Paper Digest: SIGIR 2021 Highlights

July 12, 2021August 24, 2021 admin

Download SIGIR-2021-Paper-Digests.pdf– highlights of all SIGIR-2021 papers. Readers can also choose to read this highlight article on our console, which allows users to filter out papers using keywords, authors, etc. SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval) is one of the top information retrieval conferences in the world. In 2021, it is to be held online.

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TABLE 1: Paper Digest: SIGIR 2021 Highlights

	Paper	Author(s)
1	Deconfounded Video Moment Retrieval with Causal Intervention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill the research gap, we propose a causality-inspired VMR framework that builds structural causal model to capture the true effect of query and video content on the prediction.	Xun Yang; Fuli Feng; Wei Ji; Meng Wang; Tat-Seng Chua;
2	Causal Intervention for Leveraging Popularity Bias in Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To achieve our goal, we propose a new training and inference paradigm for recommendation named Popularity-bias Deconfounding and Adjusting (PDA).	Yang Zhang; Fuli Feng; Xiangnan He; Tianxin Wei; Chonggang Song; Guohui Ling; Yongdong Zhang;
3	AutoDebias: Learning to Debias for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To move this idea forward, we propose AotoDebias that leverages another (small) set of uniform data to optimize the debiasing parameters by solving the bi-level optimization problem with meta-learning.	Jiawei Chen; Hande Dong; Yang Qiu; Xiangnan He; Xin Xin; Liang Chen; Guli Lin; Keping Yang;
4	Mitigating Sentiment Bias for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper reveals an unexplored type of bias, i.e., sentiment bias.	Chen Lin; Xinyi Liu; Guipeng Xv; Hui Li;
5	Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel and theoretic sound counterfactual approach to adjusting the user feedbacks and learning the recommendation models, called CBDF (Counterfactual Bandit with Delayed Feedback).	Xiao Zhang; Haonan Jia; Hanjing Su; Wenhan Wang; Jun Xu; Ji-Rong Wen;
6	Joint Knowledge Pruning and Recurrent Graph Convolution for News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose a novel knowledge pruning based recurrent graph convolutional network (named Kopra) for news recommendation.	Yu Tian; Yuhao Yang; Xudong Ren; Pengfei Wang; Fangzhao Wu; Qian Wang; Chenliang Li;
7	Personalized News Recommendation with Knowledge-aware Interactive Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a knowledge-aware interactive matching method for news recommendation.	Tao Qi; Fangzhao Wu; Chuhan Wu; Yongfeng Huang;
8	Enhanced Graph Learning for Collaborative Filtering Via Mutual Information Maximization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study how to better learn enhanced graph structure for CF. We argue that node embedding learning and graph structure learning can mutually enhance each other in CF, as updated node embeddings are learned from previous graph structure, and vice versa ~(i.e., newly updated graph structure are optimized based on current node embedding results).	Yonghui Yang; Le Wu; Richang Hong; Kun Zhang; Meng Wang;
9	ReXPlug: Explainable Recommendation Using Plug-and-Play Language Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose ReXPlug, an end-to-end framework with a plug and play way of explaining recommendations.	Deepesh V. Hada; Vijaikumar M.; Shirish K. Shevade;
10	Group Based Personalized Search By Integrating Search Behaviour and Friend Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a neural network enhanced method to highlight similar users in semantic space.	Yujia Zhou; Zhicheng Dou; Bingzheng Wei; Ruobing Xie; Ji-Rong Wen;
11	An Image Is Worth A Thousand Terms? Analysis of Visual E-Commerce Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present an in-depth comprehensive study of visual e-commerce search.	Arnon Dagan; Ido Guy; Slava Novgorodov;
12	Efficiently Teaching An Effective Dense Retriever with Balanced Topic Aware Sampling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead of relying on more compute capability, we introduce an efficient topic-aware query and balanced margin sampling technique, called TAS-Balanced.	Sebastian Hofstätter; Sheng-Chieh Lin; Jheng-Hong Yang; Jimmy Lin; Allan Hanbury;
13	Learning A Fine-Grained Review-based Transformer Model for Personalized Product Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Aware of the above limitations, we propose a review-based transformer model (RTM) for personalized product search, which encodes the sequence of query, user reviews, and item reviews with a transformer architecture.	Keping Bi; Qingyao Ai; W. Bruce Croft;
14	DepressionNet: Learning Multi-modalities with User Post Summarization for Depression Detection on Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the shortcomings in the existing automatic depression detection methods, we propose a novel computational framework for automatic depression detection that initially selects relevant content through a hybrid extractive and abstractive summarization strategy on the sequence of all user tweets leading to a more fine-grained and relevant content.	Hamad Zogan; Imran Razzak; Shoaib Jameel; Guandong Xu;
15	Look Before You Leap: Confirming Edge Signs in Random Walk with Restart for Personalized Node Ranking in Signed Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address the personalized node ranking (PNR) problem for signed networks, which aims to rank nodes in an order most relevant to a given seed node in a signed network.	Wonchang Lee; Yeon-Chang Lee; Dongwon Lee; Sang-Wook Kim;
16	Hierarchical Multi-modal Contextual Attention Network for Fake News Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these limitations, this paper proposes a novel hierarchical multi-modal contextual attention network (HMCAN) for fake news detection by jointly modeling the multi-modal context information and the hierarchical semantics of text in a unified deep model.	Shengsheng Qian; Jinguang Wang; Jun Hu; Quan Fang; Changsheng Xu;
17	DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction on social media.	Ruijie Wang; Zijie Huang; Shengzhong Liu; Huajie Shao; Dongxin Liu; Jinyang Li; Tianshi Wang; Dachun Sun; Shuochao Yao; Tarek Abdelzaher;
18	Tracing Knowledge State with Individual Cognition and Acquisition Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel model called Individual Estimation Knowledge Tracing (IEKT), which estimates the students’ cognition on the question before response prediction and assesses their knowledge acquisition sensitivity on the questions before updating the knowledge state.	Ting Long; Yunfei Liu; Jian Shen; Weinan Zhang; Yong Yu;
19	Knowledge-based Review Generation By Coherence Enhanced Text Planning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Coherence Enhanced Text Planning model (CETP) based on knowledge graphs (KGs) to improve both global and local coherence for review generation.	Junyi Li; Wayne Xin Zhao; Zhicheng Wei; Nicholas Jing Yuan; Ji-Rong Wen;
20	UGRec: Modeling Directed and Undirected Relations for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we make an effort to study the potential of integrating both types of side information (i.e., KG and item-item co-occurrence data) for recommendation.	Xinxiao Zhao; Zhiyong Cheng; Lei Zhu; Jiecai Zheng; Xueqing Li;
21	DEKR: Description Enhanced Knowledge Graph for Machine Learning Method Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a description-enhanced machine learning knowledge graph-based approach – DEKR – to help recommend appropriate ML methods for given ML datasets.	Xianshuai Cao; Yuliang Shi; Han Yu; Jihu Wang; Xinjun Wang; Zhongmin Yan; Zhiyong Chen;
22	Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a few-shot relational learning with global-local framework to address the above issues.	Guanglin Niu; Yang Li; Chengguang Tang; Ruiying Geng; Jian Dai; Qiao Liu; Hao Wang; Jian Sun; Fei Huang; Luo Si;
23	AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we extensively investigate how to naturally fuse the semantic textual information with the user behavior graph, and further propose three novel AdsGNN models to aggregate topological neighborhood from the perspectives of nodes, edges and tokens.	Chaozhuo Li; Bochen Pang; Yuming Liu; Hao Sun; Zheng Liu; Xing Xie; Tianqi Yang; Yanling Cui; Liangjie Zhang; Qi Zhang;
24	Hybrid Learning to Rank for Financial Event Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we formulate the financial event ranking task, which aims to score financial news (document) according to its influence to the given asset (query).	Fuli Feng; Moxin Li; Cheng Luo; Ritchie Ng; Tat-Seng Chua;
25	Hybrid Fusion with Intra- and Cross-Modality Attention for Image-Recipe Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel framework namedHybrid Fusion with Intra- and Cross-Modality Attention (HF-ICMA) to learn accurate image-recipe similarity.	Jiao Li; Xing Xu; Wei Yu; Fumin Shen; Zuo Cao; Kai Zuo; Heng Tao Shen;
26	PreSizE: Predicting Size in E-Commerce Using Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Predicting a proper size for an item to recommend is an important personalization challenge, and is being studied in this work.	Yotam Eshel; Or Levi; Haggai Roitman; Alex Nus;
27	Understanding and Mitigating Bias in Online Health Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Content can be biased and results may present different opinions. In addition, interpreting medically related content can be difficult for users with no medical background. All of these can lead users to incorrect conclusions regarding health related questions. In this work we address this problem from two perspectives.	Anat Hashavit; Hongning Wang; Raz Lin; Tamar Stern; Sarit Kraus;
28	Enhanced Doubly Robust Learning for Debiasing Post-Click Conversion Rate Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve these problems, we first derive the bias and variance of the DR estimator. Based on it, a more robust doubly robust (MRDR) estimator has been proposed to further reduce its variance while retaining its double robustness.	Siyuan Guo; Lixin Zou; Yiding Liu; Wenwen Ye; Suqi Cheng; Shuaiqiang Wang; Hechang Chen; Dawei Yin; Yi Chang;
29	Adapting Interactional Observation Embedding for Counterfactual Learning to Rank Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we leverage the embedding method to develop an Interactional Observation-Based Model (IOBM) to estimate the observation probability.	Mouxiang Chen; Chenghao Liu; Jianling Sun; Steven C.H. Hoi;
30	This Is Not What We Ordered: Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated Topics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better understand the mechanisms underlying SEME, we present a pre-registered, 5 x 3 factorial user study investigating whether order effects (i.e., users adopting the viewpoint pertaining to higher-ranked documents) can cause SEME.	Tim Draws; Nava Tintarev; Ujwal Gadiraju; Alessandro Bozzon; Benjamin Timmermans;
31	Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation of BERT Rankers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate these biases, we propose AdvBert, a ranking model achieved by adapting adversarial bias mitigation for IR, which jointly learns to predict relevance and remove protected attributes.	Navid Rekabsaz; Simone Kopeinik; Markus Schedl;
32	Binary Neural Network Hashing for Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel deep hashing method, called Binary Neural Network Hashing (BNNH) for fast image retrieval.	Wanqian Zhang; Dayan Wu; Yu Zhou; Bo Li; Weiping Wang; Dan Meng;
33	Unsupervised Proxy Selection for Session-based Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel framework to overcome the limitation of SRSs, named ProxySR, which imitates the missing information in SRSs (i.e., general interest of users) by modeling proxies of sessions.	Junsu Cho; SeongKu Kang; Dongmin Hyun; Hwanjo Yu;
34	XLightFM: Extremely Memory-Efficient Factorization Machine Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose an extremely memory-efficient Factorization Machine (xLightFM), where each category embedding is composited with latent vectors selected from codebooks.	Gangwei Jiang; Hao Wang; Jin Chen; Haoyu Wang; Defu Lian; Enhong Chen;
35	Counterfactual Data-Augmented Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel counterfactual data augmentation framework to mitigate the impact of the imperfect training data and empower sequential recommendation models.	Zhenlei Wang; Jingsen Zhang; Hongteng Xu; Xu Chen; Yongfeng Zhang; Wayne Xin Zhao; Ji-Rong Wen;
36	StackRec: Efficient Training of Very Deep Sequential Recommender Models By Iterative Stacking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To deal with such a challenge, we present StackRec, a simple, yet very effective and efficient training framework for deep SR models by iterative layer stacking.	Jiachun Wang; Fajie Yuan; Jian Chen; Qingyao Wu; Min Yang; Yang Sun; Guoxiao Zhang;
37	CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to learn accurate and robust user representations, which are required to be less sensitive to (attack on) noisy behaviors and trust more on the indispensable ones, by modeling counterfactual data distribution.	Shengyu Zhang; Dong Yao; Zhou Zhao; Tat-Seng Chua; Fei Wu;
38	Sequential Recommendation with Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a graph neural network model called SURGE (short forSeqUential Recommendation with Graph neural nEtworks) to address these two issues.	Jianxin Chang; Chen Gao; Yu Zheng; Yiqun Hui; Yanan Niu; Yang Song; Depeng Jin; Yong Li;
39	Category-aware Collaborative Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these challenges, we propose a Category-aware Collaborative Sequential Recommender.	Renqin Cai; Jibang Wu; Aidan San; Chong Wang; Hongning Wang;
40	Event Occurrence Date Estimation Based on Multivariate Time Series Analysis Over Temporal Document Collections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose an approach to temporal profiling of event mentions in text.	Jiexin Wang; Adam Jatowt; Masatoshi Yoshikawa;
41	Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To capture these properties effectively and efficiently, we propose a novel Recurrent Evolution network based on Graph Convolution Network (GCN), called RE-GCN, which learns the evolutional representations of entities and relations at each timestamp by modeling the KG sequence recurrently.	Zixuan Li; Xiaolong Jin; Wei Li; Saiping Guan; Jiafeng Guo; Huawei Shen; Yuanzhuo Wang; Xueqi Cheng;
42	Summarize Dates First: A Paradigm Shift in Timeline Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a paradigm shift in timeline summarization aimed at overcoming the above issues.	Moreno La Quatra; Luca Cagliero; Elena Baralis; Alberto Messina; Maurizio Montagnuolo;
43	TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we present the Time-aware Incremental Embedding (TIE) framework, which combines TKG representation learning, experience replay, and temporal regularization.	Jiapeng Wu; Yishi Xu; Yingxue Zhang; Chen Ma; Mark Coates; Jackie Chi Kit Cheung;
44	Allowing for The Grounded Use of Temporal Difference Learning in Large Ranking Models Via Substate Updates Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a modification of an established reinforcement learning method to facilitate the widespread use of temporal difference learning for IR: interpolated substate temporal difference (ISSTD) learning.	Daniel Cohen;
45	Answer Complex Questions: Path Ranker Is All You Need Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a purely rank-based framework Thinking Path Re-Ranker (TPRR), which is comprised of Thinking Path Ranker (TPR) for generating document sequences called "a path" and External Path Reranker (EPR) for selecting the best path from candidate paths generated by TPR.	Xinyu Zhang; Ke Zhan; Enrui Hu; Chengzhen Fu; Lan Luo; Hao Jiang; Yantao Jia; Fan Yu; Zhicheng Dou; Zhao Cao; Lei Chen;
46	Reinforcement Learning from Reformulations In Conversational Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we take a step towards a more natural learning paradigm – from noisy and implicit feedback via question reformulations.	Magdalena Kaiser; Rishiraj Saha Roy; Gerhard Weikum;
47	Ranking User-Generated Content Via Multi-Relational Graph Convolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we demonstrate the utility of considering the implicit and explicit relational aspects across user content to assess their quality.	Kanika Narang; Adit Krishnan; Junting Wang; Chaoqi Yang; Hari Sundaram; Carolyn Sutter;
48	Answering Any-hop Open-domain Questions with Iterative Document Reranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose a unified QA framework to answer any-hop open-domain questions, which iteratively retrieves, reranks and filters documents, and adaptively determines when to stop the retrieval process.	Yuyu Zhang; Ping Nie; Arun Ramamurthy; Le Song;
49	Multimodal Activation: Awakening Dialog Robots Without Wake Words Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we work towards awaking the robot without wake words.	Liqiang Nie; Mengzhao Jia; Xuemeng Song; Ganglu Wu; Harry Cheng; Jian Gu;
50	RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we present a novel Relation map driven Cognitive Diagnosis (RCD) framework, uniformly modeling the interactive and structural relations via a multi-layer student-exercise-concept relation map.	Weibo Gao; Qi Liu; Zhenya Huang; Yu Yin; Haoyang Bi; Mu-Chun Wang; Jianhui Ma; Shijin Wang; Yu Su;
51	Self-Supervised Contrastive Learning for Code Retrieval and Summarization Via Semantic-Preserving Transformations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Corder, a self-supervised contrastive learning framework for source code model.	Nghi D. Q. Bui; Yijun Yu; Lingxiao Jiang;
52	Initiative-Aware Self-Supervised Learning for Knowledge-Grounded Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a mixed-initiative knowledge selection method (MIKe) for KGC, which explicitly distinguishes between user-initiative and system-initiative knowledge selection.	Chuan Meng; Pengjie Ren; Zhumin Chen; Zhaochun Ren; Tengxiao Xi; Maarten de Rijke;
53	Wizard of Search Engine: Access to Information Through Conversations with Search Engines Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we make three main contributions to facilitate research into CIS: (1) We formulate a pipeline for CIS with six subtasks: intent detection, keyphrase extraction, action prediction, query selection, passage selection, and response generation. (2) We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS. (3) We design a neural architecture capable of training and evaluating both jointly and separately on the six sub-tasks, and devise a pre-train/fine-tune learning scheme, that can reduce the requirements of WISE in scale by making full use of available data.	Pengjie Ren; Zhongkun Liu; Xiaomeng Song; Hongtao Tian; Zhumin Chen; Zhaochun Ren; Maarten de Rijke;
54	Semi-Supervised Variational Reasoning for Medical Dialogue Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an end-to-end variational reasoning approach to medical dialogue generation.	Dongdong Li; Zhaochun Ren; Pengjie Ren; Zhumin Chen; Miao Fan; Jun Ma; Maarten de Rijke;
55	One Chatbot Per Person: Creating Personalized Chatbots Based on Implicit User Profiles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to learn implicit user profiles automatically from large-scale user dialogue history for building personalized chatbots.	Zhengyi Ma; Zhicheng Dou; Yutao Zhu; Hanxun Zhong; Ji-Rong Wen;
56	Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper makes an attempt to thoroughly explore the impact of utilizing personas that describe either self or partner speakers on the task of response selection in retrieval-based chatbots.	Jia-Chen Gu; Hui Liu; Zhen-Hua Ling; Quan Liu; Zhigang Chen; Xiaodan Zhu;
57	Learning Recommender Systems with Implicit Feedback Via Soft Target Enhancement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose SoftRec, a new RS optimization framework to enhance item recommendation.	Mingyue Cheng; Fajie Yuan; Qi Liu; Shenyang Ge; Zhi Li; Runlong Yu; Defu Lian; Senchao Yuan; Enhong Chen;
58	Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback Based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation.	Lei Chen; Le Wu; Kun Zhang; Richang Hong; Meng Wang;
59	Package Recommendation with Intra- and Inter-Package Attention Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus, in this paper, we make a first study on package recommendation and propose an Intra- and inter-package attention network for Package Recommendation (IPRec).	Chen Li; Yuanfu Lu; Wei Wang; Chuan Shi; Ruobing Xie; Haili Yang; Cheng Yang; Xu Zhang; Leyu Lin;
60	A Guided Learning Approach for Item Recommendation Via Surrogate Loss Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address the limitations of directly optimizing the NDCG measure by proposing a guided learning approach (GuidedRec) that adopts recent advances in parameterized surrogate losses for NDCG.	Ahmed Rashed; Josif Grabocka; Lars Schmidt-Thieme;
61	Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we propose Structured Graph Convolutional Networks (SGCNs) to enhance the performance of GCNs by exploiting graph structural properties of sparsity and low rank.	Huiyuan Chen; Lan Wang; Yusan Lin; Chin-Chia Michael Yeh; Fei Wang; Hao Yang;
62	WGCN: Graph Convolutional Networks with Weighted Structural Features Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To explore the directional structural information for different nodes, we propose a GCN model with weighted structural features, named WGCN.	Yunxiang Zhao; Jianzhong Qi; Qingwei Liu; Rui Zhang;
63	Privacy Protection in Deep Multi-modal Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel PrIvacy Protection method (PIP) against malicious multi-modal retrieval models, which proactively transfers original data into adversarial data with quasi-imperceptible perturbations before releasing them.	Peng-Fei Zhang; Yang Li; Zi Huang; Hongzhi Yin;
64	Learning Discriminative Neural Representations for Event Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the challenge, we propose to learn discriminative neural representations (DNR) from texts.	Jinzhi Liao; Xiang Zhao; Xinyi Li; Lingling Zhang; Jiuyang Tang;
65	Not All Relevance Scores Are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address this problem via an efficient Bayesian framework for retrieval models which captures the model’s belief in the relevance score through a stochastic process while adding only negligible computational overhead.	Daniel Cohen; Bhaskar Mitra; Oleg Lesota; Navid Rekabsaz; Carsten Eickhoff;
66	Interpretable Graph Similarity Computation Via Differentiable Optimal Alignment of Node Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper aims at efficiently approximating these domain-agnostic similarity measures through a neural network, and simultaneously learning the alignments (i.e., explanations) similar to those of classical intractable methods.	Khoa D. Doan; Saurav Manchanda; Suchismit Mahapatra; Chandan K. Reddy;
67	MMConv: An Environment for Multimodal Conversational Search Across Multiple Domains Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this fundamental obstacle, we introduce the Multimodal Multi-domain Conversational dataset (MMConv), a fully annotated collection of human-to-human role-playing dialogues spanning over multiple domains and tasks.	Lizi Liao; Le Hong Long; Zheng Zhang; Minlie Huang; Tat-Seng Chua;
68	Video Corpus Moment Retrieval with Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Retrieval and Localization Network with Contrastive Learning (ReLoCLNet) for VCMR.	Hao Zhang; Aixin Sun; Wei Jing; Guoshun Nan; Liangli Zhen; Joey Tianyi Zhou; Rick Siow Mong Goh;
69	One Person, One Model, One World: Learning Continual User Representation Without Forgetting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we delve on research to continually learn user representations task by task, whereby new tasks are learned while using partial parameters from old ones.	Fajie Yuan; Guoxiao Zhang; Alexandros Karatzoglou; Joemon Jose; Beibei Kong; Yudong Li;
70	Learning Domain Semantics and Cross-Domain Correlations for Paper Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we investigate knowledge propagation and characterize semantic correlations for cross discipline paper recommendation.	Yi Xie; Yuqing Sun; Elisa Bertino;
71	FedCT: Federated Collaborative Transfer for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, this solution usually requires direct information sharing between service providers on the cloud which may not always be available and brings privacy concerns. In this paper, we show that one can overcome these concerns through learning on edge devices such as smartphones and laptops.	Shuchang Liu; Shuyuan Xu; Wenhui Yu; Zuohui Fu; Yongfeng Zhang; Amelie Marian;
72	Self-supervised Graph Learning for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation.	Jiancan Wu; Xiang Wang; Fuli Feng; Xiangnan He; Liang Chen; Jianxun Lian; Xing Xie;
73	Modeling Intent Graph for Search Result Diversification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: On the intent graph, documents are connected if they are similar, while the query and the document are gradually connected based on the document selection result. Then we employ graph convolutional networks (GCNs) to update the representation of the query and each document by aggregating its neighbors.	Zhan Su; Zhicheng Dou; Yutao Zhu; Xubo Qin; Ji-Rong Wen;
74	Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on enhancing both domain-level and user-level adaptivity in diversified recommendation.	Yile Liang; Tieyun Qian; Qing Li; Hongzhi Yin;
75	Graph Meta Network for Multi-Behavior Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the above challenges, we propose a Multi-Behavior recommendation framework with Graph Meta Network to incorporate the multi-behavior pattern modeling into a meta-learning paradigm.	Lianghao Xia; Yong Xu; Chao Huang; Peng Dai; Liefeng Bo;
76	Fairness Among New Items in Cold Start Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper investigates recommendation fairness among new items.	Ziwei Zhu; Jingu Kim; Trung Nguyen; Aish Fenton; James Caverlee;
77	Make It Easy: An Effective End-to-End Entity Alignment Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes EASY, an effective end-to-end EA framework, which is able to (i) remove the labor-intensive pre-processing by fully discovering the name information provided by the entities themselves; and (ii) jointly fuse the features captured by the names of entities and the structural information of the graph to improve the EA results.	Congcong Ge; Xiaoze Liu; Lu Chen; Baihua Zheng; Yunjun Gao;
78	Files of A Feather Flock Together? Measuring and Modeling How Users Perceive File Similarity in Cloud Storage Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we conducted an online study combining automated analysis of 50 Google Drive and Dropbox users’ cloud accounts with a survey asking about pairs of files from those accounts.	Will Brackenbury; Galen Harrison; Kyle Chard; Aaron Elmore; Blase Ur;
79	CINES: Explore Citation Network and Event Sequences for Citation Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to explore both the citation network and the related citation event sequences which provide valuable information for future citation forecasting.	Fang He; Wang-Chien Lee; Tao-Yang Fu; Zhen Lei;
80	Learning to Ask Appropriate Questions in Conversational Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To mitigate these issues, we propose the Knowledge-Based Question Generation System (KBQG), a novel framework for conversational recommendation.	Xuhui Ren; Hongzhi Yin; Tong Chen; Hao Wang; Zi Huang; Kai Zheng;
81	Multi-Modal Supplementary-Complementary Summarization Using Multi-Objective Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce and formally define the concepts of supplementary and complementary multi-modal summaries in the context of the overlap of information covered by different modalities in the summary output.	Anubhav Jangra; Sriparna Saha; Adam Jatowt; Mohammed Hasanuzzaman;
82	Few-Shot Conversational Dense Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a Conversational Dense Retrieval system, ConvDR, that learns contextualized embeddings for multi-turn conversational queries and retrieves documents solely using embedding dot products.	Shi Yu; Zhenghao Liu; Chenyan Xiong; Tao Feng; Zhiyuan Liu;
83	Conversational Fashion Image Retrieval Via Multiturn Natural Language Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel framework that can effectively handle conversational fashion image retrieval with multiturn natural language feedback texts.	Yifei Yuan; Wai Lam;
84	Neural Graph Matching Based Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this drawback, we propose a neural Graph Matching based Collaborative Filtering model (GMCF), which effectively captures the two types of attribute interactions through modeling and aggregating attribute interactions in a graph matching structure for recommendation.	Yixin Su; Rui Zhang; Sarah M. Erfani; Junhao Gan;
85	The World Is Binary: Contrastive Learning for Denoising Next Basket Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose a Contrastive Learning Model~(named CLEA) to automatically extract items relevant to the target item for next basket recommendation.	Yuqi Qin; Pengfei Wang; Chenliang Li;
86	Dual Attention Transfer in Session-based Recommendation with Multi-dimensional Integration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, a new method is proposed in this paper, which is called dual attention transfer based on multi-dimensional integration (DAT-MDI): (i) DAT uses a potential mapping method based on a slot attention mechanism to extract the user’s representation information in different sessions between multiple domains. (ii) MDI combines the graph neural network for the graphs (session graph and global graph) and the gate recurrent unit (GRU) for the sequence to learn the item representation in each session.	Chen Chen; Jie Guo; Bin Song;
87	User-Centric Path Reasoning Towards Explainable Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose UCPR, a user-centric path reasoning network that constantly guides the search from the aspect of user demand and enables explainable recommendations.	Chang-You Tai; Liang-Ying Huang; Chien-Kun Huang; Lun-Wei Ku;
88	On Interpretation and Measurement of Soft Attributes for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address how to robustly interpret natural language refinements (or critiques) in recommender systems.	Krisztian Balog; Filip Radlinski; Alexandros Karatzoglou;
89	How Do Online Learning to Rank Methods Adapt to Changes of Intent? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address this gap by study the capability of OLTR algorithms to adapt to user intent change.	Shengyao Zhuang; Guido Zuccon;
90	Scalable Personalised Item Ranking Through Parametric Density Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a learning-to-rank approach, which achieves convergence speed comparable to the pointwise counterpart while performing similarly to the pairwise counterpart in terms of ranking effectiveness.	Riku Togashi; Masahiro Kato; Mayu Otani; Tetsuya Sakai; Shin’ichi Satoh;
91	New Insights Into Metric Optimization for Ranking-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we dig deeper into this issue in order to learn more about the effects of the choice of the metric to optimize on the performance of a ranking-based recommender system.	Roger Zhe Li; Julián Urbano; Alan Hanjalic;
92	Fast Attention-based Learning-To-Rank Model for Structured Map Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel deep neural network LTR architecture, capable of seamlessly handling heterogeneous inputs, similar to GBDT-based methods.	Chiqun Zhang; Michael R. Evans; Max Lepikhin; Dragomir Yankov;
93	Learning to Rank for Mathematical Formula Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, the effectiveness of retrieval models and formula representations are studied to identify their relative strengths and weaknesses.	Behrooz Mansouri; Richard Zanibbi; Douglas W. Oard;
94	Investigating User Behavior in Legal Case Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we focus on investigating user behavior in the scenario of legal case retrieval.	Yunqiu Shao; Yueyue Wu; Yiqun Liu; Jiaxin Mao; Min Zhang; Shaoping Ma;
95	NeurJudge: A Circumstance-aware Neural Framework for Legal Judgment Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose a circumstance-aware legal judgment prediction framework (i.e., NeurJudge) by exploring circumstances of crime.	Linan Yue; Qi Liu; Binbin Jin; Han Wu; Kai Zhang; Yanqing An; Mingyue Cheng; Biao Yin; Dayong Wu;
96	Legal Judgment Prediction Via Relational Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, we first formalize LJP as a node classification problem over a global consistency graph derived from the training set.	Qian Dong; Shuzi Niu;
97	Legal Judgment Prediction with Multi-Stage Case Representation Learning in The Real Court Setting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a novel challenging dataset from real courtrooms to predict the legal judgment in a reasonably encyclopedic manner by leveraging the genuine input of the case – plaintiff’s claims and court debate data, from which the case’s facts are automatically recognized by comprehensively understanding the multi-role dialogues of the court debate, and then learnt to discriminate the claims so as to reach the final judgment through multi-task learning.	Luyao Ma; Yating Zhang; Tianyi Wang; Xiaozhong Liu; Wei Ye; Changlong Sun; Shikun Zhang;
98	Cross-Domain Contract Element Extraction with A Bi-directional Feedback Clause-Element Relation Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a framework, the Bi-directional Feedback cLause-Element relaTion network (Bi-FLEET), for the cross-domain CEE task that addresses the above challenges.	Zihan Wang; Hongye Song; Zhaochun Ren; Pengjie Ren; Zhumin Chen; Xiaozhong Liu; Hongsong Li; Maarten de Rijke;
99	TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider recommendation scenarios from the perspective of two sides (customers and providers).	Yao Wu; Jian Cao; Guandong Xu; Yudong Tan;
100	Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a novel algorithm: PL-Rank, that estimates the gradient of a PL ranking model w.r.t. both relevance and fairness metrics.	Harrie Oosterhuis;
101	When Fair Ranking Meets Uncertain Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we investigate how uncertainty and errors in demographic inference impact the fairness offered by fair ranking algorithms.	Avijit Ghosh; Ritam Dutt; Christo Wilson;
102	Policy-Gradient Training of Fair and Unbiased Ranking Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address both exogenous and endogenous sources of unfairness, we present the first learning-to-rank approach that addresses both presentation bias and merit-based fairness of exposure simultaneously.	Himank Yadav; Zhengxiao Du; Thorsten Joachims;
103	Towards Personalized Fairness Based on Causal Notion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we introduce a framework for achieving counterfactually fair recommendations through adversary learning by generating feature-independent user embeddings for recommendation.	Yunqi Li; Hanxiong Chen; Shuyuan Xu; Yingqiang Ge; Yongfeng Zhang;
104	DAIR: A Query-Efficient Decision-based Attack on Image Retrieval Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an optimization-based method with a smoothed utility function to overcome the challenging discrete nature of the problem.	Mingyang Chen; Junda Lu; Yi Wang; Jianbin Qin; Wei Wang;
105	Fight Fire with Fire: Towards Robust Recommender Systems Via Adversarial Poisoning Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above limitations, we propose adversarial poisoning training (APT).	Chenwang Wu; Defu Lian; Yong Ge; Zhihao Zhu; Enhong Chen; Senchao Yuan;
106	Adversarial-Enhanced Hybrid Graph Network for User Identity Linkage Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the user identity linkage task across different social media platforms based on heterogeneous multi-modal posts and social connections.	Xiaolin Chen; Xuemeng Song; Guozhen Peng; Shanshan Feng; Liqiang Nie;
107	A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work focuses on the defensive side of VRSs and provides general insights that could be further exploited to broaden the frontier in the field.	Vito Walter Anelli; Yashar Deldjoo; Tommaso Di Noia; Daniele Malitesta; Felice Antonio Merra;
108	Dynamic Modality Interaction Modeling for Image-Text Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we develop a novel modality interaction modeling network based upon the routing mechanism, which is the first unified and dynamic multimodal interaction framework towards image-text retrieval.	Leigang Qu; Meng Liu; Jianlong Wu; Zan Gao; Liqiang Nie;
109	Hierarchical Cross-Modal Graph Consistency Learning for Video-Text Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Hierarchical Cross-Modal Graph Consistency Learning Network (HCGC) for video-text retrieval task, which considers multi-level graph consistency for video-text matching.	Weike Jin; Zhou Zhao; Pengcheng Zhang; Jieming Zhu; Xiuqiang He; Yueting Zhuang;
110	PAN: Prototype-based Adaptive Network for Robust Cross-modal Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address two important issues to increase the robustness of cross-modal retrieval system for real-world applications: handling test queries from unknown category and modality-imbalanced training data.	Zhixiong Zeng; Shuai Wang; Nan Xu; Wenji Mao;
111	Multi-Type Textual Reasoning for Product-Aware Answer Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a review-attribute heterogeneous graph neural network (abbreviated as RAHGNN) to model the logical relation of all multi-type text.	Yue Feng; Zhaochun Ren; Weijie Zhao; Mingming Sun; Ping Li;
112	Heterogeneous Attention Network for Effective and Efficient Cross-modal Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a heterogeneous attention network (HAN) for effective and efficient cross-modal retrieval.	Tan Yu; Yi Yang; Yi Li; Lin Liu; Hongliang Fei; Ping Li;
113	Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Graph Meta Embedding (GME) models that can rapidly learn how to generate desirable initial embeddings for new ad IDs based on graph neural networks and meta learning.	Wentao Ouyang; Xiuwu Zhang; Shukui Ren; Li Li; Kun Zhang; Jinmei Luo; Zhaojie Liu; Yanlong Du;
114	Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With the two meta networks, we propose Meta Warm Up Framework (MWUF) which learns to warm up cold ID embeddings.	Yongchun Zhu; Ruobing Xie; Fuzhen Zhuang; Kaikai Ge; Ying Sun; Xu Zhang; Leyu Lin; Juan Cao;
115	FORM: Follow The Online Regularized Meta-Leader for Cold-Start Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an online regularized meta-leader recommendation approaches named FORM to address such problems.	Xuehan Sun; Tianyao Shi; Xiaofeng Gao; Yanrong Kang; Guihai Chen;
116	Privileged Graph Distillation for Cold Start Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As user-item interaction behaviors and user (item) attributes naturally form a heterogeneous graph structure, in this paper, we propose a privileged graph distillation model (PGD).	Shuai Wang; Kun Zhang; Le Wu; Haiping Ma; Richang Hong; Meng Wang;
117	Supporting Metacognition During Exploratory Search with The OrgBox Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present results from our study comparing the OrgBox and OrgDoc tools.	Anita Crescenzi; Austin R. Ward; Yuan Li; Rob Capra;
118	Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work focuses on the local structure discrepancy issue for testing nodes, which has received little scrutiny.	Fuli Feng; Weiran Huang; Xiangnan He; Xin Xin; Qifan Wang; Tat-Seng Chua;
119	Meta-Inductive Node Classification Across Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the problem of inductive node classification across graphs.	Zhihao Wen; Yuan Fang; Zemin Liu;
120	Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel i terative network p runing with uncertainty r egularization method for l ifelong s entiment classification (IPRLS), which leverages the principles of network pruning and weight regularization.	Binzong Geng; Min Yang; Fajie Yuan; Shupeng Wang; Xiang Ao; Ruifeng Xu;
121	Decoupling Representation Learning and Classification for GNN-based Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To be less biased by the inconsistency, we propose a simple yet effective graph SSL scheme, called Deep Cluster Infomax (DCI) for node representation learning, which captures the intrinsic graph properties in more concentrated feature spaces by clustering the entire graph into multiple parts.	Yanling Wang; Jing Zhang; Shasha Guo; Hongzhi Yin; Cuiping Li; Hong Chen;
122	"Did You Buy It Already?", Detecting Users Purchase-State From Their Product-Related Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study we address the problem of identifying the purchase-state of users, based on product-related questions they ask on an eCommerce website.	Lital Kuchy; David Carmel; Thomas Huet; Elad Kravi;
123	A Graph-Enhanced Click Model for Web Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel graph-enhanced click model (GraphCM) for web search.	Jianghao Lin; Weiwen Liu; Xinyi Dai; Weinan Zhang; Shuai Li; Ruiming Tang; Xiuqiang He; Jianye Hao; Yong Yu;
124	ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address such bottlenecks, in this paper, we propose the ScaleFreeCTR: a MixCache-based distributed training system for CTR models.	Huifeng Guo; Wei Guo; Yong Gao; Ruiming Tang; Xiuqiang He; Wenzhi Liu;
125	Looking at CTR Prediction Again: Is Attention All You Need? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we use the discrete choice model in economics to redefine the CTR prediction problem, and propose a general neural network framework built on self-attention mechanism.	Yuan Cheng; Yanbo Xue;
126	Clicks Can Be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we formulate the recommendation models as a causal graph that reflects the cause-effect factors in recommendation, and address the clickbait issue by performing counterfactual inference on the causal graph.	Wenjie Wang; Fuli Feng; Xiangnan He; Hanwang Zhang; Tat-Seng Chua;
127	A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, we propose a more general automated method for building powerful interactions named AutoPI.	Ze Meng; Jinnian Zhang; Yumeng Li; Jiancheng Li; Tanchao Zhu; Lifeng Sun;
128	How Powerful Are Interest Diffusion on Purchasing Prediction: A Case Study of Taocode Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by our observations, we propose InfNet, a dynamic GNN-based framework that models the information diffusion across Taocode.	Xuanwen Huang; Yang Yang; Ziqiang Cheng; Shen Fan; Zhongyao Wang; Juren Li; Jun Zhang; Jingmin Chen;
129	B-PROP: Bootstrapped Pre-training with Representative Words Prediction for Ad-hoc Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose a bootstrapped pre-training method (namely B-PROP) based on BERT for ad-hoc retrieval.	Xinyu Ma; Jiafeng Guo; Ruqing Zhang; Yixing Fan; Yingyan Li; Xueqi Cheng;
130	Long-Tail Hashing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Long-Tail Hashing Network (LTHNet), a novel two-stage deep hashing approach that addresses the problem of learning to hash for more realistic datasets where the data labels roughly exhibit a long-tail distribution.	Yong Chen; Yuqing Hou; Shu Leng; Qing Zhang; Zhouchen Lin; Dell Zhang;
131	PTHash: Revisiting FCH Minimal Perfect Hashing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Almost thirty years later we revisit their framework and present an improved algorithm that scales well to large sets and reduces space consumption altogether, without compromising the lookup time.	Giulio Ermanno Pibiri; Roberto Trani;
132	Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we adopt an intra-document cascading strategy, which prunes passages of a candidate document using a less expensive model, called ESM, before running a scoring model that is more expensive and effective, called ETM.	Sebastian Hofstätter; Bhaskar Mitra; Hamed Zamani; Nick Craswell; Allan Hanbury;
133	Improving Video Retrieval By Adaptive Margin Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: While most video retrieval methods overlook that phenomenon, we propose an adaptive margin changed with the distance between positive and negative pairs to solve the aforementioned issue.	Feng He; Qi Wang; Zhifan Feng; Wenbin Jiang; Yajuan Lü; Yong Zhu; Xiao Tan;
134	Comprehensive Linguistic-Visual Composition Network for Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In light of the above analysis, we propose a Comprehensive Linguistic-Visual Composition Network (CLVC-Net) for image retrieval.	Haokun Wen; Xuemeng Song; Xin Yang; Yibing Zhan; Liqiang Nie;
135	GilBERT: Generative Vision-Language Pre-Training for Image-Text Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a generative visual-linguistic pre-training approach, termed as GilBERT, to simultaneously learn generic representations of image-text data and complete the missing modality for incomplete pairs.	Weixiang Hong; Kaixiang Ji; Jiajia Liu; Jian Wang; Jingdong Chen; Wei Chu;
136	DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS, that jointly optimizes multiple criteria: (1) conciseness, (2) representativeness of important query-relevant events and (3) chronological soundness.	Safa Messaoud; Ismini Lourentzou; Assma Boughoula; Mona Zehni; Zhizhen Zhao; Chengxiang Zhai; Alexander G. Schwing;
137	Comparison-based Conversational Recommender System with Relative Bandit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To enable users to provide comparative preferences during conversational interactions, we propose a novel comparison-based conversational recommender system.	Zhihui Xie; Tong Yu; Canzhe Zhao; Shuai Li;
138	When and Whom to Collaborate with in A Changing Environment: A Collaborative Dynamic Bandit Solution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we develop a collaborative dynamic bandit solution to handle a changing environment for recommendation.	Chuanhao Li; Qingyun Wu; Hongning Wang;
139	Glider: A Reinforcement Learning Approach to Extract UI Scripts from Websites Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Glider, an automated and scalable approach to generate tasklets from a natural language task query and a website URL.	Yuanchun Li; Oriana Riva;
140	Unified Conversational Recommendation Policy Learning Via Graph-based Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the light of these challenges, we propose to formulate these three decision-making problems in CRS as a unified policy learning task.	Yang Deng; Yaliang Li; Fei Sun; Bolin Ding; Wai Lam;
141	Conversations Powered By Cross-Lingual Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose the task of cross-lingual knowledge grounded conversation (CKGC), where we leverage large-scale knowledge sources in another language to generate informative responses.	Weiwei Sun; Chuan Meng; Qi Meng; Zhaochun Ren; Pengjie Ren; Zhumin Chen; Maarten de Rijke;
142	Transformer Reasoning Network for Personalized Review Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel transformer-based reasoning framework for personalized review summarization.	Hongyan Xu; Hongtao Liu; Pengfei Jiao; Wenjun Wang;
143	Leveraging Lead Bias for Zero-shot Abstractive News Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose that this lead bias can be leveraged in our favor in a simple and effective way to pre-train abstractive news summarization models on large-scale unlabeled news corpora: predicting the leading sentences using the rest of an article.	Chenguang Zhu; Ziyi Yang; Robert Gmyr; Michael Zeng; Xuedong Huang;
144	Retrieving Complex Tables with Multi-Granular Graph Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose Graph-based Table Retrieval (GTR), a generalizable NLTR framework with multi-granular graph representation learning.	Fei Wang; Kexuan Sun; Muhao Chen; Jay Pujara; Pedro Szekely;
145	TILDE: Term Independent Likelihood MoDEl for Passage Re-ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the novel, BERT-based, Term Independent Likelihood moDEl (TILDE), which ranks documents by both query and document likelihood.	Shengyao Zhuang; Guido Zuccon;
146	Path-based Deep Network for Candidate Item Matching in Recommenders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel matching architecture, Path-based Deep Network (named PDN), through incorporating both personalization and diversity to enhance matching performance.	Houyi Li; Zhihong Chen; Chenliang Li; Rong Xiao; Hongbo Deng; Peng Zhang; Yongchao Liu; Haihong Tang;
147	Optimizing Dense Retrieval Model Training with Hard Negatives Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose two training strategies named a Stable Training Algorithm for dense Retrieval (STAR) and a query-side training Algorithm for Directly Optimizing Ranking pErformance (ADORE), respectively.	Jingtao Zhan; Jiaxin Mao; Yiqun Liu; Jiafeng Guo; Min Zhang; Shaoping Ma;
148	Bootstrapping User and Item Representations for One-Class Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel OCCF framework, named as BUIR, which does not require negative sampling.	Dongha Lee; SeongKu Kang; Hyunjun Ju; Chanyoung Park; Hwanjo Yu;
149	Standing in Your Shoes: External Assessments for Personalized Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents the first attempt to incorporate external assessments into preference labeling and recommendation evaluation.	Hongyu Lu; Weizhi Ma; Min Zhang; Maarten de Rijke; Yiqun Liu; Shaoping Ma;
150	Evaluation Measures Based on Preference Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, we propose an evaluation measure that computes the similarity between a directed multigraph of preferences and an actual ranking generated by a ranker.	Charles L.A. Clarke; Chengxi Luo; Mark D. Smucker;
151	Do Affective Cues Validate Behavioural Metrics for Search? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we analysed longitudinal, in-situ, search behaviours of 152 information workers, over the course of several weeks while simultaneously tracking their facial expressions.	Daniel McDuff; Paul Thomas; Nick Craswell; Kael Rowan; Mary Czerwinski;
152	Current Challenges and Future Directions in Podcast Information Access Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this perspective paper, we highlight the many differences between podcasts and other media, and discuss our perspective on challenges and future research directions in the domain of podcast information access.	Rosie Jones; Hamed Zamani; Markus Schedl; Ching-Wei Chen; Sravana Reddy; Ann Clifton; Jussi Karlgren; Helia Hashemi; Aasish Pappu; Zahra Nazari; Longqi Yang; Oguz Semerci; Hugues Bouchard; Ben Carterette;
153	MS MARCO: Benchmarking Ranking Models in The Large-Data Regime Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We show how the design of the evaluation effort can encourage or discourage certain outcomes, and raising questions about internal and external validity of results.	Nick Craswell; Bhaskar Mitra; Emine Yilmaz; Daniel Campos; Jimmy Lin;
154	Towards Multi-Modal Conversational Information Seeking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on this research, we propose and implement a practical open-source framework for facilitating MMCIS research.	Yashar Deldjoo; Johanne R. Trippas; Hamed Zamani;
155	AMM: Attentive Multi-field Matching for News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an Attentive Multi-field Matching (AMM) framework for news recommendation which captures the semantic matching representations between each browsed news and candidate news, and then aggregates them as final user-news matching signal.	Qi Zhang; Qinglin Jia; Chuyuan Wang; Jingjie Li; Zhaowei Wang; Xiuqiang He;
156	An ALBERT-based Similarity Measure for Mathematical Answer Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The aim of this work is to explore the modeling capabilities of state-of-the-art unsupervised deep learning methods to create such representations.	Anja Reusch; Maik Thiele; Wolfgang Lehner;
157	An Exploration of Tester-based Evaluation of User Simulators for Comparing Interactive Retrieval Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Tester-based evaluation approach to evaluating the reliability of user simulators, in which we would construct a Tester based on a set of IR systems with an expected performance pattern and apply such a Tester to a user simulator to see if the user simulator would generate the expected performance pattern.	Sahiti Labhishetty; Chengxiang Zhai;
158	APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel deep neural model named Attentive Pseudo Relevance Feedback Network (APRF-Net) to enhance the representation of rare queries for query categorization.	Ali Ahmadvand; Sayyed M. Zahiri; Simon Hughes; Khalifeh Al Jadda; Surya Kallumadi; Eugene Agichtein;
159	Augmenting Sequential Recommendation with Pseudo-Prior Items Via Reversely Pre-training Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new framework for Augmenting Sequential Recommendation with Pseudo-prior items (ASReP).	Zhiwei Liu; Ziwei Fan; Yu Wang; Philip S. Yu;
160	Cluster-Based Bandits: Fast Cold-Start for Recommender System New Users Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we introduce a new type of online learning algorithm, cluster-based bandits, to address this challenge.	Sulthana Shams; Daron Anderson; Douglas Leith;
161	Contextualized Offline Relevance Weighting for Efficient and Effective Neural Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the recent advances in transformer-based document expansion technique, we propose to trade offline relevance weighting for online retrieval efficiency by utilizing the powerful BERT ranker to weight the neighbour documents collected by generated pseudo-queries for each document.	Xuanang Chen; Ben He; Kai Hui; Yiran Wang; Le Sun; Yingfei Sun;
162	Conversational Vs Traditional: Comparing Search Behavior and Outcome in Legal Case Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the collected data, we compare search behavior and outcome of these two different kinds of interaction paradigms.	Bulou Liu; Yueyue Wu; Yiqun Liu; Fan Zhang; Yunqiu Shao; Chenliang Li; Min Zhang; Shaoping Ma;
163	Counterfactual Explanations for Neural Recommenders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose ACCENT, the first general framework for finding counterfactual explanations for neural recommenders.	Khanh Hiep Tran; Azin Ghazimatin; Rishiraj Saha Roy;
164	Cross-Batch Negative Sampling for Training Two-Tower Recommenders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on such facts, we propose a simple yet effective sampling strategy called Cross-Batch Negative Sampling (CBNS), which takes advantage of the encoded item embeddings from recent mini-batches to boost the model training.	Jinpeng Wang; Jieming Zhu; Xiuqiang He;
165	De-Biased Modeling of Search Click Behavior with Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the De-biased Reinforcement Learning Click model (DRLC).	Jianghong Zhou; Sayyed M. Zahiri; Simon Hughes; Khalifeh Al Jadda; Surya Kallumadi; Eugene Agichtein;
166	DOZEN: Cross-Domain Zero Shot Named Entity Recognition with Knowledge Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Cross Domain Zero Shot Named Entity Recognition with Knowledge Graph (DOZEN), which learns the relations between entities across different domains from an existing ontology of external knowledge and a set of analogies linking entities and domains.	Hoang-Van Nguyen; Francesco Gelli; Soujanya Poria;
167	Dual Unbiased Recommender Learning for Implicit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a dual recommender learning framework that simultaneously eliminates the bias of clicked and unclicked data.	Jae-woong Lee; Seongmin Park; Jongwuk Lee;
168	Empowering News Recommendation with Pre-trained Language Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we report our work on pre-trained language models empowered news recommendation (PLM-NR).	Chuhan Wu; Fangzhao Wu; Tao Qi; Yongfeng Huang;
169	Entangled Bidirectional Encoder to Autoregressive Decoder for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel sequential recommendation model, Entangled BART for Recommendation (E-BART4Rec) that entangles bidirectional encoder and auto-regressive decoder with noisy transformations for user interaction.	Taegwan Kang; Hwanhee Lee; Byeongjin Choe; Kyomin Jung;
170	Entity Retrieval Using Fine-Grained Entity Aspects Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using entity aspect links, we improve upon the current state-of-the-art in entity retrieval.	Shubham Chatterjee; Laura Dietz;
171	Evaluating The Predictivity of IR Experiments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we explore the role of evaluation on a collection as a prediction of relative performance on collections that have different characteristics.	Lida Rashidi; Justin Zobel; Alistair Moffat;
172	FedCMR: Federated Cross-Modal Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the recent success of federated learning, we propose the federated cross-modal retrieval (FedCMR), which learns the model with decentralized multi-modal data.	Linlin Zong; Qiujie Xie; Jiahui Zhou; Peiran Wu; Xianchao Zhang; Bo Xu;
173	Gazetteer Enhanced Named Entity Recognition for Code-Mixed Web Queries Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work tackles NER for code-mixed queries, where entities and non-entity query terms co-exist simultaneously in different languages.	Besnik Fetahu; Anjie Fang; Oleg Rokhlenko; Shervin Malmasi;
174	Hyperbolic Online Time Stream Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose the first Hierarchical Time-Aware Hyperbolic LSTM (HTLSTM), which leverages the Riemannian manifold for encoding the scale-free nature of a sequence of text in a time-aware fashion.	Ramit Sawhney; Shivam Agarwal; Megh Thakkar; Arnav Wadhwa; Rajiv Ratn Shah;
175	ICAI-SR: Item Categorical Attribute Integrated Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an Item Categorical Attribute Integrated Sequential Recommendation (ICAI-SR) framework, which consists of an Item-Attribute Aggregation (IAA) model and Entity Sequential (ES) models.	Xu Yuan; Dongsheng Duan; Lingling Tong; Lei Shi; Cheng Zhang;
176	Identifying Queries in Instant Search Logs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new approach to identifying interactions belonging to the same query in instant query logs.	Markus Fischer; Kristof Komlossy; Benno Stein; Martin Potthast; Matthias Hagen;
177	Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel Conformer layer as an alternative approach to scale TK to longer input sequences.	Bhaskar Mitra; Sebastian Hofstätter; Hamed Zamani; Nick Craswell;
178	Info-flow Enhanced GANs for Recommender Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new GAN model to enhance the information flow within the generator based on the information flow between the original generator and discriminator.	Yuan Lin; Zhang Xie; Bo Xu; Kan Xu; Hongfei Lin;
179	Investigating Session Search Behavior with Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We conduct a query log-based analysis of users’ query reformulation and document clicking behavior.	Xiangsheng Li; Maarten de Rijke; Yiqun Liu; Jiaxin Mao; Weizhi Ma; Min Zhang; Shaoping Ma;
180	Is Query Performance Prediction With Multiple Query Variations Harder Than Topic Performance Prediction? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: By generalizing the existing evaluation framework of Query Performance Prediction (QPP) we explore the causes of these differences in prediction quality in the two scenarios.	Oleg Zendel; J. Shane Culpepper; Falk Scholer;
181	Joint Learning of Deep Retrieval Model and Product Quantization Based Embedding Index Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel method called Poeem, which stands for product quantization based embedding index jointly trained with deep retrieval model, to unify the two separate steps within an end-to-end training, by utilizing a few techniques including the gradient straight-through estimator, warm start strategy, optimal space decomposition and Givens rotation.	Han Zhang; Hongwei Shen; Yiming Qiu; Yunjiang Jiang; Songlin Wang; Sulong Xu; Yun Xiao; Bo Long; Wen-Yun Yang;
182	Learning Passage Impacts for Inverted Indexes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose DeepImpact, a new document term-weighting scheme suitable for efficient retrieval using a standard inverted index.	Antonio Mallia; Omar Khattab; Torsten Suel; Nicola Tonellotto;
183	Learning Robust Dense Retrieval Models from Incomplete Relevance Labels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper improves upon ANCE by proposing a robust negative sampling strategy for scenarios where the training data lacks complete relevance annotations.	Prafull Prakash; Julian Killingback; Hamed Zamani;
184	Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose the low-rank decomposed self-attention networks (LightSANs) to overcome these problems.	Xinyan Fan; Zheng Liu; Jianxun Lian; Wayne Xin Zhao; Xing Xie; Ji-Rong Wen;
185	Motif-aware Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above limitation, we propose a novel model called Motif-aware Sequential Recommendation (MoSeR), which captures the motifs hidden in behavior sequences to model the micro-structure features.	Zeyu Cui; Yinjiang Cai; Shu Wu; Xibo Ma; Liang Wang;
186	Neural Representations in Hybrid Recommender Systems: Prediction Versus Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we define the neural representation for prediction (NRP) framework and apply it to the autoencoder-based recommendation systems.	Ramin Raziperchikolaei; Tianyu Li; Young-joo Chung;
187	On The Orthogonality of Bias and Utility in Ad Hoc Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we empirically study this tradeoff and explore whether it would be possible to reduce bias while maintaining similar retrieval utility.	Amin Bigdeli; Negar Arabzadeh; Shirin Seyedsalehi; Morteza Zihayat; Ebrahim Bagheri;
188	Passage Retrieval for Outside-Knowledge Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we address multi-modal information needs that contain text questions and images by focusing on passage retrieval for outside-knowledge visual question answering.	Chen Qu; Hamed Zamani; Liu Yang; W. Bruce Croft; Erik Learned-Miller;
189	Predicting Links on Wikipedia with Anchor Text Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the transductive and the inductive tasks of link prediction on several subsets of the English Wikipedia and identify some key challenges behind automatic linking based on anchor text information.	Robin Brochier; Frédéric Béchet;
190	Propensity-Independent Bias Recovery in Offline Learning-to-Rank Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we propose a new counterfactual method that uses a two-stage correction approach and jointly addresses selection and position bias in learning-to-rank systems without relying on propensity scores.	Zohreh Ovaisi; Kathryn Vasilaky; Elena Zheleva;
191	Revisiting The Tag Relevance Prediction Problem Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the dataset from a different study, where the authors collected relevance scores on movie-tag pairs.	Denis Kotkov; Alexandr Maslov; Mats Neovius;
192	RMBERT: News Recommendation Via Recurrent Reasoning Memory Network Over BERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present Recurrent Reasoning Memory Network over BERT (RMBERT) for news recommendation.	Qinglin Jia; Jingjie Li; Qi Zhang; Xiuqiang He; Jieming Zhu;
193	Robust Neural Text Classification and Entailment Via Mixup Regularized Adversarial Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we propose mixup regularized adversarial training (MRAT) against multi-level attack.	Jiahao Zhao; Penghui Wei; Wenji Mao;
194	Sequential Recommendation for Cold-start Users with Meta Transitional Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to improve sequential recommendation for cold-start users with a novel framework named MetaTL, which learns to model the transition patterns of users through meta-learning.	Jianling Wang; Kaize Ding; James Caverlee;
195	Social Recommendation with Implicit Social Influence Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we concern two kinds of implicit influence: Local Implicit Influence of persons on unobserved interpersonal relations, and Global Implicit Influence of items broadcasted to users.	Changhao Song; Bo Wang; Qinxue Jiang; Yehua Zhang; Ruifang He; Yuexian Hou;
196	Synthetic Target Domain Supervision for Open Retrieval QA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we stress-test the Dense Passage Retriever (DPR)—a state-of-the-art (SOTA) open domain neural retrieval model—on closed and specialized target domains such as COVID-19, and find that it lags behind standard BM25 in this important real-world setting.	Revanth Gangi Reddy; Bhavani Iyer; Md Arafat Sultan; Rong Zhang; Avirup Sil; Vittorio Castelli; Radu Florian; Salim Roukos;
197	Temporal Augmented Graph Neural Networks for Session-Based Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a memory-efficient framework – TASRec.	Huachi Zhou; Qiaoyu Tan; Xiao Huang; Kaixiong Zhou; Xiaoling Wang;
198	Text-to-Text Multi-view Learning for Passage Re-ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this work, we propose a text-to-text multi-view learning framework by incorporating an additional view—the text generation view—into a typical single-view passage ranking model.	Jia-Huei Ju; Jheng-Hong Yang; Chuan-Ju Wang;
199	The Winner Takes It All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider data coming from a real-world platform and analyze the distribution of the recommendations w.r.t. the geographical provenience of the teachers.	Elizabeth Gómez; Carlos Shui Zhang; Ludovico Boratto; Maria Salamó; Mirko Marras;
200	Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With the advantage of meta learning which has good generalization ability to novel tasks, we propose a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta stage.	Yongchun Zhu; Kaikai Ge; Fuzhen Zhuang; Ruobing Xie; Dongbo Xi; Xu Zhang; Leyu Lin; Qing He;
201	Underestimation Refinement: A General Enhancement Strategy for Exploration in Recommendation Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we first benchmark state-of-the-art exploration methods in the recommendation system setting.	Yuhai Song; Lu Wang; Haoming Dang; Weiwei Zhou; Jing Guan; Xiwei Zhao; Changping Peng; Yongjun Bao; Jingping Shao;
202	Web Document Encoding for Structure-Aware Keyphrase Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our key contribution is encoding Web documents to leverage structure, such as title or anchors, by building a graph of words representing both (a) position-based proximity and (b) structural relations.	Jihyuk Kim; Young-In Song; Seung-won Hwang;
203	Adapted Graph Reasoning and Filtration for Description-Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To eliminate the mismatch, we introduce a novel problem about description-image retrieval and propose the specially designed method, named Adapted Graph Reasoning and Filtration (AGRF).	Shiqian Chen; Zhiling Luo; Yingqi Gao; Wei Zhou; Chenliang Li; Haiqing Chen;
204	Affective Dependency Graph for Sarcasm Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we revisit sarcasm detection from a novel perspective, so as to account for the long-range literal sentiment inconsistencies.	Chenwei Lou; Bin Liang; Lin Gui; Yulan He; Yixue Dang; Ruifeng Xu;
205	Automatic Form Filling with Form-BERT Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we describe Form-BERT — a Transformer-based model which is optimized for auto-filling listing attributes given the following inputs: free-text, list of known attribute names, and zero or more attribute values.	Gilad Fuchs; Haggai Roitman; Matan Mandelbrod;
206	Circumstances Enhanced Criminal Court View Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose a novel Circumstances enhanced Criminal Court View Generation (C3VG) method, consisting of the extraction and generation stage.	Linan Yue; Qi Liu; Han Wu; Yanqing An; Li Wang; Senchao Yuan; Dayong Wu;
207	Cross Interaction Network for Natural Language Guided Video Moment Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a self-attention together with cross interaction multi-head-attention mechanism in an early fusion scheme to capture video-query intra-dependencies as well as inter-relation from both directions (query-to-video and video-to-query).	Xinli Yu; Mohsen Malmir; Xin He; Jiangning Chen; Tong Wang; Yue Wu; Yue Liu; Yang Liu;
208	Cross-Graph Attention Enhanced Multi-Modal Correlation Learning for Fine-Grained Image-Text Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Toward this end, we propose a Cross-Graph Attention model (CGAM) to explicitly learn the shared semantic concepts, which can be well utilized to guide the feature learning process of each modality and promote the common embedding learning.	Yi He; Xin Liu; Yiu-Ming Cheung; Shu-Juan Peng; Jinhan Yi; Wentao Fan;
209	DCSpell: A Detector-Corrector Framework for Chinese Spelling Error Correction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, we propose a cloze-style detector-corrector framework (DCSpell) that firstly detects whether a character is erroneous before correcting it.	Jing Li; Dafei Yin; Haozhao Wang; Yonggang Wang;
210	Decoupling Representation and Regressor for Long-Tailed Information Cascade Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a general decoupling prediction solution — first extracting the representation, then fine-tuning the regressor, which combines the original prediction value and weighted bias generated by a sub-network (SUB) that we designed.	Fan Zhou; Liu Yu; Xovee Xu; Goce Trajcevski;
211	Deep Music Retrieval for Fine-Grained Videos By Exploiting Cross-Modal-Encoded Voice-Overs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we also investigate the widely added voice-overs in short videos and propose a novel framework to retrieve BGM for fine-grained short videos.	Tingtian Li; Zixun Sun; Haoruo Zhang; Jin Li; Ziming Wu; Hui Zhan; Yipeng Yu; Hengcan Shi;
212	Deep Position-wise Interaction Network for CTR Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Deep Position-wise Interaction Network (DPIN) to efficiently combine all candidate items and positions for estimating CTR at each position, achieving consistency between offline and online as well as modeling the deep non-linear interaction among position, user, context and item under the limit of serving performance.	Jianqiang Huang; Ke Hu; Qingtao Tang; Mingjian Chen; Yi Qi; Jia Cheng; Jun Lei;
213	Deep User Match Network for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose a novel Deep User Match Network (DUMN) which measures the user-to-user relevance for CTR prediction.	Zai Huang; Mingyuan Tao; Bufeng Zhang;
214	Distant Supervision Based Machine Reading Comprehension for Extractive Summarization in Customer Service Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to solve the above challenges, we propose a Distant Supervision based Machine Reading Comprehension model for extractive Summarization (DSMRC-S).	Bing Ma; Cao Liu; Jingyu Wang; Shujie Hu; Fan Yang; Xunliang Cai; Guanglu Wan; Jiansong Chen; Jianxin Liao;
215	Does BERT Pay Attention to Cyberbullying? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we examine the use of BERT for cyberbullying detection on various datasets and attempt to explain its performance by analyzing its attention weights and gradient-based feature importance scores for textual and linguistic features.	Fatma Elsafoury; Stamos Katsigiannis; Steven R. Wilson; Naeem Ramzan;
216	ECG Data Modeling and Analyzing Via Deep Representation Learning and Nonparametric Hidden Markov Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The purpose of this paper is to propose a novel model-based clustering approach for analyzing ECG data.	Jiaojiao Zhu; Wentao Fan;
217	Faster Index Reordering with Bipartite Graph Partitioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We revisit the Bipartite Graph Partitioning approach to document reordering (Dhulipala et al., KDD 2016), and consider a range of algorithmic and heuristic refinements that lead to faster computation of index-minimizing document orderings.	Joel Mackenzie; Matthias Petri; Alistair Moffat;
218	Follow The Prophet: Accurate Online Conversion Rate Prediction in The Face of Delayed Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to tackle the delayed feedback problem in online advertising by "Following the Prophet" (FTP for short).	Haoming Li; Feiyang Pan; Xiang Ao; Zhao Yang; Min Lu; Junwei Pan; Dapeng Liu; Lei Xiao; Qing He;
219	GAIPS: Accelerating Maximum Inner Product Search with GPU Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the GAIPS framework for efficient maximum inner product search (MIPS) on GPU.	Long Xiang; Xiao Yan; Lan Lu; Bo Tang;
220	Generalized Zero-shot Intent Detection Via Commonsense Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose RIDE: an intent detection model that leverages commonsense knowledge in an unsupervised fashion to overcome the issue of training data scarcity.	A.B. Siddique; Fuad Jamour; Luxun Xu; Vagelis Hristidis;
221	Graph-Structured Context Understanding for Knowledge-grounded Response Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we establish a context graph from both conversation utterances and external knowledge, and develop a novel graph-based encoder to better understand the conversation context.	Yanran Li; Wenjie Li; Zhitao Wang;
222	Hierarchical Dependence-aware Evaluation Measures for Conversational Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We overcome this issue by proposing a framework for defining evaluation measures that are aware of the conversation context and the utterance semantic dependencies.	Guglielmo Faggioli; Marco Ferrante; Nicola Ferro; Raffaele Perego; Nicola Tonellotto;
223	Improving Response Quality with Backward Reasoning in Open-domain Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate this problem, we propose to train the generation model in a bidirectional manner by adding a backward reasoning step to the vanilla encoder-decoder training.	Ziming Li; Julia Kiseleva; Maarten de Rijke;
224	Knowledge Based Hyperbolic Propagation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a knowledge-based hyperbolic propagation framework (KBHP) which includes hyperbolic components for calculating the importance of KG attributes relative to achieve better knowledge propagation.	Chang-You Tai; Chien-Kun Huang; Liang-Ying Huang; Lun-Wei Ku;
225	Learning to Select Instance: Simultaneous Transfer Learning and Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Adaptive Clustering Transfer Learning (ACTL) method to improve transferability.	Zhaoxin Huan; Yulong Wang; Yong He; Xiaolu Zhang; Chilin Fu; Weichang Wu; Jun Zhou; Ke Ding; Liang Zhang; Linjian Mo;
226	LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel Language-Prior Feedback (LPF) objective function, to re-balance the proportion of each answer’s loss value in the total VQA loss.	Zujie Liang; Haifeng Hu; Jiaying Zhu;
227	LS-DST: Long and Sparse Dialogue State Tracking with Smart History Collector in Insurance Marketing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To deal with these long and sparse dialogues, we propose a new dialogue state tracking architecture containing three components: dialogue encoder, Smart History Collector (SHC) and dialogue state classifier.	Liqiang Song; Mengqiu Yao; Ye Bi; Zhenyu Wu; Jianming Wang; Jing Xiao; Juan Wen; Xin Yu;
228	Medical Triage Chatbot Diagnosis Improvement Via Multi-relational Hyperbolic Graph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Multi-relational Hyperbolic Diagnosis Predictor (MHDP) — a novel multi-relational hyperbolic graph neural network-based approach, to build a disease predictive model.	Zheng Liu; Xiaohan Li; Zeyu You; Tao Yang; Wei Fan; Philip Yu;
229	Meta-Learned Specific Scenario Interest Network for User Preference Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Meta-Learned Specific Scenario Interest Network (Meta-SSIN) to predict user preference of target item by capturing specific scenario interests.	Yinan Sun; Kang Yin; Hehuan Liu; Si Li; Yajing Xu; Jun Guo;
230	On The Privacy of Federated Pipelines Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we draw attention to another risk associated with FL: Even if federated algorithms are individually privacy-preserving, combining them into pipelines is not necessarily privacy-preserving.	Reza Nasirigerdeh; Reihaneh Torkzadehmahani; Jan Baumbach; David B. Blumenthal;
231	On The Two-Sample Randomisation Test for IR Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using real runs and a test collection from the NTCIR-15 WWW-3 Task, the present study compares the properties of three two-sample significance tests for comparing two systems: Student’s t-test (i.e., the classical parametric test), the Wilcoxon rank sum test (i.e., the classical nonparametric test), and the randomisation test (i.e., a population-free method that utilises modern computational power).	Tetsuya Sakai;
232	Position Enhanced Mention Graph Attention Network for Dialogue Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle both local and speaker dependency challenges, we explicitly construct a unified mention co-occurrence graph within a local utterance window or all utterances of a speaker from different entities.	Xinwei Long; Shuzi Niu; Yucheng Li;
233	Predicting Patient Readmission Risk from Medical Text Via Knowledge Graph Enhanced Multiview Graph Convolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new method that uses medical text of Electronic Health Records (EHRs) for prediction, which provides an alternative perspective to previous studies that heavily depend on numerical and time-series features of patients.	Qiuhao Lu; Thien Huu Nguyen; Dejing Dou;
234	Predicting User Demography and Device from News Comments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study predicting users’ demographics based on both news browsing data and the associated user generated comments.	Ohad Rozen; Joel Oren; Ariel Raviv;
235	Proactive Retrieval-based Chatbots Based on Relevant Knowledge and Goals Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new multi-task learning framework for retrieval-based knowledge-grounded proactive dialogue.	Yutao Zhu; Jian-Yun Nie; Kun Zhou; Pan Du; Hao Jiang; Zhicheng Dou;
236	Pseudo Siamese Network for Few-shot Intent Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Pseudo Siamese Network (PSN) to generate labeled data for few-shot intents and alleviate this problem.	Congying Xia; Caiming Xiong; Philip Yu;
237	Retrieving Implicit Information for Stock Movement Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop a neural network framework that jointly learns with a news selection mechanism to extract implicit information from the chaotic daily news pool.	Tsun-Hsien Tang; Chung-Chi Chen; Hen-Hsen Huang; Hsin-Hsi Chen;
238	Retrieving Skill-Based Teams from Collaboration Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We learn dense representations for skills and experts based on previous collaborations and bootstrap the training process through transfer learning. We also propose to fine-tune the representation of skills and experts while learning the mapping function.	Radin Hamidi Rad; Ebrahim Bagheri; Mehdi Kargar; Divesh Srivastava; Jaroslaw Szlichta;
239	Rumor Detection on Social Media with Event Augmentations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this challenge, we present a new solution, Rumor Detection on social media with Event Augmentations (RDEA), which innovatively integrates three augmentation strategies by modifying both reply attributes and event structure to extract meaningful rumor propagation patterns and to learn intrinsic representations of user engagement.	Zhenyu He; Ce Li; Fan Zhou; Yi Yang;
240	Similar Trademark Detection Via Semantic, Phonetic and Visual Similarity Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we focus on the textual trademark in Chinese, and propose a model for finding similar trademarks for a given one.	Yingchi Liu; Quanzhi Li; Changlong Sun; Luo Si;
241	Structured Fine-Tuning of Contextual Embeddings for Effective Biomedical Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the suitability of leveraging biomedical abstract sections for fine-tuning pretrained contextual language models at a finer granularity.	Alberto Ueda; Rodrygo L. T. Santos; Craig Macdonald; Iadh Ounis;
242	Towards A Better Tradeoff Between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection Based Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel pre-ranking approach is proposed which supports complicated models with interaction-focused architecture.	Xu Ma; Pengjie Wang; Hui Zhao; Shaoguo Liu; Chuhan Zhao; Wei Lin; Kuang-Chih Lee; Jian Xu; Bo Zheng;
243	Towards An Online Empathetic Chatbot with Emotion Causes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To gather emotion causes in online environments, we leverage counseling strategies and develop an empathetic chatbot to utilize the causal emotion information.	Yanran Li; Ke Li; Hongke Ning; Xiaoqiang Xia; Yalong Guo; Chen Wei; Jianwei Cui; Bin Wang;
244	User Feedback and Ranking In-a-Loop: Towards Self-Adaptive Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we make the first attempt to attack the problem by implementing a user feedback enhanced reranking strategy, and propose a self-adaptive dialogue system (AdaDial) for conversational AI agents.	Chen Shi; Yuxiang Hu; Zengming Zhang; Liang Shao; Feijun Jiang;
245	User Preference-aware Fake News Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this paper, we study the novel problem of exploiting user preference for fake news detection.	Yingtong Dou; Kai Shu; Congying Xia; Philip S. Yu; Lichao Sun;
246	Utility of Missing Concepts in Query-biased Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our study focuses on reducing user effort in finding relevant documents by exposing the information in the query that is missing in the retrieved results.	Sheikh Muhammad Sarwar; Felipe Moraes; Jiepu Jiang; James Allan;
247	Variational Autoencoders for Top-K Recommendation with Implicit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce Joint Variational Autoencoder (JoVA), an ensemble of two VAEs, which jointly learns both user and item representations to predict user preferences.	Bahare Askari; Jaroslaw Szlichta; Amirali Salehi-Abari;
248	Vera: Prediction Techniques for Reducing Harmful Misinformation in Consumer Health Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To rectify this, we propose a label prediction technique that can separate helpful from harmful content.	Ronak Pradeep; Xueguang Ma; Rodrigo Nogueira; Jimmy Lin;
249	Visual Question Rewriting for Increasing Response Rate Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We developed some baseline sequence to sequence models and more advanced transformer-based models, which take a bland question and a related image as input, and output a rewritten question that’s expected to be more attractive.	Jiayi Wei; Xilian Li; Yi Zhang; Xin Eric Wang;
250	X-2ch: Quad-Channel Collaborative Graph Network Over Knowledge-Embedded Edges Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a quad-channel graph model (X-2ch) to tackle these problems.	Kachun Lo; Tsukasa Ishigaki;
251	A Systematic Evaluation of Transfer Learning and Pseudo-labeling with BERT-based Ranking Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We, therefore, carry out a systematic evaluation of transferability of BERT-based neural ranking models across five English datasets.	Iurii Mokrii; Leonid Boytsov; Pavel Braslavski;
252	Abstractive Text Summarization with Hierarchical Multi-scale Abstraction Modeling and Dynamic Memory Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel abstractive text summarization method with hierarchical multi-scale abstraction modeling and dynamic memory (called MADY).	Lihan Wang; Min Yang; Chengming Li; Ying Shen; Ruifeng Xu;
253	Accelerating Neural Architecture Search for Natural Language Processing with Knowledge Distillation and Earth Mover’s Distance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to accelerate neural architecture search for natural language processing based on knowledge distillation (called KD-NAS).	Jianquan Li; Xiaokang Liu; Sheng Zhang; Min Yang; Ruifeng Xu; Fengqing Qin;
254	Automated Graph Learning Via Population Based Self-Tuning GCN Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to automate the training of GCN models through hyperparameter optimization.	Ronghang Zhu; Zhiqiang Tao; Yaliang Li; Sheng Li;
255	AutoName: A Corpus-Based Set Naming Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose AutoName, an unsupervised framework that extracts a name for a set of query entities from a large-scale text corpus.	Zhiqi Huang; Razieh Rahimi; Puxuan Yu; Jingbo Shang; James Allan;
256	Backretrieval: An Image-Pivoted Evaluation Metric for Cross-Lingual Text Representations Without Parallel Corpora Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an automatic metric for evaluating the quality of cross-lingual textual representations using images as a proxy in a paired image-text evaluation dataset.	Mikhail Fain; Niall Twomey; Danushka Bollegala;
257	Bayesian Critiquing with Keyphrase Activation Vectors for VAE-based Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these deficiencies, we propose a novel Bayesian Keyphrase critiquing VAE (BK-VAE) framework that builds on the strengths of VAE-CF, but avoids the problematic second head of CE-VAE.	Hojin Yang; Tianshu Shen; Scott Sanner;
258	Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Reading Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a simple and effective strategy for data augmentation for low-resource machine reading comprehension (MRC).	Hoang Van; Vikas Yadav; Mihai Surdeanu;
259	CIFDM: Continual and Interactive Feature Distillation for Multi-Label Stream Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel continual and interactive feature distillation-based learning framework (CIFDM), to effectively classify instances with novel labels.	Yigong Wang; Zhuoyi Wang; Yu Lin; Latifur Khan; Dingcheng Li;
260	Clustering-Based Online News Topic Detection and Tracking Through Hierarchical Bayesian Nonparametric Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a clustering-based online news topic detection and tracking (TDT) approach based on hierarchical Bayesian nonparametric framework that allows topics to be shared across different news stories in a corpus.	Wentao Fan; Zhiyan Guo; Nizar Bouguila; Wenjuan Hou;
261	Communication Efficient Distributed Hypergraph Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an algorithm framework for distributed hypergraph clustering based on spectral hypergraph sparsification.	Chun Jiang Zhu; Qinqing Liu; Jinbo Bi;
262	Composite Code Sparse Autoencoders for First Stage Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a Composite Code Sparse Autoencoder (CCSA) approach for Approximate Nearest Neighbor (ANN) search of document representations based on Siamese-BERT models.	Carlos Lassance; Thibault Formal; Stéphane Clinchant;
263	ConsisRec: Enhancing GNN for Social Recommendation Via Consistent Neighbor Aggregation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to sample consistent neighbors by relating sampling probability with consistency scores between neighbors.	Liangwei Yang; Zhiwei Liu; Yingtong Dou; Jing Ma; Philip S. Yu;
264	DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel domain-specific generative pre-training (DSGPT) method for text generation and apply it to the product title and review summarization problems on E-commerce mobile display.	Xueying Zhang; Yunjiang Jiang; Yue Shang; Zhaomeng Cheng; Chi Zhang; Xiaochuan Fan; Yun Xiao; Bo Long;
265	Dual-View Distilled BERT for Sentence Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Dual-view distilled BERT~(DvBERT) for sentence matching with sentence embeddings.	Xingyi Cheng;
266	Enhanced Representation Learning for Examination Papers with Hierarchical Document Structure Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose a novel Examination Organization Encoder (EOE) to learn a robust representation of the examination paper with the hierarchical document structure.	Yixiao Ma; Shiwei Tong; Ye Liu; Likang Wu; Qi Liu; Enhong Chen; Wei Tong; Zi Yan;
267	Explicit Semantic Cross Feature Learning Via Pre-trained Graph Neural Networks for CTR Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take the first step in learning the explicit semantic cross features and propose Pre-trained Cross Feature learning Graph Neural Networks (PCF-GNN), a GNN based pre-trained model aiming at generating cross features in an explicit fashion.	Feng Li; Bencheng Yan; Qingqing Long; Pengjie Wang; Wei Lin; Jian Xu; Bo Zheng;
268	GemNN: Gating-enhanced Multi-task Neural Networks with Feature Interaction Learning for CTR Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present Baidu’s recently updated CTR training framework, called Gating-enhanced Multi-task Neural Networks (GemNN).	Hongliang Fei; Jingyuan Zhang; Xingxuan Zhou; Junhao Zhao; Xinyang Qi; Ping Li;
269	Graph Learning Regularization and Transfer Learning for Few-Shot Event Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we propose to transfer knowledge from open-domain word sense disambiguation into few-shot learning models for ED to improve their generalization to new event types.	Viet Dac Lai; Minh Van Nguyen; Thien Huu Nguyen; Franck Dernoncourt;
270	Graph Pooling Via Coarsened Graph Infomax Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To achieve mutual information neural maximization, we apply contrastive learning and propose a self-attention-based algorithm for learning positive and negative samples.	Yunsheng Pang; Yunxiang Zhao; Dongsheng Li;
271	GraphPAS: Parallel Architecture Search for Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a parallel graph architecture search (GraphPAS) framework for graph neural networks.	Jiamin Chen; Jianliang Gao; Yibo Chen; Moctard Babatounde Oloulade; Tengfei Lyu; Zhao Li;
272	Hierarchically Modeling Micro and Macro Behaviors Via Multi-Task Learning for Conversion Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by this observation, we propose a novel CVR prediction method by Hierarchically Modeling both Micro and Macro behaviors (HM3).	Hong Wen; Jing Zhang; Fuyu Lv; Wentian Bao; Tianyi Wang; Zulong Chen;
273	Improving Bi-encoder Document Ranking Models with Two Rankers and Multi-teacher Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a method where multi-teacher distillation is applied to a cross-encoder NRM and a bi-encoder NRM to produce a bi-encoder NRM with two rankers.	Jaekeol Choi; Euna Jung; Jangwon Suh; Wonjong Rhee;
274	Improving Neural Text Style Transfer By Introducing Loss Function Sequentiality Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we follow the latter approach and show that the sequential introduction of different loss functions into the learning process can boost the performance of a standard model.	Chinmay Rane; Gaël Dias; Alexis Lechervy; Asif Ekbal;
275	Inductive Representation Learning in Temporal Networks Via Mining Neighborhood and Community Influences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose a new inductive network representation learning method called MNCI by mining neighborhood and community influences in temporal networks.	Meng Liu; Yong Liu;
276	KeyBLD: Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We follow here a slightly different approach in which one first selects key blocks of a long document by local query-block pre-ranking, and then aggregates few blocks to form a short document that can be processed by a model such as BERT.	Minghan Li; Eric Gaussier;
277	Knowledge Graph Embedding Via Metagraph Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we define the metagraph of a knowledge graph by proposing a new affinity metric that measures the structural similarity between entities, and then grouping close entities by hypergraph clustering.	Chanyoung Chung; Joyce Jiyoung Whang;
278	Learning Early Exit Strategies for Additive Ranking Ensembles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose LEAR, a novel – learned – technique aimed to reduce the average number of trees traversed by documents to accumulate the scores, thus reducing the overall query response time.	Francesco Busolin; Claudio Lucchese; Franco Maria Nardini; Salvatore Orlando; Raffaele Perego; Salvatore Trani;
279	LSTPR: Graph-based Matrix Factorization with Long Short-term Preference Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the issue, we propose LSTPR, a graph-based matrix factorization model that incorporates both high-order graph information and long short-term user preferences into the modeling process.	Chih-Hen Lee; Jun-En Ding; Chih-Ming Chen; Jing-Kai Lou; Ming-Feng Tsai; Chuan-Ju Wang;
280	Maximal Multipolarized Cliques Search in Signed Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To conquer the limitations of the existing work, in this paper, we present a novel cohesive subgraph model based on structural clusterable theory, named maximal multipolarized clique (MMC), which can be partitioned into k polarized subcliques such that the edges in subcliques are positive and the edges between subcliques are negative.	Jie Gao; Fei Hao; Geyong Min; Zhipeng Cai;
281	MetaP: Meta Pattern Learning for One-Shot Knowledge Graph Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a meta pattern learning framework (MetaP) to predict new facts of relations under a challenging setting where there is only one reference for each relation.	Zhiyi Jiang; Jianliang Gao; Xinqi Lv;
282	MSSM: A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we design a novel architecture named the Multiple-level Sparse Sharing Model (MSSM), which can learn features selectively and share knowledge across all tasks efficiently.	Ke Ding; Xin Dong; Yong He; Lei Cheng; Chilin Fu; Zhaoxin Huan; Hai Li; Tan Yan; Liang Zhang; Xiaolu Zhang; Linjian Mo;
283	NIP-GCN: An Augmented Graph Convolutional Network with Node Interaction Patterns Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an augmented Graph Convolutional Network (GCN) mechanism wherein additional information of local interaction patterns between a node with its neighbors (specifically, in the form of distribution of cosine similarity values of a pre-trained node vector with its neighbors) is used to enrich a node’s representation prior to training a GCN.	Manish Chandra; Debasis Ganguly; Pabitra Mitra; Bithika Pal; James Thomas;
284	Podcast Metadata and Content: Episode Relevance and Attractiveness in Ad Hoc Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe a set of diverse podcast information needs and different approaches to assessing retrieved content for relevance.	Ben Carterette; Rosie Jones; Gareth F. Jones; Maria Eskevich; Sravana Reddy; Ann Clifton; Yongze Yu; Jussi Karlgren; Ian Soboroff;
285	Propensity-scored Probabilistic Label Trees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on the problem of optimal predictions under this model for probabilistic label trees, a popular approach for XMLC problems.	Marek Wydmuch; Kalina Jasinska-Kobus; Rohit Babbar; Krzysztof Dembczynski;
286	ReadsRE: Retrieval-Augmented Distantly Supervised Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new paradigm named retrieval-augmented distantly supervised relation extraction (ReadsRE), which can incorporate large-scale open-domain knowledge (e.g., Wikipedia) into the retrieval step.	Yue Zhang; Hongliang Fei; Ping Li;
287	Regularized Dual-PPMI Co-clustering for Text Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose a novel co-clustering approach based on a matrix formulation of vMF model-based co-clustering.	Séverine Affeldt; Lazhar Labiod; Mohamed Nadif;
288	RLNF: Reinforcement Learning Based Noise Filtering for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address such serious issue, we propose a reinforcement learning based noise filtering approach, dubbed RLNF, which employs a noise filter to select effective negative samples.	Pu Zhao; Chuan Luo; Cheng Zhou; Bo Qiao; Jiale He; Liangjie Zhang; Qingwei Lin;
289	SDG: A Simplified and Dynamic Graph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To bridge this gap, in this paper, we propose a simplified and dynamic graph neural network model, called SDG.	Dongqi Fu; Jingrui He;
290	Semantic Query Labeling Through Synthetic Query Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, basing on the assumption that a corpus already contains the information the users search, we propose a method for the automatic generation of semantically labeled queries and show that a semantic tagger — based on BERT, gazetteers-based features, and Conditional Random Fields — trained on our synthetic queries achieves results comparable to those obtained by the same model trained on real-world data.	Elias Bassani; Gabriella Pasi;
291	Significant Improvements Over The State of The Art? A Case Study of The MS MARCO Document Ranking Leaderboard Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Against the backdrop of recent IR debates on scale types, our study proposes an evaluation framework that explicitly treats certain outcomes as distinct and avoids aggregating them into a single-point metric.	Jimmy Lin; Daniel Campos; Nick Craswell; Bhaskar Mitra; Emine Yilmaz;
292	SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present a new first-stage ranker based on explicit sparsity regularization and a log-saturation effect on term weights, leading to highly sparse representations and competitive results with respect to state-of-the-art dense and sparse methods.	Thibault Formal; Benjamin Piwowarski; Stéphane Clinchant;
293	Stopping Criteria for Technology Assisted Reviews Based on Counting Processes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces two modifications to existing approaches: application of a Cox Process (a counting process which has not previously been used for this problem) and use of a rate function based on a power law.	Alison Sneyd; Mark Stevenson;
294	Targeted Attack and Defense for Deep Hashing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel targeted attack method and the first defense scheme for deep hashing based retrieval.	Xunguang Wang; Zheng Zhang; Guangming Lu; Yong Xu;
295	Understanding The Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis.	Rajdeep Mukherjee; Atharva Naik; Sriyash Poddar; Soham Dasgupta; Niloy Ganguly;
296	Unsupervised Ensemble Learning with Noisy Label Correction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a novel approach to improve the label accuracy based on unsupervised ensemble learning.	Xupeng Zou; Zhongnan Zhang; Zhen He; Liang Shi;
297	Unsupervised Extractive Text Summarization with Distance-Augmented Sentence Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes an unsupervised approach to extractive text summarization, which uses an automatically constructed sentence graph from each document to select salient sentences for summarization based on both the similarities and relative distances in the neighborhood of each sentences.	Jingzhou Liu; Dominic J. D. Hughes; Yiming Yang;
298	When Choice Happens: A Systematic Examination of Mouse Movement Length for Decision Making in Web Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We model these scenarios with recurrent neural nets and study the effect of mouse sequence padding and truncating to different lengths.	Lukas Brückner; Ioannis Arapakis; Luis A. Leiva;
299	Window Navigation with Adaptive Probing for Executing BlockMax WAND Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper studies a boosting approach that further accelerates document retrieval by executing BMW, or one of its variants, on a sequence of posting windows with an order prioritized to tighten the threshold bound earlier.	Jinjin Shao; Yifan Qiao; Shiyu Ji; Tao Yang;
300	A Multilingual Dataset for Named Entity Recognition, Entity Linking and Stance Detection in Historical Newspapers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the development of the NewsEye resource, a multilingual dataset for named entity recognition and linking enriched with stances towards named entities.	Ahmed Hamdi; Elvys Linhares Pontes; Emanuela Boros; Thi Tuyet Hai Nguyen; Günter Hackl; Jose G. Moreno; Antoine Doucet;
301	How Deep Is Your Learning: The DL-HARD Annotated Deep Learning Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on this data, we introduce a framework for identifying challenging queries.	Iain Mackie; Jeffrey Dalton; Andrew Yates;
302	LeCaRD: A Legal Case Retrieval Dataset for Chinese Law System Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we construct the Chinese Legal Case Retrieval Dataset (LeCaRD), which contains 107 query cases and over 43,000 candidate cases.	Yixiao Ma; Yunqiu Shao; Yueyue Wu; Yiqun Liu; Ruizhe Zhang; Min Zhang; Shaoping Ma;
303	Morphologically Annotated Amharic Text Corpora Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents morphologically annotated Amharic lexicons as well as stem-based and root-based morphologically annotated corpora which could be used by the research community as benchmark collections either to evaluate morphological analyzers or information retrieval for Amharic.	Tilahun Yeshambel; Josiane Mothe; Yaregal Assabie;
304	Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.	Jimmy Lin; Xueguang Ma; Sheng-Chieh Lin; Jheng-Hong Yang; Ronak Pradeep; Rodrigo Nogueira;
305	REGIS: A Test Collection for Geoscientific Documents in Portuguese Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: With the aim of bridging this gap, in this paper, we developed REGIS (Retrieval Evaluation for Geoscientific Information Systems), a test collection for the geoscientific domain in Portuguese.	Lucas Lima de Oliveira; Regis Kruel Romeu; Viviane Pereira Moreira;
306	TREC Deep Learning Track: Reusable Test Collections in The Large Data Regime Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper supports the reuse of the TREC DL test collections in three ways.	Nick Craswell; Bhaskar Mitra; Emine Yilmaz; Daniel Campos; Ellen M. Voorhees; Ian Soboroff;
307	WWW3E8: 259,000 Relevance Labels for Studying The Effect of Document Presentation Order for Relevance Assessors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To help researchers directly address this question using their favourite methods of analysis, we have released a large-scale data set called WWW3E8.	Tetsuya Sakai; Sijie Tao; Zhaohao Zeng;
308	Advancements in The Music Information Retrieval Framework AMUSE Over The Last Decade Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present several substantial contributions to AMUSE since its first presentation at ISMIR 2010.	Igor Vatolkin; Philipp Ginsel; Günter Rudolph;
309	Conversational Entity Linking: Problem Definition and Datasets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study entity linking for conversational systems.	Hideaki Joko; Faegheh Hasibi; Krisztian Balog; Arjen P. de Vries;
310	CopyCat: Near-Duplicates Within and Between The ClueWeb and The Common Crawl Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce ChatNoir-CopyCat-21, which simplifies deduplication significantly.	Maik Fröbe; Janek Bevendorff; Lukas Gienapp; Michael Völske; Benno Stein; Martin Potthast; Matthias Hagen;
311	Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Elliot is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple configuration file.	Vito Walter Anelli; Alejandro Bellogin; Antonio Ferrara; Daniele Malitesta; Felice Antonio Merra; Claudio Pomo; Francesco Maria Donini; Tommaso Di Noia;
312	HOOPS: Human-in-the-Loop Graph Reasoning for Conversational Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a Human-in-the-Loop (HitL) graph reasoning paradigm and develop a corresponding dataset named HOOPS for the task of KG-driven conversational recommendation.	Zuohui Fu; Yikun Xian; Yaxin Zhu; Shuyuan Xu; Zelong Li; Gerard de Melo; Yongfeng Zhang;
313	On The Quality of The TREC-COVID IR Test Collections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper examines the quality of the resulting TREC-COVID test collections, and in doing so, offers a critique of the state-of-the-art in building reusable IR test collections.	Ellen M. Voorhees; Kirk Roberts;
314	Simplified Data Wrangling with Ir_datasets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To help mitigate these challenges, we introduce a new robust and lightweight tool (ir_datasets) for acquiring, managing, and performing typical operations over datasets used in IR.	Sean MacAvaney; Andrew Yates; Sergey Feldman; Doug Downey; Arman Cohan; Nazli Goharian;
315	Wiki-Reliability: A Large Scale Dataset for Content Reliability on Wikipedia Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We select the 10 most popular reliability-related templates on Wikipedia, and propose an effective method to label almost 1M samples of Wikipedia article revisions as positive or negative with respect to each template.	KayYen Wong; Miriam Redi; Diego Saez-Trumper;
316	WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce the Wikipedia-based Image Text (WIT) Dataset to better facilitate multimodal, multilingual learning.	Krishna Srinivasan; Karthik Raman; Jiecao Chen; Michael Bendersky; Marc Najork;
317	A Test Collection for Ad-hoc Dataset Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a new test collection for ad-hoc dataset retrieval, which have been developed through a shared task called Data Search in the fifteenth NTCIR.	Makoto P. Kato; Hiroaki Ohshima; Ying-Hsang Liu; Hsin-Liang Chen;
318	Booking.com Multi-Destination Trips Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a novel dataset of real multi-destination trips booked through Booking.com’s online travel platform.	Dmitri Goldenberg; Pavel Levin;
319	EXTRA: Explanation Ranking Datasets for Explainable Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To achieve a standard way of evaluating recommendation explanations, we provide three benchmark datasets for EXplanaTion RAnking (denoted as EXTRA), on which explainability can be measured by ranking-oriented metrics.	Lei Li; Yongfeng Zhang; Li Chen;
320	Pchatbot: A Large-Scale Dataset for Personalized Chatbot Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce Pchatbot, a large-scale dialogue dataset that contains two subsets collected from Weibo and Judicial forums respectively.	Hongjin Qian; Xiaohe Li; Hanxun Zhong; Yu Guo; Yueyuan Ma; Yutao Zhu; Zhanliang Liu; Zhicheng Dou; Ji-Rong Wen;
321	POINTREC: A Test Collection for Narrative-driven Point of Interest Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a test collection for contextual point of interest (POI) recommendation in a narrative-driven scenario.	Jafar Afzali; Aleksander Mark Drzewiecki; Krisztian Balog;
322	Seer-Dock: A General-Purpose Dockerized Scholarly Document Collection and Management Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce Seer-Dock, a novel and easy-to deploy general-purpose dockerized framework to build a scholarly document harvesting and management system.	Dina Sayed; Mohamed Nour; Heiko Schuldt;
323	Select, Substitute, Search: A New Benchmark for Knowledge-Augmented Visual Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In response, we identify a key structural idiom in OKVQA ,viz., S3 (select, substitute and search), and build a new data set and challenge around it.	Aman Jain; Mayank Kothyari; Vishwajeet Kumar; Preethi Jyothi; Ganesh Ramakrishnan; Soumen Chakrabarti;
324	Simulating User Satisfaction for The Evaluation of Task-oriented Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To help build a human-like user simulator that can measure the quality of a dialogue, we propose the following task: simulating user satisfaction for the evaluation of task-oriented dialogue systems.	Weiwei Sun; Shuo Zhang; Krisztian Balog; Zhaochun Ren; Pengjie Ren; Zhumin Chen; Maarten de Rijke;
325	TripClick: The Log Files of A Large Health Web Search Engine Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We release a large-scale domain-specific dataset of click logs, obtained from user interactions of the Trip Database health web search engine.	Navid Rekabsaz; Oleg Lesota; Markus Schedl; Jon Brassey; Carsten Eickhoff;
326	WTR: A Test Collection for Web Table Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe the development, characteristics and availability of a test collection for the task of Web table retrieval, which uses a large-scale Web Table Corpora extracted from the Common Crawl.	Zhiyu Chen; Shuo Zhang; Brian D. Davison;
327	Chatty Goose: A Python Framework for Conversational Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our aim is to lower the barrier of entry for research in conversational search by providing reproducible baselines that researchers can build on top of.	Edwin Zhang; Sheng-Chieh Lin; Jheng-Hong Yang; Ronak Pradeep; Rodrigo Nogueira; Jimmy Lin;
328	DarkJargon.net: A Platform for Understanding Underground Conversation with Latent Meaning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel online platform that caters to the understating of underground conversation with latent meaning.	Dominic Seyler; Wei Liu; Yunan Zhang; XiaoFeng Wang; ChengXiang Zhai;
329	OpenMatch: An Open Source Library for Neu-IR Research Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: OpenMatch is a Python-based library that serves for Neural Information Retrieval (Neu-IR) research.	Zhenghao Liu; Kaitao Zhang; Chenyan Xiong; Zhiyuan Liu; Maosong Sun;
330	Precision Medicine Search for Paediatric Oncology Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a search engine aimed to help clinicians find targeted treatments for children with cancer.	Bevan Koopman; Tracey Wright; Natacha Omer; Veronica McCabe; Guido Zuccon;
331	PYA0: A Python Toolkit for Accessible Math-Aware Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present PyA0, an easy-to-use Python toolkit built on Approach Zero that improves its accessibility to researchers.	Wei Zhong; Jimmy Lin;
332	QuARk: A GUI for Quality-Aware Ranking of Arguments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present QuARk, a GUI that allows users to retrieve arguments from a focused debate collection for their queries.	Markus Nilles; Lorik Dumani; Ralf Schenkel;
333	The Information Retrieval Anthology Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present the IR Anthology, a corpus of information retrieval publications accessible via a metadata browser and a full-text search engine.	Martin Potthast; Sebastian Günther; Janek Bevendorff; Jan Philipp Bittner; Alexander Bondarenko; Maik Fröbe; Christian Kahmann; Andreas Niekler; Michael Völske; Benno Stein; Matthias Hagen;
334	A Web-based Knowledge Hub for Exploration of Multiple Research Article Collections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we build an online knowledge hub (http://covid19knowledgehub.herokuapp.com/) particularly for Covid-19 related research articles per the requirement from researchers in a hospital.	Wei Emma Zhang; Miao Liu; Alan Pallath; Gokul Tamilventhan;
335	FedNLP: An Interpretable NLP System to Decode Federal Reserve Communications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present FedNLP, an interpretable multi-component Natural Language Processing (NLP) system to decode Federal Reserve communications.	Jean Lee; Hoyoul Luis Youn; Nicholas Stevens; Josiah Poon; Soyeon Caren Han;
336	GeoWINE: Geolocation Based Wiki, Image, News and Event Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present the GeoWINE (Geolocation-based Wiki-Image-News-Event retrieval) demonstrator, an effective modular system for multimodal retrieval which expects only a single image as input.	Golsa Tahmasebzadeh; Endri Kacupaj; Eric Müller-Budack; Sherzod Hakimov; Jens Lehmann; Ralph Ewerth;
337	OrgBox: Supporting Cognitive and Metacognitive Activities During Exploratory Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we describe the features and implementation of the OrgBox tool.	Austin R. Ward; Robert Capra;
338	QuTI! Quantifying Text-Image Consistency in Multimodal Documents Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a web-based demo application that automatically quantifies the cross-modal relations of entities~(persons, locations, and events) in image and text.	Matthias Springstein; Eric Müller-Budack; Ralph Ewerth;
339	Towards Trustworthiness in The Context of Explainable Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present an Explainable Search system with a focus on evaluating the XAI aspect of Trustworthiness along with the retrieval performance.	Sayantan Polley; Rashmi Raju Koparde; Akshaya Bindu Gowri; Maneendra Perera; Andreas Nuernberger;
340	YASBIL: Yet Another Search Behaviour (and) Interaction Logger Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present YASBIL, a two-component logging solution comprising a browser extension and a WordPress plugin.	Nilavra Bhattacharya; Jacek Gwizdka;
341	Big Brother: A Drop-In Website Interaction Logging Service Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a generic, application-independent service for logging interactions in web-pages, specifically targetting user studies.	Harrisen Scells; Jimmy; Guido Zuccon;
342	DiffIR: Exploring Differences in Ranking Models’ Behavior Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: DiffIR is a new open-source web tool to assist with qualitative ranking analysis by visually ‘diffing’ system rankings at the individual result level for queries where behavior significantly diverges.	Kevin Martin Jose; Thong Nguyen; Sean MacAvaney; Jeffrey Dalton; Andrew Yates;
343	Interacting with Information in Immersive Virtual Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we demonstrate the Information Interactions in Virtual Reality (IIVR) system designed and implemented to study how users interact with abstract information objects in immersive virtual environments in the context of information retrieval.	Austin R. Ward; Yiyin Gu; Sandeep Avula; Praneeth Chakravarthula;
344	News2PubMed: A Browser Extension for Linking Health News to Medical Literature Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This demo system presents a browser extension that allows the reader of a health news article to quickly retrieve related medical/health research papers.	Jun Wang; Bei Yu;
345	PECAN: A Platform for Searching Chat Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a new task for addressing this problem, called searching for conversations, whereby the aim is to retrieve and rank groups of related messages given a search query.	Kunpeng Qin; Harrisen Scells; Guido Zuccon;
346	Privacy-Aware Remote Information Retrieval User Experiments Logging Tool Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a Privacy-Aware Remote User Logging Tool for remotely collecting user behaviors and explicit experience feedback, with a special care for user privacy.	Hanyu Li; Hongyu Lu; Songhao Huang; Weizhi Ma; Min Zhang; Yiqun Liu; Shaoping Ma;
347	Science2Cure: A Clinical Trial Search Prototype Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a search system that given a patient profile searches over clinical trials for potential matches.	Maciej Rybinski; Sarvnaz Karimi; Aleney Khoo;
348	Learning with Little Data: Industry Challenges and Innovations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: There are two challenges in collecting human annotated data. First, the human annotation process does not scale and it is hard to obtain large volumes of annotations in multiple languages. Second, annotators must query existing systems to obtain samples for auditing, resulting in very few mismatched examples (data skewness) and counterfactual biases. In this talk, we address these challenges using two recent advances in deep learning.	Nikhil Rao;
349	Restoring Healthy Online Discourse By Detecting and Reducing Controversy, Misinformation, and Toxicity Online Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this position paper, we argue that both detecting online information disorders and deploying novel, real-world content moderation tools is crucial in promoting empathy in social networks, and maintaining free expression and discourse.	Shiri Dori-Hacohen; Keen Sung; Jengyu Chou; Julian Lustig-Gonzalez;
350	From Producer Success to Retention: A New Role of Search and Recommendation Systems on Marketplaces Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this talk, we discuss how these systems will need to evolve from the traditional formulations by incorporating the producer value into their objectives.	Viet Ha-Thuc; Matthew Wood; Yunli Liu; Jagadeesan Sundaresan;
351	Putting The Role of Personalization Into Context Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This talk focuses on examining the role of recommenders and their ability to adapt to customer feedback.	Dmitri Goldenberg;
352	SearchGCN: Powering Embedding Retrieval By Graph Convolution Networks for E-Commerce Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this proposal, we introduce our approach, namely SearchGCN, for embedding-based candidate retrieval in one of the largest e-commerce search engine in the world.	Xinlin Xia; Shang Wang; Han Zhang; Songlin Wang; Sulong Xu; Yun Xiao; Bo Long; Wen-Yun Yang;
353	AliMe Avatar: Multi-modal Content Production and Presentation for Live-streaming E-commerce Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we mainly focus on the product broadcasting part, demonstrate the system, present the underlying techniques, and share our experience in dealing with live-streaming E-commerce.	Feng-Lin Li; Zhongzhou Zhao; Qin Lu; Xuming Lin; Hehong Chen; Bo Chen; Liming Pu; Jiashuo Zhang; Fu Sun; Xikai Liu; Liqun Xie; Qi Huang; Ji Zhang; Haiqing Chen;
354	AliMe DA: A Data Augmentation Framework for Question Answering in Cold-start Scenarios Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose AliMe DA, a practical data augmentation (DA) framework that consists of data production, denoising and consumption, to alleviate this problem.	Guohai Xu; Yan Shao; Chenliang Li; Feng-Lin Li; Bin Bi; Ji Zhang; Haiqing Chen;
355	AI Based Information Retrieval System for Identifying Harmful Online Gaming Patterns Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this proposal, we present an automated, data driven, AI powered, Responsible Game Play (RGP) framework cum tool which has been integrated in our online skill gaming platform.	Deepanshi Seth; Rukma Talwadker; Tridib Mukherjee; Usama Chitapure; Nagesh Adiga; Avantika Gupta;
356	Transformer-based Banking Products Recommender System Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a modeling framework for item recommendation using a Transformer encoder [6] and a novel input data representation accounting for the temporal context of item ownership and user metadata.	Davide Liu; George Philippe Farajalla; Alexandre Boulenger;