation metrics while considering the user’s context (dataset, machine learning model, XAI needs and constraints). |
Robin Cugny; Julien Aligon; Max Chevalier; Geoffrey Roman Jimenez; Olivier Teste; |
| 32 | Meta-Path-based Fake News Detection Leveraging Multi-level Social Context Information Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To overcome all three difficulties, we propose a novel fake news detection framework, Hetero-SCAN. |
Jian Cui; Kwanwoo Kim; Seung Ho Na; Seungwon Shin; |
| 33 | Inductive Knowledge Graph Reasoning for Multi-batch Emerging Entities Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a walk-based inductive reasoning model to tackle the new setting. |
Yuanning Cui; Yuxin Wang; Zequn Sun; Wenqiang Liu; Yiqiao Jiang; Kexin Han; Wei Hu; |
| 34 | Scaling Up Maximal K-plex Enumeration Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the problem of enumerating all large maximal k-plexes of a graph and develop several new and efficient techniques to solve the problem. |
Qiangqiang Dai; Rong-Hua Li; Hongchao Qin; Meihao Liao; Guoren Wang; |
| 35 | When Should We Use Linear Explanations? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Hence, this paper introduces Adapted Post-hoc Explanations (APE), a novel method that characterizes the decision boundary of a black-box classifier and identifies when a linear model constitutes a reliable explanation. |
Julien Delaunay; Luis Galárraga; Christine Largouët; |
| 36 | Efficient Trajectory Similarity Computation with Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To enable effective and efficient trajectory similarity computation, we propose a novel robust model, namely <u>C</u>ontrastive <u>L</u>earning based <u>T</u>rajectory <u>Sim</u>ilarity Computation (CL-TSim). |
Liwei Deng; Yan Zhao; Zidan Fu; Hao Sun; Shuncheng Liu; Kai Zheng; |
| 37 | Weakly-Supervised Online Hashing with Refined Pseudo Tags Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, only one weakly-supervised online hashing method has been proposed, but it is still far from enough to alleviate the negative effects of tags. In this paper, to address the above problems, we propose a new method, termed Weakly-Supervised Online Hashing with Refined Pseudo Tags (RPT-WOH). |
Chen-Lu Ding; Xin Luo; Xiao-Ming Wu; Yu-Wei Zhan; Rui Li; Hui Zhang; Xin-Shun Xu; |
| 38 | GDOD: Effective Gradient Descent Using Orthogonal Decomposition for Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a novel optimization approach for MTL, named GDOD, which manipulates gradients of each task using an orthogonal basis decomposed from the span of all task gradients. |
Xin Dong; Ruize Wu; Chao Xiong; Hai Li; Lei Cheng; Yong He; Shiyou Qian; Jian Cao; Linjian Mo; |
| 39 | Contrastive Learning with Bidirectional Transformers for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To tackle that, we propose a novel framework named Contrastive learning with Bidirectional Transformers for sequential recommendation (CBiT). |
Hanwen Du; Hui Shi; Pengpeng Zhao; Deqing Wang; Victor S. Sheng; Yanchi Liu; Guanfeng Liu; Lei Zhao; |
| 40 | Optimal Action Space Search: An Effective Deep Reinforcement Learning Method for Algorithmic Trading Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an end-to-end DRL method that explores solutions on the whole graph via a probabilistic dynamic programming algorithm. |
Zhongjie Duan; Cen Chen; Dawei Cheng; Yuqi Liang; Weining Qian; |
| 41 | Inferring Sensitive Attributes from Model Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, current literature has limited discussion on privacy risks of model explanations. We focus on the specific privacy risk of attribute inference attack wherein an adversary infers sensitive attributes of an input (e.g., Race and Sex) given its model explanations. |
Vasisht Duddu; Antoine Boutet; |
| 42 | Higher-order Clustering and Pooling for Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, they are not only questioned by recent work showing on par performance with random pooling, but also ignore completely higher-order connectivity patterns. To tackle this issue, we propose HoscPool, a clustering-based graph pooling operator that captures higher-order information hierarchically, leading to richer graph representations. |
Alexandre Duval; Fragkiskos Malliaros; |
| 43 | Federated K-Private Set Intersection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we develop a new approach for the PSI problem within the federated analytics framework. |
Ahmed Roushdy Elkordy; Yahya H. Ezzeldin; Salman Avestimehr; |
| 44 | Detecting Significant Differences Between Information Retrieval Systems Via Generalized Linear Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Among the most popular tools for statistical significance testing, we list t-test and ANOVA that belong to the linear models family. |
Guglielmo Faggioli; Nicola Ferro; Norbert Fuhr; |
| 45 | Risk-Aware Bid Optimization for Online Display Advertisement Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a risk-aware data-driven bid optimization model that maximizes the expected profit for the advertiser by exploiting historical data to design upfront a bidding policy, mapping the type of advertisement opportunity to a bid price, and accounting for the risk of violating the budget constraint during a given period of time. |
Rui Fan; Erick Delage; |
| 46 | Smart Contract Scams Detection with Topological Data Analysis on Account Interaction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We adopt interactive features extracted from dynamic interaction information of accounts and propose a framework named TTG-SCSD to utilize the features and Topological Data Analysis for smart contract scams detection. |
Shuhui Fan; Shaojing Fu; Yuchuan Luo; Haoran Xu; Xuyun Zhang; Ming Xu; |
| 47 | MonitorLight: Reinforcement Learning-based Traffic Signal Control Using Mixed Pressure Monitoring Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Due to the lack of exploring dynamic phase duration, the overall performance and convergence rate of RL-based TSC approaches cannot be guaranteed, which may result in poor adaptability of RL methods to different traffic conditions. To address these issues, in this paper, we formulate a novel phase-duration-aware TSC (PDA-TSC) problem and propose an effective RL-based TSC approach, named MonitorLight. |
Zekuan Fang; Fan Zhang; Ting Wang; Xiang Lian; Mingsong Chen; |
| 48 | Few-Shot Relational Triple Extraction with Perspective Transfer Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Nevertheless, they fail to utilize the triple-level information to verify the plausibility of extracted relational triples, and ignore the proper transfer among the perspectives of entity, relation and triple. To fill in these gaps, in this work, we put forward a novel perspective transfer network (PTN) to address few-shot RTE. |
Junbo Fei; Weixin Zeng; Xiang Zhao; Xuanyi Li; Weidong Xiao; |
| 49 | Aries: Accurate Metric-based Representation Learning for Fast Top-k Trajectory Similarity Query Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing representation-based computing modes remain two major problems understudied: the low quality of trajectory representation and insufficient support for various trajectory similarity metrics, which make them difficult to apply in practice. Therefore, we propose an Accurate metric-based representation learning approach for fast top-k trajectory similarity query, named Aries. |
Chunhui Feng; Zhicheng Pan; Junhua Fang; Jiajie Xu; Pengpeng Zhao; Lei Zhao; |
| 50 | MGMAE: Molecular Representation Learning By Reconstructing Heterogeneous Graphs with A High Mask Ratio Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: (2) Although a high mask ratio that corresponds to a challenging reconstruction task has been proved beneficial in the vision domain, it cannot be trivially leveraged on molecular graphs as there is less redundancy of information in graph data. To resolve these issues, we propose a novel framework, Molecular Graph Mask AutoEncoder (MGMAE). |
Jinjia Feng; Zhen Wang; Yaliang Li; Bolin Ding; Zhewei Wei; Hongteng Xu; |
| 51 | GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Additional, different approaches are applied one at a time, while there lacks an overall framework to jointly leverage multiple solutions to the over-smoothing challenge. To solve these problems, we propose GraTO, a framework based on neural architecture search to automatically search for GNNs architecture. |
Xinshun Feng; Herun Wan; Shangbin Feng; Hongrui Wang; Qinghua Zheng; Jun Zhou; Minnan Luo; |
| 52 | DP-HORUS: Differentially Private Hierarchical Count Histograms Under Untrusted Server Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Hierarchical count histograms is the task of publishing count statistics at different granularity as per hierarchy defined on a dimension table in a data warehouse, which has wide applications in On-line Analytical Processing (OLAP) scenarios. In this paper, we systematically investigate this task subjected to the rigorous privacy-preserving constraint under the untrusted server setting. |
Congcong Fu; Hui Li; Jian Lou; Jiangtao Cui; |
| 53 | KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To the best of our knowledge, this is the first real-world fully-observed data with millions of user-item interactions. With this unique dataset, we conduct a preliminary analysis of how the two factors – data density and exposure bias – affect the evaluation results of multi-round conversational recommendation. |
Chongming Gao; Shijun Li; Wenqiang Lei; Jiawei Chen; Biao Li; Peng Jiang; Xiangnan He; Jiaxin Mao; Tat-Seng Chua; |
| 54 | Consistent, Balanced, and Overlapping Label Trees for Extreme Multi-label Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Notably, the expected label trees should accurately find the right leaf nodes for future instances (i.e., effectiveness) and generate balanced leaf nodes (i.e., efficiency). To achieve this, we propose a novel generic method of label tree, namely Consistent, Balanced, and Overlapping Label Tree (CBOLT). |
Zhiqi Ge; Yuanyuan Guan; Ximing Li; Bo Fu; |
| 55 | PromptORE – A Novel Approach Towards Fully Unsupervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To mitigate the reliance on hyperparameters, we propose PromptORE, a "Prompt-based Open Relation Extraction" model. |
Pierre-Yves Genest; Pierre-Edouard Portier; Elöd Egyed-Zsigmond; Laurent-Walter Goix; |
| 56 | Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Though many approaches are proposed to predict a paper’s future citation, most ignore the dynamic heterogeneous graph structure or node importance in academic networks. To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers. |
Hao Geng; Deqing Wang; Fuzhen Zhuang; Xuehua Ming; Chenguang Du; Ting Jiang; Haolong Guo; Rui Liu; |
| 57 | Robust Recurrent Classifier Chains for Multi-Label Learning with Missing Labels Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: RCCs fail in this missing label scenario, predicting many false negatives and potentially missing important classes. In this work, we propose Robust-RCC, the first strategy for tackling this open problem of RCCs failing formulti-label missing-label data. |
Walter Gerych; Thomas Hartvigsen; Luke Buquicchio; Emmanuel Agu; Elke Rundensteiner; |
| 58 | Spatio-temporal Trajectory Learning Using Simulation Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, models for data generation must be derived from abstract studies using probabilities. To solve these problems, we present Multi-Agent-Trajectory-Learning (MATL), a state transition model to learn and generate human-like Spatio-temporal trajectory data. |
Daniel Glake; Fabian Panse; Ulfia Lenfers; Thomas Clemen; Norbert Ritter; |
| 59 | Gromov-Wasserstein Multi-modal Alignment and Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Unfortunately, these two assumptions are often questionable in practice and thus limit the feasibility of many multi-modal clustering methods. In this work, we develop a new multi-modal clustering method based on the Gromovization of optimal transport distance, which relaxes the dependence on the above two assumptions. |
Fengjiao Gong; Yuzhou Nie; Hongteng Xu; |
| 60 | ITSM-GCN: Informative Training Sample Mining for Graph Convolutional Network-based Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite their successes, we argue that existing methods have not yet properly explored more effective sampling strategy, including both positive sampling and negative sampling. To tackle this limitation, a novel framework named ITSM-GCN is proposed to carry out our designed Informative Training Sample Mining (ITSM) sampling strategy for the learning of GCN-based CF models. |
Kaiqi Gong; Xiao Song; Senzhang Wang; Songsong Liu; Yong Li; |
| 61 | Evolutionary Preference Learning Via Graph Nested GRU ODE for Session-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To this end, we propose Graph Nested GRU ordinary differential equation (ODE), namely GNG-ODE, a novel continuum model that extends the idea of neural ODEs to continuous-time temporal session graphs. |
Jiayan Guo; Peiyan Zhang; Chaozhuo Li; Xing Xie; Yan Zhang; Sunghun Kim; |
| 62 | Learning Hypersphere for Few-shot Anomaly Detection on Attributed Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As a latent hazard in existing systems, anomalies can be transformed into important instruction information once we detect them, e.g., computer network admins can react to the leakage of sensitive data if network traffic anomalies are identified. Extensive research in anomaly detection on attributed networks has proposed various techniques, which do improve the quality of data in networks, while they rarely cope with the few-shot anomaly detection problem. |
Qiuyu Guo; Xiang Zhao; Yang Fang; Shiyu Yang; Xuemin Lin; Dian Ouyang; |
| 63 | KiCi: A Knowledge Importance Based Class Incremental Learning Method for Wearable Activity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This is contrary to our expectations of identifying new activity classes while remembering existing ones. To address this problem, we propose a knowledge importance-based class incremental learning method called KiCi and construct an incremental learning model based on the framework of self-iterative knowledge distillation for dynamic activity recognition. |
Shuai Guo; Yang Gu; Shijie Wen; Yuan Ma; Yiqiang Chen; Jiwei Wang; Chunyu Hu; |
| 64 | Bootstrap-based Causal Structure Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, in many real-world settings, owing to inevitable data quality issues (e.g. noise and small sample), existing local-to-global CSL methods often yield many asymmetric edges (e.g., given anasymmetric edge containing variables A and B, the learned skeleton of A contains B, but the learned skeleton of B does not contain A), which make it difficult to construct a high quality global skeleton. To tackle this problem, this paper proposes a <u>B</u>ootstrap sampling based <u>C</u>ausal <u>S</u>tructure <u>L</u>earning (BCSL) algorithm. |
Xianjie Guo; Yujie Wang; Xiaoling Huang; Shuai Yang; Kui Yu; |
| 65 | RAGUEL: Recourse-Aware Group Unfairness Elimination Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce the notion of ranked group-level recourse fairness, and develop a ‘recourse-aware ranking’ solution that satisfies ranked recourse fairness constraints while minimizing the cost of suggested modifications. |
Aparajita Haldar; Teddy Cunningham; Hakan Ferhatosmanoglu; |
| 66 | Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-Video Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, a novel Multi-aggregator Time-warping Heterogeneous Graph Neural Network (MTHGNN) is proposed for personalized news nature micro-video recommendation based on sequential sessions, where characteristics of micro-videos are comprehensively studied, users’ preference is mined via multi-aggregator, the temporal and dynamic changes of users’ preference are captured, and timeliness is considered. |
Jinkun Han; Wei Li; Zhipeng Cai; Yingshu Li; |
| 67 | Rethinking Conversational Recommendations: Is Decision Tree All You Need? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we explore a simpler alternative and propose a decision tree based solution to CRS. |
A S M Ahsan-Ul Haque; Hongning Wang; |
| 68 | Stop&Hop: Early Classification of Irregular Time Series Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Such series are notoriously pervasive in impactful domains like healthcare. We bridge this gap and study early classification of irregular time series, a new setting for early classifiers that opens doors to more real-world problems. |
Thomas Hartvigsen; Walter Gerych; Jidapa Thadajarassiri; Xiangnan Kong; Elke Rundensteiner; |
| 69 | Change Detection for Local Explainability in Evolving Data Streams Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we discuss the impact of temporal change on local feature attributions. |
Johannes Haug; Alexander Braun; Stefan Zürn; Gjergji Kasneci; |
| 70 | Modeling Diverse Chemical Reactions for Single-step Retrosynthesis Via Discrete Latent Variables Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we aim to increase reaction diversity and generate various reactants using discrete latent variables. |
Hua-Rui He; Jie Wang; Yunfei Liu; Feng Wu; |
| 71 | AutoMARS: Searching to Compress Multi-Modality Recommendation Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we leverage the tools of neural architecture search (NAS) and distillation and propose Auto Multi-modAlity Recommendation System (AutoMARS), a unified modality-aware model compression framework dedicated to multi-modality recommendation systems. |
Duc Hoang; Haotao Wang; Handong Zhao; Ryan Rossi; Sungchul Kim; Kanak Mahadik; Zhangyang Wang; |
| 72 | Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions Using Enhanced Reduction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose ColBERTer, a neural retrieval model using contextualized late interaction (ColBERT) with enhanced reduction. |
Sebastian Hofstätter; Omar Khattab; Sophia Althammer; Mete Sertkan; Allan Hanbury; |
| 73 | Prediction-based One-shot Dynamic Parking Pricing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To this end, we present a proactive prediction-driven optimization framework to dynamically adjust parking prices. |
Seoyoung Hong; Heejoo Shin; Jeongwhan Choi; Noseong Park; |
| 74 | Can We Have Both Fish and Bear’s Paw?: Improving Performance, Reliability, and Both of Them for Relation Extraction Under Label Shift Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we make contributions by answering the following three questions: 1) How to improve performance of DS-RE models under label shift? |
Yu Hong; Zhixu Li; Jianfeng Qu; Jiaqing Liang; Yi Luo; Miyu Zhang; Yanghua Xiao; Wei Wang; |
| 75 | One Rating to Rule Them All?: Evidence of Multidimensionality in Human Assessment of Topic Labeling Quality Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In contrast, this paper provides evidence that human assessments about the quality of topic labels consist of multiple latent dimensions. |
Amin Hosseiny Marani; Joshua Levine; Eric P.S. Baumer; |
| 76 | Cross-Domain Aspect Extraction Using Transformers Augmented with Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, these methods show a drastic decrease in performance when applied to cross-domain settings where the domain of the testing data differs from that of the training data. To address this lack of extensibility and robustness, we propose a novel approach for automatically constructing domain-specific knowledge graphs that contain information relevant to the identification of aspect terms. |
Phillip Howard; Arden Ma; Vasudev Lal; Ana Paula Simoes; Daniel Korat; Oren Pereg; Moshe Wasserblat; Gadi Singer; |
| 77 | Memory Bank Augmented Long-tail Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, most existing sequential recommendation methods do not focus on solving the long-tail item recommendation problem that is caused by the imbalanced distribution of item data. To solve this problem, we propose a novel sequential recommendation framework, named MASR (ie <u>M</u>emory Bank <u>A</u>ugmented Long-tail <u>S</u>equential <u>R</u>ecommendation). |
Yidan Hu; Yong Liu; Chunyan Miao; Yuan Miao; |
| 78 | An Uncertainty-Aware Imputation Framework for Alleviating The Sparsity Problem in Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Given a (sparse) user-item interaction matrix, our key idea is to quantify uncertainty on each missing entry and then the cells with the lowest uncertainty are selectively imputed. |
Sunghyun Hwang; Dong-Kyu Chae; |
| 79 | Beyond Learning from Next Item: Sequential Recommendation Via Personalized Interest Sustainability Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a recommender system taking advantages of the models in both categories. |
Dongmin Hyun; Chanyoung Park; Junsu Cho; Hwanjo Yu; |
| 80 | Discovering Fine-Grained Semantics in Knowledge Graph Relations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The correct semantic interpretation of KG relations is necessary for many downstream applications such as entity classification and question answering. We present the problem of fine-grained relation discovery and a data-driven method towards this task that leverages the vector representations of the knowledge graph entities and relations available from relational learning models. |
Nitisha Jain; Ralf Krestel; |
| 81 | Accurate Action Recommendation for Smart Home Via Two-Level Encoders and Commonsense Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose SmartSense, an accurate action recommendation method for smart home. |
Hyunsik Jeon; Jongjin Kim; Hoyoung Yoon; Jaeri Lee; U Kang; |
| 82 | Diverse Effective Relationship Exploration for Cooperative Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To tackle the problem, we propose a diverse effective relationship exploration based multi-agent reinforcement learning (DERE) method. |
Hao Jiang; Yuntao Liu; Shengze Li; Jieyuan Zhang; Xinhai Xu; Donghong Liu; |
| 83 | Estimating Causal Effects on Networked Observational Data Via Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the causal effects estimation problem on networked observational data. |
Song Jiang; Yizhou Sun; |
| 84 | Towards Federated Learning Against Noisy Labels Via Local Self-Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Specifically, we propose a Local Self-Regularization method, which effectively regularizes the local training process via implicitly hindering the model from memorizing noisy labels and explicitly narrowing the model output discrepancy between original and augmented instances using self distillation. |
Xuefeng Jiang; Sheng Sun; Yuwei Wang; Min Liu; |
| 85 | Multi-Scale User Behavior Network for Entire Space Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Concretely, we introduce a hierarchical framework, where the lower layer models the user’s engagement behaviors while the upper layer estimates the user’s satisfaction behaviors. |
Jiarui Jin; Xianyu Chen; Weinan Zhang; Yuanbo Chen; Zaifan Jiang; Zekun Zhu; Zhewen Su; Yong Yu; |
| 86 | Extracting Drug-drug Interactions from Biomedical Texts Using Knowledge Graph Embeddings and Multi-focal Loss Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The use of knowledge graphs can alleviate this problem by incorporating different relationships for each, thus allowing them to be distinguished. Thus, we propose a novel framework to integrate the neural network with a knowledge graph, where the features from these components are complementary. |
Xin Jin; Xia Sun; Jiacheng Chen; Richard Sutcliffe; |
| 87 | X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Secondly, how to effectively model the multiplexity of the real-world graphs, where nodes are connected by various relations and each relation could form a homogeneous graph layer. To solve these problems, we propose a novel multiple<u>x</u> heterogeneous <u>g</u>raph pr<u>o</u>totypical contr<u>a</u>stive <u>l</u>eaning (X-GOAL) framework to extract node embeddings. |
Baoyu Jing; Shengyu Feng; Yuejia Xiang; Xi Chen; Yu Chen; Hanghang Tong; |
| 88 | Can Adversarial Training Benefit Trajectory Representation?: An Investigation on Robustness for Trajectory Similarity Computation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, how to infer an accurate and robust similarity over two trajectories is difficult due to the some trajectory characteristics in practice, e.g. non-uniform sampling rate, nonmalignant fluctuation, and noise points, etc. To circumvent such challenges, we in this paper introduce the adversarial training idea into the trajectory representation learning for the first time to enhance the robustness and accuracy. |
Quanliang Jing; Shuo Liu; Xinxin Fan; Jingwei Li; Di Yao; Baoli Wang; Jingping Bi; |
| 89 | Efficient Optimization of Dominant Set Clustering with Frank-Wolfe Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study Frank-Wolfe algorithms – standard, pairwise, and away-steps – for efficient optimization of Dominant Set Clustering. We present a unified and computationally efficient framework to employ the different variants of Frank-Wolfe methods, and we investigate its effectiveness via several experimental studies. |
Carl Johnell; Morteza Haghir Chehreghani; |
| 90 | Contrastive Representation Learning for Conversational Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This work proposes a contrastive representation learning-based approach to rank KG paths effectively. |
Endri Kacupaj; Kuldeep Singh; Maria Maleshkova; Jens Lehmann; |
| 91 | Sharper Utility Bounds for Differentially Private Models: Smooth and Non-smooth Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, by introducing Generalized Bernstein condition, we propose the first O(√p over n∈ ) high probability excess population risk bound for differentially private algorithms under the assumptions G-Lipschitz, L-smooth, and Polyak-Łojasiewicz condition, based on gradient perturbation method. |
Yilin Kang; Yong Liu; Jian Li; Weiping Wang; |
| 92 | Residual Correction in Real-Time Traffic Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, to capture the correlation of errors, we introduce ResCAL, a residual estimation module for traffic forecasting, as a widely applicable add-on module to existing traffic forecasting models. |
Daejin Kim; Youngin Cho; Dongmin Kim; Cheonbok Park; Jaegul Choo; |
| 93 | FedRN: Exploiting K-Reliable Neighbors Towards Robust Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a robust federated learning method called FedRN, which exploits k-reliable neighbors with high data expertise or similarity. |
Sangmook Kim; Wonyoung Shin; Soohyuk Jang; Hwanjun Song; Se-Young Yun; |
| 94 | SWAG-Net: Semantic Word-Aware Graph Network for Temporal Video Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, to effectively capture non-sequential dependencies among semantic words for temporal video grounding, we propose a novel framework called Semantic Word-Aware Graph Network (SWAG-Net), which adopts graph-guided semantic word embedding in an end-to-end manner. |
Sunoh Kim; Taegil Ha; Kimin Yun; Jin Young Choi; |
| 95 | MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: More importantly, we experimentally observe that the learning procedures of existing works fail to preserve the intrinsic modality-specific properties of items. To address above limitations, we propose an accurate multimedia recommendation framework, named MARIO, based on modality-aware attention and modality-preserving decoders. |
Taeri Kim; Yeon-Chang Lee; Kijung Shin; Sang-Wook Kim; |
| 96 | Semorph: A Morphology Semantic Enhanced Pre-trained Model for Chinese Spam Text Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, based on the diversity of Chinese characters in glyphs, the spammers frequently wrap the spam content in another visually close text to fool the model but make sure people understand. This paper proposes to adopt the essence of human cognition of these adversarial texts into spam text detection models, by designing a pre-trained model to learn the morphology semantics of Chinese characters and represent their contextual meanings from scratch. |
Kaiting Lai; Yinong Long; Bowen Wu; Ying Li; Baoxun Wang; |
| 97 | Loyalty-based Task Assignment in Spatial Crowdsourcing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study a loyalty-based task assignment problem, which aims to maximize the overall rewards of workers while considering worker loyalty. |
Tinghao Lai; Yan Zhao; Weizhu Qian; Kai Zheng; |
| 98 | Legal Charge Prediction Via Bilinear Attention Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, inspired by the vision-language information fusion techniques in the multi-modal field, we propose a novel model (denoted as LeapBank) by fusing the representations of text and labels to enhance the legal charge prediction task. |
Yuquan Le; Yuming Zhao; Meng Chen; Zhe Quan; Xiaodong He; Kenli Li; |
| 99 | Accelerating CNN Via Dynamic Pattern-based Pruning Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Nevertheless, most dynamic pruning methods fail to achieve actual acceleration due to the extra overheads caused by indexing and weight-copying to implement the dynamic sparse patterns for every input sample. To address this issue, we propose Dynamic Pattern-based Pruning Network (DPPNet), which preserves the advantages of both static and dynamic networks. |
Gwanghan Lee; Saebyeol Shin; Simon S. Woo; |
| 100 | Sliding Cross Entropy for Self-Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a novel plug-in approach called Sliding Cross Entropy (SCE) method, which can be combined with existing self-knowledge distillation to significantly improve the performance. |
Hanbeen Lee; Jeongho Kim; Simon S. Woo; |
| 101 | Relational Self-Supervised Learning on Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To fully benefit from the relational information inherent in the graph-structured data, we propose a novel GRL method, called RGRL, that learns from the relational information generated from the graph itself. |
Namkyeong Lee; Dongmin Hyun; Junseok Lee; Chanyoung Park; |
| 102 | Maximum Norm Minimization: A Single-Policy Multi-Objective Reinforcement Learning to Expansion of The Pareto Front Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Maximum Norm Minimization (MNM), a single-policy Multi-Objective Reinforcement Learning (MORL) algorithm to solve the multi-objective RL problem. |
Seonjae Lee; Myoung Hoon Lee; Jun Moon; |
| 103 | Parallel Skyline Processing Using Space Pruning on GPU Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a grid-based structure that enables grid cell domination checks. |
Chuanwen Li; Yu Gu; Jianzhong Qi; Ge Yu; |
| 104 | Automated Spatio-Temporal Synchronous Modeling with Multiple Graphs for Traffic Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel neural architecture search framework, entitled AutoSTS, for automated spatio-temporal synchronous modeling in traffic prediction. |
Fuxian Li; Huan Yan; Guangyin Jin; Yue Liu; Yong Li; Depeng Jin; |
| 105 | MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Existing works, while encouraging, typically adopt simple aggregation operation on the user-item bipartite graph to model user and item representations, but neglect to mine the sufficient dependencies between nodes, e.g., the relationships between users/items and their neighbors (or congeners), resulting in inadequate graph representation learning. To address these problems, we propose a novel Multi-Dependency Graph Collaborative Filtering (MDGCF) model, which mines the neighborhood- and homogeneous-level dependencies to enhance the representation power of graph-based CF models. |
Guohui Li; Zhiqiang Guo; Jianjun Li; Chaoyang Wang; |
| 106 | CoPatE: A Novel Contrastive Learning Framework for Patent Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This type of strategy leads to poor performance and costs too much labor power to filter in post-processing. To address these issues, we proposed CoPatE: a novel Contrastive Learning Framework for Patent Embeddings to capture the high-level semantics of the large-scale patents, where a patent semantic compression module learns the informative claims to reduce the computational complexity, and a tags auxiliary learning module is to enhance the semantics of a patent from the structure to learn the high-quality patent embeddings. |
Huahang Li; Shuangyin Li; Yuncheng Jiang; Gansen Zhao; |
| 107 | SK2: Integrating Implicit Sentiment Knowledge and Explicit Syntax Knowledge for Aspect-Based Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite its versatility, its performance is still sub-optimal since ABSA tasks depend heavily on both sentiment and syntax knowledge, but existing task-specific knowledge integration methods are hardly applicable to such a unified framework. Therefore, we propose a brand-new unified framework for ABSA in this work, which incorporates both implicit sentiment knowledge and explicit syntax knowledge to better complete all ABSA tasks. |
Jia Li; Yuyuan Zhao; Zhi Jin; Ge Li; Tao Shen; Zhengwei Tao; Chongyang Tao; |
| 108 | SPOT: Knowledge-Enhanced Language Representations for Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, existing models still struggle to represent entities and relationships simultaneously. To address these problems, we propose a new pre-trained model that learns representations of both entities and relationships from token spans and span pairs in the text respectively. |
Jiacheng Li; Yannis Katsis; Tyler Baldwin; Ho-Cheol Kim; Andrew Bartko; Julian McAuley; Chun-Nan Hsu; |
| 109 | Multi-agent Transformer Networks for Multimodal Human Activity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Also, the potential of attention-based methods still has not been fully explored to better extract the multimodal spatial-temporal relationship and produce robust results. In this work, we propose Multi-agent Transformer Network (MATN), a multi-agent attention-based deep learning algorithm, to address the above issues in multimodal human activity recognition. |
Jingcheng Li; Lina Yao; Binghao Li; Xianzhi Wang; Claude Sammut; |
| 110 | Frequent Itemset Mining with Local Differential Privacy Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper adopts padding-and-sampling-based frequent oracle (PSFO), combined with an interactive query-response method satisfying local differential privacy, to identify frequent itemsets in an efficient and accurate way. Therefore, this paper proposes FIML, an improved algorithm for finding frequent itemsets in the LDP setting of transaction data. |
Junhui Li; Wensheng Gan; Yijie Gui; Yongdong Wu; Philip S. Yu; |
| 111 | AdaDebunk: An Efficient and Reliable Deep State Space Model for Adaptive Fake News Early Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work focuses on the adaptive FNED problem and proposes a novel efficient and reliable deep state space model, namely AdaDebunk, which models the complex probabilistic dependencies. |
Ke Li; Bin Guo; Siyuan Ren; Zhiwen Yu; |
| 112 | Heterogeneous Graph Attention Network for Drug-Target Interaction Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose the heterogeneous graph attention network (HGAN) to capture the complex structures and rich semantics in the biological heterogeneous graph for DTI prediction. |
Mei Li; Xiangrui Cai; Linyu Li; Sihan Xu; Hua Ji; |
| 113 | ℘-MinHash Algorithm for Continuous Probability Measures: Theory and Application to Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, we formalize the definition of ℐ℘ in continuous measure space, and propose a general ℘-MinHash sampling algorithm which generates samples following any target distribution, and preserves ℐ℘ between two distributions by the hash collision. |
Ping Li; Xiaoyun Li; Gennady Samorodnitsky; |
| 114 | GCWSNet: Generalized Consistent Weighted Sampling for Scalable and Accurate Training of Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose using "powered generalized min-max” (pGMM) hashed (linearized) via the "generalized consistent weighted sampling” (GCWS) for training (deep) neural networks (hence the name "GCWSNet”). |
Ping Li; Weijie Zhao; |
| 115 | Gromov-Wasserstein Guided Representation Learning for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, due to the commercial privacy policy and the sensitivity of user data, it is unrealistic to explicitly share the user mapping relations and behavior data. Therefore, in this paper, we consider a more practical cross-domain scenario, where there is no explicit overlap between the source and target domains in terms of users/items. |
Xinhang Li; Zhaopeng Qiu; Xiangyu Zhao; Zihao Wang; Yong Zhang; Chunxiao Xing; Xian Wu; |
| 116 | Spatiotemporal-aware Session-based Recommendation with Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address them, we propose a novel solution named STAGE in this paper. |
Yinfeng Li; Chen Gao; Xiaoyi Du; Huazhou Wei; Hengliang Luo; Depeng Jin; Yong Li; |
| 117 | Dynamic Network Embedding Via Temporal Path Adjacency Matrix Factorization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this article, a novel dynamic network embedding model named TPANE (Temporal Path Adjacency Matrix based Network Embedding) is proposed. |
Zhuoming Li; Darong Lai; |
| 118 | TrajFormer: Efficient Trajectory Classification with Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Second, transformers often suffer high computational costs, especially for long trajectories. In this paper, we address these challenges by presenting a novel transformer architecture entitled TrajFormer. |
Yuxuan Liang; Kun Ouyang; Yiwei Wang; Xu Liu; Hongyang Chen; Junbo Zhang; Yu Zheng; Roger Zimmermann; |
| 119 | Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we systematically study the problem of popularity bias in CRSs. |
Allen Lin; Jianling Wang; Ziwei Zhu; James Caverlee; |
| 120 | Cascade Variational Auto-Encoder for Hierarchical Disentanglement Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Hence, we in this paper propose a novel generative modeling paradigm by introducing a Bayesian network-based regularize on a cascade Variational Auto-Encoder (VAE). |
Fudong Lin; Xu Yuan; Lu Peng; Nian-Feng Tzeng; |
| 121 | High-quality Task Division for Large-scale Entity Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we design the DivEA framework for large-scale EA with high-quality task division. |
Bing Liu; Wen Hua; Guido Zuccon; Genghong Zhao; Xia Zhang; |
| 122 | Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present an end-to-end model using streaming physiological time series to predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery. |
Hanyang Liu; Michael Montana; Dingwen Li; Chase Renfroe; Thomas Kannampallil; Chenyang Lu; |
| 123 | Task Assignment with Federated Preference Learning in Spatial Crowdsourcing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As we have transitioned from crowdsourcing applications (e.g., Wikipedia) to SC applications (e.g., Uber), there is a substantial precedent that SC systems have a responsibility not only to effective task assignment but also to privacy protection. To address these often-conflicting responsibilities, we propose a framework, Task Assignment with Federated Preference Learning, which performs task assignment based on worker preferences while keeping the data decentralized and private in each platform center (e.g., each delivery center of an SC company). |
Jiaxin Liu; Liwei Deng; Hao Miao; Yan Zhao; Kai Zheng; |
| 124 | DA-Net: Distributed Attention Network for Temporal Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel model for predicting future events, namely Distributed Attention Network (DA-Net). |
Kangzheng Liu; Feng Zhao; Hongxu Chen; Yicong Li; Guandong Xu; Hai Jin; |
| 125 | Unsupervised Hierarchical Graph Pooling Via Substructure-Sensitive Mutual Information Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose SMIP, a novel unsupervised hierarchical graph pooling method based on substructure-sensitive MI maximization. |
Ning Liu; Songlei Jian; Dongsheng Li; Hongzuo Xu; |
| 126 | Efficient Learning with Pseudo Labels for Query Cost Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a plan-based query cost estimation framework, called Saturn, which can e<u>S</u>timate c<u>a</u>rdinality and la<u>t</u>ency acc<u>ur</u>ately and efficie<u>n</u>tly, for any query plan structures. |
Shuncheng Liu; Xu Chen; Yan Zhao; Jin Chen; Rui Zhou; Kai Zheng; |
| 127 | HeGA: Heterogeneous Graph Aggregation Network for Trajectory Prediction in High-Density Traffic Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a novel trajectory prediction network called <u>He</u>terogeneous <u>G</u>raph <u>A</u>ggregation (HeGA) for high-density heterogeneous traffic, where the traffic agents of various categories interact densely with each other. |
Shuncheng Liu; Xu Chen; Ziniu Wu; Liwei Deng; Han Su; Kai Zheng; |
| 128 | I Know What You Do Not Know: Knowledge Graph Embedding Via Co-distillation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Recent studies have used pre-trained language models to learn embeddings based on the textual information of entities and relations, but they cannot take advantage of graph structures. In the paper, we show empirically that these two kinds of features are complementary for KG embedding. |
Yang Liu; Zequn Sun; Guangyao Li; Wei Hu; |
| 129 | Social Graph Transformer Networks for Pedestrian Trajectory Prediction in Complex Social Scenarios Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose Social Graph Transformer Networks for multi-modal prediction of pedestrian trajectories, where we combine Graph Convolutional Network and Transformer Network by generating stable resolution pseudo-images from Spatio-temporal graphs through a designed stacking and interception method. |
Yao Liu; Lina Yao; Binghao Li; Xianzhi Wang; Claude Sammut; |
| 130 | Improving Personality Consistency in Conversation By Persona Extending Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To alleviate the problem, in this paper we propose a novel retrieval-to-prediction paradigm consisting of two subcomponents, namely, (1) Persona Retrieval Model (PRM), it retrieves a persona from a global collection based on a Natural Language Inference (NLI) model, the inferred persona is consistent with the predefined personas; and (2) Posterior-scored Transformer (PS-Transformer), it adopts a persona posterior distribution that further considers the actual personas used in the ground response, maximally mitigating the gap between training and inferring. |
Yifan Liu; Wei Wei; Jiayi Liu; Xianling Mao; Rui Fang; Dangyang Chen; |
| 131 | Are Gradients on Graph Structure Reliable in Gray-box Attacks? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we discuss and analyze the errors caused by the unreliability of the structural gradients. |
Zihan Liu; Yun Luo; Lirong Wu; Siyuan Li; Zicheng Liu; Stan Z. Li; |
| 132 | Learning Chinese Word Embeddings By Discovering Inherent Semantic Relevance in Sub-characters Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Learning Chinese word embeddings is important in many tasks of Chinese language information processing, such as entity linking, entity extraction, and knowledge graph. A Chinese … |
Wei Lu; Zhaobo Zhang; Pingpeng Yuan; Hai Jin; Qiangsheng Hua; |
| 133 | Dual-Task Learning for Multi-Behavior Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To release the potential of multi-behavior interaction sequences, we propose a novel framework named NextIP that adopts a dual-task learning strategy to convert the problem to two specific tasks, i.e., <u>next</u>-<u>i</u>tem prediction and <u>p</u>urchase prediction. |
Jinwei Luo; Mingkai He; Xiaolin Lin; Weike Pan; Zhong Ming; |
| 134 | HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a specifically tailor-made Hybrid Static and Adaptive Graph Embedding (HySAGE) network for context-drifting recommendations. |
Sichun Luo; Xinyi Zhang; Yuanzhang Xiao; Linqi Song; |
| 135 | OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models. |
Fuyuan Lyu; Xing Tang; Hong Zhu; Huifeng Guo; Yingxue Zhang; Ruiming Tang; Xue Liu; |
| 136 | Faithful Abstractive Summarization Via Fact-aware Consistency-constrained Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this way, the summarization results could be vulnerable to hallucinations, i.e., the semantic-level inconsistency between a summary and corresponding original document. To deal with this challenge, in this paper, we propose a novel fact-aware abstractive summarization model, named Entity-Relation Pointer Generator Network (ERPGN). |
Yuanjie Lyu; Chen Zhu; Tong Xu; Zikai Yin; Enhong Chen; |
| 137 | DEMO: Disentangled Molecular Graph Generation Via An Invertible Flow Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose DEMO, a flow-based model for <u>D</u>is<u>E</u>ntangled <u>M</u>olecular graph generati<u>O</u>n in a completely unsupervised manner, which is able to generate molecular graphs w.r.t. the learned disentangled latent factors that are relevant to molecular semantic features and interpretable structural patterns. |
Changsheng Ma; Qiang Yang; Xin Gao; Xiangliang Zhang; |
| 138 | NEST: Simulating Pandemic-like Events for Collaborative Filtering By Modeling User Needs Evolution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an adaptive ensemble method that can effectively apply optimal algorithms to cope with the change brought about by different stages of the event. |
Chenglong Ma; Yongli Ren; Pablo Castells; Mark Sanderson; |
| 139 | Towards Robust False Information Detection on Social Networks with Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, we empirically find that slight perturbations in the conversation graph can cause the predictions of existing models to collapse. To address this problem, we present RDCL, a contrastive learning framework for false information detection on social networks, to obtain robust detection results. |
Guanghui Ma; Chunming Hu; Ling Ge; Junfan Chen; Hong Zhang; Richong Zhang; |
| 140 | Knowledge-Sensed Cognitive Diagnosis for Intelligent Education Platforms Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a Knowledge-Sensed Cognitive Diagnosis (KSCD) framework, aiming at learning intrinsic relations among knowledge concepts from student response logs and incorporating them for inferring students’ mastery over all knowledge concepts in an end-to-end manner. |
Haiping Ma; Manwei Li; Le Wu; Haifeng Zhang; Yunbo Cao; Xingyi Zhang; Xuemin Zhao; |
| 141 | MORN: Molecular Property Prediction Based on Textual-Topological-Spatial Multi-View Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, the scarcity of labeled molecular data limits the accuracy of the molecular property prediction model. To address the above issues, we proposed a two-stage method, named MORN, for learning molecular representations for molecular property prediction from a multi-view perspective. |
Runze Ma; Yidan Zhang; Xinye Wang; Zhenyang Yu; Lei Duan; |
| 142 | Scattered or Connected? An Optimized Parameter-efficient Tuning Approach for Information Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To analyze the underlying reason, we conduct a theoretical analysis and show that the separation of the inserted trainable modules makes the optimization difficult. To alleviate this issue, we propose to inject additional modules alongside the pre-trained models (PTMs) to make the original scattered modules connected. |
Xinyu Ma; Jiafeng Guo; Ruqing Zhang; Yixing Fan; Xueqi Cheng; |
| 143 | Hierarchical Spatio-Temporal Graph Neural Networks for Pandemic Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: One drawback with existing Graph Neural Networks (GNNs) for pandemic forecasting is that they generally perform information propagation in a flat way and thus ignore the inherent community structure in a mobility graph. To bridge this gap, we propose a Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to perform pandemic forecasting, which learns both spatial and temporal information from a sequence of dynamic mobility graphs. |
Yihong Ma; Patrick Gerard; Yijun Tian; Zhichun Guo; Nitesh V. Chawla; |
| 144 | Adaptive Re-Ranking with A Corpus Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a novel approach for overcoming the recall limitation based on the well-established clustering hypothesis. |
Sean MacAvaney; Nicola Tonellotto; Craig Macdonald; |
| 145 | Jointly Contrastive Representation Learning on Road Network and Trajectory Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we aim to propose a unified framework that jointly learns the road network and trajectory representations end-to-end. |
Zhenyu Mao; Ziyue Li; Dedong Li; Lei Bai; Rui Zhao; |
| 146 | Cascade-based Echo Chamber Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Despite echo chambers in social media have been under considerable scrutiny, general models for their detection and analysis are missing. In this work, we aim to fill this gap by proposing a probabilistic generative model that explains social media footprints—i.e., social network structure and propagations of information—through a set of latent communities, characterized by a degree of echo-chamber behavior and by an opinion polarity. |
Marco Minici; Federico Cinus; Corrado Monti; Francesco Bonchi; Giuseppe Manco; |
| 147 | Mining Reaction and Diffusion Dynamics in Social Activities Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To tackle the problem, we propose FluxCube, which is an effective mining method that forecasts large collections of co-evolving online user activity and provides good interpretability. |
Taichi Murayama; Yasuko Matsubara; Yasushi Sakurai; None None; |
| 148 | Network Aware Forecasting for ECommerce Supply Planning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We assume that the base forecasts follow a distribution from exponential family and are provided as input to supply chain planning by specifying the distribution form and parameters. With this in mind, following are the contributions of our paper. |
K.V.M. Naidu; Praveen Gupta; Vaishnavi Gujjula; |
| 149 | Domain-Agnostic Contrastive Representations for Learning from Label Proportions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to learn diverse representations of instances within the same bags to effectively utilize the weak bag-level supervision. |
Jay Nandy; Rishi Saket; Prateek Jain; Jatin Chauhan; Balaraman Ravindran; Aravindan Raghuveer; |
| 150 | Rationale Aware Contrastive Learning Based Approach to Classify and Summarize Crisis-Related Microblogs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we take advantage of state-of-the-art methods, such as transformers and contrastive learning to build an interpretable classifier. |
Thi Huyen Nguyen; Koustav Rudra; |
| 151 | Automatic Meta-Path Discovery for Effective Graph-Based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Thus, to discover effective meta-paths automatically, we propose the Reinforcement learning-based Meta-path Selection (RMS) framework. |
Wentao Ning; Reynold Cheng; Jiajun Shen; Nur Al Hasan Haldar; Ben Kao; Xiao Yan; Nan Huo; Wai Kit Lam; Tian Li; Bo Tang; |
| 152 | MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose MetaTrader, a novel two-stage RL-based approach for portfolio management, which learns to integrate diverse trading policies to adapt to various market conditions. |
Hui Niu; Siyuan Li; Jian Li; |
| 153 | Rank List Sensitivity of Recommender Systems to Interaction Perturbations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Prediction models can exhibit sensitivity with respect to training data: small changes in the training data can produce models that assign conflicting predictions to individual data points during test time. In this work, we study this sensitivity in recommender systems, where users’ recommendations are drastically altered by minor perturbations in other unrelated users’ interactions. |
Sejoon Oh; Berk Ustun; Julian McAuley; Srijan Kumar; |
| 154 | Asymmetrical Context-aware Modulation for Collaborative Filtering Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Symmetrical methods may fail to sufficiently and reasonably extract the features of user and item as their interaction data have diverse semantic properties. To address the above issues, a novel model called Asymmetrical context-awaRe modulation for collaBorative filtering REcommendation (ARBRE) is proposed. |
Yi Ouyang; Peng Wu; Li Pan; |
| 155 | Sequence Prediction Under Missing Data: An RNN Approach Without Imputation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In particular, we propose novel variants of Encoder-Decoder (Seq2Seq) RNNs for this. |
Soumen Pachal; Avinash Achar; |
| 156 | Analysis of Knowledge Transfer in Kernel Regime Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, there has been little or no theoretical analysis of this phenomenon. To bridge this gap, we propose to approach the problem of knowledge transfer by regularizing the fit between the teacher and the student with PI provided by the teacher. |
Ashkan Panahi; Arman Rahbar; Chiranjib Bhattacharyya; Devdatt Dubhashi; Morteza Haghir Chehreghani; |
| 157 | SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: By simplifying LightGCN, we show the close connection between GCN-based and low-rank methods such as Singular Value Decomposition (SVD) and Matrix Factorization (MF), where stacking graph convolution layers is to learn a low-rank representation by emphasizing (suppressing) components with larger (smaller) singular values. Based on this observation, we replace the core design of GCN-based methods with a flexible truncated SVD and propose a simplified GCN learning paradigm dubbed SVD-GCN, which only exploits K-largest singular vectors for recommendation. |
Shaowen Peng; Kazunari Sugiyama; Tsunenori Mine; |
| 158 | Malicious Repositories Detection with Adversarial Heterogeneous Graph Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel model adversarial contrastive learning on heterogeneous graph (CLA-HG) to detect malicious repository in GitHub. |
Yiyue Qian; Yiming Zhang; Nitesh Chawla; Yanfang Ye; Chuxu Zhang; |
| 159 | A Multi-Interest Evolution Story: Applying Psychology in Query-based Recommendation for Inferring Customer Intention Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In addition, the diversity and evolution of user interests also bring challenges to inferring user intentions correctly. In this paper, we propose a predecessor task to infer two important customer intentions, which are purchasing and browsing respectively, and we introduce a novel Psychological Intention Prediction Model (PIPM for short) to address this issue. |
Yuqi Qin; Pengfei Wang; Biyu Ma; Zhe Zhang; |
| 160 | Reinforced Continual Learning for Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a graph continual learning strategy that combines the architecture-based and memory-based approaches. |
Appan Rakaraddi; Lam Siew Kei; Mahardhika Pratama; Marcus de Carvalho; |
| 161 | RSD: A Reinforced Siamese Network with Domain Knowledge for Early Diagnosis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the early diagnosis of diseases. |
Houxing Ren; Jingyuan Wang; Wayne Xin Zhao; |
| 162 | Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. |
Jiaqian Ren; Lei Jiang; Hao Peng; Lingjuan Lyu; Zhiwei Liu; Chaochao Chen; Jia Wu; Xu Bai; Philip S. Yu; |
| 163 | From Known to Unknown: Quality-aware Self-improving Graph Neural Network For Open Set Social Event Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, to fully exploit the labeled data, we propose a novel supervised pairwise loss with an additional orthogonal inter-class relation constraint to train the backbone GNN encoder. |
Jiaqian Ren; Lei Jiang; Hao Peng; Yuwei Cao; Jia Wu; Philip S. Yu; Lifang He; |
| 164 | Flow-based Perturbation for Cause-effect Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: A new causal discovery method is introduced to solve the bivariate causal discovery problem. |
Shaogang Ren; Ping Li; |
| 165 | Unbiased Learning to Rank with Biased Continuous Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Accordingly, we design a novel unbiased LTR algorithm to tackle the challenges, which innovatively models position bias in the pairwise fashion and introduces the pairwise trust bias to separate the position bias, trust bias, and user relevance explicitly and can work for both continuous and categorical feedback. |
Yi Ren; Hongyan Tang; Siwen Zhu; |
| 166 | Deep Extreme Mixture Model for Time Series Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Assuming light tailed distributions, such as Gaussian distribution, on time series data does not do justice to the modeling of extreme points. To tackle this issue, we develop a novel approach towards improving attention to extreme event prediction. |
Abilasha S; Sahely Bhadra; Ahmed Zaheer Dadarkar; Deepak P; |
| 167 | Crowdsourced Fact-Checking at Twitter: How Does The Crowd Compare With Experts? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we study the first large-scale effort of crowdsourced fact-checking deployed in practice, started by Twitter with the Birdwatch program. |
Mohammed Saeed; Nicolas Traub; Maelle Nicolas; Gianluca Demartini; Paolo Papotti; |
| 168 | PLAID: An Efficient Engine for Late Interaction Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To dramatically speed up the search latency of late interaction, we introduce the Performance-optimized Late Interaction Driver (PLAID) engine. |
Keshav Santhanam; Omar Khattab; Christopher Potts; Matei Zaharia; |
| 169 | Towards Principled User-side Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we provide theoretical justification of user-side recommender systems. |
Ryoma Sato; |
| 170 | Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing CTR methods still struggle in the product search settings due to the following three challenges including how to more effectively extract users’ short-term interest with respect to multiple aspects, how to extract and fuse users’ long-term interest with short-term interest, how to address the entangling characteristic of long and short-term interests. To resolve these challenges, in this paper, we propose a new approach named Hierarchical Interests Fusing Network (HIFN), which consists of four basic modules namely Short-term Interest Extractor (SIE), Long-term Interest Extractor (LIE), Interest Fusion Module (IFM) and Interest Disentanglement Module (IDM). |
Qijie Shen; Hong Wen; Jing Zhang; Qi Rao; |
| 171 | A Transformer-Based User Satisfaction Prediction for Proactive Interaction Mechanism in DuerOS Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, they are trained and evaluated on the benchmark datasets with adequate labels, which are expensive to obtain in a commercial dialogue system. To face these challenges, we propose a pipeline to predict the user satisfaction to help DuerOS decide whether to ask for clarification in each turn. |
Wei Shen; Xiaonan He; Chuheng Zhang; Xuyun Zhang; Jian Xie; |
| 172 | Personalizing Task-oriented Dialog Systems Via Zero-shot Generalizable Reward Function Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel framework, P-ToD, to personalize task-oriented dialog systems capable of adapting to a wide range of user profiles in an unsupervised fashion using a zero-shot generalizable reward function. |
A.B. Siddique; M.H. Maqbool; Kshitija Taywade; Hassan Foroosh; |
| 173 | Perturbation Effect: A Metric to Counter Misleading Validation of Feature Attribution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a novel metric, the Perturbation Effect Size, and demonstrate how it complements existing metrics to offer a more faithful assessment of importance attribution. |
Ilija Šimić; Vedran Sabol; Eduardo Veas; |
| 174 | Cross-domain Recommendation Via Adversarial Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to specifically align the data distributions between the source domain and the target domain to alleviate imbalanced sample distribution and thus challenge the data scarcity issue in the target domain. |
Hongzu Su; Yifei Zhang; Xuejiao Yang; Hua Hua; Shuangyang Wang; Jingjing Li; |
| 175 | Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: long- and short-term) has been proved to be of great value in capturing user interests. Both industry and academy have paid much attention to this topic and propose different approaches to modeling with long-term and short-term user behavior data. |
Huinan Sun; Guangliang Yu; Pengye Zhang; Bo Zhang; Xingxing Wang; Dong Wang; |
| 176 | A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a novel self-supervised Riemannian graph neural network (SelfℛGNN). |
Li Sun; Junda Ye; Hao Peng; Philip S. Yu; |
| 177 | Serpens: Privacy-Preserving Inference Through Conditional Separable of Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we find that the inference procedure of CNNs can be separated and performed synergistically by many parties. |
Longlong Sun; Hui Li; Yanguo Peng; Jiangtao Cui; |
| 178 | Position-aware Structure Learning for Graph Topology-imbalance By Relieving Under-reaching and Over-squashing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we provide a new understanding of topology-imbalance from a global view of the supervision information distribution in terms of under-reaching and over-squashing, which motivates two quantitative metrics as measurements. |
Qingyun Sun; Jianxin Li; Haonan Yuan; Xingcheng Fu; Hao Peng; Cheng Ji; Qian Li; Philip S. Yu; |
| 179 | DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, a vast majority of existing RL methods focus on the relatively low frequency trading scenarios (e.g., day-level) and fail to capture the fleeting intraday investment opportunities due to two major challenges: 1) how to effectively train profitable RL agents for intraday investment decision-making, which involves high-dimensional fine-grained action space; 2) how to learn meaningful multi-modality market representation to understand the intraday behaviors of the financial market at tick-level. Motivated by the efficient workflow of professional human intraday traders, we propose DeepScalper, a deep reinforcement learning framework for intraday trading to tackle the above challenges. |
Shuo Sun; Wanqi Xue; Rundong Wang; Xu He; Junlei Zhu; Jian Li; Bo An; |
| 180 | RobustFed: A Truth Inference Approach for Robust Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel robust aggregation algorithm inspired by the truth inference methods in crowdsourcing by incorporating the clients’ reliability into aggregation. |
Farnaz Tahmasebian; Jian Lou; Li Xiong; |
| 181 | Temporality- and Frequency-aware Graph Contrastive Learning for Temporal Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Graph contrastive learning (GCL) methods aim to learn more distinguishable representations by contrasting positive and negative samples. |
Shiyin Tan; Jingyi You; Dongyuan Li; |
| 182 | Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting Across Cities Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To this end, this paper aims to propose a novel transferable traffic forecasting framework: Domain Adversarial Spatial-Temporal Network (DASTNet). |
Yihong Tang; Ao Qu; Andy H.F. Chow; William H.K. Lam; S.C. Wong; Wei Ma; |
| 183 | CROLoss: Towards A Customizable Loss for Retrieval Models in Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we proposed the Customizable R[email protected] Optimization Loss (CROLoss), a loss function that can directly optimize the [email protected] metrics and is customizable for different choices of N. |
Yongxiang Tang; Wentao Bai; Guilin Li; Xialong Liu; Yu Zhang; |
| 184 | Temporal Contrastive Pre-Training for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In order to better model temporal characteristics of user behavior sequences, we propose a Temporal Contrastive Pre-training method for Sequential Recommendation (TCPSRec for short). |
Changxin Tian; Zihan Lin; Shuqing Bian; Jinpeng Wang; Wayne Xin Zhao; |
| 185 | Dr. Can See: Towards A Multi-modal Disease Diagnosis Virtual Assistant Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Artificial Intelligence-based clinical decision support is gaining ever-growing popularity and demand in both the research and industry communities. |
Abhisek Tiwari; Manisimha Manthena; Sriparna Saha; Pushpak Bhattacharyya; Minakshi Dhar; Sarbajeet Tiwari; |
| 186 | A Context-Enhanced Generate-then-Evaluate Framework for Chinese Abbreviation Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce a fresh perspective to evaluate the quality of abbreviations within their textual contexts with pre-trained language model. |
Hanwen Tong; Chenhao Xie; Jiaqing Liang; Qianyu He; Zhiang Yue; Jingping Liu; Yanghua Xiao; Wenguang Wang; |
| 187 | Dense Retrieval with Entity Views Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we investigate methods for enriching dense query and document representations with entity information from an external source. |
Hai Dang Tran; Andrew Yates; |
| 188 | Intersection of Parallels As An Early Stopping Criterion Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a method to spot an early stopping point in the training iterations of an overparameterized (NN) without the need for a validation set. |
Ali Vardasbi; Maarten de Rijke; Mostafa Dehghani; |
| 189 | Adaptive Multi-Source Causal Inference from Observational Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a new approach to estimate causal effects from observational data. |
Thanh Vinh Vo; Pengfei Wei; Trong Nghia Hoang; Tze Yun Leong; |
| 190 | Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we model the conditional inter-dependence of time and mark to overcome the limitations of conditionally independent models. |
Govind Waghmare; Ankur Debnath; Siddhartha Asthana; Aakarsh Malhotra; |
| 191 | ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we introduce a new question answering dataset based on image-ChiQA. |
Bingning Wang; Feiyang Lv; Ting Yao; Jin Ma; Yu Luo; Haijin Liang; |
| 192 | Target Interest Distillation for Multi-Interest Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to enhance multi-interest recommendation by predicting the target user interest with a separate interest predictor and a specifically designed distillation loss. |
Chenyang Wang; Zhefan Wang; Yankai Liu; Yang Ge; Weizhi Ma; Min Zhang; Yiqun Liu; Junlan Feng; Chao Deng; Shaoping Ma; |
| 193 | Explanation Guided Contrastive Learning for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: When the positive and negative sequences turn out to be false positive and false negative respectively, it may lead to degraded recommendation performance. In this work, we address the above problem by proposing Explanation Guided Augmentations (EGA) and Explanation Guided Contrastive Learning for Sequential Recommendation (EC4SRec) model framework. |
Lei Wang; Ee-Peng Lim; Zhiwei Liu; Tianxiang Zhao; |
| 194 | Generative-Free Urban Flow Imputation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we for the first time study the urban flow imputation problem and propose a generative-free Attention-based Spatial-Temporal Combine and Mix Completion Network model (AST-CMCN for short) to effectively address it. |
Senzhang Wang; Jiyue Li; Hao Miao; Junbo Zhang; Junxing Zhu; Jianxin Wang; |
| 195 | Interpretable Emotion Analysis Based on Knowledge Graph and OCC Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, an analysis framework is constructed for interpreting casual association based on the emotional logic. |
Shuo Wang; Yifei Zhang; Bochen Lin; Boxun Li; |
| 196 | AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To bridge the gap between large-scale graph training and contrastive learning, we propose adaptive subgraph contrastive learning (AdaGCL). |
Yili Wang; Kaixiong Zhou; Rui Miao; Ninghao Liu; Xin Wang; |
| 197 | ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, current methods suffer from the following issues: 1) sparsity of user-item interactions, 2) uncertainty of sequential records, 3) long-tail items. In this paper, we propose to incorporate contrastive learning into the framework of Variational AutoEncoders to address these challenges simultaneously. |
Yu Wang; Hengrui Zhang; Zhiwei Liu; Liangwei Yang; Philip S. Yu; |
| 198 | Imbalanced Graph Classification Via Graph-of-Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To this end, we introduce a novel framework, Graph-of-Graph Neural Networks (G2GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from stochastic augmentations of graphs. |
Yu Wang; Yuying Zhao; Neil Shah; Tyler Derr; |
| 199 | Latent Coreset Sampling Based Data-Free Continual Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Accordingly, how to select a suitable coreset during continual learning becomes significant in such setting. In this work, we propose a novel approach that leverages continual coreset sampling (CCS) to address these challenges. |
Zhuoyi Wang; Dingcheng Li; Ping Li; |
| 200 | Bandit Learning in Many-to-One Matching Markets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus we consider a more general bandit learning problem in many-to-one matching markets where each arm has a fixed capacity and agents make choices with multiple rounds of iterations. |
Zilong Wang; Liya Guo; Junming Yin; Shuai Li; |
| 201 | Multi-level Contrastive Learning Framework for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the problem of SR and propose a novel multi-level contrastive learning framework for sequential recommendation, named MCLSR. |
Ziyang Wang; Huoyu Liu; Wei Wei; Yue Hu; Xian-Ling Mao; Shaojian He; Rui Fang; Dangyang Chen; |
| 202 | Dynamic Hypergraph Learning for Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: From a learning perspective, we argue that the fixed heuristic topology of hypergraph may become a limitation and thus potentially compromise the recommendation performance. To tackle this issue, we propose a novel dynamic hypergraph learning framework for collaborative filtering (DHLCF), which learns hypergraph structures and makes recommendations collectively in a unified framework. |
Chunyu Wei; Jian Liang; Bing Bai; Di Liu; |
| 203 | Dynamic Transfer Gaussian Process Regression Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we work on a challenging dynamic transfer regression problem where domains come in a streaming manner. |
Pengfei Wei; Xinghua Qu; Wen Song; Zejun Ma; |
| 204 | Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Neural ranking models (NRMs) have achieved promising results in information retrieval. |
Chen Wu; Ruqing Zhang; Jiafeng Guo; Wei Chen; Yixing Fan; Maarten de Rijke; Xueqi Cheng; |
| 205 | RelpNet: Relation-based Link Prediction Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a relation-based link prediction neural network named RelpNet, which aggregates edge features along the structural interactions between two target nodes and directly represents their relationship. |
Ensen Wu; Hongyan Cui; Zunming Chen; |
| 206 | Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To mitigate the aforementioned problems, we propose a novel training framework named Triplet Importance Learning (TIL), which adaptively learns the importance score of training triplets. |
Haolun Wu; Chen Ma; Yingxue Zhang; Xue Liu; Ruiming Tang; Mark Coates; |
| 207 | Contrastive Label Correlation Enhanced Unified Hashing Encoder for Cross-modal Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To overcome modality heterogeneity, we propose a shared transformer encoder (UniHash) to unify the cross-modal hashing into the same semantic space. |
Hongfa Wu; Lisai Zhang; Qingcai Chen; Yimeng Deng; Joanna Siebert; Yunpeng Han; Zhonghua Li; Dejiang Kong; Zhao Cao; |
| 208 | Leveraging Multiple Types of Domain Knowledge for Safe and Effective Drug Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing work either missed/underutilized the important information lying in the drug molecule structure in drug encoding or has insufficient control over Drug-Drug Interactions (DDIs) rates within the predictions. To address these limitations, we propose CSEDrug, which enhances the drug encoding and DDIs controlling by leveraging multi-faceted drug knowledge, including molecule structures of drugs, Synergistic DDIs (SDDIs), and Antagonistic DDIs (ADDIs). |
Jialun Wu; Buyue Qian; Yang Li; Zeyu Gao; Meizhi Ju; Yifan Yang; Yefeng Zheng; Tieliang Gong; Chen Li; Xianli Zhang; |
| 209 | FedCDR: Federated Cross-Domain Recommendation for Privacy-Preserving Rating Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose FedCDR, a federated learning based cross-domain recommendation system that effectively trains the recommendation model while keeping users’ raw data and private user-specific parameters located on their own devices. |
Wu Meihan; Li Li; Chang Tao; Eric Rigall; Wang Xiaodong; Xu Cheng-Zhong; |
| 210 | Incorporating Peer Reviews and Rebuttal Counter-Arguments for Meta-Review Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To generate a comprehensive meta-review that well organizes reviewers’ opinions and authors’ responses, we present a novel generation model that is capable of explicitly modeling the complicated argumentation structure from not only arguments between the reviewers and the authors but also the inter-reviewer discussions. |
Po-Cheng Wu; An-Zi Yen; Hen-Hsen Huang; Hsin-Hsi Chen; |
| 211 | A Gumbel-based Rating Prediction Framework for Imbalanced Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In contrast to the normal assumption, we propose a novel Gumbel-based Variational Network framework (GVN) to model rating imbalance and augment feature representations by the Gumbel distributions. |
Yuexin Wu; Xiaolei Huang; |
| 212 | RISE: A Velocity Control Framework with Minimal Impacts Based on Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a velocity control framework based on reinforcement learning, called RISE (contRol velocIty for autonomouS vEhicle). |
Yuyang Xia; Shuncheng Liu; Xu Chen; Zhi Xu; Kai Zheng; Han Su; |
| 213 | Representation Matters When Learning From Biased Feedback in Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing methods for dealing with selection bias are usually affected by the error of propensity weight estimation, have high variance, or assume access to uniform data, which is expensive to be collected in practice. In this work, we address these issues by proposing Learning De-biased Representations (LDR), a framework derived from the representation learning perspective. |
Teng Xiao; Zhengyu Chen; Suhang Wang; |
| 214 | MARINA: An MLP-Attention Model for Multivariate Time-Series Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, in this paper, we propose a Multi-layer perceptron (<u>M</u>LP)-<u>a</u>ttention based multivariate time-se<u>ri</u>es a<u>na</u>lysis model MARINA. |
Jiandong Xie; Yue Cui; Feiteng Huang; Chao Liu; Kai Zheng; |
| 215 | Large-scale Entity Alignment Via Knowledge Graph Merging, Partitioning and Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, such a partitioning and learning process results in an excessive loss of structure and alignment. Therefore, in this work, we propose a scalable GNN-based entity alignment approach to reduce the structure and alignment loss from three perspectives. |
Kexuan Xin; Zequn Sun; Wen Hua; Wei Hu; Jianfeng Qu; Xiaofang Zhou; |
| 216 | AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQL Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This study investigates the task of knowledge-based question generation (KBQG). |
Guanming Xiong; Junwei Bao; Wen Zhao; Youzheng Wu; Xiaodong He; |
| 217 | Dually Enhanced Propensity Score Estimation in Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Inspired by the observation, we propose to estimate the propensity scores from the views of user and item, called Dually Enhanced Propensity Score Estimation (DEPS). |
Chen Xu; Jun Xu; Xu Chen; Zhenghua Dong; Ji-Rong Wen; |
| 218 | Taxonomy-Enhanced Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce Taxonomy-Enhanced Graph Neural Networks (Taxo-GNN). |
Lingjun Xu; Shiyin Zhang; Guojie Song; Junshan Wang; Tianshu Wu; Guojun Liu; |
| 219 | Traffic Speed Imputation with Spatio-Temporal Attentions and Cycle-Perceptual Training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel solution called STCPA for the speed imputation problem. |
Qianxiong Xu; Sijie Ruan; Cheng Long; Liang Yu; Chen Zhang; |
| 220 | Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching Via Prompt Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we consider five common text matching tasks including document retrieval, open-domain question answering, retrieval-based dialogue, paraphrase identification, and natural language inference. |
Shicheng Xu; Liang Pang; Huawei Shen; Xueqi Cheng; |
| 221 | Dynamic Causal Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we design a causal graph with loops to describe the dynamic process of recommendation. |
Shuyuan Xu; Juntao Tan; Zuohui Fu; Jianchao Ji; Shelby Heinecke; Yongfeng Zhang; |
| 222 | Evidence-aware Document-level Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, there often exist strong semantic correlations between evidence sentences that collaborate together to describe a specific relation. To address these challenges, we propose a novel evidence-aware model for document-level RE. |
Tianyu Xu; Wen Hua; Jianfeng Qu; Zhixu Li; Jiajie Xu; An Liu; Lei Zhao; |
| 223 | Effects of Stubbornness on Opinion Dynamics Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study a popular opinion model in the presence of inhomogeneous stubbornness. |
Wanyue Xu; Liwang Zhu; Jiale Guan; Zuobai Zhang; Zhongzhi Zhang; |
| 224 | Drive Less But Finish More: Food Delivery Based on Multi-Level Workers in Spatial Crowdsourcing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the problem of on-demand food delivery in a new setting where two groups of workers — riders and taxi drivers (drivers for short) — cooperate with each other for better service. |
Xiaojia Xu; An Liu; Guanfeng Liu; Zhixu Li; Lei Zhao; |
| 225 | Dissecting Cross-Layer Dependency Inference on Multi-Layered Inter-Dependent Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, to tackle the sparsity challenge for static networks, we propose FITO-S, which incorporates a position embedding matrix generated by random walk with restart and the embedding space transformation function. |
Yuchen Yan; Qinghai Zhou; Jinning Li; Tarek Abdelzaher; Hanghang Tong; |
| 226 | Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the problem, this paper treats vertices and hyperedges equally and proposes a new hypergraph expansion named the line expansion(LE) for hypergraphs learning. |
Chaoqi Yang; Ruijie Wang; Shuochao Yao; Tarek Abdelzaher; |
| 227 | Hierarchical Representation for Multi-view Clustering: From Intra-sample to Intra-view to Inter-view Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a novel hierarchical representation for MVC method via the integration of intra-sample, intra-view, and inter-view representation learning models. |
Jing-Hua Yang; Chuan Chen; Hong-Ning Dai; Meng Ding; Le-Le Fu; Zibin Zheng; |
| 228 | GROWN+UP: A ”Graph Representation Of A Webpage" Network Utilizing Pre-training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We aim to close the gap by introducing an agnostic deep graph neural network feature extractor that can ingest webpage structures, pre-train self-supervised on massive unlabeled data, and fine-tune to arbitrary tasks on webpages effectually. |
Benedict Yeoh; Huijuan Wang; |
| 229 | Scalable Graph Sampling on GPUs with Compressed Graph Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we introduce a Chunk-wise Graph Compression format (CGC) to effectively reduce the graph size and save the graph transfer cost. |
Hongbo Yin; Yingxia Shao; Xupeng Miao; Yawen Li; Bin Cui; |
| 230 | A Biased Sampling Method for Imbalanced Personalized Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We thus propose an efficient <u>Vi</u>tal <u>N</u>egative <u>S</u>ampler (VINS) to alleviate the class-imbalance issue for pairwise ranking model, in particular for deep learning models optimized by gradient methods. |
Lu Yu; Shichao Pei; Feng Zhu; Longfei Li; Jun Zhou; Chuxu Zhang; Xiangliang Zhang; |
| 231 | The Interaction Graph Auto-encoder Network Based on Topology-aware for Transferable Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As a result, we propose an Interaction Graph Auto-encoder Network (IGA) based on topology-aware to address the transferable recommendation problem. |
Ruiyun Yu; Kang Yang; Bingyang Guo; |
| 232 | Cognize Yourself: Graph Pre-Training Via Core Graph Cognizing and Differentiating Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Motivated by Meta-Cognitive theory, we propose a novel model named Core Graph Cognizing and Differentiating (CORE) to deal with the problem in an effective approach. |
Tao Yu; Yao Fu; Linghui Hu; Huizhao Wang; Weihao Jiang; Shiliang Pu; |
| 233 | Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19 Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose contrastive adaptation network for early misinformation detection (CANMD). |
Zhenrui Yue; Huimin Zeng; Ziyi Kou; Lanyu Shang; Dong Wang; |
| 234 | LTE4G: Long-Tail Experts for Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel framework for training GNNs, called Long-Tail Experts for Graphs (LTE4G), which jointly considers the class long-tailedness, and the degree long-tailedness for node classification. |
Sukwon Yun; Kibum Kim; Kanghoon Yoon; Chanyoung Park; |
| 235 | Joint Clothes Detection and Attribution Prediction Via Anchor-free Framework with Decoupled Representation Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In addition, it may also confront with optimization contradiction problem in the training procedure, as the clothes detection and attribution prediction branches demand diverse optimization. In this work, to handle the above problems, we aim to develop an end-to-end anchor-free framework by involving an additional branch for joint clothes detection and attribution prediction. |
Fankai Zeng; Mingbo Zhao; Zhao Zhang; Shanchuan Gao; Lu Cheng; |
| 236 | Causal Learning Empowered OD Prediction for Urban Planning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose designs to solve both challenges. |
Jinwei Zeng; Guozhen Zhang; Can Rong; Jingtao Ding; Jian Yuan; Yong Li; |
| 237 | Interactive Contrastive Learning for Self-Supervised Entity Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose an interactive contrastive learning model for self-supervised EA. |
Kaisheng Zeng; Zhenhao Dong; Lei Hou; Yixin Cao; Minghao Hu; Jifan Yu; Xin Lv; Lei Cao; Xin Wang; Haozhuang Liu; Yi Huang; Junlan Feng; Jing Wan; Juanzi Li; Ling Feng; |
| 238 | Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To address the challenges, we propose AutoSMOTE, an automated over-sampling algorithm that can jointly optimize different levels of decisions. |
Daochen Zha; Kwei-Herng Lai; Qiaoyu Tan; Sirui Ding; Na Zou; Xia Ben Hu; |
| 239 | Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we demonstrate the importance of separately evaluating the two capabilities of neural retrieval models. |
Jingtao Zhan; Xiaohui Xie; Jiaxin Mao; Yiqun Liu; Jiafeng Guo; Min Zhang; Shaoping Ma; |
| 240 | TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD for short, to exploit both time and frequency domains for performance improvement. |
Chaoli Zhang; Tian Zhou; Qingsong Wen; Liang Sun; |
| 241 | Hierarchical Item Inconsistency Signal Learning for Sequence Denoising in Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a novel sequence denoising paradigm for sequential recommendation by learning hierarchical item inconsistency signals. |
Chi Zhang; Yantong Du; Xiangyu Zhao; Qilong Han; Rui Chen; Li Li; |
| 242 | Look Twice As Much As You Say: Scene Graph Contrastive Learning for Self-Supervised Image Caption Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose SGCL, a novel Scene Graph Contrastive Learning model for self-supervised image caption generation. |
Chunhui Zhang; Chao Huang; Youhuan Li; Xiangliang Zhang; Yanfang Ye; Chuxu Zhang; |
| 243 | Along The Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We empirically find that current models have difficulty distinguishing representations of the same entity or relation at different timestamps. In this regard, we propose a TimeLine-Traced Knowledge Graph Embedding method (TLT-KGE) for temporal knowledge graph completion. |
Fuwei Zhang; Zhao Zhang; Xiang Ao; Fuzhen Zhuang; Yongjun Xu; Qing He; |
| 244 | Control-based Bidding for Mobile Livestreaming Ads with Exposure Guarantee Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a bidding-based ad delivery algorithm for mobile livestreaming ads that can provide advertisers with bidding strategies for optimizing diverse marketing objectives under general ad performance guaranteed constraints, such as ad exposure and cost-efficiency constraints. |
Haoqi Zhang; Junqi Jin; Zhenzhe Zheng; Fan Wu; Haiyang Xu; Jian Xu; |
| 245 | Disentangling Past-Future Modeling in Sequential Recommendation Via Dual Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To alleviate the training-inference gap, we propose a new framework DualRec, which achieves past-future disentanglement and past-future mutual enhancement by a novel dual network. |
Hengyu Zhang; Enming Yuan; Wei Guo; Zhicheng He; Jiarui Qin; Huifeng Guo; Bo Chen; Xiu Li; Ruiming Tang; |
| 246 | Dismantling Complex Networks By A Neural Model Trained from Tiny Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Can we employ one neural model to efficiently dismantle many complex yet unique networks? |
Jiazheng Zhang; Bang Wang; |
| 247 | Disentangled Representation for Long-tail Senses of Word Sense Disambiguation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel DR by constraining the covariance matrix of a multivariate Gaussian distribution, which can enhance the strength of independence among features compared to β-VAE. |
Junwei Zhang; Ruifang He; Fengyu Guo; Jinsong Ma; Mengnan Xiao; |
| 248 | Handling RDF Streams: Harmonizing Subgraph Matching, Adaptive Incremental Maintenance, and Matching-free Updates Together Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel graph-based framework, referred as IncTreeRDF, towards continuous SPARQL query evaluation over RDF data streams. |
Qianzhen Zhang; Deke Guo; Xiang Zhao; Lailong Luo; |
| 249 | Contrastive Knowledge Graph Error Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To this end, we propose a novel framework – ContrAstive knowledge Graph Error Detection (CAGED). |
Qinggang Zhang; Junnan Dong; Keyu Duan; Xiao Huang; Yezi Liu; Linchuan Xu; |
| 250 | A Simple Meta-path-free Framework for Heterogeneous Network Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Attempting to break the limitations of pre-set meta-paths and non-global node learning in existing models, we propose a simple but effective framework for heterogeneous network embedding learning by encoding the original multi-type nodes and relations directly in a self-supervised way. |
Rui Zhang; Arthur Zimek; Peter Schneider-Kamp; |
| 251 | Unsupervised Representation Learning on Attributed Multiplex Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we propose a novel and flexible unsupervised network embedding method for attributed multiplex networks to generate more precise node embeddings by simplified Bernstein encoders and alternate contrastive learning between local and global. |
Rui Zhang; Arthur Zimek; Peter Schneider-Kamp; |
| 252 | Automating DBSCAN Via Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Abstract: DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality. However, due to its high sensitivity parameters, the accuracy of the … |
Ruitong Zhang; Hao Peng; Yingtong Dou; Jia Wu; Qingyun Sun; Yangyang Li; Jingyi Zhang; Philip S. Yu; |
| 253 | GBERT: Pre-training User Representations for Ephemeral Group Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Ephemeral groups are formed ad-hoc for one-time activities, and therefore they suffer severely from data sparsity and cold-start problems. To deal with such problems, we propose a pre-training and fine-tuning method called GBERT for improved group recommendations, which employs BERT to enhance the expressivity and capture group-specific preferences of members. |
Song Zhang; Nan Zheng; Danli Wang; |
| 254 | DeepVT: Deep View-Temporal Interaction Network for News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Personalized news recommendation aims to provide people with customized content, which can effectively improve the reading experience. Because user interests in news are diverse … |
Xuanyu Zhang; Qing Yang; Dongliang Xu; |
| 255 | RuDi: Explaining Behavior Sequence Models By Automatic Statistics Generation and Rule Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we bridge the gap between effective but black-box models and transparent rule models. |
Yao Zhang; Yun Xiong; Yiheng Sun; Caihua Shan; Tian Lu; Hui Song; Yangyong Zhu; |
| 256 | Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To fill the gap, we propose two novel BAFT settings, cross-domain and cross-domain cross-architecture BAFT, which only assume that (1) the target model for attacking is a fine-tuned model, and (2) the source domain data is known and accessible. |
Yinghua Zhang; Yangqiu Song; Kun Bai; Qiang Yang; |
| 257 | Towards Understanding The Overfitting Phenomenon of Deep Click-Through Rate Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In the context of Click-Through Rate (CTR) prediction, we observe an interesting one-epoch overfitting problem: the model performance exhibits a dramatic degradation at the beginning of the second epoch. |
Zhao-Yu Zhang; Xiang-Rong Sheng; Yujing Zhang; Biye Jiang; Shuguang Han; Hongbo Deng; Bo Zheng; |
| 258 | MAE4Rec: Storage-saving Transformer for Sequential Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a novel storage-saving SRS framework, MAE4Rec, based on a unidirectional self-attentive mechanism and masked autoencoder. |
Kesen Zhao; Xiangyu Zhao; Zijian Zhang; Muyang Li; |
| 259 | CPEE: Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, in this paper, we formalize CCJP as a multi-task learning problem and propose a CCJP method centering on the trial mode of essential elements, CPEE, which explores the practical judicial process and analyzes comprehensive legal essential elements to make judgment predictions. |
Lili Zhao; Linan Yue; Yanqing An; Yuren Zhang; Jun Yu; Qi Liu; Enhong Chen; |
| 260 | Two-Level Graph Path Reasoning for Conversational Recommendation with User Realistic Preference Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the aforementioned issues, a novel method that combines graph path reasoning with multi-turn conversation is proposed, called Graph Path reasoning for conversational Recommendation (GPR). |
Rongmei Zhao; Shenggen Ju; Jian Peng; Ning Yang; Fanli Yan; Siyu Sun; |
| 261 | End-to-end Modularity-based Community Co-partition in Bipartite Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For instance, the building blocks of general heterogeneous networks are the bipartite model, which is a ubiquitous structure where two types of nodes co-exist. In view of these challenges, we study the end-to-end community co-partition of two types of nodes in bipartite networks. |
Cangqi Zhou; Yuxiang Wang; Jing Zhang; Jiqiong Jiang; Dianming Hu; |
| 262 | MentorGNN: Deriving Curriculum for Pre-Training GNNs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose an end-to-end model named MentorGNN that aims to supervise the pre-training process of GNNs across graphs with diverse structures and disparate feature spaces. |
Dawei Zhou; Lecheng Zheng; Dongqi Fu; Jiawei Han; Jingrui He; |
| 263 | D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Most Graph Neural Networks (GNNs) exhibit poor performance on digraphs due to the neglect of modeling neighborhoods and preserving asymmetry. In this paper, we address these notable challenges by leveraging hyperbolic collaborative learning from multi-ordered and partitioned neighborhoods, and regularizers inspired by socio-psychological factors. |
Honglu Zhou; Advith Chegu; Samuel S. Sohn; Zuohui Fu; Gerard de Melo; Mubbasir Kapadia; |
| 264 | Multi-task Learning with Adaptive Global Temporal Structure for Predicting Alzheimer’s Disease Progression Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a multi-task learning approach for predicting the progression of Alzheimer’s disease (AD), known as the most common form of dementia. |
Menghui Zhou; Yu Zhang; Tong Liu; Yun Yang; Po Yang; |
| 265 | Adversarial Robustness Through Bias Variance Decomposition: A New Perspective for Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Federated learning learns a neural network model by aggregating the knowledge from a group of distributed clients under the privacy-preserving constraint. In this work, we show that this paradigm might inherit the adversarial vulnerability of the centralized neural network, i.e., it has deteriorated performance on adversarial examples when the model is deployed. |
Yao Zhou; Jun Wu; Haixun Wang; Jingrui He; |
| 266 | Decoupled Hyperbolic Graph Attention Network for Modeling Substitutable and Complementary Item Relationships Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel Decoupled Hyperbolic Graph Attention Network (DHGAN). |
Zhiheng Zhou; Tao Wang; Linfang Hou; Xinyuan Zhou; Mian Ma; Zhuoye Ding; |
| 267 | Personalized Query Suggestion with Searching Dynamic Flow for Online Recruitment Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose <u>D</u>ynamic <u>S</u>earching <u>F</u>low <u>M</u>odel (DSFM), a query suggestion framework that is capable of modeling and refining user search intent progressively in recruitment scenarios by leveraging a dynamic flow mechanism. |
Zile Zhou; Xiao Zhou; Mingzhe Li; Yang Song; Tao Zhang; Rui Yan; |
| 268 | From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a curriculum learning framework for context-aware document ranking, in which the ranking model learns matching signals between the search context and the candidate document in an easy-to-hard manner. |
Yutao Zhu; Jian-Yun Nie; Yixuan Su; Haonan Chen; Xinyu Zhang; Zhicheng Dou; |
| 269 | Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a new label inference model, namely LInDT, which integrates both Bayesian label transition and topology-based label propagation for improving the robustness of GNNs against topological perturbations. |
Jun Zhuang; Mohammad Al Hasan; |
| 270 | Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we focus on an important, practical, but often overlooked task: domain-level zero-shot recommendation (DZSR). |
Jianhuan Zhuo; Jianxun Lian; Lanling Xu; Ming Gong; Linjun Shou; Daxin Jiang; Xing Xie; Yinliang Yue; |
| 271 | Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring contrastive learning in KGR and propose a novel multi-level interactive contrastive learning mechanism, to alleviate the aforementioned challenges. |
Ding Zou; Wei Wei; Ziyang Wang; Xian-Ling Mao; Feida Zhu; Rui Fang; Dangyang Chen; |
| 272 | Hierarchical Conversational Preference Elicitation with Bandit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We conduct a survey and analyze a real-world dataset to find that, unlike assumptions made in prior works, key-term rewards are mainly affected by rewards of representative items. We propose two bandit algorithms, Hier-UCB and Hier-LinUCB, that leverage this observed relationship and the hierarchical structure between key-terms and items to efficiently learn which items to recommend. |
Jinhang Zuo; Songwen Hu; Tong Yu; Shuai Li; Handong Zhao; Carlee Joe-Wong; |
| 273 | A Case Study in Educational Recommenders: Recommending Music Partitures at Tomplay Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here we provide a study on the benefits of recommendation technologies in an educational platform with a focus on music learning. |
Ahmad Ajalloeian; Michalis Vlachos; Johannes Schneider; Alexis Steinmann; |
| 274 | E-Commerce Promotions Personalization Via Online Multiple-Choice Knapsack with Uplift Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an Online Constrained Multiple-Choice Promotions Personalization framework, driven by causal incremental estimations achieved by uplift modeling. |
Javier Albert; Dmitri Goldenberg; |
| 275 | Will This Online Shopping Session Succeed? Predicting Customer’s Purchase Intention Using Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a customer embedding representation that is based on the customer’s click-events recorded during browsing sessions. |
Miguel Alves Gomes; Richard Meyes; Philipp Meisen; Tobias Meisen; |
| 276 | Improving Text-based Similar Product Recommendation for Dynamic Product Advertising at Yahoo Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel product name generation model that fine tunes a pre-trained Transformer-based language model with a sequence to sequence objective. |
Xiao Bai; Lei Duan; Richard Tang; Gaurav Batra; Ritesh Agrawal; |
| 277 | Efficient and Effective SPARQL Autocompletion on Very Large Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: For existing SPARQL engines, these queries are impractically slow on large knowledge graphs. We present various algorithmic and engineering improvements of an open-source SPARQL engine such that these queries are executed efficiently. |
Hannah Bast; Johannes Kalmbach; Theresa Klumpp; Florian Kramer; Niklas Schnelle; |
| 278 | Graph Neural Networks Pretraining Through Inherent Supervision for Molecular Property Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This is challenging as creating these datasets requires labeled data or expert knowledge. To aid in solving this pressing issue, we introduce MISU – Molecular Inherent SUpervision, a unique method for pretraining graph neural networks for molecular property prediction. |
Roy Benjamin; Uriel Singer; Kira Radinsky; |
| 279 | Debiased Balanced Interleaving at Amazon Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a new interleaving method that utilizes a counterfactual evaluation framework for credit attribution while sticking to the simple ranking merge policy of balanced interleaving, and formally derive an unbiased estimator for comparing rankers with theoretical guarantees. |
Nan Bi; Pablo Castells; Daniel Gilbert; Slava Galperin; Patrick Tardif; Sachin Ahuja; |
| 280 | A Relevant and Diverse Retrieval-enhanced Data Augmentation Framework for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing neural sequential recommendation models may not perform well in practice due to the sparsity of the real-world data especially in cold-start scenarios. To tackle this problem, we propose the model ReDA, which stands for Retrieval-enhanced Data Augmentation for modeling sequential user behaviors. |
Shuqing Bian; Wayne Xin Zhao; Jinpeng Wang; Ji-Rong Wen; |
| 281 | Fooling MOSS Detection with Pretrained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we explore whether transformers can be used to solve introductory level programming assignments while bypassing commonly used AI tools to detect similarities between pieces of software. |
Stella Biderman; Edward Raff; |
| 282 | A Context-Enhanced Transformer with Abbr-Recover Policy for Chinese Abbreviation Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we proposed a Context-Enhanced Transformer with Abbr-Recover policy, namely CETAR, for Chinese abbreviation prediction. |
Kaiyan Cao; Deqing Yang; Jingping Liu; Jiaqing Liang; Yanghua Xiao; Feng Wei; Baohua Wu; Quan Lu; |
| 283 | Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To this end, we propose a simulation-informed revenue extrapolation (SiRE) algorithm that generates fine-grained long-term revenue predictions on small datasets and short time-series. |
Lele Cao; Sonja Horn; Vilhelm von Ehrenheim; Richard Anselmo Stahl; Henrik Landgren; |
| 284 | GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose GIFT, an efficient Graph-guIded Feature Transfer system, to fully take advantages of the rich information of warmed-up videos to compensate for the cold-start ones. |
Yi Cao; Sihao Hu; Yu Gong; Zhao Li; Yazheng Yang; Qingwen Liu; Shouling Ji; |
| 285 | Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose SDIM (Sampling-based Deep Interest Modeling), a simple yet effective sampling-based end-to-end approach for modeling long-term user behaviors. |
Yue Cao; Xiaojiang Zhou; Jiaqi Feng; Peihao Huang; Yao Xiao; Dayao Chen; Sheng Chen; |
| 286 | Numerical Feature Representation with Hybrid N-ary Encoding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To emphasize both continuity and discriminability for numerical features, we propose an end-to-end representation learning framework named NaryDis. |
Bo Chen; Huifeng Guo; Weiwen Liu; Yue Ding; Yunzhe Li; Wei Guo; Yichao Wang; Zhicheng He; Ruiming Tang; Rui Zhang; |
| 287 | Generating Persuasive Responses to Customer Reviews with Multi-Source Prior Knowledge in E-commerce Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a Multi-Source Multi-Aspect Attentive Generation model for persuasive response generation. |
Bo Chen; Jiayi Liu; Mieradilijiang Maimaiti; Xing Gao; Ji Zhang; |
| 288 | Hierarchically Constrained Adaptive Ad Exposure in Feeds Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we focus on the application-level performance optimization under hierarchical constraints in feeds and formulate adaptive ad exposure as a Dynamic Knapsack Problem. |
Dagui Chen; Qi Yan; Chunjie Chen; Zhenzhe Zheng; Yangsu Liu; Zhenjia Ma; Chuan Yu; Jian Xu; Bo Zheng; |
| 289 | Approximate Nearest Neighbor Search Under Neural Similarity Metric for Large-Scale Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel method to extend ANN search to arbitrary matching functions, e.g., a deep neural network. |
Rihan Chen; Bin Liu; Han Zhu; Yaoxuan Wang; Qi Li; Buting Ma; Qingbo Hua; Jun Jiang; Yunlong Xu; Hongbo Deng; Bo Zheng; |
| 290 | ReLiable: Offline Reinforcement Learning for Tactical Strategies in Professional Basketball Games Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present ReLiable (ReinforcemEnt Learning In bAsketBaLl gamEs). |
Xiusi Chen; Jyun-Yu Jiang; Kun Jin; Yichao Zhou; Mingyan Liu; P. Jeffrey Brantingham; Wei Wang; |
| 291 | Mitigating Biases in Student Performance Prediction Via Attention-Based Personalized Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a methodology for predicting student performance from their online learning activities that optimizes inference accuracy over different demographic groups such as race and gender. |
Yun-Wei Chu; Seyyedali Hosseinalipour; Elizabeth Tenorio; Laura Cruz; Kerrie Douglas; Andrew Lan; Christopher Brinton; |
| 292 | Hierarchical Capsule Prediction Network for Marketing Campaigns Effect Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Furthermore, each subject is always under the interference of several marketing campaigns simultaneously. Therefore, we cannot easily parse and evaluate the effect of a single marketing campaign. |
Zhixuan Chu; Hui Ding; Guang Zeng; Yuchen Huang; Tan Yan; Yulin Kang; Sheng Li; |
| 293 | Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application Under The Clean Water Act Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper introduces a new, highly consequential setting for the use of computer vision for environmental sustainability. |
Ben Chugg; Nicolas Rothbacher; Alex Feng; Xiaoqi Long; Daniel E. Ho; |
| 294 | DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu Maps Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents not only DuMapper I, which imitates the process of POI verification conducted by expert mappers, but also proposes DuMapper II, a highly efficient framework to accelerate POI verification by means of deep multimodal embedding and approximate nearest neighbor (ANN) search. |
Miao Fan; Jizhou Huang; Haifeng Wang; |
| 295 | Towards Practical Large Scale Non-Linear Semi-Supervised Learning with Balancing Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, to make the balancing constraint handle different proportions of positive and negative samples among labeled and unlabeled data, we propose a soft balancing constraint for S3VM. |
Zhengqing Gao; Huimin Wu; Martin Takáč; Bin Gu; |
| 296 | Cascaded Debiasing: Studying The Cumulative Effect of Multiple Fairness-Enhancing Interventions Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Understanding the cumulative effect of multiple fairness-enhancing interventions at different stages of the machine learning (ML) pipeline is a critical and underexplored facet of the fairness literature. Such knowledge can be valuable to data scientists/ML practitioners in designing fair ML pipelines. |
Bhavya Ghai; Mihir Mishra; Klaus Mueller; |
| 297 | RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion Using Cascaded Set Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a computational approach for recipe ideation, a downstream task that helps users select and gather ingredients for creating dishes. |
Mogan Gim; Donghee Choi; Kana Maruyama; Jihun Choi; Hajung Kim; Donghyeon Park; Jaewoo Kang; |
| 298 | Real-time Short Video Recommendation on Mobile Devices Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, as users continue to watch videos and feedback, the changing context leads the ranking of the server-side recommendation system inaccurate. In this paper, we propose to deploy a short video recommendation framework on mobile devices to solve these problems. |
Xudong Gong; Qinlin Feng; Yuan Zhang; Jiangling Qin; Weijie Ding; Biao Li; Peng Jiang; Kun Gai; |
| 299 | Towards Fairer Classifier Via True Fairness Score Path Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Such a method is obviously time consuming, besides it cannot guarantee to find the fairer classifier (i.e., original fairness constraint is less than a smaller threshold). To address this challenging problem, we propose a novel true fairness score path algorithm which guarantees to find fairer classifiers efficiently. |
Bin Gu; Zhou Zhai; Xiang Li; Heng Huang; |
| 300 | UDM: A Unified Deep Matching Framework in Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the item-to-item relevance between user interacted item and target item is not considered in the deep matching models which is computationally prohibitive for large-scale applications. In this paper, we propose a unified deep matching framework called UDM for the matching stage to mitigate this issue. |
Long Guo; Fei Fang; Binqiang Zhao; Bin Cui; |
| 301 | Sentaur: Sensor Observable Data Model for Smart Spaces Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: By supporting mechanisms to map/translate data, concepts, and queries between the two levels, Sentaur relieves application developers from having to know or reason about either capabilities of sensors or write sensor specific code. This paper describes Sentaur’s data model, its translation strategy, and highlights its benefits through real-world case studies. |
Peeyush Gupta; Sharad Mehrotra; Shantanu Sharma; Roberto Yus; Nalini Venkatasubramanian; |
| 302 | Addressing Cold Start in Product Search Via Empirical Bayes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a principled approach to deal with cold start in a large-scale e-commerce search system. |
Cuize Han; Pablo Castells; Parth Gupta; Xu Xu; Vamsi Salaka; |
| 303 | PROPN: Personalized Probabilistic Strategic Parameter Optimization in Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a personalized probabilistic solution for strategic parameter optimization. |
Pengfei He; Haochen Liu; Xiangyu Zhao; Hui Liu; Jiliang Tang; |
| 304 | BLUTune: Query-informed Multi-stage IBM Db2 Tuning Via ML Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a query informed tuning system called BLUTune which uses machine learning (ML)-deep reinforcement learning based on advantage actor critic neural networks-to tune configurations within defined resource constraints. |
Connor Henderson; Spencer Bryson; Vincent Corvinelli; Parke Godfrey; Piotr Mierzejewski; Jaroslaw Szlichta; Calisto Zuzarte; |
| 305 | DuETA: Traffic Congestion Propagation Pattern Modeling Via Efficient Graph Learning for ETA Prediction at Baidu Maps Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The ETA prediction models previously deployed at Baidu Maps have addressed the factors of spatial-temporal interaction (ConSTGAT) and driving behavior (SSML). In this work, we believe that modeling traffic congestion propagation patterns is of great importance toward accurately performing ETA prediction, and we focus on this factor to improve ETA performance. |
Jizhou Huang; Zhengjie Huang; Xiaomin Fang; Shikun Feng; Xuyi Chen; Jiaxiang Liu; Haitao Yuan; Haifeng Wang; |
| 306 | DuIVRS: A Telephonic Interactive Voice Response System for Large-Scale POI Attribute Acquisition at Baidu Maps Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present our efforts and findings from a 3-year longitudinal study on designing and implementing DuIVRS, which is an alternative, fully automatic, and production-proven solution for large-scale POI attribute acquisition via completely machine-directed dialogues. |
Jizhou Huang; Haifeng Wang; Shaolei Wang; |
| 307 | Incorporating Fairness in Large-scale Evacuation Planning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: On the positive side, we present a heuristic optimization method MIP-LNS, based on the well-known Large Neighborhood Search framework, that can find good approximate solutions in reasonable amount of time. |
Kazi Ashik Islam; Da Qi Chen; Madhav Marathe; Henning Mortveit; Samarth Swarup; Anil Vullikanti; |
| 308 | Bridging Self-Attention and Time Series Decomposition for Periodic Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study how to capture explicit periodicity to boost the accuracy of deep models in univariate time series forecasting. |
Song Jiang; Tahin Syed; Xuan Zhu; Joshua Levy; Boris Aronchik; Yizhou Sun; |
| 309 | Adaptive Domain Interest Network for Multi-domain Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present Adaptive Domain Interest Network(ADIN) that adaptively handles the commonalities and diversities across scenarios, making full use of multi-scenarios data during training. |
Yuchen Jiang; Qi Li; Han Zhu; Jinbei Yu; Jin Li; Ziru Xu; Huihui Dong; Bo Zheng; |
| 310 | RaDaR: A Real-Word Dataset for AI Powered Run-time Detection of Cyber-Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents RaDaR, an open real-world dataset for run-time behavioral analysis of Windows malware. |
Sareena Karapoola; Nikhilesh Singh; Chester Rebeiro; Kamakoti V.; |
| 311 | PAVE: Lazy-MDP Based Ensemble to Improve Recall of Product Attribute Extraction Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present PAVE: Product Attribute Value Ensemble, a novel reinforcement learning model that usesLazy-MDP formalism to solve for low recall by aggregating information from a sequence of product neighbors. |
Kushal Kumar; Anoop Saladi; |
| 312 | Billion-user Customer Lifetime Value Prediction: An Industrial-scale Solution from Kuaishou Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a complete set of industrial-level LTV modeling solutions. |
Kunpeng Li; Guangcui Shao; Naijun Yang; Xiao Fang; Yang Song; |
| 313 | An Adaptive Framework for Confidence-constraint Rule Set Learning Algorithm in Large Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Confidence-constraint Rule Set Learning (CRSL) framework consisting of three main components, i.e. rule miner, rule ranker, and rule subset selector. |
Meng Li; Lu Yu; Ya-Lin Zhang; Xiaoguang Huang; Qitao Shi; Qing Cui; Xinxing Yang; Longfei Li; Wei Zhu; Yanming Fang; Jun Zhou; |
| 314 | Query Rewriting in TaoBao Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present Contrastive Learning Enhanced Query Rewriting (CLE-QR), the solution used in Taobao product search. |
Sen Li; Fuyu Lv; Taiwei Jin; Guiyang Li; Yukun Zheng; Tao Zhuang; Qingwen Liu; Xiaoyi Zeng; James Kwok; Qianli Ma; |
| 315 | Cognitive Diagnosis Focusing on Knowledge Concepts Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, in order to more comprehensively simulate the interaction between students and exercises, we developed a neural network-based CDMFKC model for cognitive diagnosis. |
Sheng Li; Quanlong Guan; Liangda Fang; Fang Xiao; Zhenyu He; Yizhou He; Weiqi Luo; |
| 316 | Predicting Multi-level Socioeconomic Indicators from Structural Urban Imagery Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we show how to leverage city structural information and urban imagery like satellite images and street view images to accurately predict multi-level socioeconomic indicators. |
Tong Li; Shiduo Xin; Yanxin Xi; Sasu Tarkoma; Pan Hui; Yong Li; |
| 317 | IntTower: The Next Generation of Two-Tower Model for Pre-Ranking System Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we show it is possible to design a two-tower model that emphasizes both information interactions and inference efficiency. |
Xiangyang Li; Bo Chen; Huifeng Guo; Jingjie Li; Chenxu Zhu; Xiang Long; Sujian Li; Yichao Wang; Wei Guo; Longxia Mao; Jinxing Liu; Zhenhua Dong; Ruiming Tang; |
| 318 | PlatoGL: Effective and Scalable Deep Graph Learning System for Graph-enhanced Real-Time Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a new deep graph learning system called PlatoGL, where (1) an effective block-based graph storage is designed with non-trivial insertion/deletion mechanism for updating the graph topology in-milliseconds, (2) a non-trivial multi-blocks neighbour sampling method is proposed for efficient graph query, and (3) a cache technique is exploited to improve the storage stability. |
Dandan Lin; Shijie Sun; Jingtao Ding; Xuehan Ke; Hao Gu; Xing Huang; Chonggang Song; Xuri Zhang; Lingling Yi; Jie Wen; Chuan Chen; |
| 319 | Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a Sparse Attentive Memory (SAM) network for long sequential user behavior modeling. |
Qianying Lin; Wen-Ji Zhou; Yanshi Wang; Qing Da; Qing-Guo Chen; Bing Wang; |
| 320 | Knowledge Enhanced Multi-Interest Network for The Generation of Recommendation Candidates Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Knowledge Enhanced Multi-Interest Network: KEMI, which exploits knowledge graphs to help learn users’ diverse interest representations via heterogeneous graph neural networks (HGNNs) and a novel dual memory network. |
Danyang Liu; Yuji Yang; Mengdi Zhang; Wei Wu; Xing Xie; Guangzhong Sun; |
| 321 | Multi-Faceted Hierarchical Multi-Task Learning for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work proposes a Multi-Faceted Hierarchical MTL model (MFH) that exploits the multidimensional task relations in large scale MTLs with a nested hierarchical tree structure. |
Junning Liu; Xinjian Li; Bo An; Zijie Xia; Xu Wang; |
| 322 | BRIGHT – Graph Neural Networks in Real-time Fraud Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Second, the latency of graph query and GNN model inference is usually up to hundreds of milliseconds, which is costly for some critical online services. To tackle these challenges, we propose a Batch and Real-time Inception GrapH Topology (BRIGHT) framework to conduct an end-to-end GNN learning that allows efficient online real-time inference. |
Mingxuan Lu; Zhichao Han; Susie Xi Rao; Zitao Zhang; Yang Zhao; Yinan Shan; Ramesh Raghunathan; Ce Zhang; Jiawei Jiang; |
| 323 | STARDOM: Semantic Aware Deep Hierarchical Forecasting Model for Search Traffic Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, to exploit hierarchical structure, we propose a reconciliation learning module. |
Yucheng Lu; Qiang Ji; Liang Wang; Tianshu Wu; Hongbo Deng; Jian Xu; Bo Zheng; |
| 324 | Towards Fair Workload Assessment Via Homogeneous Order Grouping in Last-mile Delivery Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we design, implement, and deploy a nationwide homogeneous order grouping system called FHOG for improving the accuracy of homogeneous order grouping in last-mile delivery for fair courier’s workload assessment. |
Wenjun Lyu; Kexin Zhang; Baoshen Guo; Zhiqing Hong; Guang Yang; Guang Wang; Yu Yang; Yunhuai Liu; Desheng Zhang; |
| 325 | Efficient Compression Method for Roadside LiDAR Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we consider the problem of compressing roadside (i.e. static) LiDAR data in real-time that provides a unique condition unexplored by current methods. |
Md Parvez Mollah; Biplob Debnath; Murugan Sankaradas; Srimat Chakradhar; Abdullah Mueen; |
| 326 | MEMENTO: Neural Model for Estimating Individual Treatment Effects for Multiple Treatments Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Learning individual level treatment effects from observational data is a problem of growing interest. For instance, inferring the effect of delivery promises on purchase of … |
Abhirup Mondal; Anirban Majumder; Vineet Chaoji; |
| 327 | Ensure A/B Test Quality at Scale with Automated Randomization Validation and Sample Ratio Mismatch Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We discuss two automated test quality monitoring processes and methodologies, namely randomization validation using population stability index (PSI) and sample ratio mismatch (a.k.a. sample delta) detection using sequential analysis. |
Keyu Nie; Zezhong Zhang; Bingquan Xu; Tao Yuan; |
| 328 | MIC: Model-agnostic Integrated Cross-channel Recommender Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a model-agnostic integrated cross-channel (MIC) approach for the large-scale recommendation, which maximally leverages the inherent multi-channel mutual information to enhance the matching performance. |
Ping Nie; Yujie Lu; Shengyu Zhang; Ming Zhao; Ruobing Xie; William Yang Wang; Yi Ren; |
| 329 | Guided Text-based Item Exploration Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we develop GUIDES, a framework for guided Text-based Item Exploration (TIE). |
Behrooz Omidvar-Tehrani; Aurelien Personnaz; Sihem Amer-Yahia; |
| 330 | Multimodal Meta-Learning for Cold-Start Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the task of cold-start sequential recommendation, where new users with very short interaction sequences come with time. |
Xingyu Pan; Yushuo Chen; Changxin Tian; Zihan Lin; Jinpeng Wang; He Hu; Wayne Xin Zhao; |
| 331 | Learning-to-Spell: Weak Supervision Based Query Correction in E-Commerce Search with Small Strong Labels Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present a novel approach towards spell correction that effectively solves a very diverse set of spell errors and outperforms several state-of-the-art systems in the domain of E-commerce search. |
Madhura Pande; Vishal Kakkar; Manish Bansal; Surender Kumar; Chinmay Sharma; Himanshu Malhotra; Praneet Mehta; |
| 332 | Observability of SQL Hints in Oracle Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we describe the design and implementation of Oracle’s hint observability framework which provides a comprehensive usage report of all hints, manual or otherwise, used to compile a query. |
Krishna Kantikiran Pasupuleti; Dinesh Das; Satyanarayana R Valluri; Mohamed Zait; |
| 333 | High Availability Framework and Query Fault Tolerance for Hybrid Distributed Database Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper introduces a new framework that allows a host database to efficiently manage the availability of a massive secondary distributed system and describes a mechanism to achieve query fault tolerance at the primary database by transparently re-executing query (sub)plans on the secondary distributed system. |
Krishna Kantikiran Pasupuleti; Boris Klots; Vijayakrishnan Nagarajan; Ananthakiran Kandukuri; Nipun Agarwal; |
| 334 | Sub-Task Imputation Via Self-Labelling to Train Image Moderation Models on Sparse Noisy Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Additionally, the sourced labels can be noisy due to annotator biases or policy rules clubbing multiple types of transgressions into a single category. Therefore, training advert image moderation models necessitates an approach that can effectively improve the sample efficiency of training, weed out noise and discover latent moderation sub-labels in one go. |
Indraneil Paul; Sumit Negi; |
| 335 | MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose the molecular omics network (MOOMIN) a multimodal graph neural network used by AstraZeneca oncologists to predict the synergy of drug combinations for cancer treatment. |
Benedek Rozemberczki; Anna Gogleva; Sebastian Nilsson; Gavin Edwards; Andriy Nikolov; Eliseo Papa; |
| 336 | E-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present techniques we used to train large-scale representation learning models and share solutions that address domain-specific challenges. |
Wonyoung Shin; Jonghun Park; Taekang Woo; Yongwoo Cho; Kwangjin Oh; Hwanjun Song; |
| 337 | Selective Tensorized Multi-layer LSTM for Orbit Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this research, we propose Selective Tensorized multi-layer LSTM (ST-LSTM) for orbit prediction, which not only improves the orbit prediction performance but also compresses the size of the model that can be applied in practical deployable scenarios. |
Youjin Shin; Eun-Ju Park; Simon S. Woo; Okchul Jung; Daewon Chung; |
| 338 | PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a new approach, namely Physics properties Enhanced Molecular Property prediction (PEMP), to utilize relations between molecular properties revealed by previous physics theory and physical chemistry studies. |
Yuancheng Sun; Yimeng Chen; Weizhi Ma; Wenhao Huang; Kang Liu; Zhiming Ma; Wei-Ying Ma; Yanyan Lan; |
| 339 | WARNER: Weakly-Supervised Neural Network to Identify Eviction Filing Hotspots in The Absence of Court Records Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To circumvent existing challenges in data acquisition, we propose WARNER, a novel Machine Learning (ML) framework that predicts eviction filing hotspots in US counties from unlabeled satellite imagery dataset. |
Maryam Tabar; Wooyong Jung; Amulya Yadav; Owen Wilson Chavez; Ashley Flores; Dongwon Lee; |
| 340 | A Dual Channel Intent Evolution Network for Predicting Period-Aware Travel Intentions at Fliggy Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Existing studies on user’s intent are largely sub-optimal for users’ travel intent prediction at OTPs, since they rarely pay attentions to the characteristics of the travel industry, namely, user behavior sparsity due to low frequency of travel, spatial-temporal periodicity patterns, and the correlations between user’s online and offline behaviors. In this paper, to address these challenges, we propose a dual channel intent evolution network based online-offline periodicity-aware network, DCIEN, for user’s future travel intent prediction. |
Wanjie Tao; Zhang-Hua Fu; Liangyue Li; Zulong Chen; Hong Wen; Yuanyuan Liu; Qijie Shen; Peilin Chen; |
| 341 | Towards An Awareness of Time Series Anomaly Detection Models’ Adversarial Vulnerability Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The overarching goal of this research is to raise awareness towards the adversarial vulnerabilities of time series anomaly detectors. |
Shahroz Tariq; Binh M. Le; Simon S. Woo; |
| 342 | CTRL: Cooperative Traffic Tolling Via Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we put the dynamic tolling problem in a reinforcement learning setting and try to tackle the three key challenges of complex state representation, pricing action credit assignment, and route price relative competition. |
Yiheng Wang; Hexi Jin; Guanjie Zheng; |
| 343 | Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This further hinders the exploration of RL agents and causes poor sample efficiency. To address this problem, we propose a novel RL-based approach for ads allocation which learns better list-wise representations by leveraging task-specific signals on Meituan food delivery platform. |
Ze Wang; Guogang Liao; Xiaowen Shi; Xiaoxu Wu; Chuheng Zhang; Yongkang Wang; Xingxing Wang; Dong Wang; |
| 344 | DuARUS: Automatic Geo-object Change Detection with Street-view Imagery for Updating Road Database at Baidu Maps Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In addition, the artificially prescribed production standards make the geo-object position in the map data deviate from its position in the real world, as well as some geo-objects do not need to be updated (e.g., temporary speed limit), which further yields many false-positive detections and significantly increases the labor costs of existing systems. To address these challenges, we propose a novel framework called DuARUS for automatic geo-object change detection with street-view imagery. |
Deguo Xia; Jizhou Huang; Jianzhong Yang; Xiyan Liu; Haifeng Wang; |
| 345 | DuTraffic: Live Traffic Condition Prediction with Trajectory Data and Street Views at Baidu Maps Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In addition, the trajectory information alone is insufficient to provide evidence for sudden traffic situations and perception of street-wise elements. To alleviate these problems, in this paper, we present DuTraffic, which is a robust and production-ready solution for live traffic condition prediction by taking both trajectory data and street views into account. |
Deguo Xia; Xiyan Liu; Wei Zhang; Hui Zhao; Chengzhou Li; Weiming Zhang; Jizhou Huang; Haifeng Wang; |
| 346 | Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Besides, they cannot effectively response to the dynamic changes in relation graphs. Therefore, in this paper, we propose a temporal and heterogeneous graph neural network-based (THGNN) approach to learn the dynamic relations among price movements in financial time series. |
Sheng Xiang; Dawei Cheng; Chencheng Shang; Ying Zhang; Yuqi Liang; |
| 347 | Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents the network load balancing problem, a challenging real-world task for multi-agent reinforcement learning (MARL) methods. |
Zhiyuan Yao; Zihan Ding; Thomas Clausen; |
| 348 | An Actor-critic Reinforcement Learning Model for Optimal Bidding in Online Display Advertising Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we address the application of RTB to mobile gaming where the in-app purchase action is of high uncertainty, making it challenging to evaluate individual impression opportunities. |
Congde Yuan; Mengzhuo Guo; Chaoneng Xiang; Shuangyang Wang; Guoqing Song; Qingpeng Zhang; |
| 349 | Offline Reinforcement Learning for Mobile Notifications Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we argue that reinforcement learning is a better framework for notification systems in terms of performance and iteration speed. |
Yiping Yuan; Ajith Muralidharan; Preetam Nandy; Miao Cheng; Prakruthi Prabhakar; |
| 350 | Hierarchical Reinforcement Learning Using Gaussian Random Trajectory Generation in Autonomous Furniture Assembly Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Gaussian Random Trajectory guided Hierarchical Reinforcement Learning (GRT-HL) method for autonomous furniture assembly. |
Won Joon Yun; David Mohaisen; Soyi Jung; Jong-Kook Kim; Joongheon Kim; |
| 351 | Graph-based Weakly Supervised Framework for Semantic Relevance Learning in E-commerce Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Besides, we propose a contrastive learning framework suitable for e-commerce scenarios with targeted improvements in data augmentation and training objectives. |
Zhiyuan Zeng; Yuzhi Huang; Tianshu Wu; Hongbo Deng; Jian Xu; Bo Zheng; |
| 352 | QuickSkill: Novice Skill Estimation in Online Multiplayer Games Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This is known as the "cold-start" problem for matchmaking rating algorithms. To overcome this conundrum, this paper proposes QuickSKill, a deep learning based novice skill estimation framework to quickly probe abilities of new players in online multiplayer games. |
Chaoyun Zhang; Kai Wang; Hao Chen; Ge Fan; Yingjie Li; Lifang Wu; Bingchao Zheng; |
| 353 | SwiftPruner: Reinforced Evolutionary Pruning for Efficient Ad Relevance Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose SwiftPruner – an efficient framework that leverages evolution-based search to automatically find the best-performing layer-wise sparse BERT model under the desired latency constraint. |
Li Lyna Zhang; Youkow Homma; Yujing Wang; Min Wu; Mao Yang; Ruofei Zhang; Ting Cao; Wei Shen; |
| 354 | Measuring Friendship Closeness: A Perspective of Social Identity Theory Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, most existing measures for friendship closeness only consider the information between the source and target but ignore the information of groups where they are located, which renders inferior results. To address this issue, we present new measures for friendship closeness based on the social identity theory (SIT), which describes the inclination that a target endorses behaviors of users inside the same group. |
Shiqi Zhang; Jiachen Sun; Wenqing Lin; Xiaokui Xiao; Bo Tang; |
| 355 | Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Scenario-Adaptive and Self-Supervised (SASS) model to solve the three challenges mentioned above. |
Yuanliang Zhang; Xiaofeng Wang; Jinxin Hu; Ke Gao; Chenyi Lei; Fei Fang; |
| 356 | KEEP: An Industrial Pre-Training Framework for Online Recommendation Via Knowledge Extraction and Plugging Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, we argue that such data usage may lead to sub-optimal online performance because of thedata sparsity. To alleviate this issue, we propose to extract knowledge from thesuper-domain that contains web-scale and long-time impression data, and further assist the online recommendation task (downstream task). |
Yujing Zhang; Zhangming Chan; Shuhao Xu; Weijie Bian; Shuguang Han; Hongbo Deng; Bo Zheng; |
| 357 | Network Report: A Structured Description for Network Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Network report extends the idea of dataset reports (e.g., Datasheets for Datasets) from prior work with network-specific descriptions of the non-i.i.d. nature, demographic information, network characteristics, etc. |
Xinyi Zheng; Ryan A. Rossi; Nesreen K. Ahmed; Dominik Moritz; |
| 358 | Towards Edge-Cloud Collaborative Machine Learning: A Quality-aware Task Partition Framework Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Accordingly, we propose Quality-aware Task Partition (QTP) problem, in which final tasks are partitioned by the performance of task models. |
Zimu Zheng; Yunzhe Li; Han Song; Lanjun Wang; Fei Xia; |
| 359 | A Practical Distributed ADMM Solver for Billion-Scale Generalized Assignment Problems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we are interested in solving constrained assignment problems with hundreds of millions of items. |
Jun Zhou; Feng Qi; Zhigang Hua; Daohong Jian; Ziqi Liu; Hua Wu; |
| 360 | SASNet: Stage-aware Sequential Matching for Online Travel Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to capture the deep sequential context by modeling the evolving of user stages, and develop a novel stage-aware deep sequential matching network (SASNet) that incorporates inter-stage and intra-stage dependencies over stage-augmented interaction sequence for more accurate and interpretable recommendation. |
Fanwei Zhu; Zulong Chen; Fan Zhang; Jiazhen Lou; Hong Wen; Shui Liu; Qi Rao; Tengfei Yuan; Shenghua Ni; Jinxin Hu; Fuzhen Sun; Quan Lu; |
| 361 | Breast Cancer Early Detection with Time Series Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a household solution that utilizes pairs of sensors embedded in the bra to measure the thermal and moisture time series data (BTMTSD) of the breast surface and conduct time series classification (TSC) to diagnose breast cancer. |
Haoren Zhu; Pengfei Zhao; Yiu-Pong Chan; Hong Kang; Dik Lun Lee; |
| 362 | Cross-Domain Product Search with Knowledge Graph Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, we propose a simple yet effective knowledge graph based information propagation framework for cross-domain product search (named KIPS). |
Rui Zhu; Yiming Zhao; Wei Qu; Zhongyi Liu; Chenliang Li; |
| 363 | Approximated Doubly Robust Search Relevance Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to unify the counterfactual evaluating and learning approaches for unbiased relevance estimation on search queries with various popularities. |
Lixin Zou; Changying Hao; Hengyi Cai; Shuaiqiang Wang; Suqi Cheng; Zhicong Cheng; Wenwen Ye; Simiu Gu; Dawei Yin; |
| 364 | Scaling Up Mass-Based Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, we observe that mass-based clustering requires information about only a tiny fraction of all possible data point pairs. We propose three optimizations to MBScan for quickly finding such pairs and computing their distances. |
Nidhi Ahlawat; Amit Awekar; |
| 365 | Probing The Robustness of Pre-trained Language Models for Entity Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we aim at investigating whether PLM-based entity matching models can be trusted in real-world applications where data distribution is different from that of training. |
Mehdi Akbarian Rastaghi; Ehsan Kamalloo; Davood Rafiei; |
| 366 | SERF: Interpretable Sleep Staging Using Embeddings, Rules, and Features Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Using sleep as a case study, we propose a generalizable method to combine clinical interpretability with high accuracy derived from black-box deep learning. |
Irfan Al-Hussaini; Cassie S. Mitchell; |
| 367 | Improving Imitation Learning By Merging Experts Trajectories Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes an original approach based on expert trajectories combination and Deep Reinforcement Learning to provide a better MineCraft player. |
Pegah Alizadeh; Aomar Osmani; Sammy Taleb; |
| 368 | TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper we present the novel, relevance judgement test collection TripJudge for TripClick health retrieval. |
Sophia Althammer; Sebastian Hofstätter; Suzan Verberne; Allan Hanbury; |
| 369 | Interpretability of BERT Latent Space Through Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we analyze the latent vector space resulting from the BERT context-aware word embeddings. |
Vito Walter Anelli; Giovanni Maria Biancofiore; Alessandro De Bellis; Tommaso Di Noia; Eugenio Di Sciascio; |
| 370 | Unsupervised Question Clarity Prediction Through Retrieved Item Coherency Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an unsupervised method for predicting the need for clarification. |
Negar Arabzadeh; Mahsa Seifikar; Charles L.A. Clarke; |
| 371 | IEEE13-AdvAttack A Novel Dataset for Benchmarking The Power of Adversarial Attacks Against Fault Prediction Systems in Smart Electrical Grid Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present IEEE13-AdvAttack, a large-scaled simulated dataset based on the IEEE-13 test node feeder suitable for supervised tasks under SG. |
Carmelo Ardito; Yashar Deldjoo; Tommaso Di Noia; Eugenio Di Sciascio; Fatemeh Nazary; |
| 372 | A Multi-Domain Benchmark for Personalized Search Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we put forward a novel evaluation benchmark for Personalized Search with more than 18 million documents and 1.9 million queries across four domains. |
Elias Bassani; Pranav Kasela; Alessandro Raganato; Gabriella Pasi; |
| 373 | CS-MLGCN: Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a query-driven graph convolutional network in multiplex networks, CS-MLGCN, that can capture flexible community structures by learning from the ground-truth communities in a data-driven fashion. |
Ali Behrouz; Farnoosh Hashemi; |
| 374 | A Mask-based Output Layer for Multi-level Hierarchical Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a novel mask-based output layer for multi-level hierarchical classification, addressing the limitations of existing methods which (i) often do not embed the taxonomy structure being used, (ii) use a complex backbone neural network with n disjoint output layers that do not constraint each other, (iii) may output predictions that are often inconsistent with the taxonomy in place, and (iv) have often a fixed value of n. Specifically, we propose a model agnostic output layer that embeds the taxonomy and that can be combined with any model. |
Tanya Boone-Sifuentes; Mohamed Reda Bouadjenek; Imran Razzak; Hakim Hacid; Asef Nazari; |
| 375 | Marine-tree: A Large-scale Marine Organisms Dataset for Hierarchical Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Experimental results demonstrate thatMarine-tree and the proposed hierarchical loss function are a good contribution for both research in underwater imagery and hierarchical classification. |
Tanya Boone-Sifuentes; Asef Nazari; Imran Razzak; Mohamed Reda Bouadjenek; Antonio Robles-Kelly; Daniel Ierodiaconou; Elizabeth S. Oh; |
| 376 | Deep Ordinal Neural Network for Length of Stay Estimation in The Intensive Care Units Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a Deep Ordinal neural network for Length of stay Estimation in the intensive care units (DOSE). |
Derun Cai; Moxian Song; Chenxi Sun; Baofeng Zhang; Shenda Hong; Hongyan Li; |
| 377 | Predicting Guiding Entities for Entity Aspect Linking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel entity aspect linking approach that outperforms several neural and non-neural baselines on a large-scale entity aspect linking test collection. |
Shubham Chatterjee; Laura Dietz; |
| 378 | DialogID: A Dialogic Instruction Dataset for Improving Teaching Effectiveness in Online Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In spite of the popularity and advantages of online learning, the education technology and educational data mining communities still suffer from the lack of large-scale, high-quality, and well-annotated teaching instruction datasets to study computational approaches to automatically detect online dialogic instructions and further improve the online teaching effectiveness. Therefore, in this paper, we present a dataset of online dialogic instruction detection, DialogID, which contains 30,431 effective dialogic instructions. |
Jiahao Chen; Shuyan Huang; Zitao Liu; Weiqi Luo; |
| 379 | Discriminative Language Model Via Self-Teaching for Dense Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Taking the pre-trained language models (PLMs) as the text encoders has become a popular choice in DR. However, the learned representations based on these PLMs often lose the discriminative power, and thus hurt the recall performance, particularly as PLMs consider too much content of the input texts. Therefore, in this work, we propose to pre-train a discriminative language representation model, called DiscBERT, for DR. The key idea is that a good text representation should be able to automatically keep those discriminative features that could well distinguish different texts from each other in the semantic space. |
Lu Chen; Ruqing Zhang; Jiafeng Guo; Yixing Fan; Xueqi Cheng; |
| 380 | Knowledge Tracing Model with Learning and Forgetting Behavior Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, based on educational psychology theory, we propose a knowledge tracing model with learning and forgetting behavior (LFBKT). |
Mingzhi Chen; Quanlong Guan; Yizhou He; Zhenyu He; Liangda Fang; Weiqi Luo; |
| 381 | An Empirical Cross Domain-Specific Entity Recognition with Domain Vector Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Most existing methods focus on recognizing generic entities, but few methods are to recognize the domain-specific entities across domains due to the very large discrepancy of entity representations between the source and target domains. To address this issue, we introduce domain vectors and context vectors to represent domain-specific semantics of entities and domain-irrelevant semantics of the context words, respectively. |
Wei Chen; Songqiao Han; Hailiang Huang; |
| 382 | Trusted Media Challenge Dataset and User Study Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To enable further research, we are releasing the dataset from the TMC, consists of 4,380 fake and 2,563 real videos, with various video and audio manipulation methods employed to produce different types of fake media. |
Weiling Chen; Sheng Lun Benjamin Chua; Stefan Winkler; See-Kiong Ng; |
| 383 | Scalable Graph Representation Learning Via Locality-Sensitive Hashing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Due to the sparsity, proximity-based methods are incapable of deriving satisfactory representations for these tail nodes. To address this challenge, we propose a novel approach, Content-Preserving Locality-Sensitive Hashing~(CP-LSH), by incorporating the content information for representation learning. |
Xiusi Chen; Jyun-Yu Jiang; Wei Wang; |
| 384 | CFS-MTL: A Causal Feature Selection Mechanism for Multi-task Learning Via Pseudo-intervention Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper explains negative transfer in FS-MTL from a causal perspective and presents a novel architecture called Causal Feature Selection for Multi-task Learning(CFS-MTL). |
Zhongde Chen; Ruize Wu; Cong Jiang; Honghui Li; Xin Dong; Can Long; Yong He; Lei Cheng; Linjian Mo; |
| 385 | Dynamic Explicit Embedding Representation for Numerical Features in Deep CTR Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a Dynamic Explicit Embedding Representation (DEER) for numerical features in deep CTR prediction, which can provide explicit and dynamic embedding representation for numerical features. |
Yuan Cheng; |
| 386 | A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce a publicly available dataset for the burned area detection problem for semantic segmentation. |
Luca Colomba; Alessandro Farasin; Simone Monaco; Salvatore Greco; Paolo Garza; Daniele Apiletti; Elena Baralis; Tania Cerquitelli; |
| 387 | On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, it is not well understood how GNNs work without natural node features, especially regarding the various ways to construct artificial ones. In this paper, we point out the two types of artificial node features, i.e., positional and structural node features, and provide insights on why each of them is more appropriate for certain tasks, i.e., positional node classification, structural node classification, and graph classification. |
Hejie Cui; Zijie Lu; Pan Li; Carl Yang; |
| 388 | LCD: Adaptive Label Correction for Denoising Music Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Music recommendation is usually modeled as a Click-Through Rate (CTR) prediction problem, which estimates the probability of a user listening a recommended song. CTR prediction … |
Quanyu Dai; Yalei Lv; Jieming Zhu; Junjie Ye; Zhenhua Dong; Rui Zhang; Shu-Tao Xia; Ruiming Tang; |
| 389 | Effective Neural Team Formation Via Negative Samples Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present an optimization objective that leverages both successful and virtually unsuccessful teams to overcome the long-tailed distribution problem. |
Arman Dashti; Saeed Samet; Hossein Fani; |
| 390 | OpeNTF: A Benchmark Library for Neural Team Formation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We contribute OpeNTF, an open-source python-based benchmark library to support neural team formation research. |
Arman Dashti; Karan Saxena; Dhwani Patel; Hossein Fani; |
| 391 | GFlow-FT: Pick A Child Network Via Gradient Flow for Efficient Fine-Tuning in Recommendation Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a general and efficient transfer learning method called Gradient-Flow based Fine-Tuning (GFlow-FT), which only needs to update a subset of parameters (called child network) via pruning the gradients to restrain gradient norm against over-fitting. |
Ke Ding; Yong He; Xin Dong; Jieyu Yang; Liang Zhang; Ang Li; Xiaolu Zhang; Linjian Mo; |
| 392 | MASR: A Model-Agnostic Sparse Routing Architecture for Arbitrary Order Feature Sharing in Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a model-agnostic sparse routing architecture called MASR, which emphasizes arbitrary order feature sharing for multi-task learning. |
Xin Dong; Ruize Wu; Chao Xiong; Hai Li; Lei Cheng; Yong He; Shiyou Qian; Jian Cao; Linjian Mo; |
| 393 | Semi-Supervised Learning with Data Augmentation for Tabular Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the tabular data does not possess an obvious invariant structure, and therefore similar data augmentation methods do not apply to it. To fill this gap, we present a simple yet efficient data augmentation method particular designed for tabular data and apply it to the SSL algorithm: SDAT (Semi-supervised learning with Data Augmentation for Tabular data). |
Junpeng Fang; Caizhi Tang; Qing Cui; Feng Zhu; Longfei Li; Jun Zhou; Wei Zhu; |
| 394 | Adaptive Graph Spatial-Temporal Transformer Network for Traffic Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the recent success of transformers in the graph domain, in this paper, we propose to directly model the cross-spatial-temporal correlations on the adaptive spatial-temporal graph using local multi-head self-attentions. |
Aosong Feng; Leandros Tassiulas; |
| 395 | Subspace Co-clustering with Two-Way Graph Convolution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: It has shown good results on the task of image clustering but text clustering, using document-term matrices, proved more impervious to advances based on this approach. We hypothesize that this is because, compared to image data, text data is generally higher dimensional and sparser. |
Chakib Fettal; Lazhar Labiod; Mohamed Nadif; |
| 396 | On The Mining of Time Series Data Counterfactual Explanations Using Barycenters Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose TimeX, a new model-agnostic time series counterfactual explanation algorithm that provides sparse, interpretable, and contiguous explanations. |
Soukaïna Filali Boubrahimi; Shah Muhammad Hamdi; |
| 397 | MalNet: A Large-Scale Image Database of Malicious Software Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: MalNet-Image contains over 1.2 million malware images-across 47 types and 696 families—democratizing image-based malware capabilities by enabling researchers and practitioners to evaluate techniques that were previously reported in propriety settings. |
Scott Freitas; Rahul Duggal; Duen Horng Chau; |
| 398 | KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we present KuaiRand, an unbiased sequential recommendation dataset containing millions of intervened interactions on randomly exposed videos, collected from the video-sharing mobile App, Kuaishou. |
Chongming Gao; Shijun Li; Yuan Zhang; Jiawei Chen; Biao Li; Wenqiang Lei; Peng Jiang; Xiangnan He; |
| 399 | End-to-end Multi-task Learning Framework for Spatio-Temporal Grounding in Video Corpus Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we consider a novel task, Video Corpus Spatio-Temporal Grounding (VCSTG) for material selection and spatio-temporal adaption in intelligent video editing. |
Yingqi Gao; Zhiling Luo; Shiqian Chen; Wei Zhou; |
| 400 | Local Contrastive Feature Learning for Tabular Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a new local contrastive feature learning (LoCL) framework, and our theme is to learn local patterns/features from tabular data. |
Zhabiz Gharibshah; Xingquan Zhu; |
| 401 | Binary Transformation Method for Multi-Label Stream Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we propose a novel Problem Transformation method for Multi-Label Stream Classification called Binary Transformation, which utilizes regression algorithms by transforming the labels into a continuous value. |
Ege Berkay Gulcan; Isin Su Ecevit; Fazli Can; |
| 402 | SpCQL: A Semantic Parsing Dataset for Converting Natural Language Into Cypher Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose the first Text-to-CQL dataset, SpCQL, which contains one Neo4j graph database, 10,000 manually annotated natural language queries and the matching Cypher queries (CQL). |
Aibo Guo; Xinyi Li; Guanchen Xiao; Zhen Tan; Xiang Zhao; |
| 403 | Fusing Geometric and Scene Information for Cross-View Geo-Localization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a Geometric and Scene Information Fusion (GSIF) model for more accurate cross-view geo-localization. |
Siyuan Guo; Tianying Liu; Wengen Li; Jihong Guan; Shuigeng Zhou; |
| 404 | Calibrated Conversion Rate Prediction Via Knowledge Distillation Under Delayed Feedback in Online Advertising Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to calibrate conversion rate prediction models considering delayed feedback via the knowledge distillation technique. |
Yuyao Guo; Haoming Li; Xiang Ao; Min Lu; Dapeng Liu; Lei Xiao; Jie Jiang; Qing He; |
| 405 | SciTweets – A Dataset and Annotation Framework for Detecting Scientific Online Discourse Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we contribute (a) an annotation framework and corresponding definitions for different forms of scientific relatedness of online discourse in tweets, (b) an expert-annotated dataset of 1261 tweets obtained through our labeling framework reaching an average Fleiss Kappa κ of 0.63, (c) a multi-label classifier trained on our data able to detect science- relatedness with 89% F1 and also able to detect distinct forms of scientific knowledge (claims, references). |
Salim Hafid; Sebastian Schellhammer; Sandra Bringay; Konstantin Todorov; Stefan Dietze; |
| 406 | OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we will introduce OpenHGNN, an open-source toolkit for HGNNs. |
Hui Han; Tianyu Zhao; Cheng Yang; Hongyi Zhang; Yaoqi Liu; Xiao Wang; Chuan Shi; |
| 407 | Long-tail Mixup for Extreme Multi-label Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The XMC problem inherently poses two challenges: scalability and label sparsity – the number of labels is too large, and labels follow the long-tail distribution. To resolve these problems, we propose a novel Mixup-based augmentation method for long-tail labels, called TailMix. |
Sangwoo Han; Eunseong Choi; Chan Lim; Hyunjung Shim; Jongwuk Lee; |
| 408 | Stochastic Optimization of Text Set Generation for Learning Multiple Query Intent Representations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we argue that the model should not be penalized as long as it generates an accurate and complete set of intent representations. Based on this intuition, we propose a stochastic permutation invariant approach for optimizing such networks. |
Helia Hashemi; Hamed Zamani; W. Bruce Croft; |
| 409 | Unified Knowledge Prompt Pre-training for Customer Service Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel unified knowledge prompt pre-training framework, UFA (Unified Model F or All Tasks), for customer service dialogues. |
Keqing He; Jingang Wang; Chaobo Sun; Wei Wu; |
| 410 | Causal Intervention for Sentiment De-biasing in Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we resolve the sentiment bias with causal reasoning. |
Ming He; Xin Chen; Xinlei Hu; Changshu Li; |
| 411 | Query-Aware Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we argue that user queries should be an important contextual cue for sequential recommendation. |
Zhankui He; Handong Zhao; Zhaowen Wang; Zhe Lin; Ajinkya Kale; Julian Mcauley; |
| 412 | Semi-supervised Continual Learning with Meta Self-training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel method, namely Meta-SSCL, which combines meta-learning with pseudo-labeling and data augmentations to learn a sequence of semi-supervised tasks without catastrophic forgetting. |
Stella Ho; Ming Liu; Lan Du; Yunfeng Li; Longxiang Gao; Shang Gao; |
| 413 | Extreme Systematic Reviews: A Large Literature Screening Dataset to Support Environmental Policymaking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work introduces the ISA literature screening dataset and the associated research challenges to the information and knowledge management community. |
Jingwen Hou; Xiaochen Wang; Jean-Jacques Dubois; R. Byron Rice; Amanda Haddock; Yue Wang; |
| 414 | META-CODE: Community Detection Via Exploratory Learning in Topologically Unknown Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, due to privacy concerns or access restrictions, the network structure is often unknown, thereby rendering established community detection approaches ineffective without costly data acquisition. To tackle this challenge, we present META-CODE, a novel end-to-end solution for detecting overlapping communities in networks with unknown topology via exploratory learning aided by easy-to-collect node metadata. |
Yu Hou; Cong Tran; Won-Yong Shin; |
| 415 | AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel click-through rate (CTR) prediction model with heterogeneous graph neural network, called AMinerGNN, to recommend papers with two queries. |
Zepeng Huai; Zhe Wang; Yifan Zhu; Peng Zhang; |
| 416 | Pattern Adaptive Specialist Network for Learning Trading Patterns in Stock Market Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel training process Pattern Adaptive Training based on Optimal Transport (OT) to train a set of predictors specializing in diverse patterns while without any prior pattern knowledge and inconsistent assumption. |
Huiling Huang; Jianliang Gao; Cong Xu; Xiaoting Ying; |
| 417 | Deep Presentation Bias Integrated Framework for CTR Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a Deep Presentation Bias Integrated Framework (DPBIF). |
Jianqiang Huang; Xingyuan Tang; Zhe Wang; Shaolin Jia; Yin Bai; Zhiwei Liu; Jia Cheng; Jun Lei; Yan Zhang; |
| 418 | GDA-HIN: A Generalized Domain Adaptive Model Across Heterogeneous Information Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, for given source and target heterogeneous networks, they generally contain shared and private node types, where private types bring an extra challenge for graph domain adaptation. In this paper, we investigate Heterogeneous Information Networks (HINs) with both shared and private node types and propose a Generalized Domain Adaptive model across HINs (GDA-HIN) to handle the domain shift between them. |
Tiancheng Huang; Ke Xu; Donglin Wang; |
| 419 | LGP: Few-Shot Class-Evolutionary Learning on Dynamic Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the challenges caused by the phenomenon, in this paper, we propose a novel algorithm named Learning to Generate Parameters (LGP) to deal with few-shot class-evolutionary learning on dynamic graphs. |
Tiancheng Huang; Feng Zhao; Donglin Wang; |
| 420 | An Empirical Study on The Membership Inference Attack Against Tabular Data Synthesis Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we investigate the membership inference attack in the context of tabular data synthesis. |
Jihyeon Hyeong; Jayoung Kim; Noseong Park; Sushil Jajodia; |
| 421 | NILK: Entity Linking Dataset Targeting NIL-linking Cases Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents NILK, a new dataset for NIL-linking processing, constructed from WikiData and Wikipedia dumps from two different timestamps. |
Anastasiia Iurshina; Jiaxin Pan; Rafika Boutalbi; Steffen Staab; |
| 422 | RealGraphGPU: A High-Performance GPU-Based Graph Engine Toward Large-Scale Real-World Network Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Via an in-depth analysis of RealGraph, however, we found that there is still a chance for more performance improvement in the computation part of RealGraph despite its great I/O processing ability. Motivated by this, in this paper, we propose RealGraphGPU, a GPU-based single-machine graph engine. |
Myung-Hwan Jang; Yunyong Ko; Dongkyu Jeong; Jeong-Min Park; Sang-Wook Kim; |
| 423 | Intra-session Context-aware Feed Recommendation in Live Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, such type of exposure bias is generally ignored or not explicitly modeled in most feed recommendation studies. In this paper, we model this effect as part of intra-session context, and propose a novel intra-session Context-aware Feed Recommendation (INSCAFER) framework to maximize the total views and total clicks simultaneously. |
Luo Ji; Gao Liu; Mingyang Yin; Hongxia Yang; |
| 424 | MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we define the task of Medical-domain Chinese Spelling Correction (MCSC) and propose MCSCSet, a large-scale specialist-annotated dataset that contains about 200k samples. |
Wangjie Jiang; Zhihao Ye; Zijing Ou; Ruihui Zhao; Jianguang Zheng; Yi Liu; Bang Liu; Siheng Li; Yujiu Yang; Yefeng Zheng; |
| 425 | AI-Augmented Art Psychotherapy Through A Hierarchical Co-Attention Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present M2C (Multimodal classification with 2-stage Co-attention), a deep learning model that predicts stress from art therapy psychological test data. |
Seungwan Jin; Hoyoung Choi; Kyungsik Han; |
| 426 | GReS: Graphical Cross-domain Recommendation for Supply Chain Platform Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we take the catering platform as an example and propose GReS, a graphical CDR model. |
Zhiwen Jing; Ziliang Zhao; Yang Feng; Xaochen Ma; Nan Wu; Shengqiao Kang; Cheng Yang; Yujia Zhang; Hao Guo; |
| 427 | Personal Entity, Concept, and Named Entity Linking in Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we introduce a collection and a tool for entity linking in conversations. |
Hideaki Joko; Faegheh Hasibi; |
| 428 | Commonsense Knowledge Base Completion with Relational Graph Attention Network and Pre-trained Language Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a commonsense knowledge base completion (CKBC) model which learns the structural representations and contextual representations of CKG nodes and relations, respectively by a relational graph attention network and a pre-trained language model. |
Jinhao Ju; Deqing Yang; Jingping Liu; |
| 429 | Convolutional Transformer Networks for Epileptic Seizure Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a promising epilepsy detection model based on convolutional transformer networks. |
Nan Ke; Tong Lin; Zhouchen Lin; Xiao-Hua Zhou; Taoyun Ji; |
| 430 | Mining Entry Gates for Points of Interest Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose two algorithms for identifying entry gates for Points of Interest (PoIs) using polygon representations of the PoIs (PoI polygons) and the Global Positioning System (GPS) trajectories of the Delivery Partners (DPs) obtained from their smartphones in the context of online food delivery platforms. |
Tanya Khanna; Abhinav Ganesan; Jose Mathew; Kranthi Mitra Adusimilli; |
| 431 | Models and Benchmarks for Representation Learning of Partially Observed Subgraphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we formulate a novel task of learning representations of partially observed subgraphs. |
Dongkwan Kim; Jiho Jin; Jaimeen Ahn; Alice Oh; |
| 432 | Bootstrapped Knowledge Graph Embedding Based on Neighbor Expansion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel framework, Bootstrapped Knowledge graph Embedding based on Neighbor Expansion (BKENE), which learns representations of KG without using negative samples. |
Jun Seon Kim; Seong Jin Ahn; Myoung Ho Kim; |
| 433 | Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a simple but effective approach, neighbor aggregation via inverse propensity (NAVIP) for GNNs. |
Minseok Kim; Jinoh Oh; Jaeyoung Do; Sungjin Lee; |
| 434 | Context-aware Traffic Flow Forecasting in New Roads Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel prediction model based on Generative Adversarial Networks (GAN) that learns the subtle patterns of the changes in the traffic flow according to the various contextual factors. |
Namhyuk Kim; Dong-Kyu Chae; Jung Ah Shin; Sang-Wook Kim; Duen Horng Chau; Sunghwan Park; |
| 435 | Is It Enough Just Looking at The Title?: Leveraging Body Text To Enrich Title Words Towards Accurate News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Such a behavior is possible since, when viewing the title, humans naturally think of the contextual meaning of each title word by leveraging their own background knowledge. Motivated by this, we propose a novel personalized news recommendation framework CAST (Context-aware Attention network with a Selection module for Title word representation), which is capable of enriching title words by leveraging body text that fully provides the whole content of a given article as the context. |
Taeho Kim; Yungi Kim; Yeon-Chang Lee; Won-Yong Shin; Sang-Wook Kim; |
| 436 | EEG-Oriented Self-Supervised Learning and Cluster-Aware Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose novel EEG-oriented self-supervised learning methods to discover complex and diverse patterns of spatio-spectral characteristics and spatio-temporal dynamics. |
Wonjun Ko; Heung-Il Suk; |
| 437 | Neuron Specific Pruning for Communication Efficient Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper introduces a federated pruning method based on Neuron Importance Scope Propagation (NISP) algorithm. |
Gaurav Kumar; Durga Toshniwal; |
| 438 | Efficient Data Augmentation Policy for Electrocardiograms Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present the taxonomy of data augmentation for electrocardiogram (ECG) after reviewing various ECG augmentation methods. |
Byeong Tak Lee; Yong-Yeon Jo; Seon-Yu Lim; Youngjae Song; Joon-myoung Kwon; |
| 439 | A Multi-grained Dataset for News Event Triggered Knowledge Update Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we introduce a novel task, news event triggered knowledge update. |
Yu-Ting Lee; Ying-Jhe Tang; Yu-Chung Cheng; Pai-Lin Chen; Tsai-Yen Li; Hen-Hsen Huang; |
| 440 | A Hierarchical User Behavior Modeling Framework for Cross-Domain Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel hierarchical user behavior modeling framework for cross-domain CTR prediction, named HBMNet. |
Hai Li; Xin Dong; Lei Cheng; Linjian Mo; |
| 441 | Do Simpler Statistical Methods Perform Better in Multivariate Long Sequence Time-Series Forecasting? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We investigate two simple statistical methods for MLSTF and provide analysis to indicate that linear regression owns a lower upper bound of error than deep learning methods and SNaive can act as an effective nonparametric method with unpredictable trends. |
Hao Li; Jie Shao; Kewen Liao; Mingjian Tang; |
| 442 | Cooperative Max-Pressure Enhanced Traffic Signal Control Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This results in highly sensitive and unstable performances of next actions. The paper proposes a <u>C</u>ooperative <u>M</u>ax-<u>P</u>ressure enhanced <u>S</u>tate <u>L</u>earning for the traffic signal control (CMP-SL), which is inspired by the advanced pressure definition for an intersection in the transportation field to cope with this problem. |
Lin Li; Renbo Li; Yuquan Peng; Chuanming Huang; Jingling Yuan; |
| 443 | An Exploratory Study of Information Cocoon on Short-form Video Platform Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we focus on theinformation cocoon that measures overwhelmingly homogeneity of users’ video consumption. |
Nian Li; Chen Gao; Jinghua Piao; Xin Huang; Aizhen Yue; Liang Zhou; Qingmin Liao; Yong Li; |
| 444 | Prototypical Contrastive Learning and Adaptive Interest Selection for Candidate Generation in Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we aim to advance the typical two-tower DNN candidate generation model. |
Ningning Li; Qunwei Li; Xichen Ding; Shaohu Chen; Wenliang Zhong; |
| 445 | Dual-Augment Graph Neural Network for Fraud Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Dual-Augment Graph Neural Network (DAGNN) for fraud detection tasks. |
Qiutong Li; Yanshen He; Cong Xu; Feng Wu; Jianliang Gao; Zhao Li; |
| 446 | CNewsTS – A Large-scale Chinese News Dataset with Hierarchical Topic Category and Summary Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a large Chinese news article dataset with 4.4 million articles. |
Quanzhi Li; Yingchi Liu; Yang Chao; |
| 447 | SmartQuery: An Active Learning Framework for Graph Neural Networks Through Hybrid Uncertainty Reduction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel method SMARTQUERY, a framework to learn a graph neural network with very few labeled nodes using a hybrid uncertainty reduction function. |
Xiaoting Li; Yuhang Wu; Vineeth Rakesh; Yusan Lin; Hao Yang; Fei Wang; |
| 448 | An Extreme Semi-supervised Framework Based on Transformer for Network Intrusion Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing NID methods usually require a large amount of labeled data for training, which is impractical in many real application scenarios due to the high cost. To address this issue, we proposed an extreme semi-supervised framework based on transformer (ESet) for NID. |
Yangmin Li; Xinhang Yuan; Wengen Li; |
| 449 | Heterogeneous Hypergraph Neural Network for Friend Recommendation with Human Mobility Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the fact that hyperedges can connect multiple nodes of different types, we model user trajectories and check-in records as hyperedges in a novel heterogeneous LBSN hypergraph to represent complex spatio-temporal information. |
Yongkang Li; Zipei Fan; Jixiao Zhang; Dengheng Shi; Tianqi Xu; Du Yin; Jinliang Deng; Xuan Song; |
| 450 | Relation-aware Blocking for Scalable Recommendation Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Current hashing techniques mainly focus on hashing for directly related (i.e. hop-1) features. This paper proposes to develop relation-aware hashing techniques to bridge this gap. |
Huizhi Liang; Zehao Liu; Thanet Markchom; |
| 451 | Invariance Testing and Feature Selection Using Sparse Linear Layers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel invariance testing approach that does not utilise traditional statistical scores. |
Zukang Liao; Michael Cheung; |
| 452 | JavaScript&Me, A Tool to Support Research Into Code Transformation and Browser Security Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Although JavaScript code is widely available on various sources, such as package managers, code hosting platforms, and websites, collecting a large corpus of JavaScript and curating it is not a simple task. We present a novel open-source tool that helps with this task by allowing the automatic and systematic collection, processing, and transformation of JavaScript code. |
Susana Lima; Ricardo Morla; João Routar; |
| 453 | Knowledge Distillation Via Hypersphere Features Distribution Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, these methods ignore important features distribution in the teacher network space, which leads to the defect of current KD approaches in the fine-grained categorization task, e.g., metric learning. For this, we propose a novel approach that transfers features distribution in the hyperspherical space from the teacher network to the student network. |
Boheng Liu; Tianrui Zhang; Ligang Miao; |
| 454 | Learning Rate Perturbation: A Generic Plugin of Learning Rate Schedule Towards Flatter Local Minima Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To boost the performance of the obtained sub-optimal learning rate schedule, we propose a generic learning rate schedule plugin, called LEArning Rate Perturbation (LEAP), which can be applied to various learning rate schedules to improve the model training by introducing a certain perturbation to the learning rate. |
Hengyu Liu; Qiang Fu; Lun Du; Tiancheng Zhang; Ge Yu; Shi Han; Dongmei Zhang; |
| 455 | Efficient Non-sampling Expert Finding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel Efficient Non-sampling Expert Finding model, named ENEF, which could learn accurate representations of questions and experts from whole training data. |
Hongtao Liu; Zhepeng Lv; Qing Yang; Dongliang Xu; Qiyao Peng; |
| 456 | ExpertBert: Pretraining Expert Finding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an expert-level pretraining language model named ExpertBert, aiming to model questions, experts as well as question-expert matching effectively in a pretraining manner. |
Hongtao Liu; Zhepeng Lv; Qing Yang; Dongliang Xu; Qiyao Peng; |
| 457 | Embedding Global and Local Influences for Dynamic Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Unlike transductive learning, inductive learning attempts to dynamically generate node embeddings over time even for unseen nodes, which is more suitable for real-world applications. Therefore, we propose an inductive dynamic graph embedding method called AGLI by aggregating <u>g</u>lobal and <u>l</u>ocal <u>i</u>nfluences. |
Meng Liu; Jiaming Wu; Yong Liu; |
| 458 | Memory Augmented Graph Learning Networks for Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a memory augmented graph learning network (MAGL), which captures the spatial correlations in terms of the global historical features of MTS. |
Xiangyue Liu; Xinqi Lyu; Xiangchi Zhang; Jianliang Gao; Jiamin Chen; |
| 459 | MomNet: Gender Prediction Using Mechanism of Working Memory Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a novel gender prediction framework based on user-posted living Moments MomNet). |
Sijie Long; Lin Li; Jingling Yuan; Jianquan Liu; |
| 460 | Meta-Reinforcement Learning for Multiple Traffic Signals Control Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite the success of recent reinforcement learning (RL) in traffic signal control which has shown to outperform the conventional control methods, current RL-based methods require large amounts of samples to learn and lack the generalization ability to a new environment. In order to solve these problems, we propose a new context-based meta-RL model that disentangles task inference and control, which improves the meta-training efficiency and accelerates the learning process in a new environment. |
Yican Lou; Jia Wu; Yunchuan Ran; |
| 461 | Sampling Enclosing Subgraphs for Link Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper presents a scalable link prediction solution, that we call ScaLed, which utilizes sparse enclosing subgraphs to make predictions. |
Paul Louis; Shweta Ann Jacob; Amirali Salehi-Abari; |
| 462 | PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose a pipeline-based application programming interface (API) so all machine learning workflows follow a standardized six-step pipeline. |
Haiping Lu; Xianyuan Liu; Shuo Zhou; Robert Turner; Peizhen Bai; Raivo E. Koot; Mustafa Chasmai; Lawrence Schobs; Hao Xu; |
| 463 | Scalable Multiple Kernel K-means Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the issue, we propose to employ the Nystrom scheme for MKKM clustering, termed scalable multiple kernel k-means clustering. |
Yihang Lu; Haonan Xin; Rong Wang; Feiping Nie; Xuelong Li; |
| 464 | Self-Paced and Discrete Multiple Kernel K-Means Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the issue, we propose a novel Self-Paced and Discrete Multiple Kernel K-Means (SPD-MKKM). |
Yihang Lu; Xuan Zheng; Jitao Lu; Rong Wang; Feiping Nie; Xuelong Li; |
| 465 | Personalized Federated Recommendation Via Joint Representation Learning, User Clustering, and Model Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Graph Neural Network based Personalized Federated Recommendation (PerFedRec) framework via joint representation learning, user clustering, and model adaptation. |
Sichun Luo; Yuanzhang Xiao; Linqi Song; |
| 466 | Urban Region Profiling Via Multi-Graph Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, we propose a multi-graph representation learning framework, called Region2Vec, for urban region profiling. |
Yan Luo; Fu-lai Chung; Kai Chen; |
| 467 | See Clicks Differently: Modeling User Clicking Alternatively with Multi Classifiers for CTR Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: User-item click probability is then estimated as a single term, and the basic assumption is that the user has different preferences over items. This is presumably true, but from real-world data, we observe that some people are naturally more active in clicking on items while some are not. |
Shiwei Lyu; Hongbo Cai; Chaohe Zhang; Shuai Ling; Yue Shen; Xiaodong Zeng; Jinjie Gu; Guannan Zhang; Haipeng Zhang; |
| 468 | A Prerequisite Attention Model for Knowledge Proficiency Diagnosis of Students Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a Prerequisite Attention model for Knowledge Proficiency diagnosis of students (PAKP) to learn the attentive weights of precursor concepts on successor concepts and model it for inferring the knowledge proficiency. |
Haiping Ma; Jinwei Zhu; Shangshang Yang; Qi Liu; Haifeng Zhang; Xingyi Zhang; Yunbo Cao; Xuemin Zhao; |
| 469 | Curriculum Contrastive Learning for Fake News Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the idea of curriculum learning, we propose a curriculum contrastive model (CCFD) for fake news detection which automatically select and train negative samples with different difficulty at different training stages. |
Jiachen Ma; Yong Liu; Meng Liu; Meng Han; |
| 470 | A Contrastive Pre-training Approach to Discriminative Autoencoder for Dense Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: More importantly, the discriminative ability of learned representations may be limited since each token is treated equally important in decoding the input texts. To address the above problems, in this paper, we propose a contrastive pre-training approach to learn a discriminative autoencoder with a lightweight multi-layer perception (MLP) decoder. |
Xinyu Ma; Ruqing Zhang; Jiafeng Guo; Yixing Fan; Xueqi Cheng; |
| 471 | Robustness of Sketched Linear Classifiers to Adversarial Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose novel Fast-Gradient-Sign Method (FGSM) attacks for sketched classifiers in full, partial, and black-box information settings with regards to their internal parameters. |
Ananth Mahadevan; Arpit Merchant; Yanhao Wang; Michael Mathioudakis; |
| 472 | Locality Aware Temporal FMs for Crime Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Locality Aware Temporal Factorization Machines (LTFMs) for crime prediction. |
Sameen Mansha; Abdur Rehman; Shaaf Abdullah; Faisal Kamiran; Hongzhi Yin; |
| 473 | Contextualized Formula Search Using Math Abstract Meaning Representation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce MathAMR, a new unified representation for sentences containing math. |
Behrooz Mansouri; Douglas W. Oard; Richard Zanibbi; |
| 474 | Not All Neighbors Are Friendly: Learning to Choose Hop Features to Improve Node Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we show that selective aggregation leads to better performance than default aggregation on the node classification task. |
Sunil Kumar Maurya; Xin Liu; Tsuyoshi Murata; |
| 475 | Music4All-Onion — A Large-Scale Multi-faceted Content-Centric Music Recommendation Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We organize distinct item content features in an onion model according to their semantics, and perform a comprehensive examination of the impact of different layers of this model (e.g., audio features, user-generated content, and derivative content) on content-driven music recommendation, demonstrating how various content features influence accuracy, novelty, and fairness of music recommendation systems. |
Marta Moscati; Emilia Parada-Cabaleiro; Yashar Deldjoo; Eva Zangerle; Markus Schedl; |
| 476 | Towards Confidence-aware Calibrated Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work, presents a set of properties that address various aspects of a desired calibrated recommender system. Considering these properties, we propose a confidence-aware optimization-based re-ranking algorithm to find the balance between calibration, relevance, and item diversity, while simultaneously accounting for calibration confidence based on user profile size. |
Mohammadmehdi Naghiaei; Hossein A. Rahmani; Mohammad Aliannejadi; Nasim Sonboli; |
| 477 | Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: It is often difficult to correctly infer a writer’s emotion from text exchanged online, and differences in recognition between writers and readers can be problematic. In this paper, we propose a new framework for detecting sentences that create differences in emotion recognition between the writer and the reader and for detecting the kinds of expressions that cause such differences. |
Tsubasa Nakagawa; Shunsuke Kitada; Hitoshi Iyatomi; |
| 478 | Locality Sensitive Hashing with Temporal and Spatial Constraints for Efficient Population Record Linkage Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a novel method to improve the scalability and robustness of min-hash LSH for linking large population databases by exploiting temporal and spatial information available in personal data, and by filtering record pairs based on block sizes and min-hash similarity. |
Charini Nanayakkara; Peter Christen; |
| 479 | ReFine: Re-randomization Before Fine-tuning for Cross-domain Few-shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a simple yet powerful method that re-randomizes the parameters fitted on the source domain before adapting to the target data. |
Jaehoon Oh; Sungnyun Kim; Namgyu Ho; Jin-Hwa Kim; Hwanjun Song; Se-Young Yun; |
| 480 | Implicit Session Contexts for Next-Item Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose \method, which implicitly contextualizes sessions. |
Sejoon Oh; Ankur Bhardwaj; Jongseok Han; Sungchul Kim; Ryan A. Rossi; Srijan Kumar; |
| 481 | Cross-domain Prototype Learning from Contaminated Faces Via Disentangling Latent Factors Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a disentangled heterogeneous prototype learning framework, dubbed DisHPL, which consists of one encoder-decoder generator and two discriminators. |
Meng Pang; Binghui Wang; Shengbo Chen; Yiu-ming Cheung; Rong Zou; Wei Huang; |
| 482 | GradAlign+: Empowering Gradual Network Alignment Using Attribute Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Although NA methods have achieved remarkable success in a myriad of scenarios, their satisfactory performance is not without prior anchor link information and/or node attributes, which may not always be available. In this paper, we propose Grad-Align+, a novel NA method using node attribute augmentation that is quite robust to the absence of such additional information. |
Jin-Duk Park; Cong Tran; Won-Yong Shin; Xin Cao; |
| 483 | Improving Graph-based Document-Level Relation Extraction Model with Novel Graph Structure Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In addition, there is a possibility of losing lexical information of the relations among entities directly expressed in a sentence. To address this issue, we propose two novel graph structures: an anaphoric graph and a local-context graph. |
Seongsik Park; Dongkeun Yoon; Harksoo Kim; |
| 484 | Plotly.plus, An Improved Dataset for Visualization Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Advances in this research area are hindered by the absence of reliable datasets on which to train the recommender systems. To the best of our knowledge, Plotly corpus is the only publicly available dataset, but as complained by many authors and discussed in this article, it contains many labeling errors, which greatly limits its usefulness. |
Luca Podo; Paola Velardi; |
| 485 | GRETEL: Graph Counterfactual Explanation Evaluation Framework Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present GRETEL, a unified framework to develop and test GCE methods in several settings. |
Mario Alfonso Prado-Romero; Giovanni Stilo; |
| 486 | CLNews: The First Dataset of The Chilean Social Outbreak for Disinformation Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents a new dataset with Twitter claims verified by fact-checkers along with the propagation structure of retweets and replies. |
Eliana Providel; Daniel Toro; Fabián Riquelme; Marcelo Mendoza; Eduardo Puraivan; |
| 487 | Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we perform a beyond-accuracy analysis of the state-of-the-art approaches to assess the presence of disparate impact and disparate mistreatment, meaning that users characterised by a given sensitive feature are unintentionally, but systematically, classified worse than their counterparts. |
Erasmo Purificato; Ludovico Boratto; Ernesto William De Luca; |
| 488 | FwSeqBlock: A Field-wise Approach for Modeling Behavior Representation in Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a pluggable module, FwSeqBlock, to enhance the expressiveness of behavior representations. |
Hao Qian; Qintong Wu; MingHao Li; Zhengwei Wu; Zhiqiang Zhang; Jun Zhou; Lihong Gu; Jinjie Gu; |
| 489 | Robust Semi-supervised Domain Adaptation Against Noisy Labels Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To remedy it, we have introduced the adversarial disturbance to raise the divergence across differently augmented views. |
Can Qin; Yizhou Wang; Yun Fu; |
| 490 | Explainable Graph-based Fraud Detection Via Neural Meta-graph Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we attempt to achieve high performance for graph-based fraud detection while considering model explainability. |
Zidi Qin; Yang Liu; Qing He; Xiang Ao; |
| 491 | Probabilistic Model Incorporating Auxiliary Covariates to Control FDR Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We incorporate auxiliary covariates among test-level covariates in a deep Black-Box framework (named as NeurT-FDR) which boosts statistical power and controls FDR for multiple hypothesis testing. |
Lin Qiu; Nils Murrugarra-Llerena; Vítor Silva; Lin Lin; Vernon M. Chinchilli; |
| 492 | Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, this tech- nique may not always work, especially for two scenarios: a corpus that contains very different text from the general corpus Wikipedia, or a task that learns embedding spacial distribution for a specific purpose (e.g., approximate nearest neighbor search). In this paper, to tackle the above two scenarios that we have encountered in an industrial e-commerce search system, we propose customized and novel pre-training tasks for two critical modules: user intent detec- tion and semantic embedding retrieval. |
Yiming Qiu; Chenyu Zhao; Han Zhang; Jingwei Zhuo; Tianhao Li; Xiaowei Zhang; Songlin Wang; Sulong Xu; Bo Long; Wen-Yun Yang; |
| 493 | SCC – A Test Collection for Search in Chat Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present SCC, a test collection for evaluating search in chat conversations. |
Ismail Sabei; Ahmed Mourad; Guido Zuccon; |
| 494 | A Model-Centric Explainer for Graph Neural Network Based Node Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a neighborhood generator as an explainer that generates optimal neighborhoods to maximize a particular class prediction of the trained GNN model. |
Sayan Saha; Monidipa Das; Sanghamitra Bandyopadhyay; |
| 495 | Cost-constrained Minimal Steiner Tree Enumeration Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The BDD-based algorithm constructs a BDD that compactly represents the set of minimal Steiner trees and then traverses the BDD for enumeration. We develop a novel frontier-based algorithm to construct such BDDs efficiently. |
Yuya Sasaki; |
| 496 | Twin Papers: A Simple Framework of Causal Inference for Citations Via Coupling Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The research process includes many decisions, e.g., how to entitle and where to publish the paper. In this paper, we introduce a general framework for investigating the effects of such decisions. |
Ryoma Sato; Makoto Yamada; Hisashi Kashima; |
| 497 | Measuring and Comparing The Consistency of IR Models for Query Pairs with Similar and Different Information Needs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While a match between the user expectations and system outputs can be sought at different levels of granularity, we study this alignment for search intent transformation across a pair of queries. Specifically, we propose a consistency metric, which for a given pair of queries – one reformulated from the other with at least one term in common, measures if the change in the set of the top-retrieved documents induced by this reformulation is as per a user’s expectation. |
Procheta Sen; Sourav Saha; Debasis Ganguly; Manisha Verma; Dwaipayan Roy; |
| 498 | Spatial-Temporal Identity: A Simple Yet Effective Baseline for Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we identify the indistinguishability of samples in both spatial and temporal dimensions as a key bottleneck, and propose a simple yet effective baseline for MTS forecasting by attaching <u>S</u>patial and <u>T</u>emporal <u>ID</u>entity information (STID), which achieves the best performance and efficiency simultaneously based on simple Multi-Layer Perceptrons (MLPs). |
Zezhi Shao; Zhao Zhang; Fei Wang; Wei Wei; Yongjun Xu; |
| 499 | A Graph-based Spatiotemporal Model for Energy Markets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Due to the interconnected nature of the energy markets, the supply-demand constraints in one region can impact prices in another connected region. To incorporate these spatiotemporal relationships, we propose a novel graph neural network architecture incorporating multidimensional time-series features to forecast price (node attribute) and energy flow (edge attribute) between regions simultaneously. |
Swati Sharma; Srinivasan Iyengar; Shun Zheng; Kshitij Kapoor; Wei Cao; Jiang Bian; Shivkumar Kalyanaraman; John Lemmon; |
| 500 | Early Stage Sparse Retrieval with Entity Linking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose boosting the performance of sparse retrievers by expanding both the queries and the documents with linked entities in two formats for the entity names: 1) explicit and 2) hashed. |
Dahlia Shehata; Negar Arabzadeh; Charles L. A. Clarke; |
| 501 | PubMed Author-assigned Keyword Extraction (PubMedAKE) Benchmark Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: PubMedAKE is an author-assigned keyword extraction dataset that contains the title, abstract, and keywords of over 843,269 articles from the PubMed open access subset database. |
Jiasheng Sheng; Zelalem Gero; Joyce C. Ho; |
| 502 | CStory: A Chinese Large-scale News Storyline Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the development of these methods is strongly limited by the size and quality of existing storyline datasets since news storylines are expensive to annotate as they contain a myriad of unlabeled relationships growing quadratically with the number of news events. Working around these difficulties, we propose a sophisticated pre-processing method to filter candidate news pairs by entity co-occurrence and semantic similarity. |
Kaijie Shi; Xiaozhi Wang; Jifan Yu; Lei Hou; Juanzi Li; Jingtong Wu; Dingyu Yong; Jinghui Xiao; Qun Liu; |
| 503 | Multi-task Generative Adversarial Network for Missing Mobility Data Imputation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a multi-task generative adversarial network, termed as MDI-MG, to mitigate the negative impact of missing mobility data by imputing possible missing records. |
Meihui Shi; Derong Shen; Yue Kou; Tiezheng Nie; Ge Yu; |
| 504 | On The Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Interacting with a speech interface to query a Question Answering (QA) system is becoming increasingly popular. Typically, QA systems rely on passage retrieval to select candidate … |
Georgios Sidiropoulos; Svitlana Vakulenko; Evangelos Kanoulas; |
| 505 | Data Oversampling with Structure Preserving Variational Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: It leads to various issues like poor modelling of the structure of the data, resulting in data overlapping between minority and majority classes that lead to poor classification performance of minority class(es). To overcome these limitations, we propose a novel data oversampling architecture called Structure Preserving Variational Learning (SPVL). |
Indu Solomon; Senthilnath Jayavelu; Md Meftahul Ferdaus; Uttam Kumar; |
| 506 | Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we rethink pre-ranking system from the perspective of the entire sample space, and propose Entire-chain Cross-domain Models (ECM), which leverage samples from the whole cascaded stages to effectively alleviate SSB problem. |
Jinbo Song; Ruoran Huang; Xinyang Wang; Wei Huang; Qian Yu; Mingming Chen; Yafei Yao; Chaosheng Fan; Changping Peng; Zhangang Lin; Jinghe Hu; Jingping Shao; |
| 507 | ST-GAT: A Spatio-Temporal Graph Attention Network for Accurate Traffic Speed Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, with the evidence via preliminary analysis, we point out the importance of considering individual dependencies between two speeds from all possible points in space and time for accurate traffic speed prediction. |
Junho Song; Jiwon Son; Dong-hyuk Seo; Kyungsik Han; Namhyuk Kim; Sang-Wook Kim; |
| 508 | A Preliminary Exploration of Extractive Multi-Document Summarization in Hyperbolic Space Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the above structural property is hard to model in the Euclidean space. Inspired by the above issues, we explore extractive summarization in the hyperbolic space and propose a new Hyperbolic Siamese Network for the matching-based extractive summarization (HyperSiameseNet). |
Mingyang Song; Yi Feng; Liping Jing; |
| 509 | Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel time series dissimilarity measure named RobustDTW to reduce the effects of noises and outliers. |
Xiaomin Song; Qingsong Wen; Yan Li; Liang Sun; |
| 510 | Targeted Influence with Community and Gender-Aware Seeding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To tackle the challenge of maximizing information spread on certain target groups, e.g., females, we introduce the concept of the community and gender-aware potential of users. |
Maciej Styczen; Bing-Jyue Chen; Ya-Wen Teng; Yvonne-Anne Pignolet; Lydia Chen; De-Nian Yang; |
| 511 | Multi-Aspect Embedding of Dynamic Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To solve the problem, in this paper we propose a Dynamic Graph Multi-Aspect Embedding (DGMAE) to automatically learn the proper number of aspects and their distributions in each temporal duration based on a distance dependent Chinese Restaurant Process. |
Aimin Sun; Zhiguo Gong; |
| 512 | Confidence-Guided Learning Process for Continuous Classification of Time Series Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, we propose a novel Confidence-guided method for CCTS (C3TS). |
Chenxi Sun; Moxian Song; Derun Cai; Baofeng Zhang; Shenda Hong; Hongyan Li; |
| 513 | Global and Local Feature Interaction with Vision Transformer for Few-shot Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we propose a new method, named GL-ViT, to integrate both global and local features to fully exploit the few-shot samples for image classification. |
Mingze Sun; Weizhi Ma; Yang Liu; |
| 514 | Improving Downstream Task Performance By Treating Numbers As Entities Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we attempt to tap the potential of state-of-the-art language models and transfer their ability to boost performance in related downstream tasks dealing with numbers. |
Dhanasekar Sundararaman; Vivek Subramanian; Guoyin Wang; Liyan Xu; Lawrence Carin; |
| 515 | ML-1M++: MovieLens-Compatible Additional Preferences for More Robust Offline Evaluation of Sequential Recommenders Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, we propose an assessment interface tailored to the sequential recommendation task and ask crowd workers to assess the (potential) relevance of each candidate item in MovieLens 1M, a commonly used dataset. |
Kazutoshi Umemoto; |
| 516 | Leveraging The Graph Structure of Neural Network Training Dynamics Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Thus, in this work, we propose a compact, expressive temporal graph framework that effectively captures the dynamics of many workhorse architectures in computer vision. |
Fatemeh Vahedian; Ruiyu Li; Puja Trivedi; Di Jin; Danai Koutra; |
| 517 | Towards A Learned Cost Model for Distributed Spatial Join: Data, Code & Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Geospatial data comprise around 60% of all the publicly available data. One of the essential and most complex operations that brings together multiple geospatial datasets is the … |
Tin Vu; Alberto Belussi; Sara Migliorini; Ahmed Eldawy; |
| 518 | Self-supervision Meets Adversarial Perturbation: A Novel Framework for Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel and powerful framework, dubbed as SLA2P, for unsupervised anomaly detection. |
Yizhou Wang; Can Qin; Rongzhe Wei; Yi Xu; Yue Bai; Yun Fu; |
| 519 | Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Data from these entrances has low coverage, which makes it difficult for the agent to learn. To address this challenge, we propose Similarity-based Hybrid Transfer for Ads Allocation (SHTAA), which effectively transfers samples as well as knowledge from data-rich entrance to data-poor entrance. |
Ze Wang; Guogang Liao; Xiaowen Shi; Xiaoxu Wu; Chuheng Zhang; Bingqi Zhu; Yongkang Wang; Xingxing Wang; Dong Wang; |
| 520 | MNCM: Multi-level Network Cascades Model for Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, inspired by the Multi-task Network Cascades (MNC) and Adaptive Information Transfer Multi-task (AITM) frameworks, we propose a Multi-level Network Cascades Model (MNCM) based on the pattern of specific and shared experts separation. |
Haotian Wu; |
| 521 | Disentangled Contrastive Learning for Social Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, such an approach may fail to model the users’ heterogeneous behavior patterns in two domains, impairing the expressiveness of user representations. In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations (DcRec). |
Jiahao Wu; Wenqi Fan; Jingfan Chen; Shengcai Liu; Qing Li; Ke Tang; |
| 522 | Nonlinear Causal Discovery in Time Series Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper contributes in particular to a practical FCM-based causal learning approach, which can maintain effectiveness for real-world nonstationary data with general nonlinear relationships and unlimited variable scale.Specifically, the non-stationarity of time series data is first exploited with the nonlinear independent component analysis, to discover the underlying components or latent disturbances. |
Tianhao Wu; Xingyu Wu; Xin Wang; Shikang Liu; Huanhuan Chen; |
| 523 | HQANN: Efficient and Robust Similarity Search for Hybrid Queries with Structured and Unstructured Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present HQANN, a simple yet highly efficient hybrid query processing framework which can be easily embedded into existing proximity graph-based ANNS algorithms. |
Wei Wu; Junlin He; Yu Qiao; Guoheng Fu; Li Liu; Jin Yu; |
| 524 | Efficiently Answering Minimum Reachable Label Set Queries in Edge-Labeled Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To fill the gap, in this paper, we propose and investigate the minimum reachable label set (MRLS) problem in edge-labeled graphs. |
Yanping Wu; Renjie Sun; Chen Chen; Xiaoyang Wang; Xianming Fu; |
| 525 | Balancing Utility and Exposure Fairness for Integrated Ranking with Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: During this process, apart from optimizing the system’s utility like the total number of clicks, a fair allocation of the exposure opportunities over different channels also needs to be satisfied. To address this problem, we propose an integrated ranking model called <u>I</u>ntegrated <u>D</u>eep-<u>Q</u> <u>N</u>etwork (iDQN), which jointly considers user preferences, the platform’s utility, and the exposure fairness. |
Wei Xia; Weiwen Liu; Yifan Liu; Ruiming Tang; |
| 526 | Multi-granularity Fatigue in Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel multi-granularity fatigue, modeling user fatigue from coarse to fine. |
Ruobing Xie; Cheng Ling; Shaoliang Zhang; Feng Xia; Leyu Lin; |
| 527 | BidH: A Bidirectional Hierarchical Model for Nested Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing methods have two drawbacks, i.e., 1) error propagation when identifying entities at different nesting levels and 2) unable to uncover and utilize the complex correlations between the inner and outer entities. To address these two defects, we propose a bidirectional hierarchical(BidH) model for nested name entity recognition. |
Wanyang Xu; Wengen Li; Jihong Guan; Shuigeng Zhou; |
| 528 | Modeling Latent Autocorrelation for Session-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel and concise SBR model inspired by the basic concept of autocorrelation in the Stochastic Process. |
Xianghong Xu; Kai Ouyang; Liuyin Wang; Jiaxin Zou; Yanxiong Lu; Hai-Tao Zheng; Hong-Gee Kim; |
| 529 | Texture BERT for Cross-modal Texture Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose Texture BERT, a model describing visual attributes of texture using natural language. |
Zelai Xu; Tan Yu; Ping Li; |
| 530 | Visual Encoding and Debiasing for CTR Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the extracted visual features are coarse-grained and/or biased. In this paper, we present a visual encoding framework for CTR prediction to overcome these problems. |
Guipeng Xv; Si Chen; Chen Lin; Wanxian Guan; Xingyuan Bu; Xubin Li; Hongbo Deng; Jian Xu; Bo Zheng; |
| 531 | Lightweight Unbiased Multi-teacher Ensemble for Review-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents LUME (a Lightweight Unbiased Multi-teacher Ensemble) for RRS. |
Guipeng Xv; Xinyi Liu; Chen Lin; Hui Li; Chenliang Li; Zhenhua Huang; |
| 532 | A Multi-granularity Network for Emotion-Cause Pair Extraction Via Matrix Capsule Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We argue that the clause-level encoders are ill-suited to the ECPE task where text information has many granularity features. In this paper, we design a Matrix Capsule-based multi-granularity framework (MaCa) for this task. |
Cheng Yang; Zhongwei Zhang; Jie Ding; Wenjun Zheng; Zhiwen Jing; Ying Li; |
| 533 | Task Similarity Aware Meta Learning for Cold-Start Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: And for tasks with different distributions, most meta-learning-based methods are difficult to achieve better performance under a single initialization. To address the limitations mentioned above and combine the strengths of both methods, we propose a Task Similarity Aware Meta-Learning (TSAML) framework from two aspects. |
Jieyu Yang; Zhaoxin Huan; Yong He; Ke Ding; Liang Zhang; Xiaolu Zhang; Jun Zhou; Linjian Mo; |
| 534 | AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose AdaSparse for multi-domain CTR prediction, which learns adaptively sparse structure for each domain, achieving better generalization across domains with lower computational cost. |
Xuanhua Yang; Xiaoyu Peng; Penghui Wei; Shaoguo Liu; Liang Wang; Bo Zheng; |
| 535 | Calibrate Automated Graph Neural Network Via Hyperparameter Uncertainty Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, we propose a hyperparameter uncertainty-induced graph convolutional network (HyperU-GCN) with a bilevel formulation, where the upper-level problem explicitly reasons uncertainties by developing a probabilistic hypernetworks through a variational Bayesian lens, while the lower-level problem learns how the GCN weights respond to a hyperparameter distribution. |
Xueying Yang; Jiamian Wang; Xujiang Zhao; Sheng Li; Zhiqiang Tao; |
| 536 | Unanswerable Question Correction and Explanation Over Personal Knowledge Base Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we seek not only to correct unanswerable questions based on a personal knowledge base, but also to explain the reason of the correction. |
An-Zi Yen; Hen-Hsen Huang; Hsin-Hsi Chen; |
| 537 | MetaRule: A Meta-path Guided Ensemble Rule Set Learning for Explainable Fraud Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose to learn interpretable models for fraud detection as a simple rule set. |
Lu Yu; Meng Li; Xiaoguang Huang; Wei Zhu; Yanming Fang; Jun Zhou; Longfei Li; |
| 538 | Multi-scale Multi-modal Dictionary BERT For Effective Text-image Retrieval in Multimedia Advertising Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we improve the multi-modal dictionary BERT by developing a multi-scale multi-modal dictionary and propose a Multi-scale Multi-modal Dictionary BERT (M^2D-BERT). |
Tan Yu; Jie Liu; Zhipeng Jin; Yi Yang; Hongliang Fei; Ping Li; |
| 539 | The SimIIR 2.0 Framework: User Types, Markov Model-Based Interaction Simulation, and Advanced Query Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present an extended SimIIR 2.0 version with new components for dynamic user type-specific Markov model-based interactions and more realistic query generation. |
Saber Zerhoudi; Sebastian Günther; Kim Plassmeier; Timo Borst; Christin Seifert; Matthias Hagen; Michael Granitzer; |
| 540 | A Hyperbolic-to-Hyperbolic User Representation with Multi-aspect for Social Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we design a novel hyperbolic-to-hyperbolic user representation with multi-aspect social recommender system, namely H2HMSR, which directly works in hyperbolic space. |
Hang Zhang; Hao Wang; Guifeng Wang; Jiayu Liu; Qi Liu; |
| 541 | Binary Classification with Positive Labeling Sources Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, in this work, we study the application of WS on binary classification tasks with positive labeling sources only. |
Jieyu Zhang; Yujing Wang; Yaming Yang; Yang Luo; Alexander Ratner; |
| 542 | An Enhanced Gated Graph Neural Network for E-commerce Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, since more and more people are surfing the Internet without logging in, it is no longer capable to provide accurate recommendations based on the historical data or profiles. To tackle this issue, we propose MentalNet-a mental model for e-commerce recommendation by enhancing the gated Graph Neural Network (GNN) and capturing user intent in a short session. |
Jihai Zhang; Fangquan Lin; Cheng Yang; Ziqiang Cui; |
| 543 | Graph Representation Learning Via Adaptive Multi-layer Neighborhood Diffusion Contrast Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, most GNNs focus on using the message passing mechanism to guide the information aggregation between neighbors, which results in the over-smoothing and weak robustness. To address the above issues, we propose a novel graph representation learning framework via <u>A</u>daptive <u>M</u>ulti-layer <u>N</u>eighborhood <u>D</u>iffusion <u>C</u>ontrast, called AM-NDC in this paper. |
Jijie Zhang; Yan Yang; Yong Liu; Meng Han; Shaowei Yin; |
| 544 | Selectively Expanding Queries and Documents for News Background Linking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing approaches of background article search tend to apply a single ranking method to all types of search topics. In this paper, we focus on exploring search topics on news articles by classifying them into two types:time-sensitive andnon-time-sensitive. |
Lirong Zhang; Hideo Joho; Sumio Fujita; Hai-Tao Yu; |
| 545 | Deep Contrastive Multiview Network Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, they neglect the semantic consistency between fused and view representations and have difficulty in modeling complementary information between different views. To deal with these deficiencies, this work presents a novel Contrastive leaRning framEwork for Multiview network Embedding (CREME). |
Mengqi Zhang; Yanqiao Zhu; Qiang Liu; Shu Wu; Liang Wang; |
| 546 | Co-Training with Validation: A Generic Framework for Semi-Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a generic SRE paradigm, called Co-Training with Validation (CTV), for making full use of learners to benefit more from unlabeled corpus. |
Shun Zhang; Xiangkui Lu; Jun Wu; |
| 547 | Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding Via GAN Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel framework dubbed GF2 to alleviate the cold-start problem in deep learning based CTR prediction. |
Xuxin Zhang; Di Wang; Dehong Gao; Wen Jiang; Wei Ning; Yang Zhou; Chen Wang; |
| 548 | WDRASS: A Web-scale Dataset for Document Retrieval and Answer Sentence Selection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present WDRASS, a dataset for ODQA based on answer sentence selection (AS2) models, which consider sentences as candidate answers for QA systems. |
Zeyu Zhang; Thuy Vu; Sunil Gandhi; Ankit Chadha; Alessandro Moschitti; |
| 549 | SuGeR: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Furthermore, they usually lack the transferability from one domain with sufficient supervision to another domain which might suffer from the label scarcity issue. In this work, we propose a subgraph-based Graph Neural Network model, SuGeR, for bundle recommendation to handle these limitations. |
Zhenning Zhang; Boxin Du; Hanghang Tong; |
| 550 | KSG: Knowledge and Skill Graph Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: The knowledge graph (KG) is an essential form of knowledge representation that has grown in prominence in recent years. Because it concentrates on nominal entities and their … |
Feng Zhao; Ziqi Zhang; Donglin Wang; |
| 551 | RecBole 2.0: Towards A More Up-to-Date Recommendation Library Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures. |
Wayne Xin Zhao; Yupeng Hou; Xingyu Pan; Chen Yang; Zeyu Zhang; Zihan Lin; Jingsen Zhang; Shuqing Bian; Jiakai Tang; Wenqi Sun; Yushuo Chen; Lanling Xu; Gaowei Zhang; Zhen Tian; Changxin Tian; Shanlei Mu; Xinyan Fan; Xu Chen; Ji-Rong Wen; |
| 552 | Multiple Instance Learning for Uplift Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: Uplift modeling is widely used in performance marketing to estimate effects of promotion campaigns (e.g., increase of customer retention rate). Since it is impossible to observe … |
Yao Zhao; Haipeng Zhang; Shiwei Lyu; Ruiying Jiang; Jinjie Gu; Guannan Zhang; |
| 553 | Multi-Interest Refinement By Collaborative Attributes Modeling for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we novelly propose to integrate a dynamic collaborative attribute routing module into Transformer. |
Huachi Zhou; Jiaqi Fan; Xiao Huang; Ka Ho Li; Zhenyu Tang; Dahai Yu; |
| 554 | Dialogue State Tracking Based on Hierarchical Slot Attention and Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Besides, to address the problem that the dialogue utterance is semantically distant from the corresponding slot value, we introduce the contrastive learning to make the utterance embedding mapped under each slot name more suitable with the ground truth value and away from other slot values. |
Yihao Zhou; Guoshuai Zhao; Xueming Qian; |
| 555 | Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel elastic demand function that captures the price elasticity of demand in hotel occupancy prediction. |
Fanwei Zhu; Wendong Xiao; Yao Yu; Ziyi Wang; Zulong Chen; Quan Lu; Zemin Liu; Minghui Wu; Shenghua Ni; |
| 556 | CAPER: Coarsen, Align, Project, Refine – A General Multilevel Framework for Network Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We propose CAPER, a multilevel alignment framework that Coarsens the input graphs, Aligns the coarsened graphs, Projects the alignment solution to finer levels and Refines the alignment solution. |
Jing Zhu; Danai Koutra; Mark Heimann; |
| 557 | Spherical Graph Embedding for Item Retrieval in Recommendation System Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a simple and effective graph-based retrieval method, which does not need any graph infrastructures. |
Wenqiao Zhu; Yesheng Xu; Xin Huang; Qiyang Min; Xun Zhou; |
| 558 | Molecular Substructure-Aware Network for Drug-Drug Interaction Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a novel model, Molecular Substructure-Aware Network (MSAN), to effectively predict potential DDIs from molecular structures of drug pairs. |
Xinyu Zhu; Yongliang Shen; Weiming Lu; |
| 559 | SEERa: A Framework for Community Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite abundant community detection libraries, there is yet to be one that provides access to the possible user communities in future time intervals. To bridge this gap, we contribute SEERa, an open-source end-to-end community prediction framework to identify future user communities in a text streaming social network. |
Soroush Ziaeinejad; Saeed Samet; Hossein Fani; |