Paper Digest: SIGIR 2022 Highlights
Download SIGIR-2022-Paper-Digests.pdf– highlights of all SIGIR-2022 papers. Readers can choose to read all these highlights on our console, which allows users to filter out papers using keywords and find related papers, patents, etc. To browse papers by author, here is a list of top SIGIR-2022 authors. You may also like to explore our “Best Paper” Digest (SIGIR), which lists the most influential SIGIR papers since 1981.
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) is one of the top information retrieval conferences in the world. In 2022, it is to be held in Madrid, Spain.
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TABLE 1: Paper Digest: SIGIR 2022 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Users: Can’t Work With Them, Can’t Work Without Them? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: If we could design the ideal IR effectiveness experiment (as distinct from an IR efficiency experiment), what would it look like? |
Alistair Moffat; |
| 2 | Few-shot Information Extraction Is Here: Pre-train, Prompt and Entail Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Manually annotated entailment datasets covering multiple inference phenomena have been used to infuse inference capabilities to PLMs. This talk will review these recent developments, and will present an approach that combines prompts and PLMs fine-tuned for textual entailment that yields state-of-the-art results on Information Extraction (IE) using only a small fraction of the annotations. |
Eneko Agirre; |
| 3 | Searching for A New and Better Future of Work Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this talk I will give an overview of what research tells us about emerging work practices, and explore how the SIGIR community can build on these findings to help create a new – and better – future of work. |
Jaime Teevan; |
| 4 | Intelligent Conversational Agents for Ambient Computing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this talk, we present some early steps we are taking with Alexa, Amazon’s Conversational AI system, to move from supervised learning to self-learning methods, where the AI relies on customer interactions for supervision in our journey to ambient intelligence. |
Ruhi Sarikaya; |
| 5 | Can Clicks Be Both Labels and Features?: Unbiased Behavior Feature Collection and Uncertainty-aware Learning to Rank Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we explore the possibility of incorporating user clicks as both training labels and ranking features for learning to rank. |
Tao Yang; Chen Luo; Hanqing Lu; Parth Gupta; Bing Yin; Qingyao Ai; |
| 6 | Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper we study how to effectively exploit implicit feedback in Dense Retrievers (DRs). |
Shengyao Zhuang; Hang Li; Guido Zuccon; |
| 7 | Bilateral Self-unbiased Learning from Biased Implicit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel unbiased recommender learning model, namely BIlateral SElf-unbiased Recommender (BISER), to eliminate the exposure bias of items caused by recommender models. |
Jae-woong Lee; Seongmin Park; Joonseok Lee; Jongwuk Lee; |
| 8 | Interpolative Distillation for Unifying Biased and Debiased Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we aim to develop a win-win recommendation method that is strong on both tests. |
Sihao Ding; Fuli Feng; Xiangnan He; Jinqiu Jin; Wenjie Wang; Yong Liao; Yongdong Zhang; |
| 9 | Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder Whether the existing graph-based CF models alleviate or exacerbate the popularity bias of recommender systems? To answer this question, we first investigate the two-fold performances w.r.t. accuracy and novelty for existing graph-based CF methods. |
Minghao Zhao; Le Wu; Yile Liang; Lei Chen; Jian Zhang; Qilin Deng; Kai Wang; Xudong Shen; Tangjie Lv; Runze Wu; |
| 10 | Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To make better use of the popularity bias, we propose a co-training disentangled domain adaptation network (CD$^2$AN), which can co-train both biased and unbiased models. |
Zhihong Chen; Jiawei Wu; Chenliang Li; Jingxu Chen; Rong Xiao; Binqiang Zhao; |
| 11 | Hypergraph Contrastive Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, two key challenges have not been well explored in existing solutions: i) The over-smoothing effect with deeper graph-based CF architecture, may cause the indistinguishable user representations and degradation of recommendation results. ii) The supervision signals (i.e., user-item interactions) are usually scarce and skewed distributed in reality, which limits the representation power of CF paradigms. To tackle these challenges, we propose a new self-supervised recommendation framework Hypergraph Contrastive Collaborative Filtering (HCCF) to jointly capture local and global collaborative relations with a hypergraph-enhanced cross-view contrastive learning architecture. |
Lianghao Xia; Chao Huang; Yong Xu; Jiashu Zhao; Dawei Yin; Jimmy Huang; |
| 12 | Geometric Disentangled Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The Euclidean-based models may be inadequate to fully uncover the intent factors beneath such hybrid-geometry interactions. To remedy this deficiency, in this paper, we study the novel problem of Geometric Disentangled Collaborative Filtering (GDCF), which aims to reveal and disentangle the latent intent factors across multiple geometric spaces. |
Yiding Zhang; Chaozhuo Li; Xing Xie; Xiao Wang; Chuan Shi; Yuming Liu; Hao Sun; Liangjie Zhang; Weiwei Deng; Qi Zhang; |
| 13 | INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Besides, the number of model parameters heavily depends on the number of all users and items, restricting their scalability to real-world applications. To solve the above challenges, in this paper, we propose a novel model-agnostic and scalable Inductive Embedding Module for collaborative filtering, namely INMO. |
Yunfan Wu; Qi Cao; Huawei Shen; Shuchang Tao; Xueqi Cheng; |
| 14 | Improving Implicit Alternating Least Squares with Ring-based Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel ring-based regularization to penalize significant differences of each user’s preferences between each item and some other items. |
Rui Fan; Jin Chen; Jin Zhang; Defu Lian; Enhong Chen; |
| 15 | Graph Trend Filtering Networks for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we investigate the drawbacks (e.g., non-adaptive propagation and non-robustness) of existing GNN-based recommendation methods. |
Wenqi Fan; Xiaorui Liu; Wei Jin; Xiangyu Zhao; Jiliang Tang; Qing Li; |
| 16 | Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Although there have been several studies on data denoising in recommender systems, they either neglect direct intervention of noisy interaction in the message-propagation of GNN, or fail to preserve the diversity of recommendation when denoising. To tackle the above issues, this paper presents a novel GNN-based CF model, named Robust Graph Collaborative Filtering (RGCF), to denoise unreliable interactions for recommendation. |
Changxin Tian; Yuexiang Xie; Yaliang Li; Nan Yang; Wayne Xin Zhao; |
| 17 | Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, building a human-like simulator is still an open challenge. In this work, we focus on how users reformulate their utterances when a conversational agent fails to understand them. |
Shuo Zhang; Mu-Chun Wang; Krisztian Balog; |
| 18 | Conversational Question Answering on Heterogeneous Sources Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper addresses the novel issue of jointly tapping into all of these together, this way boosting answer coverage and confidence. We present CONVINSE, an end-to-end pipeline for ConvQA over heterogeneous sources, operating in three stages: i) learning an explicit structured representation of an incoming question and its conversational context, ii) harnessing this frame-like representation to uniformly capture relevant evidences from KB, text, and tables, and iii) running a fusion-in-decoder model to generate the answer. |
Philipp Christmann; Rishiraj Saha Roy; Gerhard Weikum; |
| 19 | Structured and Natural Responses Co-generation for Conversational Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The latter emphasizes generating natural responses but fails to predict structured acts. Therefore, we propose a neural co-generation model that gener- ates the two concurrently. |
Chenchen Ye; Lizi Liao; Fuli Feng; Wei Ji; Tat-Seng Chua; |
| 20 | Variational Reasoning About User Preferences for Conversational Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we address the problem of accurately recognizing and maintaining user preferences in CRSs. |
Zhaochun Ren; Zhi Tian; Dongdong Li; Pengjie Ren; Liu Yang; Xin Xin; Huasheng Liang; Maarten de Rijke; Zhumin Chen; |
| 21 | Curriculum Contrastive Context Denoising for Few-shot Conversational Dense Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we reveal that not all historical conversational turns are necessary for understanding the intent of the current query. |
Kelong Mao; Zhicheng Dou; Hongjin Qian; |
| 22 | Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose SPACE, a novel unified pre-trained dialog model learning from large-scale dialog corpora with limited annotations, which can be effectively fine-tuned on a wide range of downstream dialog tasks. |
Wanwei He; Yinpei Dai; Min Yang; Jian Sun; Fei Huang; Luo Si; Yongbin Li; |
| 23 | COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose COSPLAY(COncept Set guided PersonaLized dialogue generation Across both partY personas) that considers both parties as a "team": expressing self-persona while keeping curiosity toward the partner, leading responses around mutual personas, and finding the common ground. |
Chen Xu; Piji Li; Wei Wang; Haoran Yang; Siyun Wang; Chuangbai Xiao; |
| 24 | Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We argue that the targets of reaching the goal topic quickly and maintaining a high user satisfaction are not always converged, because the topics close to the goal and the topics user preferred may not be the same. Towards this issue, we propose a new solution named I-Pro that can learn Proactive policy in the Interactive setting. |
Wenqiang Lei; Yao Zhang; Feifan Song; Hongru Liang; Jiaxin Mao; Jiancheng Lv; Zhenglu Yang; Tat-Seng Chua; |
| 25 | User-Centric Conversational Recommendation with Multi-Aspect User Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To systematically model the multi-aspect information, we propose a User-Centric Conversational Recommendation (UCCR) model, which returns to the essence of user preference learning in CRS tasks. |
Shuokai Li; Ruobing Xie; Yongchun Zhu; Xiang Ao; Fuzhen Zhuang; Qing He; |
| 26 | Generating Clarifying Questions with Web Search Results Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For such a query, it is unable to generate an informative question. To alleviate this problem, we propose leveraging top search results of the query to help generate better descriptions because we deem that the top retrieved documents contain rich and relevant contexts of the query. |
Ziliang Zhao; Zhicheng Dou; Jiaxin Mao; Ji-Rong Wen; |
| 27 | ADPL: Adversarial Prompt-based Domain Adaptation for Dialogue Summarization with Knowledge Disentanglement Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Overall, our work introduces a prompt-based perspective to the zero-shot learning for dialogue summarization task and provides valuable findings and insights for future research. |
Lulu Zhao; Fujia Zheng; Weihao Zeng; Keqing He; Ruotong Geng; Huixing Jiang; Wei Wu; Weiran Xu; |
| 28 | Learning to Infer User Implicit Preference in Conversational Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the limitations of existing methods, we propose a new CRS framework called Conversational Recommender with Implicit Feedback (CRIF). |
Chenhao Hu; Shuhua Huang; Yansen Zhang; Yubao Liu; |
| 29 | DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Grounded in the information theory, we propose DisenCDR, a novel model to disentangle the domain-shared and domain-specific information. |
Jiangxia Cao; Xixun Lin; Xin Cong; Jing Ya; Tingwen Liu; Bin Wang; |
| 30 | Structure-Aware Semantic-Aligned Network for Universal Cross-Domain Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Compared to CDR, the UCDR task is more challenging due to (1) visually diverse data from multi-source domains, (2) the domain shift between seen and unseen domains, and (3) the semantic shift across seen and unseen categories. To tackle these problems, we propose a novel model termed Structure-Aware Semantic-Aligned Network (SASA) to align the heterogeneous representations of multi-source domains without loss of generalizability for the UCDR task. |
Jialin Tian; Xing Xu; Kai Wang; Zuo Cao; Xunliang Cai; Heng Tao Shen; |
| 31 | Dynamics-Aware Adaptation for Reinforcement Learning Based Cross-Domain Interactive Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Few have explored how the temporally dynamic user-item interaction patterns transform across domains. Motivated by the above consideration, we propose DACIR, a novel Doubly-Adaptive deep RL-based framework for Cross-domain Interactive Recommendation. |
Junda Wu; Zhihui Xie; Tong Yu; Handong Zhao; Ruiyi Zhang; Shuai Li; |
| 32 | Exploring Modular Task Decomposition in Cross-domain Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we aim to explore the task decomposition in cross-domain NER. |
Xinghua Zhang; Bowen Yu; Yubin Wang; Tingwen Liu; Taoyu Su; Hongbo Xu; |
| 33 | Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we focus on the Partially Overlapped Cross-Domain Recommendation (POCDR) problem, that is, how to leverage the information of both the overlapped and non-overlapped users to improve recommendation performance. |
Weiming Liu; Xiaolin Zheng; Jiajie Su; Mengling Hu; Yanchao Tan; Chaochao Chen; |
| 34 | HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Based on this observation, in this paper, we propose a novel approach Hierarchical Intention Embedding Network (HIEN), which considers dependencies of attributes based on bottom-up tree aggregation in the constructed attribute graph. |
Zuowu Zheng; Changwang Zhang; Xiaofeng Gao; Guihai Chen; |
| 35 | NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Therefore, it is essential to explore a more efficient architecture search method. To achieve this goal, we propose NAS-CTR, a differentiable neural architecture search approach for CTR prediction. |
Guanghui Zhu; Feng Cheng; Defu Lian; Chunfeng Yuan; Yihua Huang; |
| 36 | Enhancing CTR Prediction with Context-Aware Feature Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel module named Feature Refinement Network (FRNet), which learns context-aware feature representations at bit-level for each feature in different contexts. |
Fangye Wang; Yingxu Wang; Dongsheng Li; Hansu Gu; Tun Lu; Peng Zhang; Ning Gu; |
| 37 | Neighbour Interaction Based Click-Through Rate Prediction Via Graph-masked Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Although these methods have made great progress, they are often limited by the recommender system’s direct exposure and inactive interactions, and thus fail to mine all potential user interests. To tackle these problems, we propose Neighbor-Interaction based CTR prediction (NI-CTR), which considers this task under a Heterogeneous Information Network (HIN) setting. |
Erxue Min; Yu Rong; Tingyang Xu; Yatao Bian; Da Luo; Kangyi Lin; Junzhou Huang; Sophia Ananiadou; Peilin Zhao; |
| 38 | ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB) for CVR estimation, where the CVR estimate is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where ESMM might overlook the causality from click to conversion. |
Hao Wang; Tai-Wei Chang; Tianqiao Liu; Jianmin Huang; Zhichao Chen; Chao Yu; Ruopeng Li; Wei Chu; |
| 39 | Target-aware Abstractive Related Work Generation with Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Hence, in this paper, we propose an abstractive target-aware related work generator (TAG), which can generate related work sections consisting of new sentences. |
Xiuying Chen; Hind Alamro; Mingzhe Li; Shen Gao; Rui Yan; Xin Gao; Xiangliang Zhang; |
| 40 | A Study of Cross-Session Cross-Device Search Within An Academic Digital Library Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To support such searching, we have developed an academic digital library search interface that assists searchers in managing cross-session search tasks even when moving between mobile and desktop environments. |
Sebastian Gomes; Miriam Boon; Orland Hoeber; |
| 41 | DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation Based on Correlation Graph Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, traditional keyword search methods for APIs often suffer from several critical issues such as functional compatibility and limited diversity in search results, which may lead to mashup creation failures and lower development productivity. To deal with these challenges, this paper designs DAWAR, a diversity-aware Web APIs recommendation approach that finds diversified and compatible APIs for mashup creation. |
Wenwen Gong; Xuyun Zhang; Yifei Chen; Qiang He; Amin Beheshti; Xiaolong Xu; Chao Yan; Lianyong Qi; |
| 42 | Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Besides, the existing diagnosis results of knowledge tracing are not convincing enough since they neglect hierarchical relations between exercises. To solve the above problems, we propose a hierarchical graph knowledge tracing model called HGKT to explore the latent complex relations between exercises. |
Hanshuang Tong; Zhen Wang; Yun Zhou; Shiwei Tong; Wenyuan Han; Qi Liu; |
| 43 | A Robust Computerized Adaptive Testing Approach in Educational Question Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, we present a generic optimization criterion Robust Adaptive Testing (RAT) for proficiency estimation via fusing multiple estimates at each step, which maintains a multi-facet description of student’s potential proficiency. |
Yan Zhuang; Qi Liu; Zhenya Huang; Zhi Li; Binbin Jin; Haoyang Bi; Enhong Chen; Shijin Wang; |
| 44 | Assessing Student’s Dynamic Knowledge State By Exploring The Question Difficulty Effect Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we focus on exploring the question difficulty effect on learning to improve student’s knowledge state assessment and propose the DIfficulty Matching Knowledge Tracing (DIMKT) model. |
Shuanghong Shen; Zhenya Huang; Qi Liu; Yu Su; Shijin Wang; Enhong Chen; |
| 45 | Incorporating Retrieval Information Into The Truncation of Ranking Lists for Better Legal Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: These existing efforts also treat result list truncation as an isolated task instead of a component in the entire ranking process, limiting the usage of truncation in practical systems. To tackle these limitations, we propose LeCut, a ranking list truncation model for legal case retrieval. |
Yixiao Ma; Qingyao Ai; Yueyue Wu; Yunqiu Shao; Yiqun Liu; Min Zhang; Shaoping Ma; |
| 46 | MetaCare++: Meta-Learning with Hierarchical Subtyping for Cold-Start Diagnosis Prediction in Healthcare Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we first propose a novel Meta-learning framework for cold-start diagnosis prediction in healthCare data (MetaCare). By explicitly encoding the effects of disease progress over time as a generalization prior, MetaCare dynamically predicts future diagnosis and timestamp for infrequent patients. Then, to model complicated relations among rare diseases, we propose to utilize domain knowledge of hierarchical relations among diseases, and further perform diagnosis subtyping to mine the latent syndromic relations among diseases. |
Yanchao Tan; Carl Yang; Xiangyu Wei; Chaochao Chen; Weiming Liu; Longfei Li; Jun Zhou; Xiaolin Zheng; |
| 47 | Interpreting Patient Descriptions Using Distantly Supervised Similar Case Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, retrieval augmented strategies have only had limited success, as it is rare to find sentences which express the exact type of knowledge that is needed for interpreting a given patient description. For this reason, rather than attempting to retrieve explicit medical knowledge, we instead propose to rely on a nearest neighbour strategy. |
Israa Alghanmi; Luis Espinosa-Anke; Steven Schockaert; |
| 48 | Few-shot Node Classification on Attributed Networks with Graph Meta-learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: More specifically, we introduce an efficient method for learning expressive node representations even on heterophilic graphs and propose utilizing a prototype-based approach to initialize parameters in meta-learning. |
Yonghao Liu; Mengyu Li; Ximing Li; Fausto Giunchiglia; Xiaoyue Feng; Renchu Guan; |
| 49 | Personalized Fashion Compatibility Modeling Via Metapath-guided Heterogeneous Graph Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite their significance, these PFCM methods mainly concentrate on the user and item entities, as well as their interactions, but ignore the attribute entities, which contain rich semantics. To address this problem, we propose to fully explore the related entities and their relations involved in PFCM to boost the PFCM performance. |
Weili Guan; Fangkai Jiao; Xuemeng Song; Haokun Wen; Chung-Hsing Yeh; Xiaojun Chang; |
| 50 | KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Therefore, it is critical to not only consider the connections between users and threads, but also the descriptions of users’ symptoms and clinical conditions. In this paper, towards this problem of thread recommendation in online healthcare forums, we propose a knowledge graph enhanced Threads Recommendation (KETCH) model, which leverages graph neural networks to model the interactions among users and threads, and learn their representations. |
Limeng Cui; Dongwon Lee; |
| 51 | Recognizing Medical Search Query Intent By Few-shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In addition, many intents only have a few labeled data. To handle these problems, we propose a few-shot learning method for medical search query intent recognition called MEDIC. |
Yaqing Wang; Song Wang; Yanyan Li; Dejing Dou; |
| 52 | Single-shot Embedding Dimension Search in Recommender System Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Single-Shot Embedding Dimension Search method, called SSEDS, which can efficiently assign dimensions for each feature field via a single-shot embedding pruning operation while maintaining the recommendation accuracy of the model. |
Liang Qu; Yonghong Ye; Ningzhi Tang; Lixin Zhang; Yuhui Shi; Hongzhi Yin; |
| 53 | Forest-based Deep Recommender Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we propose a Deep Forest-based Recommender (DeFoRec for short) for an efficient recommendation. |
Chao Feng; Defu Lian; Zheng Liu; Xing Xie; Le Wu; Enhong Chen; |
| 54 | Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose an efficient exploration strategy for online interactive neural ranker learning based on bootstrapping. |
Yiling Jia; Hongning Wang; |
| 55 | On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we explore ultra-compact models for next-item recommendation, by loosing the constraint of dimensionality consistency in tensor decomposition. |
Xin Xia; Hongzhi Yin; Junliang Yu; Qinyong Wang; Guandong Xu; Quoc Viet Hung Nguyen; |
| 56 | IR Evaluation and Learning in The Presence of Forbidden Documents Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work we propose nDCGf, a novel extension of the nDCGmin metric[14], which measures both ranking and filtering quality of the search results. |
David Carmel; Nachshon Cohen; Amir Ingber; Elad Kravi; |
| 57 | Human Preferences As Dueling Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We frame the problem of finding best items as a dueling bandits problem. |
Xinyi Yan; Chengxi Luo; Charles L. A. Clarke; Nick Craswell; Ellen M. Voorhees; Pablo Castells; |
| 58 | A Flexible Framework for Offline Effectiveness Metrics Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here we introduce a user behavior framework that extends the C/W/L family. |
Alistair Moffat; Joel Mackenzie; Paul Thomas; Leif Azzopardi; |
| 59 | Ranking Interruptus: When Truncated Rankings Are Better and How to Measure That Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we provide both theoretical and experimental contributions. We first define formal properties to analyze how effectiveness metrics behave when evaluating truncated rankings. |
Enrique Amigó; Stefano Mizzaro; Damiano Spina; |
| 60 | Offline Retrieval Evaluation Without Evaluation Metrics Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose recall-paired preference (RPP), a metric-free evaluation method based on directly computing a preference between ranked lists. |
Fernando Diaz; Andres Ferraro; |
| 61 | Information Need Awareness: An EEG Study Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper aims to investigate this research question by inferring the variability of brain activity based on the contrast of a state of IN with the two other (no-IN) scenarios. |
Dominika Michalkova; Mario Parra-Rodriguez; Yashar Moshfeghi; |
| 62 | Offline Evaluation of Ranked Lists Using Parametric Estimation of Propensities Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A majority of offline evaluation approaches invoke the well studied inverse propensity weighting to adjust for biases inherent in logged data. In this paper, we propose the use of parametric estimates for these propensities. |
Vishwa Vinay; Manoj Kilaru; David Arbour; |
| 63 | Why Don’t You Click: Understanding Non-Click Results in Web Search with Brain Signals Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we analyze the differences in brain signals between the examination of non-click search results in different usefulness levels. |
Ziyi Ye; Xiaohui Xie; Yiqun Liu; Zhihong Wang; Xuancheng Li; Jiaji Li; Xuesong Chen; Min Zhang; Shaoping Ma; |
| 64 | Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we conceptualized three novel properties that model the quality of the explanations (linking interaction recency, shared entity popularity, and explanation type diversity) and proposed re-ranking approaches able to optimize for these properties. |
Giacomo Balloccu; Ludovico Boratto; Gianni Fenu; Mirko Marras; |
| 65 | Explainable Legal Case Matching Via Inverse Optimal Transport-based Rationale Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This task has a high demand on the explainability of matching results because of its critical impacts on downstream applications — the matched legal cases may provide supportive evidence for the judgments of target cases and thus influence the fairness and justice of legal decisions. Focusing on this challenging task, we propose a novel and explainable method, namely IOT-Match, with the help of computational optimal transport, which formulates the legal case matching problem as an inverse optimal transport (IOT) problem. |
Weijie Yu; Zhongxiang Sun; Jun Xu; Zhenhua Dong; Xu Chen; Hongteng Xu; Ji-Rong Wen; |
| 66 | Towards Explainable Search Results: A Listwise Explanation Generator Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Furthermore, these models provide a single query aspect for each document, even though documents often cover multiple query aspects. To overcome these limitations, we propose LiEGe, an approach that jointly explains all documents in a search result list. |
Puxuan Yu; Razieh Rahimi; James Allan; |
| 67 | Explainable Fairness in Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the problem ofexplainable fairness, which helps to gain insights about why a system is fair or unfair, and guides the design of fair recommender systems with a more informed and unified methodology. |
Yingqiang Ge; Juntao Tan; Yan Zhu; Yinglong Xia; Jiebo Luo; Shuchang Liu; Zuohui Fu; Shijie Geng; Zelong Li; Yongfeng Zhang; |
| 68 | PEVAE: A Hierarchical VAE for Personalized Explainable Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, we aim to extend VAE to explainable recommendation. |
Zefeng Cai; Zerui Cai; |
| 69 | Joint Multisided Exposure Fairness for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation. |
Haolun Wu; Bhaskar Mitra; Chen Ma; Fernando Diaz; Xue Liu; |
| 70 | Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we present a novel way of representing permutation distributions, based on the notion of permutation graphs. |
Ali Vardasbi; Fatemeh Sarvi; Maarten de Rijke; |
| 71 | Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We aim to bridge the gap between theoretical and practical ap-plication of these metrics. In this paper we describe several fair ranking metrics from the existing literature in a common notation, enabling direct comparison of their approaches and assumptions, and empirically compare them on the same experimental setup and data sets in the context of three information access tasks. |
Amifa Raj; Michael D. Ekstrand; |
| 72 | Optimizing Generalized Gini Indices for Fairness in Rankings Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the domain of inequality measurement in economics, this paper explores the use of generalized Gini welfare functions (GGFs) as a means to specify the normative criterion that recommender systems should optimize for. |
Virginie Do; Nicolas Usunier; |
| 73 | Pareto-Optimal Fairness-Utility Amortizations in Rankings with A DBN Exposure Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We lay out the structure of a new geometrical object (the DBN-expohedron), and propose for it a Carathéodory decomposition algorithm of complexity $O(n^3)$, where n is the number of documents to rank. |
Till Kletti; Jean-Michel Renders; Patrick Loiseau; |
| 74 | Fairness of Exposure in Light of Incomplete Exposure Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we discuss how to approach fairness of exposure in cases where the policy contains rankings of which, due to inter-item dependencies, we cannot reliably estimate the exposure distribution. |
Maria Heuss; Fatemeh Sarvi; Maarten de Rijke; |
| 75 | CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side in a joint objective framework. |
Mohammadmehdi Naghiaei; Hossein A. Rahmani; Yashar Deldjoo; |
| 76 | Structure and Semantics Preserving Document Representations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose here a holistic approach to learning document representations by integrating intra-document content with inter-document relations. |
Natraj Raman; Sameena Shah; Manuela Veloso; |
| 77 | Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A shortcoming of this approach is that users often do not know which query will provide the best retrieval performance on the current information retrieval system, meaning that any query autocompletion methods trained to mimic user behavior can lead to suboptimal query suggestions. To overcome this limitation, we propose a new approach that explicitly optimizes the query suggestions for downstream retrieval performance. |
Adam Block; Rahul Kidambi; Daniel N. Hill; Thorsten Joachims; Inderjit S. Dhillon; |
| 78 | Risk-Sensitive Deep Neural Learning to Rank Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the risk-sensitive measures described in the literature have a non-smooth behavior, making them difficult, if not impossible, to be optimized by DNNs. In this work we solve this difficult problem by proposing a family of new loss functions — \riskloss\ — that support a smooth risk-sensitive optimization. |
Pedro Henrique Silva Rodrigues; Daniel Xavier Sousa; Thierson Couto Rosa; Marcos André Gonçalves; |
| 79 | RankFlow: Joint Optimization of Multi-Stage Cascade Ranking Systems As Flows Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper provides an elaborate analysis of this commonly used solution to reveal its limitations. By studying the essence of cascade ranking, we propose a joint training framework named RankFlow to alleviate the SSB issue and exploit the interactions between the cascade rankers, which is the first systematic solution for this topic. |
Jiarui Qin; Jiachen Zhu; Bo Chen; Zhirong Liu; Weiwen Liu; Ruiming Tang; Rui Zhang; Yong Yu; Weinan Zhang; |
| 80 | LoL: A Comparative Regularization Loss Over Query Reformulation Losses for Pseudo-Relevance Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Ideally, if a PRF model can distinguish between irrelevant and relevant information in the feedback, the more feedback documents there are, the better the revised query will be. To bridge this gap, we propose the Loss-over-Loss (LoL) framework to compare the reformulation losses between different revisions of the same query during training. |
Yunchang Zhu; Liang Pang; Yanyan Lan; Huawei Shen; Xueqi Cheng; |
| 81 | Few-Shot Stance Detection Via Target-Aware Prompt Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, inspired by the potential capability of pre-trained language models (PLMs) serving as knowledge bases and few-shot learners, we propose to introduce prompt-based fine-tuning for stance detection. |
Yan Jiang; Jinhua Gao; Huawei Shen; Xueqi Cheng; |
| 82 | Pre-train A Discriminative Text Encoder for Dense Retrieval Via Contrastive Span Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, we argue that 1) it is not discriminative to decode all the input texts and, 2) even a weak decoder has the bypass effect on the encoder. Therefore, in this work, we introduce a novel contrastive span prediction task to pre-train the encoder alone, but still retain the bottleneck ability of the autoencoder. |
Xinyu Ma; Jiafeng Guo; Ruqing Zhang; Yixing Fan; Xueqi Cheng; |
| 83 | Co-clustering Interactions Via Attentive Hypergraph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, they only model and leverage part of the information in real entire interactions, i.e., either decompose the entire interaction into several pair-wise sub-interactions for simplification, or only focus on clustering some specific types of objects, which limits the performance and explainability of clustering. To tackle this issue, we propose to Co-cluster the Interactions via Attentive Hypergraph neural network (CIAH). |
Tianchi Yang; Cheng Yang; Luhao Zhang; Chuan Shi; Maodi Hu; Huaijun Liu; Tao Li; Dong Wang; |
| 84 | Adaptable Text Matching Via Meta-Weight Regulator Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, adapting a model trained on the abundant source data to a few-shot target dataset or task is challenging. To tackle this challenge, we propose a Meta-Weight Regulator (MWR), which is a meta-learning approach that learns to assign weights to the source examples based on their relevance to the target loss. |
Bo Zhang; Chen Zhang; Fang Ma; Dawei Song; |
| 85 | Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, it is challenging to precisely conduct inductive relation prediction as there exists requirements of entity-independent relation modeling and discrete logical reasoning for interoperability. To this end, we propose a novel model ConGLR to incorporate context graph with logical reasoning. |
Qika Lin; Jun Liu; Fangzhi Xu; Yudai Pan; Yifan Zhu; Lingling Zhang; Tianzhe Zhao; |
| 86 | Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a hybrid transformer with multi-level fusion to address those issues. |
Xiang Chen; Ningyu Zhang; Lei Li; Shumin Deng; Chuanqi Tan; Changliang Xu; Fei Huang; Luo Si; Huajun Chen; |
| 87 | Re-thinking Knowledge Graph Completion Evaluation from An Information Retrieval Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Due to the incomplete nature of the large-scale knowledge bases, such an entity ranking setting is likely affected by unlabelled top-ranked positive examples, raising questions on whether the current evaluation protocol is sufficient to guarantee a fair comparison of KGC systems. To this end, this paper presents a systematic study on whether and how the label sparsity affects the current KGC evaluation with the popular micro metrics. |
Ying Zhou; Xuanang Chen; Ben He; Zheng Ye; Le Sun; |
| 88 | Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings. |
Mingyang Chen; Wen Zhang; Yushan Zhu; Hongting Zhou; Zonggang Yuan; Changliang Xu; Huajun Chen; |
| 89 | Multimodal Entity Linking with Gated Hierarchical Fusion and Contrastive Training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Consequently, we conceive the idea of introducing valuable information of other modalities, and propose a novel multimodal entity linking method with gated hierarchical multimodal fusion and contrastive training (GHMFC). |
Peng Wang; Jiangheng Wu; Xiaohang Chen; |
| 90 | CRET: Cross-Modal Retrieval Transformer for Efficient Text-Video Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel EDB method CRET (Cross-modal REtrieval Transformer), which not only demonstrates promising efficiency in retrieval tasks, but also achieves better accuracy than existing MDB methods. |
Kaixiang Ji; Jiajia Liu; Weixiang Hong; Liheng Zhong; Jian Wang; Jingdong Chen; Wei Chu; |
| 91 | Multimodal Disentanglement Variational AutoEncoders for Zero-Shot Cross-Modal Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Generally, these methods largely rely on auxiliary semantic embeddings for knowledge transfer across classes and unconsciously neglect the effect of the data reconstruction manner in the adopted generative model. To address this issue, we propose a novel ZS-CMR model termed Multimodal Disentanglement Variational AutoEncoders (MDVAE), which consists of two coupled disentanglement variational autoencoders (DVAEs) and a fusion-exchange VAE (FVAE). |
Jialin Tian; Kai Wang; Xing Xu; Zuo Cao; Fumin Shen; Heng Tao Shen; |
| 92 | CenterCLIP: Token Clustering for Efficient Text-Video Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, to reduce the number of redundant video tokens, we design a multi-segment token clustering algorithm to find the most representative tokens and drop the non-essential ones. |
Shuai Zhao; Linchao Zhu; Xiaohan Wang; Yi Yang; |
| 93 | Bit-aware Semantic Transformer Hashing for Multi-modal Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For solving these problems, in this paper, we propose a Bit-aware Semantic Transformer Hashing (BSTH) framework to excavate bit-wise semantic concepts and simultaneously align the heterogeneous modalities for multi-modal hash learning on the concept-level. |
Wentao Tan; Lei Zhu; Weili Guan; Jingjing Li; Zhiyong Cheng; |
| 94 | V2P: Vision-to-Prompt Based Multi-Modal Product Summary Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Although existing methods have achieved great success, they still suffer from three key limitations: 1) overlook the benefit of pre-training, 2) lack the representation-level supervision, and 3) ignore the diversity of the seller-generated data. To address these limitations, in this work, we propose a Vision-to-Prompt based multi-modal product summary generation framework, dubbed as V2P, where a Generative Pre-trained Language Model (GPLM) is adopted as the backbone. |
Xuemeng Song; Liqiang Jing; Dengtian Lin; Zhongzhou Zhao; Haiqing Chen; Liqiang Nie; |
| 95 | Learn from Unlabeled Videos for Near-duplicate Video Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In addition, most retrieval systems are based on frame-level features for video similarity searching, making it expensive both storage wise and search wise. To address the above issues, we propose a video representation learning (VRL) approach to effectively address the above shortcomings. |
Xiangteng He; Yulin Pan; Mingqian Tang; Yiliang Lv; Yuxin Peng; |
| 96 | Progressive Learning for Image Retrieval with Hybrid-Modality Queries Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we decompose the CTI-IR task into a three-stage learning problem to progressively learn the complex knowledge for image retrieval with hybrid-modality queries. |
Yida Zhao; Yuqing Song; Qin Jin; |
| 97 | You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Previous methods tend to perform self-modal learning and cross-modal interaction in a coarse manner, which neglect fine-grained clues contained in video content, query context, and their alignment. To this end, we propose a novel Multi-Granularity Perception Network (MGPN) that perceives intra-modality and inter-modality information at a multi-granularity level. |
Xin Sun; Xuan Wang; Jialin Gao; Qiong Liu; Xi Zhou; |
| 98 | Video Moment Retrieval from Text Queries Via Single Frame Annotation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we look closer into the annotation process and propose a new paradigm called "glance annotation". |
Ran Cui; Tianwen Qian; Pai Peng; Elena Daskalaki; Jingjing Chen; Xiaowei Guo; Huyang Sun; Yu-Gang Jiang; |
| 99 | HTKG: Deep Keyphrase Generation with Neural Hierarchical Topic Guidance Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we focus on how to effectively exploit the hierarchical topic to improve the keyphrase generation performance (HTKG). |
Yuxiang Zhang; Tao Jiang; Tianyu Yang; Xiaoli Li; Suge Wang; |
| 100 | Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Also, it is demanding to uncover the logical structures of the text and further fuse the discrete logic to the continuous text embedding. To tackle the above issues, we propose an end-to-end model Logiformer which utilizes a two-branch graph transformer network for logical reasoning of text. |
Fangzhi Xu; Jun Liu; Qika Lin; Yudai Pan; Lingling Zhang; |
| 101 | Personalized Abstractive Opinion Tagging Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we focus on the task of personalized abstractive opinion tagging. |
Mengxue Zhao; Yang Yang; Miao Li; Jingang Wang; Wei Wu; Pengjie Ren; Maarten de Rijke; Zhaochun Ren; |
| 102 | Contrastive Learning with Hard Negative Entities for Entity Set Expansion Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Although previous ESE methods have achieved great progress, most of them still lack the ability to handle hard negative entities (i.e., entities that are difficult to distinguish from the target entities), since two entities may or may not belong to the same semantic class based on different granularity levels we analyze on. To address this challenge, we devise an entity-level masked language model with contrastive learning to refine the representation of entities. |
Yinghui Li; Yangning Li; Yuxin He; Tianyu Yu; Ying Shen; Hai-Tao Zheng; |
| 103 | Unifying Cross-lingual Summarization and Machine Translation with Compression Rate Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel task, Cross-lingual Summarization with Compression rate (CSC), to benefit Cross-Lingual Summarization by large-scale Machine Translation corpus. |
Yu Bai; Heyan Huang; Kai Fan; Yang Gao; Yiming Zhu; Jiaao Zhan; Zewen Chi; Boxing Chen; |
| 104 | What Makes The Story Forward?: Inferring Commonsense Explanations As Prompts for Future Event Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel explainable FEG framework, Coep. |
Li Lin; Yixin Cao; Lifu Huang; Shu’Ang Li; Xuming Hu; Lijie Wen; Jianmin Wang; |
| 105 | A Dual-Expert Framework for Event Argument Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the Mixture of Experts (MOE), we propose a Routing-Balanced Dual Expert Framework (RBDEF), which divides all roles into "head" and "tail" two scopes and assigns the classifications of head and tail roles to two separate experts. |
Rui Li; Wenlin Zhao; Cheng Yang; Sen Su; |
| 106 | CorED: Incorporating Type-level and Instance-level Correlations for Fine-grained Event Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper simultaneously incorporates both the type-level and instance-level event correlations, and proposes a novel framework, termed as CorED. |
Jiawei Sheng; Rui Sun; Shu Guo; Shiyao Cui; Jiangxia Cao; Lihong Wang; Tingwen Liu; Hongbo Xu; |
| 107 | Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the data sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. |
Nicholas Lim; Bryan Hooi; See-Kiong Ng; Yong Liang Goh; Renrong Weng; Rui Tan; |
| 108 | GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Instead, we propose a user-agnostic global trajectory flow map and a novel Graph Enhanced Transformer model (GETNext) to better exploit the extensive collaborative signals for a more accurate next POI prediction, and alleviate the cold start problem in the meantime. |
Song Yang; Jiamou Liu; Kaiqi Zhao; |
| 109 | Learning Graph-based Disentangled Representations for Next POI Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel Disentangled Representation-enhanced Attention Network (DRAN) for next POI recommendation, which leverages the disentangled representations to explicitly model different aspects and corresponding influence for representing a POI more precisely. |
Zhaobo Wang; Yanmin Zhu; Haobing Liu; Chunyang Wang; |
| 110 | ProFairRec: Provider Fairness-aware News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a provider fairness-aware news recommendation framework (named ProFairRec), which can learn news recommendation models fair for different news providers from biased user data. |
Tao Qi; Fangzhao Wu; Chuhan Wu; Peijie Sun; Le Wu; Xiting Wang; Yongfeng Huang; Xing Xie; |
| 111 | CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Besides, users’ out-of-town travel behaviors are affected not only by their personalized preferences but heavily by others’ travel behaviors. To this end, we propose a Crowd-Aware Pre-Travel Out-of-town Recommendation framework (CAPTOR) consisting of two major modules: spatial-affined conditional random field (SA-CRF) and crowd behavior memory network (CBMN). |
Haoran Xin; Xinjiang Lu; Nengjun Zhu; Tong Xu; Dejing Dou; Hui Xiong; |
| 112 | Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Previous works tend to formulate session-based recommendation as a next item prediction task, while they neglect the implicit feedback from user behaviors, which indicates what users really like or dislike. Hence, we propose a comprehensive framework to model user behaviors through positive feedback (i.e., the articles they spend more time on) and negative feedback (i.e., the articles they choose to skip without clicking in). |
Shansan Gong; Kenny Q. Zhu; |
| 113 | A Non-Factoid Question-Answering Taxonomy Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work presents the first comprehensive taxonomy of NFQ categories and the expected structure of answers. |
Valeriia Bolotova; Vladislav Blinov; Falk Scholer; W. Bruce Croft; Mark Sanderson; |
| 114 | QUASER: Question Answering with Scalable Extractive Rationalization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce unsupervised generative models to extract dual-purpose rationales, which must not only be able to support a subsequent answer prediction, but also support a reproduction of the input query. |
Asish Ghoshal; Srinivasan Iyer; Bhargavi Paranjape; Kushal Lakhotia; Scott Wen-tau Yih; Yashar Mehdad; |
| 115 | PTAU: Prompt Tuning for Attributing Unanswerable Questions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, a capable model has to carefully appreciate the causes, and then, judiciously contrast the question with its context, in order to cast it into the right cause. In response to the challenges, we present PTAU, which refers to and implements a high-level human reading strategy such that one reads with anticipation. |
Jinzhi Liao; Xiang Zhao; Jianming Zheng; Xinyi Li; Fei Cai; Jiuyang Tang; |
| 116 | DGQAN: Dual Graph Question-Answer Attention Networks for Answer Selection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the redundancy and lengthiness issues of crowd-sourced answers limit the performance of answer selection, thus leading to difficulties in reading or even misunderstandings for community users. To solve these problems, we propose the dual graph question-answer attention networks (DGQAN) for answer selection task. |
Haitian Yang; Xuan Zhao; Yan Wang; Min Li; Wei Chen; Weiqing Huang; |
| 117 | ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we first gain a data-driven understanding of users’ repeat consumption behavior through an empirical study on six public and proprietary grocery shopping transaction datasets. |
Mozhdeh Ariannezhad; Sami Jullien; Ming Li; Min Fang; Sebastian Schelter; Maarten de Rijke; |
| 118 | User-controllable Recommendation Against Filter Bubbles Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This work proposes a new recommender prototype called User-Controllable Recommender System (UCRS), which enables users to actively control the mitigation of filter bubbles. |
Wenjie Wang; Fuli Feng; Liqiang Nie; Tat-Seng Chua; |
| 119 | Unify Local and Global Information for Top-N Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To tackle this research gap, we propose a novel duet representation learning framework named KADM to fuse local information (user-item interaction data) and global information (external knowledge graph) for the top-N recommendation, which is composed of two separate sub-models. |
Xiaoming Liu; Shaocong Wu; Zhaohan Zhang; Chao Shen; |
| 120 | Less Is More: Reweighting Important Spectral Graph Features for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Based on the two findings above, we propose a new GCN learning scheme for recommendation by replacing neihgborhood aggregation with a simple yet effective Graph Denoising Encoder (GDE), which acts as a band pass filter to capture important graph features. |
Shaowen Peng; Kazunari Sugiyama; Tsunenori Mine; |
| 121 | A Review-aware Graph Contrastive Learning Framework for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, in this paper, we propose a novel Review-aware Graph Contrastive Learning (RGCL) framework for review-based recommendation. |
Jie Shuai; Kun Zhang; Le Wu; Peijie Sun; Richang Hong; Meng Wang; Yong Li; |
| 122 | Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Meanwhile, we reveal that the graph augmentations, which used to be considered necessary, just play a trivial role. Based on this finding, we propose a simple CL method which discards the graph augmentations and instead adds uniform noises to the embedding space for creating contrastive views. |
Junliang Yu; Hongzhi Yin; Xin Xia; Tong Chen; Lizhen Cui; Quoc Viet Hung Nguyen; |
| 123 | AutoLossGen: Automatic Loss Function Generation for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, inspired by the recent development of automated machine learning, we propose an automatic loss function generation framework, AutoLossGen, which is able to generate loss functions directly constructed from basic mathematical operators without prior knowledge on loss structure. |
Zelong Li; Jianchao Ji; Yingqiang Ge; Yongfeng Zhang; |
| 124 | Locality-Sensitive State-Guided Experience Replay Optimization for Sparse Rewards in Online Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, they adapt poorly to the complex environment of online recommender systems and are inefficient in learning an optimal strategy from past experience. As a step to filling this gap, we propose a novel state-aware experience replay model, in which the agent selectively discovers the most relevant and salient experiences and is guided to find the optimal policy for online recommendations. |
Xiaocong Chen; Lina Yao; Julian McAuley; Weili Guan; Xiaojun Chang; Xianzhi Wang; |
| 125 | User-Aware Multi-Interest Learning for Candidate Matching in Recommenders Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Hence, we are interested in exploiting the benefit of user profile in multi-interest learning to enhance candidate matching performance. To this end, a user-aware multi-interest learning framework (named UMI) is proposed in this paper to exploit both user profile and behavior information for candidate matching. |
Zheng Chai; Zhihong Chen; Chenliang Li; Rong Xiao; Houyi Li; Jiawei Wu; Jingxu Chen; Haihong Tang; |
| 126 | Multi-Level Interaction Reranking with User Behavior History Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Lastly, estimating the reranking score on the ordered initial list before reranking may lead to the early scoring problem, thereby yielding suboptimal reranking performance. To address the above issues, we propose a framework named Multi-level Interaction Reranking (MIR). |
Yunjia Xi; Weiwen Liu; Jieming Zhu; Xilong Zhao; Xinyi Dai; Ruiming Tang; Weinan Zhang; Rui Zhang; Yong Yu; |
| 127 | Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here we propose a new learning paradigm—namely Prompt-Based Reinforcement Learning (PRL)—for the offline training of RL-based recommendation agents. |
Xin Xin; Tiago Pimentel; Alexandros Karatzoglou; Pengjie Ren; Konstantina Christakopoulou; Zhaochun Ren; |
| 128 | Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. |
Ding Zou; Wei Wei; Xian-Ling Mao; Ziyang Wang; Minghui Qiu; Feida Zhu; Xin Cao; |
| 129 | MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose meta graph enhanced off-policy learning (MGPolicy), which is the first recommendation model for correcting the off-policy bias via contextual information. |
Xiangmeng Wang; Qian Li; Dianer Yu; Zhichao Wang; Hongxu Chen; Guandong Xu; |
| 130 | Privacy-Preserving Synthetic Data Generation for Recommendation Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: The problem of privacy leakage still exists when directly sharing the private user interaction data with organizations or releasing them to the public. To address this problem, in this paper, we present a User Privacy Controllable Synthetic Data Generation model (short for UPC-SDG), which generates synthetic interaction data for users based on their privacy preferences. |
Fan Liu; Zhiyong Cheng; Huilin Chen; Yinwei Wei; Liqiang Nie; Mohan Kankanhalli; |
| 131 | HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a new model, called Hierarchy-Aware Knowledge Gated Network (HAKG), to tackle the aforementioned problems. |
Yuntao Du; Xinjun Zhu; Lu Chen; Baihua Zheng; Yunjun Gao; |
| 132 | Alleviating Spurious Correlations in Knowledge-aware Recommendations Through Counterfactual Generator Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: It refers to a knowledge fact that appears causal to the user behaviors (inferred by the recommender) but is not in fact. For tackling this issue, we present a novel approach to discovering and alleviating the potential spurious correlations from a counterfactual perspective. |
Shanlei Mu; Yaliang Li; Wayne Xin Zhao; Jingyuan Wang; Bolin Ding; Ji-Rong Wen; |
| 133 | Self-Guided Learning to Denoise for Robust Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we thoroughly investigate the memorization effect of recommendation models, and propose a new denoising paradigm, i.e., Self-Guided Denoising Learning (SGDL), which is able to collect memorized interactions at the early stage of the training (i.e., ”noise-resistant” period), and leverage those data as denoising signals to guide the following training (i.e., ”noise-sensitive” period) of the model in a meta-learning manner. |
Yunjun Gao; Yuntao Du; Yujia Hu; Lu Chen; Xinjun Zhu; Ziquan Fang; Baihua Zheng; |
| 134 | Deployable and Continuable Meta-learning-Based Recommender System with Fast User-Incremental Updates Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a d eployable and c ontinuable m eta-learning-based r ecommendation (DCMR) approach, which can achieve fast user-incremental updating with task replay and first-order gradient descent. |
Renchu Guan; Haoyu Pang; Fausto Giunchiglia; Ximing Li; Xuefeng Yang; Xiaoyue Feng; |
| 135 | Knowledge Graph Contrastive Learning for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Specifically, we propose a knowledge graph augmentation schema to suppress KG noise in information aggregation, and derive more robust knowledge-aware representations for items. |
Yuhao Yang; Chao Huang; Lianghao Xia; Chenliang Li; |
| 136 | CharacterBERT and Self-Teaching for Improving The Robustness of Dense Retrievers on Queries with Typos Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Current dense retrievers are not robust to out-of-domain and outlier queries, i.e. their effectiveness on these queries is much poorer than what one would expect. In this paper, we consider a specific instance of such queries: queries that contain typos. |
Shengyao Zhuang; Guido Zuccon; |
| 137 | Entity-aware Transformers for Entity Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper investigates the following question: Do BERT-based entity retrieval models benefit from additional entity information stored in knowledge graphs? To address this research question, we map entity embeddings into the same input space as a pre-trained BERT model and inject these entity embeddings into the BERT model. |
Emma J. Gerritse; Faegheh Hasibi; Arjen P. de Vries; |
| 138 | BERT-ER: Query-specific BERT Entity Representations for Entity Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present BERT Entity Representations (BERT-ER) which are query-specific vector representations of entities obtained from text that describes how an entity is relevant for a query. |
Shubham Chatterjee; Laura Dietz; |
| 139 | H-ERNIE: A Multi-Granularity Pre-Trained Language Model for Web Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In particular, we propose a novel H-ERNIE framework, which includes a query-document analysis component and a hierarchical ranking component. |
Xiaokai Chu; Jiashu Zhao; Lixin Zou; Dawei Yin; |
| 140 | Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To leverage a reliable knowledge, we propose a novel knowledge graph distillation method and obtain a knowledge meta graph as the bridge between query and passage. |
Qian Dong; Yiding Liu; Suqi Cheng; Shuaiqiang Wang; Zhicong Cheng; Shuzi Niu; Dawei Yin; |
| 141 | Webformer: Pre-training with Web Pages for Information Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to leverage large-scale web pages and their DOM (Document Object Model) tree structures to pre-train models for information retrieval. |
Yu Guo; Zhengyi Ma; Jiaxin Mao; Hongjin Qian; Xinyu Zhang; Hao Jiang; Zhao Cao; Zhicheng Dou; |
| 142 | Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose Distill-VQ, which unifies the learning of IVF and PQ within a knowledge distillation framework. |
Shitao Xiao; Zheng Liu; Weihao Han; Jianjin Zhang; Defu Lian; Yeyun Gong; Qi Chen; Fan Yang; Hao Sun; Yingxia Shao; Xing Xie; |
| 143 | Axiomatically Regularized Pre-training for Ad Hoc Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To shed light on this research question, we propose a novel pre-training method with \underlineA xiomatic \underlineRe gularization for ad hoc \underlineS earch (ARES). |
Jia Chen; Yiqun Liu; Yan Fang; Jiaxin Mao; Hui Fang; Shenghao Yang; Xiaohui Xie; Min Zhang; Shaoping Ma; |
| 144 | Automatic Expert Selection for Multi-Scenario and Multi-Task Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel Automatic Expert Selection framework for Multi-scenario and Multi-task search, named AESM2. |
Xinyu Zou; Zhi Hu; Yiming Zhao; Xuchu Ding; Zhongyi Liu; Chenliang Li; Aixin Sun; |
| 145 | Tag-assisted Multimodal Sentiment Analysis Under Uncertain Missing Modalities Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To this end, in this paper, we propose a Tag-Assisted Transformer Encoder (TATE) network to handle the problem of missing uncertain modalities. |
Jiandian Zeng; Tianyi Liu; Jiantao Zhou; |
| 146 | Mutual Disentanglement Learning for Joint Fine-Grained Sentiment Classification and Controllable Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Most of the existing work solves the FGSC and the FGSG tasks in isolation, while ignoring the complementary benefits in between. This paper combines FGSC and FGSG as a joint dual learning system, encouraging them to learn the advantages from each other. |
Hao Fei; Chenliang Li; Donghong Ji; Fei Li; |
| 147 | Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. |
Kai Zhang; Qi Liu; Zhenya Huang; Mingyue Cheng; Kun Zhang; Mengdi Zhang; Wei Wu; Enhong Chen; |
| 148 | Aspect Feature Distillation and Enhancement Network for Aspect-based Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, the cross-entropy loss lacks discriminative learning of features, which makes it difficult to exploit the implicit information of intra-class compactness and inter-class separability. To overcome these challenges, we propose an Aspect Feature Distillation and Enhancement Network (AFDEN) for the ABSA task. |
Rui Liu; Jiahao Cao; Nannan Sun; Lei Jiang; |
| 149 | IAOTP: An Interactive End-to-End Solution for Aspect-Opinion Term Pairs Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Some existing studies heavily relied on the annotated aspect terms and/or opinion terms, or adopted external knowledge/resources to figure out the task. Therefore, in this study, we propose a novel end-to-end solution, called an Interactive AOTP (IAOTP) model, for exploring AOTP. |
Ambreen Nazir; Yuan Rao; |
| 150 | Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a new training and inference paradigm, termed as Ada-Ranker, to address the challenges of dynamic online serving. |
Xinyan Fan; Jianxun Lian; Wayne Xin Zhao; Zheng Liu; Chaozhuo Li; Xing Xie; |
| 151 | Decoupled Side Information Fusion for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Also, it involves mixed correlations among the different heterogeneous information resources, which brings extra disturbance to attention calculation. Motivated by this, we propose Decoupled Side Information Fusion for Sequential Recommendation (DIF-SR), which moves the side information from the input to the attention layer and decouples the attention calculation of various side information and item representation. |
Yueqi Xie; Peilin Zhou; Sunghun Kim; |
| 152 | Multi-Agent RL-based Information Selection Model for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce a Multi-Agent RL-based Information S election Model (named MARIS) to explore an effective collaboration between different kinds of auxiliary information and sequential signals in an automatic way. |
Kaiyuan Li; Pengfei Wang; Chenliang Li; |
| 153 | When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To this end, in this paper, we propose a unified multi-grained neural model (named MGNM) via a combination of multi-interest learning and graph convolutional aggregation. |
Yu Tian; Jianxin Chang; Yanan Niu; Yang Song; Chenliang Li; |
| 154 | Multi-Behavior Sequential Transformer Recommender Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite the great successes, existing methods seem to have limitations on modelling heterogeneous item-level multi-behavior dependencies, capturing diverse multi-behavior sequential dynamics, or alleviating data sparsity problems. In this paper, we show it is possible to derive a framework to address all the above three limitations. |
Enming Yuan; Wei Guo; Zhicheng He; Huifeng Guo; Chengkai Liu; Ruiming Tang; |
| 155 | Determinantal Point Process Likelihoods for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We argue that such objective functions suffer from two inherent drawbacks: i) the dependencies among elements of a sequence are overlooked in these loss formulations; ii) instead of balancing accuracy (quality) and diversity, only generating accurate results has been over emphasized. We therefore propose two new loss functions based on the Determinantal Point Process (DPP) likelihood, that can be adaptively applied to estimate the subsequent item or items. |
Yuli Liu; Christian Walder; Lexing Xie; |
| 156 | Thinking Inside The Box: Learning Hypercube Representations for Group Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a novel representation of groups via the notion of hypercubes, which are subspaces containing innumerable points in the vector space. |
Tong Chen; Hongzhi Yin; Jing Long; Quoc Viet Hung Nguyen; Yang Wang; Meng Wang; |
| 157 | An Attribute-Driven Mirror Graph Network for Session-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a novel mirror graph enhanced neural model for session-based recommendation (MGS), to exploit item attribute information over item embeddings for more accurate preference estimation. |
Siqi Lai; Erli Meng; Fan Zhang; Chenliang Li; Bin Wang; Aixin Sun; |
| 158 | Price DOES Matter!: Modeling Price and Interest Preferences in Session-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Firstly, it is hard to handle heterogeneous information from various features of items to capture users’ price preferences. Secondly, it is difficult to model the complex relations between price and interest preferences in determining user choices. To address the above challenges, we propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation. |
Xiaokun Zhang; Bo Xu; Liang Yang; Chenliang Li; Fenglong Ma; Haifeng Liu; Hongfei Lin; |
| 159 | AutoGSR: Neural Architecture Search for Graph-based Session Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Nevertheless, due to the highly diverse types of potential information in sessions, existing GNNs-based methods perform differently on different session datasets, leading to the need for efficient design of neural networks adapted to various session recommendation scenarios. To address this problem, we propose Automated neural architecture search for Graph-based Session Recommendation, namely AutoGSR, a framework that provides a practical and general solution to automatically find the optimal GNNs-based session recommendation model. |
Jingfan Chen; Guanghui Zhu; Haojun Hou; Chunfeng Yuan; Yihua Huang; |
| 160 | Multi-Faceted Global Item Relation Learning for Session-Based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In view of the limitations of pioneering studies that explore collaborative information from other sessions, in this paper we propose a new direction to enhance session representations by learning multi-faceted session-independent global item relations. |
Qilong Han; Chi Zhang; Rui Chen; Riwei Lai; Hongtao Song; Li Li; |
| 161 | Towards Suicide Ideation Detection Through Online Conversational Context Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Moreover, psychological studies suggested that it is important to capture the fine-grained temporal irregularities in the release of vast volumes of comments, since suicidal users react quickly to online community support. Building on these limitations and psychological studies, we propose HCN, a Hyperbolic Conversation Network, which is a less user-intrusive method for suicide ideation detection. |
Ramit Sawhney; Shivam Agarwal; Atula Tejaswi Neerkaje; Nikolaos Aletras; Preslav Nakov; Lucie Flek; |
| 162 | Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e.g., posts that represent user views) into an appropriate disentangled latent space. |
Jinning Li; Huajie Shao; Dachun Sun; Ruijie Wang; Yuchen Yan; Jinyang Li; Shengzhong Liu; Hanghang Tong; Tarek Abdelzaher; |
| 163 | A Multitask Framework for Sentiment, Emotion and Sarcasm Aware Cyberbullying Detection from Multi-modal Code-Mixed Memes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The current work is the first attempt, to the best of our knowledge, in investigating the role of sentiment, emotion and sarcasm in identifying cyberbullying from multi-modal memes in a code-mixed language setting. |
Krishanu Maity; Prince Jha; Sriparna Saha; Pushpak Bhattacharyya; |
| 164 | Bias Mitigation for Toxicity Detection Via Sequential Decisions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we consider debiasing toxicity detection as a sequential decision-making process where different biases can be interdependent. |
Lu Cheng; Ahmadreza Mosallanezhad; Yasin N. Silva; Deborah L. Hall; Huan Liu; |
| 165 | A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Enlightened by Multiple Instance Learning (MIL) scheme, we propose a novel weakly supervised joint learning framework for rumor verification and stance detection which only requires bag-level class labels concerning the rumor’s veracity. |
Ruichao Yang; Jing Ma; Hongzhan Lin; Wei Gao; |
| 166 | On The Role of Relevance in Natural Language Processing Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents experimental results on two NLP tasks implemented as a two-stage cascading architecture. |
Artsiom Sauchuk; James Thorne; Alon Halevy; Nicola Tonellotto; Fabrizio Silvestri; |
| 167 | Learning to Enrich Query Representation with Pseudo-Relevance Feedback for Cross-lingual Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Two of the challenges are incorporating feedback from long documents, and cross-language knowledge transfer. To address these challenges, we propose a novel neural CLIR architecture, NCLPRF, capable of incorporating PRF feedback from multiple potentially long documents, which enables improvements to query representation in the shared semantic space between query and document languages. |
Ramraj Chandradevan; Eugene Yang; Mahsa Yarmohammadi; Eugene Agichtein; |
| 168 | CORE: Simple and Effective Session-based Recommendation Within Consistent Representation Space Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, session embedding learned by a non-linear encoder is usually not in the same representation space as item embeddings, resulting in the inconsistent prediction issue while recommending items. To address this issue, we propose a simple and effective framework named CORE, which can unify the representation space for both the encoding and decoding processes. |
Yupeng Hou; Binbin Hu; Zhiqiang Zhang; Wayne Xin Zhao; |
| 169 | Learning Disentangled Representations for Counterfactual Regression Via Mutual Information Minimization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, previous methods fail to obtain independent disentangled factors, which is a necessary condition for identifying treatment effect. In this paper, we propose Disentangled Representations for Counterfactual Regression via Mutual Information Minimization (MIM-DRCFR), which uses a multi-task learning framework to share information when learning the latent factors and incorporates MI minimization learning criteria to ensure the independence of these factors. |
Mingyuan Cheng; Xinru Liao; Quan Liu; Bin Ma; Jian Xu; Bo Zheng; |
| 170 | Multi-modal Graph Contrastive Learning for Micro-video Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, we argue that these approaches are not sufficient to encode item representations with multiple modalities, since the used methods cannot fully disentangle the users’ tastes on different modalities. To tackle this problem, we propose a novel learning method named Multi-Modal Graph Contrastive Learning (MMGCL), which aims to explicitly enhance multi-modal representation learning in a self-supervised learning manner. |
Zixuan Yi; Xi Wang; Iadh Ounis; Craig Macdonald; |
| 171 | RESETBERT4Rec: A Pre-training Model Integrating Time And User Historical Behavior for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, we propose a new model called the RE arrange S equence prE -training and T ime embedding model via BERT for sequential R ecommendation (RESETBERT4Rec ) \footnoteThis work was completed during JD internship., it further captures the information of the user’s whole click history by adding a rearrange sequence prediction task to the original BERT pre-training framework, while it integrates different views of time information. |
Qihang Zhao; |
| 172 | Item-Provider Co-learning for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, we propose two representation learning methods (single-steam and cross-stream) to learn comprehensive item and user representations based on the user’s historical item sequence and provider sequence. |
Lei Chen; Jingtao Ding; Min Yang; Chengming Li; Chonggang Song; Lingling Yi; |
| 173 | Re-weighting Negative Samples for Model-Agnostic Matching Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we find that the common practice that randomly sampling negative samples from the entire space and treating them equally is not an optimal choice, since the negative samples from different sub-spaces at different stages have different importance to a matching model. To address this issue, we propose a novel method named Unbiased Model-Agnostic Matching Approach (UMA2). |
Jiazhen Lou; Hong Wen; Fuyu Lv; Jing Zhang; Tengfei Yuan; Zhao Li; |
| 174 | Towards Event-level Causal Relation Identification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As a result, they either suffer from conflicts among causal relations predicted separately or require a set of additional constraints to resolve such conflicts. We propose to study this task in a more realistic setting, where event-level causality identification can be made. |
Chuang Fan; Daoxing Liu; Libo Qin; Yue Zhang; Ruifeng Xu; |
| 175 | Exploring Heterogeneous Data Lake Based on Unified Canonical Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study a novel keyword search. |
Qin Yuan; Ye Yuan; Zhenyu Wen; He Wang; Chen Chen; Guoren Wang; |
| 176 | Regulating Group Exposure for Item Providers in Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we consider providers as grouped based on a common characteristic in settings in which certain provider groups have low representation of items in the catalog and, thus, in the user interactions. |
Mirko Marras; Ludovico Boratto; Guilherme Ramos; Gianni Fenu; |
| 177 | L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we proposed a novel ensemble framework for the language task, termed L3E-HD, which enables efficient HDC on low-power edge devices. |
Fangxin Liu; Haomin Li; Xiaokang Yang; Li Jiang; |
| 178 | Neural Statistics for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper introduces a novel embedding technique called neural statistics that instead learns explicit semantics of categorical features by incorporating feature engineering as an innate prior into the deep architecture in an end-to-end manner. |
Yanhua Huang; Hangyu Wang; Yiyun Miao; Ruiwen Xu; Lei Zhang; Weinan Zhang; |
| 179 | Adversarial Graph Perturbations for Recommendations at Scale Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, perturbing graph structures has been far less studied in recommendations. To bridge this gap, we propose AdvGraph to model adversarial graph perturbations during the training of GNNs. |
Huiyuan Chen; Kaixiong Zhou; Kwei-Herng Lai; Xia Hu; Fei Wang; Hao Yang; |
| 180 | Graph Capsule Network with A Dual Adaptive Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Furthermore, though many GCNs variants have been proposed and obtained state-of-the-art results, they face the situation that much early information may be lost during the graph convolution step. To this end, we innovatively present an Graph Capsule Network with a Dual Adaptive Mechanism (DA-GCN) to tackle the above challenges. |
Xiangping Zheng; Xun Liang; Bo Wu; Yuhui Guo; Xuan Zhang; |
| 181 | Constrained Sequence-to-Tree Generation for Hierarchical Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we formulate HTC as a sequence generation task and introduce a sequence-to-tree framework (Seq2Tree) for modeling the hierarchical label structure. |
Chao Yu; Yi Shen; Yue Mao; |
| 182 | Relevance Under The Iceberg: Reasonable Prediction for Extreme Multi-label Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we aim to provide reasonable prediction for extreme multi-label classification with dynamic numbers of predicted labels. |
Jyun-Yu Jiang; Wei-Cheng Chang; Jiong Zhang; Cho-Jui Hsieh; Hsiang-Fu Yu; |
| 183 | Training Entire-Space Models for Target-oriented Opinion Words Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Abstract: Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA). Given a sentence and an aspect term occurring in the sentence, TOWE … |
Yuncong Li; Fang Wang; Sheng-Hua Zhong; |
| 184 | Zero-shot Query Contextualization for Conversational Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: While the proposed methods have proven effective, they still assume the availability of large-scale question resolution and conversational search datasets. To waive the dependency on the availability of such data, we adapt a pre-trained token-level dense retriever on ad-hoc search data to perform conversational search with no additional fine-tuning. |
Antonios Minas Krasakis; Andrew Yates; Evangelos Kanoulas; |
| 185 | EFLEC: Efficient Feature-LEakage Correction in GNN Based Recommendation Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose the accurate removal algorithm to generate the final embedding. |
Ishaan Kumar; Yaochen Hu; Yingxue Zhang; |
| 186 | Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to address the high latency and noise problems via Gating-adapted wavelet multiresolution analysis (Gama), which can effectively denoise the extremely long exposure sequence and adaptively capture the implied multi-dimension user interest with linear computational complexity. |
Xiaoxiao Xu; Zhiwei Fang; Qian Yu; Ruoran Huang; Chaosheng Fan; Yong Li; Yang He; Changping Peng; Zhangang Lin; Jingping Shao; Non Non; |
| 187 | Enhancing Top-N Item Recommendations By Peer Collaboration Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, this inevitably brings redundant neurons, a phenomenon referred to as over-parameterization. In this paper, we plan to exploit such redundancy phenomena for recommender systems (RS), and propose a top-N item recommendation framework called PCRec that leverages collaborative training of two recommender models of the same network structure, termed peer collaboration. |
Yang Sun; Fajie Yuan; Min Yang; Alexandros Karatzoglou; Li Shen; Xiaoyan Zhao; |
| 188 | Faster Learned Sparse Retrieval with Guided Traversal Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a novel indexing and query processing technique that exploits a traditional sparse model’s "guidance" to efficiently traverse the index, allowing the more effective learned model to execute fewer scoring operations. |
Antonio Mallia; Joel Mackenzie; Torsten Suel; Nicola Tonellotto; |
| 189 | Animating Images to Transfer CLIP for Video-Text Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel image animation strategy to transfer the image-text CLIP model to video-text retrieval effectively. |
Yu Liu; Huai Chen; Lianghua Huang; Di Chen; Bin Wang; Pan Pan; Lisheng Wang; |
| 190 | IPR: Interaction-level Preference Ranking for Explicit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose interaction-level preference ranking(IPR), a novel pairwise ranking embedding learning approach to better utilize explicit feedback for recommendation. |
Shih-Yang Liu; Hsien Hao Chen; Chih-Ming Chen; Ming-Feng Tsai; Chuan-Ju Wang; |
| 191 | News Recommendation with Candidate-aware User Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a candidate-aware user modeling method for personalized news recommendation, which can incorporate candidate news into user modeling for better matching between candidate news and user interest. |
Tao Qi; Fangzhao Wu; Chuhan Wu; Yongfeng Huang; |
| 192 | PERD: Personalized Emoji Recommendation with Dynamic User Preference Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a personalized emoji recommendation with dynamic user preference (PERD) which contains a text encoder and a personalized attention mechanism. |
Xuanzhi Zheng; Guoshuai Zhao; Li Zhu; Xueming Qian; |
| 193 | Socially-aware Dual Contrastive Learning for Cold-Start Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose socially-aware dual contrastive learning for cold-start recommendation, where cold users can be modeled in the same way as warm users. |
Jing Du; Zesheng Ye; Lina Yao; Bin Guo; Zhiwen Yu; |
| 194 | Hierarchical Task-aware Multi-Head Attention Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While related research continues to break new ground, two major limitations still remain, including (i) poor generalization to scenarios where tasks are loosely correlated; and (ii) under-investigation on global commonality and local characteristics of tasks. Our aim is to bridge these gaps by presenting a neural multi-task learning model coined Hierarchical Task-aware Multi-headed Attention Network (HTMN). |
Jing Du; Lina Yao; Xianzhi Wang; Bin Guo; Zhiwen Yu; |
| 195 | Image-Text Retrieval Via Contrastive Learning with Auxiliary Generative Features and Support-set Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we bridge the heterogeneity gap between different modalities and improve image-text retrieval by taking advantage of auxiliary image-to-text and text-to-image generative features with contrastive learning. |
Lei Zhang; Min Yang; Chengming Li; Ruifeng Xu; |
| 196 | Enhancing Event-Level Sentiment Analysis with Structured Arguments Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e.g., subject, object, time and location) that have potential effects on the sentiment are not well studied. In this paper, we redefine the task as structured event-level SA and propose an End-to-End Event-level Sentiment Analysis (E3SA) approach to solve this issue. |
Qi Zhang; Jie Zhou; Qin Chen; Qingchun Bai; Liang He; |
| 197 | Denoising Time Cycle Modeling for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Denoising Time Cycle Modeling (DiCycle), a novel approach to denoise user behaviors and select the subset of user behaviors that are highly related to the target item. |
Sicong Xie; Qunwei Li; Weidi Xu; Kaiming Shen; Shaohu Chen; Wenliang Zhong; |
| 198 | P3 Ranker: Mitigating The Gaps Between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we identify and study the two mismatches between pre-training and ranking fine-tuning: the training schema gap regarding the differences in training objectives and model architectures, and the task knowledge gap considering the discrepancy between the knowledge needed in ranking and that learned during pre-training. |
Xiaomeng Hu; Shi Yu; Chenyan Xiong; Zhenghao Liu; Zhiyuan Liu; Ge Yu; |
| 199 | Towards Results-level Proportionality for Multi-objective Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: If individual objectives are transformed to represent quality on the same scale, these result conditioning expressions may greatly contribute towards recommendations tuneability and explainability as well as user’s control over recommendations. To achieve this task, we propose an iterative algorithm inspired by the mandates allocation problem in public elections. |
Ladislav Peska; Patrik Dokoupil; |
| 200 | Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To obtain better cost/performance trade-off, we propose a novel Adversarial Filtering Model (ADFM) to model long-term user behavior. |
Xiaochen Li; Jian Liang; Xialong Liu; Yu Zhang; |
| 201 | FUM: Fine-grained and Fast User Modeling for News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a fine-grained and fast user modeling framework (FUM) to model user interest from fine-grained behavior interactions for news recommendation. |
Tao Qi; Fangzhao Wu; Chuhan Wu; Yongfeng Huang; |
| 202 | Curriculum Learning for Dense Retrieval Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose a generic curriculum learning based optimization framework called CL-DRD that controls the difficulty level of training data produced by the re-ranking (teacher) model. |
Hansi Zeng; Hamed Zamani; Vishwa Vinay; |
| 203 | Evaluation of Herd Behavior Caused By Population-scale Concept Drift in Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We conduct a study on user behavior to detect the collaborative concept drifts among users. |
Chenglong Ma; Yongli Ren; Pablo Castells; Mark Sanderson; |
| 204 | Detecting Frozen Phrases in Open-Domain Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study one particular structure, referred to as frozen phrases, that is highly expected to transfer as a whole from questions to answer passages. |
Mostafa Yadegari; Ehsan Kamalloo; Davood Rafiei; |
| 205 | Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address them, in this paper, we propose a hypergraph-based solution, HIDE. |
Yinfeng Li; Chen Gao; Hengliang Luo; Depeng Jin; Yong Li; |
| 206 | Point Prompt Tuning for Temporally Language Grounding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Therefore, how to perform TLG task efficiently and stably is a non-trivial work. Toward this end, we innovatively contribute a solution, Point Prompt Tuning (PPT), which formulates this task as a prompt-based multi-modal problem and integrates multiple sub-tasks to tuning performance. |
Yawen Zeng; |
| 207 | Value Penalized Q-Learning for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Value Penalized Q-learning (VPQ), a novel uncertainty-based offline RL algorithm that penalizes the unstable Q-values in the regression target using uncertainty-aware weights, achieving the conservative Q-function without the need of estimating the behavior policy, suitable for RS with a large number of items. |
Chengqian Gao; Ke Xu; Kuangqi Zhou; Lanqing Li; Xueqian Wang; Bo Yuan; Peilin Zhao; |
| 208 | Transform Cold-Start Users Into Warm Via Fused Behaviors in Large-Scale Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: (1) Cold-start users may have a quite different distribution of features from existing users. (2) The few behaviors of cold-start users are hard to be exploited. In this paper, we propose a recommender system called Cold-Transformer to alleviate these problems. |
Pengyang Li; Rong Chen; Quan Liu; Jian Xu; Bo Zheng; |
| 209 | Understanding User Satisfaction with Task-oriented Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a comprehensive set of user experience aspects derived from the annotators’ open comments that can influence users’ overall impression. |
Clemencia Siro; Mohammad Aliannejadi; Maarten de Rijke; |
| 210 | Distilling Knowledge on Text Graph for Social Media Attribute Inference Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, these text graphs are constructed on words, suffering from high memory consumption and ineffectiveness on few labeled texts. To address this challenge, we design a text-graph-based few-shot learning model for social media attribute inferences. |
Quan Li; Xiaoting Li; Lingwei Chen; Dinghao Wu; |
| 211 | Progressive Self-Attention Network with Unsymmetrical Positional Encoding for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel interpretable convolutional self-attention, which efficiently captures both short- and long-term patterns with a progressive attention distribution. |
Yuehua Zhu; Bo Huang; Shaohua Jiang; Muli Yang; Yanhua Yang; Wenliang Zhong; |
| 212 | Empowering Next POI Recommendation with Multi-Relational Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, most existing methods either focus on merely the user-POI visits, or handle different relations based on over-simplified assumptions while neglecting relational heterogeneities. To fill these critical voids, we propose a novel framework, MEMO, which effectively utilizes the heterogeneous relations with a multi-network representation learning module, and explicitly incorporates the inter-temporal user-POI mutual influence with the coupled recurrent neural networks. |
Zheng Huang; Jing Ma; Yushun Dong; Natasha Zhang Foutz; Jundong Li; |
| 213 | What Makes A Good Podcast Summary? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Using a collection of podcast summaries produced by different algorithms alongside human judgments of summary quality obtained from the TREC 2020 Podcasts Track, we study the correlations between various automatic evaluation metrics and human judgments, as well as the linguistic aspects of summaries that result in strong evaluations. |
Rezvaneh Rezapour; Sravana Reddy; Rosie Jones; Ian Soboroff; |
| 214 | A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a simple yet effective meta-learning paradigm for zero-shot intent classification. |
Han Liu; Siyang Zhao; Xiaotong Zhang; Feng Zhang; Junjie Sun; Hong Yu; Xianchao Zhang; |
| 215 | BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To bridge the gap, we propose a bicomponent structural and attribute learning framework (BSAL) that is designed to adaptively leverage information from topology and feature spaces. |
Bisheng Li; Min Zhou; Shengzhong Zhang; Menglin Yang; Defu Lian; Zengfeng Huang; |
| 216 | Analyzing The Support Level for Tips Extracted from Product Reviews Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work we argue that extracted tips should be examined based on the amount of support and opposition they receive from all product reviews. |
Miriam Farber; David Carmel; Lital Kuchy; Avihai Mejer; |
| 217 | Why Do Semantically Unrelated Categories Appear in The Same Session?: A Demand-aware Method Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To tackle the aforementioned issue, this paper proposes a novel demand-aware graph neural network model. |
Liqi Yang; Linhao Luo; Xiaofeng Zhang; Fengxin Li; Xinni Zhang; Zelin Jiang; Shuai Tang; |
| 218 | Enhancing Zero-Shot Stance Detection Via Targeted Background Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For human beings, we generally tend to reason the stance of a new target by linking it with the related knowledge learned from the known ones. Therefore, in this paper, to better generalize the target-related stance features learned from the known targets to the unseen ones, we incorporate the targeted background knowledge from Wikipedia into the model. |
Qinglin Zhu; Bin Liang; Jingyi Sun; Jiachen Du; Lanjun Zhou; Ruifeng Xu; |
| 219 | Translation-Based Implicit Annotation Projection for Zero-Shot Cross-Lingual Event Argument Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper investigates a translation-based method to implicitly project annotations from the source language to the target language. |
Chenwei Lou; Jun Gao; Changlong Yu; Wei Wang; Huan Zhao; Weiwei Tu; Ruifeng Xu; |
| 220 | Coarse-to-Fine Sparse Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose to model user dynamics from shopping intents and interacted items simultaneously. |
Jiacheng Li; Tong Zhao; Jin Li; Jim Chan; Christos Faloutsos; George Karypis; Soo-Min Pantel; Julian McAuley; |
| 221 | UserBERT: Pre-training User Model with Contrastive Self-supervision Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a user model pre-training method named UserBERT to learn universal user models on unlabeled user behavior data with two contrastive self-supervision tasks. |
Chuhan Wu; Fangzhao Wu; Tao Qi; Yongfeng Huang; |
| 222 | Understanding Long Programming Languages with Structure-Aware Sparse Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, codes in real-world applications are generally long, such as code searches, which cannot be processed efficiently by existing PPLMs. To solve this problem, in this paper, we present SASA, a Structure-Aware Sparse Attention mechanism, which reduces the complexity and improves performance for long code understanding tasks. |
Tingting Liu; Chengyu Wang; Cen Chen; Ming Gao; Aoying Zhou; |
| 223 | Learning Trustworthy Web Sources to Derive Correct Answers and Reduce Health Misinformation in Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: By using an existing set of health questions and their known answers, we show it is possible to learn which web hosts are trustworthy, from which we can predict the correct answers to the 2021 health questions with an accuracy of 76%. |
Dake Zhang; Amir Vakili Tahami; Mustafa Abualsaud; Mark D. Smucker; |
| 224 | MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To solve this issue, we find the soft-labeling property of pairwise labels could be utilized to alleviate the bias of pointwise labels. To this end, we propose a momentum contrast framework (\method ) that combines pointwise and pairwise learning for recommendation. |
Menghan Wang; Yuchen Guo; Zhenqi Zhao; Guangzheng Hu; Yuming Shen; Mingming Gong; Philip Torr; |
| 225 | MetaCVR: Conversion Rate Prediction Via Meta Learning in Small-Scale Recommendation Scenarios Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel CVR method named MetaCVR from a perspective of meta learning to address the DDF issue. |
Xiaofeng Pan; Ming Li; Jing Zhang; Keren Yu; Hong Wen; Luping Wang; Chengjun Mao; Bo Cao; |
| 226 | A Multi-Task Based Neural Model to Simulate Users in Goal Oriented Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a deep learning-based user simulator that predicts users’ satisfaction scores and actions while also jointly generating users’ utterances in a multi-task manner. |
To Eun Kim; Aldo Lipani; |
| 227 | Generalizing to The Future: Mitigating Entity Bias in Fake News Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose an entity debiasing framework (ENDEF) which generalizes fake news detection models to the future data by mitigating entity bias from a cause-effect perspective. |
Yongchun Zhu; Qiang Sheng; Juan Cao; Shuokai Li; Danding Wang; Fuzhen Zhuang; |
| 228 | Dialogue Topic Segmentation Via Parallel Extraction Network with Neighbor Smoothing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Besides, the ambiguity and labeling noise in dialogue segment bounds bring further challenges to existing models. In this work, we propose the Parallel Extraction Network with Neighbor Smoothing (PEN-NS) to address the above issues. |
Jinxiong Xia; Cao Liu; Jiansong Chen; Yuchen Li; Fan Yang; Xunliang Cai; Guanglu Wan; Houfeng Wang; |
| 229 | Analysing The Robustness of Dual Encoders for Dense Retrieval Against Misspellings Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we study the robustness of dense retrievers against typos in the user question. |
Georgios Sidiropoulos; Evangelos Kanoulas; |
| 230 | From Cluster Ranking to Document Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a novel supervised approach to transform cluster ranking to document ranking. |
Egor Markovskiy; Fiana Raiber; Shoham Sabach; Oren Kurland; |
| 231 | Unlearning Protected User Attributes in Recommendations with Adversarial Training Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we investigate the possibility and challenges of removing specific protected information of users from the learned interaction representations of a RS algorithm, while maintaining its effectiveness. |
Christian Ganhör; David Penz; Navid Rekabsaz; Oleg Lesota; Markus Schedl; |
| 232 | A ‘Pointwise-Query, Listwise-Document’ Based Query Performance Prediction Approach Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel end-to-end neural cross-encoder-based approach that is trained pointwise on individual queries, but listwise over the top ranked documents (split into chunks). |
Suchana Datta; Sean MacAvaney; Debasis Ganguly; Derek Greene; |
| 233 | How Does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we control the quality of the feedback signal and measure its impact on a range of PRF methods, including traditional bag-of-words methods (Rocchio), and dense vector-based methods (learnt and not learnt). |
Hang Li; Ahmed Mourad; Bevan Koopman; Guido Zuccon; |
| 234 | Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Specifically, for positive samples, we propose switch-case augmentation to flip the case of the first letter of randomly selected words in a sentence. |
Wei Wang; Liangzhu Ge; Jingqiao Zhang; Cheng Yang; |
| 235 | Expression Syntax Information Bottleneck for Math Word Problems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we turn our attention in the opposite direction, and work on how to discard redundant features containing spurious correlations for MWP. |
Jing Xiong; Chengming Li; Min Yang; Xiping Hu; Bin Hu; |
| 236 | Masking and Generation: An Unsupervised Method for Sarcasm Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In the real world scenario, however, the abundant labeled data or extra information requires high labor cost, not to mention that sufficient annotated data is unavailable in many low-resource conditions. To alleviate this dilemma, we investigate sarcasm detection from an unsupervised perspective, in which we explore a masking and generation paradigm in the context to extract the context incongruities for learning sarcastic expression. |
Rui Wang; Qianlong Wang; Bin Liang; Yi Chen; Zhiyuan Wen; Bing Qin; Ruifeng Xu; |
| 237 | Cross-Probe BERT for Fast Cross-Modal Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the inefficiency issue in exiting text-vision BERT models, in this work, we develop a novel architecture, cross-probe BERT. |
Tan Yu; Hongliang Fei; Ping Li; |
| 238 | GERE: Generative Evidence Retrieval for Fact Verification Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we proposeGERE, the first system that retrieves evidences in a generative fashion, i.e., generating the document titles as well as evidence sentence identifiers. |
Jiangui Chen; Ruqing Zhang; Jiafeng Guo; Yixing Fan; Xueqi Cheng; |
| 239 | DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Specifically, we use sentiment analysis to extract comment relation and use the k-means clustering to construct item-item correlations, and we then optimize those heterogeneous graphs in a unified framework. |
Jiadi Han; Qian Tao; Yufei Tang; Yuhan Xia; |
| 240 | Clustering Based Behavior Sampling with Long Sequential Data for CTR Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: First, there is a lot of noise in such long histories, which can seriously hurt the prediction performance. Second, feeding the long behavior sequence directly results in infeasible inference time and storage cost. In order to tackle these challenges, in this paper we propose a novel framework, which we name as User Behavior Clustering Sampling (UBCS). |
Yuren Zhang; Enhong Chen; Binbin Jin; Hao Wang; Min Hou; Wei Huang; Runlong Yu; |
| 241 | Conversational Recommendation Via Hierarchical Information Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Hierarchical Information-aware Conversational Recommender (HICR) to model the two types of hierarchical information to boost the performance of CRS. |
Quan Tu; Shen Gao; Yanran Li; Jianwei Cui; Bin Wang; Rui Yan; |
| 242 | Relation-Guided Few-Shot Relational Triple Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To address it, we propose a novel task decomposition strategy, Relation-then-Entity, for FS-RTE. |
Xin Cong; Jiawei Sheng; Shiyao Cui; Bowen Yu; Tingwen Liu; Bin Wang; |
| 243 | On Survivorship Bias in MS MARCO Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We observe that this bias could be present in the popular MS MARCO dataset, given that annotators could not find answers to 38–45% of the queries, leading to these queries being discarded in training and evaluation processes. |
Prashansa Gupta; Sean MacAvaney; |
| 244 | An Efficiency Study for SPLADE Models Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we focus on improving the efficiency of the SPLADE model since it has achieved state-of-the-art zero-shot performance and competitive results on TREC collections. |
Carlos Lassance; Stéphane Clinchant; |
| 245 | Tensor-based Graph Modularity for Text Data Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, we rely on the modularity metric, which effectively evaluates graph clustering in such circumstances. Therefore, we present a novel approach for text clustering based on both a sparse tensor representation and graph modularity. |
Rafika Boutalbi; Mira Ait-Saada; Anastasiia Iurshina; Steffen Staab; Mohamed Nadif; |
| 246 | Learned Token Pruning in Contextualized Late Interaction Over BERT (ColBERT) Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the late-interaction mechanism leads to large index size, as one needs to save a representation for each token of every document. In this work, we focus on token pruning techniques in order to mitigate this problem. |
Carlos Lassance; Maroua Maachou; Joohee Park; Stéphane Clinchant; |
| 247 | AHP: Learning to Negative Sample for Hyperedge Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose AHP, an adversarial training-based hyperedge-prediction method. |
Hyunjin Hwang; Seungwoo Lee; Chanyoung Park; Kijung Shin; |
| 248 | GraFN: Semi-Supervised Node Classification on Graph with Few Labels Via Non-Parametric Distribution Assignment Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To this end, we propose a novel semi-supervised method for graphs, GraFN, that leverages few labeled nodes to ensure nodes that belong to the same class to be grouped together, thereby achieving the best of both worlds of semi-supervised and self-supervised methods. |
Junseok Lee; Yunhak Oh; Yeonjun In; Namkyeong Lee; Dongmin Hyun; Chanyoung Park; |
| 249 | Item Similarity Mining for Multi-Market Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we explore multi-market recommendation (MMR), and propose a novel model called M$^3$Rec to improve all markets recommendation simultaneously. |
Jiangxia Cao; Xin Cong; Tingwen Liu; Bin Wang; |
| 250 | ILMART: Interpretable Ranking with Constrained LambdaMART Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: While most of the previous research efforts focus on creating post-hoc explanations, in this paper we investigate how to train effective and intrinsically-interpretable ranking models. |
Claudio Lucchese; Franco Maria Nardini; Salvatore Orlando; Raffaele Perego; Alberto Veneri; |
| 251 | Modern Baselines for SPARQL Semantic Parsing Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we focus on the task of generating SPARQL queries from natural language questions, which can then be executed on Knowledge Graphs (KGs). |
Debayan Banerjee; Pranav Ajit Nair; Jivat Neet Kaur; Ricardo Usbeck; Chris Biemann; |
| 252 | Learning-to-Rank at The Speed of Sampling: Plackett-Luce Gradient Estimation with Minimal Computational Complexity Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we introduce the novel PL-Rank-3 algorithm that performs unbiased gradient estimation with a computational complexity comparable to the best sorting algorithms. |
Harrie Oosterhuis; |
| 253 | CTnoCVR: A Novelty Auxiliary Task Making The Lower-CTR-Higher-CVR Upper Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce a novelty auxiliary task called CTnoCVR, which aims to predict the probability of events with click but no-conversion, in various state-of-the-art multi-task models of recommender systems to promote samples with high CVR but low CTR. |
Dandan Zhang; Haotian Wu; Guanqi Zeng; Yao Yang; Weijiang Qiu; Yujie Chen; Haoyuan Hu; |
| 254 | Deep Multi-Representational Item Network for CTR Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Furthermore, most existing works regard the candidate item as one fixed embedding and ignore the multi-representational characteristics of the item. To handle the above issues, we propose a Deep multi-Representational Item NetworK (DRINK) for CTR prediction. |
Jihai Zhang; Fangquan Lin; Cheng Yang; Wei Wang; |
| 255 | A New Sequential Prediction Framework with Spatial-temporal Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In addition, we find that user behavior varies greatly at different time, and most existing models fail to characterize the rich temporal information. To address the above problems, we propose a transformer-based spatial-temporal recommendation framework (STEM). |
Jihai Zhang; Fangquan Lin; Cheng Yang; Wei Jiang; |
| 256 | Rethinking Correlation-based Item-Item Similarities for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The purpose of this paper is to re-investigate the effectiveness of correlation-based nearest neighbor methods on several benchmark datasets that have been used for recommendation evaluation in recent years. |
Katsuhiko Hayashi; |
| 257 | Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Most previous works only model point-level positive feedback (i.e., click), which neglect the page-level information of feedback and other types of feedback. To this end, we propose Deep Page-level Interest Network (DPIN) to model the page-level user preference and exploit multiple types of feedback. |
Guogang Liao; Xiaowen Shi; Ze Wang; Xiaoxu Wu; Chuheng Zhang; Yongkang Wang; Xingxing Wang; Dong Wang; |
| 258 | GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we pose an entity-wise multivariate time-series anomaly detection problem that considers the time-series of each unique entity. |
Xu Chen; Qiu Qiu; Changshan Li; Kunqing Xie; |
| 259 | On Optimizing Top-K Metrics for Neural Ranking Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we follow the LambdaLoss framework and design novel and theoretically sound losses for NDCG@K metrics, while the original LambdaLoss paper can only do so using an unsound heuristic. |
Rolf Jagerman; Zhen Qin; Xuanhui Wang; Michael Bendersky; Marc Najork; |
| 260 | Bias Mitigation for Evidence-aware Fake News Detection By Causal Intervention Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the success of causal inference, we propose a novel framework for debiasing evidence-based fake news detection\footnoteCode available at https://github.com/CRIPAC-DIG/CF-FEND by causal intervention. |
Junfei Wu; Qiang Liu; Weizhi Xu; Shu Wu; |
| 261 | DisenCTR: Dynamic Graph-based Disentangled Representation for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose DisenCTR, a novel dynamic graph-based disentangled representation framework for CTR prediction. |
Yifan Wang; Yifang Qin; Fang Sun; Bo Zhang; Xuyang Hou; Ke Hu; Jia Cheng; Jun Lei; Ming Zhang; |
| 262 | Improving Conversational Recommender Systems Via Transformer-based Sequential Modelling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Transformer-based sequential conversational recommendation method, named TSCR, which models the sequential dependencies in the conversations to improve CRS. |
Jie Zou; Evangelos Kanoulas; Pengjie Ren; Zhaochun Ren; Aixin Sun; Cheng Long; |
| 263 | Neural Query Synthesis and Domain-Specific Ranking Templates for Multi-Stage Clinical Trial Matching Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose an effective multi-stage neural ranking system for the clinical trial matching problem. |
Ronak Pradeep; Yilin Li; Yuetong Wang; Jimmy Lin; |
| 264 | On Extractive Summarization for Profile-centric Neural Expert Search in Academia Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite offering a complete picture of each candidate’s scientific output, such lengthy profiles make it inefficient to leverage state-of-the-art neural architectures for inferring expertise. To overcome this limitation, we investigate the suitability of extractive summarization as a mechanism to reduce candidate profiles for semantic encoding using Transformers. |
Rennan C. Lima; Rodrygo L. T. Santos; |
| 265 | Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the two issues, we propose a hybrid CNN based attention module, unifying user’s image behaviors and category prior, for CTR prediction. |
Xin Chen; Qingtao Tang; Ke Hu; Yue Xu; Shihang Qiu; Jia Cheng; Jun Lei; |
| 266 | Joint Optimization of Ad Ranking and Creative Selection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the existing common practices in the industry usually place the creative selection after the ad ranking stage, and thus the optimal creative fails to reflect the influence on the ad ranking stage. To address these issues, we propose a novel Cascade Architecture of Creative Selection (CACS), which is built before the ranking stage to joint optimization of intra-ad creative selection and inter-ad ranking. |
Kaiyi Lin; Xiang Zhang; Feng Li; Pengjie Wang; Qingqing Long; Hongbo Deng; Jian Xu; Bo Zheng; |
| 267 | BERT-based Dense Intra-ranking and Contextualized Late Interaction Via Multi-task Learning for Long Document Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a cascaded late interaction approach using a single model for long document retrieval. |
Minghan Li; Eric Gaussier; |
| 268 | From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we build on SPLADE — a sparse expansion-based retriever — and show to which extent it is able to benefit from the same training improvements as dense models, by studying the effect of distillation, hard-negative mining as well as the Pre-trained Language Model initialization. |
Thibault Formal; Carlos Lassance; Benjamin Piwowarski; Stéphane Clinchant; |
| 269 | Which Discriminator for Cooperative Text Generation? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: A solution is to use a classifier at each generation step, resulting in a cooperative environment where the classifier guides the decoding of the language model distribution towards relevant texts for the task at hand. In this paper, we examine three families of (transformer-based) discriminators for this specific task of cooperative decoding: bidirectional, left-to-right and generative ones. |
Antoine Chaffin; Thomas Scialom; Sylvain Lamprier; Jacopo Staiano; Benjamin Piwowarski; Ewa Kijak; Vincent Claveau; |
| 270 | When Online Meets Offline: Exploring Periodicity for Travel Destination Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an online-offline periodicity-aware information gain network, OOPIN, for travel destination prediction on OTPs. |
Wanjie Tao; Liangyue Li; Chen Chen; Zulong Chen; Hong Wen; |
| 271 | Long Document Re-ranking with Modular Re-ranker Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose instead to model full query-to-document interaction, leveraging the attention operation and modular Transformer re-ranker framework. |
Luyu Gao; Jamie Callan; |
| 272 | Improving Micro-video Recommendation Via Contrastive Multiple Interests Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Recently, contrastive learning provides a new opportunity for refining the existing recommendation techniques. Therefore, in this paper, we propose to extract contrastive multi-interests and devise a micro-video recommendation model CMI. |
Beibei Li; Beihong Jin; Jiageng Song; Yisong Yu; Yiyuan Zheng; Wei Zhou; |
| 273 | Is News Recommendation A Sequential Recommendation Task? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study whether news recommendation can be regarded as a standard sequential recommendation problem. |
Chuhan Wu; Fangzhao Wu; Tao Qi; Chenliang Li; Yongfeng Huang; |
| 274 | InPars: Unsupervised Dataset Generation for Information Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we harness the few-shot capabilities of large pretrained language models as synthetic data generators for IR tasks. |
Luiz Bonifacio; Hugo Abonizio; Marzieh Fadaee; Rodrigo Nogueira; |
| 275 | Identifying Argumentative Questions in Web Search Logs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present an approach to identify argumentative questions among web search queries. |
Yamen Ajjour; Pavel Braslavski; Alexander Bondarenko; Benno Stein; |
| 276 | Smooth-AUC: Smoothing The Path Towards Rank-based CTR Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: It is noteworthy that directly optimizing AUC by gradient-descent methods is difficult due to the non-differentiable Heaviside function built-in AUC. To this end, we propose a smooth approximation of AUC, called smooth-AUC (SAUC), towards the rank-based CTR prediction. |
Shuang Tang; Fangyuan Luo; Jun Wu; |
| 277 | Diversity Vs Relevance: A Practical Multi-objective Study in Luxury Fashion Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we explored a handful of diversification strategies to rerank the output of a relevance-focused recommender system. |
João Sá; Vanessa Queiroz Marinho; Ana Rita Magalhães; Tiago Lacerda; Diogo Goncalves; |
| 278 | Revisiting Two-tower Models for Unbiased Learning to Rank Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we revisit two-tower models for ULTR. |
Le Yan; Zhen Qin; Honglei Zhuang; Xuanhui Wang; Michael Bendersky; Marc Najork; |
| 279 | Answering Count Queries with Explanatory Evidence Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper proposes a methodology for answering count queries with inference, contextualization and explanatory evidence. |
Shrestha Ghosh; Simon Razniewski; Gerhard Weikum; |
| 280 | Interactive Query Clarification and Refinement Via User Simulation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose and evaluate a fully simulated query clarification framework allowing multi-turn interactions between IR systems and user agents. |
Pierre Erbacher; Ludovic Denoyer; Laure Soulier; |
| 281 | Summarizing Legal Regulatory Documents Using Transformers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper aims at applying advanced extractive summarization to democratize the understanding of regulations, so that non-jurists can decide which regulations deserve further follow-up. |
Svea Klaus; Ria Van Hecke; Kaweh Djafari Naini; Ismail Sengor Altingovde; Juan Bernabé-Moreno; Enrique Herrera-Viedma; |
| 282 | An MLP-based Algorithm for Efficient Contrastive Graph Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Motivated by the emerging contrastive learning technique, we design a simple neighbourhood construction method in conjunction with the contrastive objective function to simulate the neighbourhood information processing of GNN. |
Siwei Liu; Iadh Ounis; Craig Macdonald; |
| 283 | Modeling User Behavior With Interaction Networks for Spam Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose SEINE (Spam DEtection using Interaction NEtworks), a spam detection model over a novel graph framework. |
Prabhat Agarwal; Manisha Srivastava; Vishwakarma Singh; Charles Rosenberg; |
| 284 | Relation Extraction As Open-book Examination: Retrieval-enhanced Prompt Tuning Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Those long-tailed or hard patterns can hardly be memorized in parameters given few-shot instances. To this end, we regard RE as an open-book examination and propose a new semiparametric paradigm of retrieval-enhanced prompt tuning for relation extraction. |
Xiang Chen; Lei Li; Ningyu Zhang; Chuanqi Tan; Fei Huang; Luo Si; Huajun Chen; |
| 285 | End-to-end Distantly Supervised Information Extraction with Retrieval Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a general paradigm (Dasiera) to resolve issues in KB-based DS. |
Yue Zhang; Hongliang Fei; Ping Li; |
| 286 | DeSCoVeR: Debiased Semantic Context Prior for Venue Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a novel semantic context prior-based venue recommendation system that uses only the title and the abstract of a paper. |
Sailaja Rajanala; Arghya Pal; Manish Singh; Raphaël C.-W. Phan; KokSheik Wong; |
| 287 | Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We show that supervised neural information retrieval (IR) models are prone to learning sparse attention patterns over passage tokens, which can result in key phrases including named entities receiving low attention weights, eventually leading to model under-performance. |
Revanth Gangi Reddy; Md Arafat Sultan; Martin Franz; Avirup Sil; Heng Ji; |
| 288 | Assessing Scientific Research Papers with Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel approach towards automatically assessing scientific publications by constructing a knowledge graph (KG) that captures a holistic view of the research contributions. |
Kexuan Sun; Zhiqiang Qiu; Abel Salinas; Yuzhong Huang; Dong-Ho Lee; Daniel Benjamin; Fred Morstatter; Xiang Ren; Kristina Lerman; Jay Pujara; |
| 289 | Matching Search Result Diversity with User Diversity Acceptance in Web Search Sessions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address this gap between offline evaluations and users’ expectations, we proposed an intuitive diversity acceptance measure and ran experiments for diversity acceptance prediction and diversity-aware re-ranking based on datasets from both controlled lab and naturalistic settings. |
Jiqun Liu; Fangyuan Han; |
| 290 | Topological Analysis of Contradictions in Text Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This study presents a topological approach to enhancing deep learning models in detecting contradictions in text. |
Xiangcheng Wu; Xi Niu; Ruhani Rahman; |
| 291 | Addressing Gender-related Performance Disparities in Neural Rankers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we investigate whether neural rankers introduce retrieval effectiveness (performance) disparities over queries related to different genders. |
Shirin Seyedsalehi; Amin Bigdeli; Negar Arabzadeh; Morteza Zihayat; Ebrahim Bagheri; |
| 292 | Alignment Rationale for Query-Document Relevance Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study how the input perturbations can be used to infer or evaluate alignments between the query and document spans, which best explain the black-box ranker’s relevance prediction. |
Youngwoo Kim; Razieh Rahimi; James Allan; |
| 293 | To Interpolate or Not to Interpolate: PRF, Dense and Sparse Retrievers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we consider the problem of combining the relevance signals from sparse and dense retrievers in the context of Pseudo Relevance Feedback (PRF). |
Hang Li; Shuai Wang; Shengyao Zhuang; Ahmed Mourad; Xueguang Ma; Jimmy Lin; Guido Zuccon; |
| 294 | A Content Recommendation Policy for Gaining Subscribers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel content recommendation policy to a brand agent for Gaining Subscribers by Messaging (GSM) over many rounds. |
Konstantinos Theocharidis; Manolis Terrovitis; Spiros Skiadopoulos; Panagiotis Karras; |
| 295 | C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Unlike monolingual retrieval, designing an appropriate auxiliary task for cross-language mappings is challenging. To address this challenge, we use comparable Wikipedia articles in different languages to further pretrain off-the-shelf multilingual pretrained models before fine-tuning on the retrieval task. |
Eugene Yang; Suraj Nair; Ramraj Chandradevan; Rebecca Iglesias-Flores; Douglas W. Oard; |
| 296 | Dual Pseudo Supervision for Semi-Supervised Text Classification with A Reliable Teacher Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the semi-supervised text classification (SSTC) by exploring both labeled and extra unlabeled data. |
Shujie Li; Min Yang; Chengming Li; Ruifeng Xu; |
| 297 | Learning to Rank Knowledge Subgraph Nodes for Entity Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel entity retrieval method that addresses the important challenge that revolves around the need to effectively represent and model context in which entities relate to each other. |
Parastoo Jafarzadeh; Zahra Amirmahani; Faezeh Ensan; |
| 298 | Mitigating The Filter Bubble While Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Historically, Maximal Marginal Relevance (MMR) has been used to diversify result lists and even mitigate filter bubbles, but suffers from three key drawbacks: (1)~MMR directly sacrifices relevance for diversity, (2)~MMR typically diversifies across all content and not just targeted dimensions (e.g., political polarization), and (3)~MMR is inefficient in practice due to the need to compute pairwise similarities between recommended items. To simultaneously address these limitations, we propose a novel methodology that trains Concept Activation Vectors (CAVs) for targeted topical dimensions (e.g., political polarization). |
Zhaolin Gao; Tianshu Shen; Zheda Mai; Mohamed Reda Bouadjenek; Isaac Waller; Ashton Anderson; Ron Bodkin; Scott Sanner; |
| 299 | Mitigating Bias in Search Results Through Contextual Document Reranking and Neutrality Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Recent research has therefore focused on developing methods for quantifying and mitigating bias in search results and applied them to contemporary retrieval systems that leverage transformer-based language models. In the present work, we expand this direction of research by considering bias mitigation within a framework for contextual document embedding reranking. |
George Zerveas; Navid Rekabsaz; Daniel Cohen; Carsten Eickhoff; |
| 300 | A Meta-learning Approach to Fair Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For example, the collected imbalance dataset on the subject of the expert search usually leads to systematic discrimination on the specific demographic groups such as race, gender, etc, which further reduces the exposure for the minority group. To solve this problem, we propose a Meta-learning based Fair Ranking (MFR) model that could alleviate the data bias for protected groups through an automatically-weighted loss. |
Yuan Wang; Zhiqiang Tao; Yi Fang; |
| 301 | Can Users Predict Relative Query Effectiveness? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Can they anticipate the effectiveness of alternative queries for the same retrieval need? To explore that question we designed and carried out a crowd-sourced user study in which we asked subjects to consider an information need statement expressed as a backstory, and then provide their opinions as to the relative usefulness of a set of queries ostensibly addressing that objective. |
Oleg Zendel; Melika P. Ebrahim; J. Shane Culpepper; Alistair Moffat; Falk Scholer; |
| 302 | ELECRec: Training Sequential Recommenders As Discriminators Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose to train the sequential recommenders as discriminators rather than generators. |
Yongjun Chen; Jia Li; Caiming Xiong; |
| 303 | Explainable Session-based Recommendation with Meta-path Guided Instances and Self-Attention Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the majority of current SR models are unexplainable and even those that claim to be interpretable cannot provide clear and convincing explanations of users’ intentions and how they influence the models’ decisions. To solve this problem, in this research, we propose a meta-path guided model which uses path instances to capture item dependencies, explicitly reveal the underlying motives, and illustrate the entire reasoning process. |
Jiayin Zheng; Juanyun Mai; Yanlong Wen; |
| 304 | MM-Rec: Visiolinguistic Model Empowered Multimodal News Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a multimodal news recommendation method that can incorporate both textual and visual information of news to learn multimodal news representations. |
Chuhan Wu; Fangzhao Wu; Tao Qi; Chao Zhang; Yongfeng Huang; Tong Xu; |
| 305 | Generative Adversarial Framework for Cold-Start Item Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To this end, we propose a general framework named Generative Adversarial Recommendation (GAR). |
Hao Chen; Zefan Wang; Feiran Huang; Xiao Huang; Yue Xu; Yishi Lin; Peng He; Zhoujun Li; |
| 306 | Inconsistent Ranking Assumptions in Medical Search and Their Downstream Consequences Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents the use of this uncertainty information as an indicator of how well downstream methods will function over a ranklist. |
Daniel Cohen; Kevin Du; Bhaskar Mitra; Laura Mercurio; Navid Rekabsaz; Carsten Eickhoff; |
| 307 | Modality-Balanced Embedding for Video Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose \modelname (short for Modality Balanced Video Retrieval) with two key components: manually generated modality-shuffled (MS) samples and a dynamic margin (DM) based on visual relevance. |
Xun Wang; Bingqing Ke; Xuanping Li; Fangyu Liu; Mingyu Zhang; Xiao Liang; Qiushi Xiao; |
| 308 | An Efficient Fusion Mechanism for Multimodal Low-resource Setting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The fusion of different modalities could even be more challenging under the low-resource setting, where we have fewer samples for training. This paper proposes a multi-representative fusion mechanism that generates diverse fusions with multiple modalities and then chooses the best fusion among them. |
Dushyant Singh Chauhan; Asif Ekbal; Pushpak Bhattacharyya; |
| 309 | QSG Transformer: Transformer with Query-Attentive Semantic Graph for Query-Focused Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the simple Transformer architecture cannot utilize the relationships between distant words and information from a query directly. In this study, we propose the QSG Transformer, a novel QFS model that leverages structure information on Query-attentive Semantic Graph (QSG) to address these issues. |
Choongwon Park; Youngjoong Ko; |
| 310 | Improving Item Cold-start Recommendation Via Model-agnostic Conditional Variational Autoencoder Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To address these problems, we propose a model-agnostic Conditional Variational Autoencoder based Recommendation(CVAR) framework with some advantages including compatibility on various backbones, no extra requirements for data, utilization of both historical data and recent emerging interactions. |
Xu Zhao; Yi Ren; Ying Du; Shenzheng Zhang; Nian Wang; |
| 311 | PST: Measuring Skill Proficiency in Programming Exercise Process Via Programming Skill Tracing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Most of existing studies on learner capability portrait only made use of the exercise results, while the rich behavioral information contained in programming exercise process remains unused. Therefore, we propose a model that measures skill proficiency in programming exercise process named Programming Skill Tracing (PST). |
Ruixin Li; Yu Yin; Le Dai; Shuanghong Shen; Xin Lin; Yu Su; Enhong Chen; |
| 312 | Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we revisited experiments on IRS with review datasets and compared RL-based models with a simple reward model that greedily recommends the item with the highest one-step reward. |
Hojoon Lee; Dongyoon Hwang; Kyushik Min; Jaegul Choo; |
| 313 | Next Point-of-Interest Recommendation with Auto-Correlation Enhanced Multi-Modal Transformer Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While many recent efforts show the effectiveness of recurrent neural network-based next POI recommendation algorithms, several important challenges have not been well addressed yet: (i) The majority of previous models only consider the dependence of consecutive visits, while ignoring the intricate dependencies of POIs in traces; (ii) The nature of hierarchical and the matching of sub-sequence in POI sequences are hardly model in prior methods; (iii) Most of the existing solutions neglect the interactions between two modals of POI and the density category. To tackle the above challenges, we propose an auto-correlation enhanced multi-modal Transformer network (AutoMTN) for the next POI recommendation. |
Yanjun Qin; Yuchen Fang; Haiyong Luo; Fang Zhao; Chenxing Wang; |
| 314 | MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents MuchSUM, a better approach for extractive text summarization. |
Qianren Mao; Hongdong Zhu; Junnan Liu; Cheng Ji; Hao Peng; Jianxin Li; Lihong Wang; Zheng Wang; |
| 315 | Neutralizing Popularity Bias in Recommendation Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Item embeddings inherit popularity bias from the data, which leads to biased recommendations. We use this observation to design two simple and effective strategies, which can be flexibly plugged into different backbone recommendation models, to learn popularity neutral item representations. |
Guipeng Xv; Chen Lin; Hui Li; Jinsong Su; Weiyao Ye; Yewang Chen; |
| 316 | Lightweight Meta-Learning for Low-Resource Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Furthermore, the need for low-resource abstractive summarization task is emerging but existing methods for the task such as transfer learning still have domain shifting and overfitting problems. To address these problems, we propose a new framework for low-resource abstractive summarization using a meta-learning algorithm that can quickly adapt to a new domain using small data. |
Taehun Huh; Youngjoong Ko; |
| 317 | Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To take both quality and efficiency into account, we propose a contrastive non-autoregressive model that captures user preferences with ingenious decoding objective. |
Penghui Wei; Shaoguo Liu; Xuanhua Yang; Liang Wang; Bo Zheng; |
| 318 | Exploiting Session Information in BERT-based Session-aware Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose three methods to improve recommendation performance by exploiting session information while minimizing additional parameters in a BERT-based sequential recommendation model: using session tokens, adding session segment embeddings, and a time-aware self-attention. |
Jinseok Jamie Seol; Youngrok Ko; Sang-goo Lee; |
| 319 | Posterior Probability Matters: Doubly-Adaptive Calibration for Neural Predictions in Online Advertising Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we propose a doubly-adaptive approach AdaCalib. |
Penghui Wei; Weimin Zhang; Ruijie Hou; Jinquan Liu; Shaoguo Liu; Liang Wang; Bo Zheng; |
| 320 | Towards Motivational and Empathetic Response Generation in Online Mental Health Support Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we develop a Virtual Assistant (VA) that serves as a first point of contact for users who are depressed or disheartened. |
Tulika Saha; Vaibhav Gakhreja; Anindya Sundar Das; Souhitya Chakraborty; Sriparna Saha; |
| 321 | Selective Fairness in Recommendation Via Prompts Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we define the selective fairness task, where users can flexibly choose which sensitive attributes should the recommendation model be bias-free. |
Yiqing Wu; Ruobing Xie; Yongchun Zhu; Fuzhen Zhuang; Ao Xiang; Xu Zhang; Leyu Lin; Qing He; |
| 322 | Multi-label Masked Language Modeling on Zero-shot Code-switched Sentiment Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we consider zero-shot setting and improve model performance on code-switched tasks via monolingual language datasets, unlabeled code-switched datasets, and semantic dictionaries. |
Zhi Li; Xing Gao; Ji Zhang; Yin Zhang; |
| 323 | Expanded Lattice Embeddings for Spoken Document Retrieval on Informal Meetings Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we evaluate different alternatives to process richer forms of Automatic Speech Recognition (ASR) output based on lattice expansion algorithms for Spoken Document Retrieval (SDR). |
Esaú Villatoro-Tello; Srikanth Madikeri; Petr Motlicek; Aravind Ganapathiraju; Alexei V. Ivanov; |
| 324 | Extractive Elementary Discourse Units for Improving Abstractive Summarization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we apply elementary discourse unit (EDU) as textual unit of content selection. |
Ye Xiong; Teeradaj Racharak; Minh Le Nguyen; |
| 325 | LightSGCN: Powering Signed Graph Convolution Network for Link Sign Prediction with Simplified Architecture Design Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we aim to simplify the architecture of signed GNNs to make it more concise and appropriate for link sign prediction. |
Haoxin Liu; |
| 326 | Dual Contrastive Network for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose D ual C ontrastive N etwork (DCN) to boost sequential recommendation, from a new perspective of integrating auxiliary user-sequence for items. |
Guanyu Lin; Chen Gao; Yinfeng Li; Yu Zheng; Zhiheng Li; Depeng Jin; Yong Li; |
| 327 | ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the intuition that humans usually reflect and learn from mistakes, in this paper, we attempt to build a self-correction continual learning loop (dubbed ReLoop) for recommender systems. |
Guohao Cai; Jieming Zhu; Quanyu Dai; Zhenhua Dong; Xiuqiang He; Ruiming Tang; Rui Zhang; |
| 328 | Task-Oriented Dialogue System As Natural Language Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we propose to formulate the task-oriented dialogue system as the purely natural language generation task, so as to fully leverage the large-scale pre-trained models like GPT-2 and simplify complicated delexicalization prepossessing. |
Weizhi Wang; Zhirui Zhang; Junliang Guo; Yinpei Dai; Boxing Chen; Weihua Luo; |
| 329 | Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i.e., preference topics generation, preference shift modeling, and social influence activation. |
Likang Wu; Hao Wang; Enhong Chen; Zhi Li; Hongke Zhao; Jianhui Ma; |
| 330 | Constructing Better Evaluation Metrics By Incorporating The Anchoring Effect Into The User Model Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we challenge the rational user assumption and introduce the anchoring effect into user models. |
Nuo Chen; Fan Zhang; Tetsuya Sakai; |
| 331 | Where Does The Performance Improvement Come From?: – A Reproducibility Concern About Image-Text Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This article aims to provide the information retrieval community with some reflections on recent advances in retrieval learning by analyzing the reproducibility of image-text retrieval models. |
Jun Rao; Fei Wang; Liang Ding; Shuhan Qi; Yibing Zhan; Weifeng Liu; Dacheng Tao; |
| 332 | State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, this finding is limited to the actor-critic method, four state encoders, and evaluation-simulators that do not debias logged user data. In response to these shortcomings, we reproduce and expand on the existing comparison of attention-based state encoders (1) in the publicly available debiased RL4Rec SOFA simulator with (2) a different RL method, (3) more state encoders, and (4) a different dataset. |
Jin Huang; Harrie Oosterhuis; Bunyamin Cetinkaya; Thijs Rood; Maarten de Rijke; |
| 333 | Another Look at Information Retrieval As Statistical Translation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we ask the simple question: What if Berger and Lafferty had access to datasets such as the MS MARCO passage ranking dataset that we take for granted today? |
Yuqi Liu; Chengcheng Hu; Jimmy Lin; |
| 334 | Experiments on Generalizability of User-Oriented Fairness in Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we re-produce a user-oriented fairness study and provide extensive experiments to analyze the dependency of their proposed method on various fairness and recommendation aspects, including the recommendation domain, nature of the base ranking model, and user grouping method. |
Hossein A. Rahmani; Mohammadmehdi Naghiaei; Mahdi Dehghan; Mohammad Aliannejadi; |
| 335 | Users and Contemporary SERPs: A (Re-)Investigation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we reproduce the user studies conducted in prior works—specifically those of~\citetarguello2012task and~\citetsiu2014first —to explore to what extent the findings from research conducted five to ten years ago still hold today as the average web user has become accustomed to SERPs with ever-increasing presentational complexity. |
Nirmal Roy; David Maxwell; Claudia Hauff; |
| 336 | Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To mitigate these issues, we first propose a unified framework covering most HGNNs, consisting of three components: heterogeneous linear transformation, heterogeneous graph transformation, and heterogeneous message passing layer. Then we build a platform Space4HGNN by defining a design space for HGNNs based on the unified framework, which offers modularized components, reproducible implementations, and standardized evaluation for HGNNs. |
Tianyu Zhao; Cheng Yang; Yibo Li; Quan Gan; Zhenyi Wang; Fengqi Liang; Huan Zhao; Yingxia Shao; Xiao Wang; Chuan Shi; |
| 337 | An Inspection of The Reproducibility and Replicability of TCT-ColBERT Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Among the most prominent of these approaches is TCT-ColBERT, which trains a light-weight "student” model from a more expensive "teacher” model. In this work, we take a closer look into TCT-ColBERT concerning its reproducibility and replicability. |
Xiao Wang; Sean MacAvaney; Craig Macdonald; Iadh Ounis; |
| 338 | Is Non-IID Data A Threat in Federated Online Learning to Rank? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this perspective paper we study the effect of non independent and identically distributed (non-IID) data on federated online learning to rank (FOLTR) and chart directions for future work in this new and largely unexplored research area of Information Retrieval. |
Shuyi Wang; Guido Zuccon; |
| 339 | Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This paper aims to explore the feasibility of using currently available quantum computing architectures to solve some quadratic feature selection algorithms for both ranking and classification. |
Maurizio Ferrari Dacrema; Fabio Moroni; Riccardo Nembrini; Nicola Ferro; Guglielmo Faggioli; Paolo Cremonesi; |
| 340 | Reduce, Reuse, Recycle: Green Information Retrieval Research Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Therefore, within the Information Retrieval community, the consequences of non-Green (in other words, Red) research should at least be considered and acknowledged. As such, the aims of this perspective paper are fourfold: (1) to review the Green literature not only for Information Retrieval but also for related domains in order to identify transferable Green techniques; (2) to provide measures for quantifying the power usage and emissions of Information Retrieval research; (3) to report the power usage and emission impacts for various current IR methods; and (4) to provide a framework to guide Green Information Retrieval research, taking inspiration from ‘reduce, reuse, recycle’ waste management campaigns, including salient examples from the literature that implement these concepts. |
Harrisen Scells; Shengyao Zhuang; Guido Zuccon; |
| 341 | Competitive Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We provide a broad perspective on recent work on competitive retrieval settings, argue that this work is the tip of the iceberg, and pose a suite of novel research directions; for example, a general game theoretic framework for competitive search, methods of learning-to-rank that account for post-ranking effects, approaches to automatic document manipulation, addressing societal aspects and evaluation. |
Oren Kurland; Moshe Tennenholtz; |
| 342 | Where Do Queries Come From? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we draw on a variety of literatures (including information seeking, psychology, and misinformation), and report some small experiments to describe what is known about where queries come from, and demonstrate a clear literature gap around the source of query variations in IR. |
Marwah Alaofi; Luke Gallagher; Dana Mckay; Lauren L. Saling; Mark Sanderson; Falk Scholer; Damiano Spina; Ryen W. White; |
| 343 | On Natural Language User Profiles for Transparent and Scrutable Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years. We focus on the challenge of supporting people’s understanding and control of these systems and explore a fundamentally new way of thinking about representation of knowledge in recommendation and personalization systems. |
Filip Radlinski; Krisztian Balog; Fernando Diaz; Lucas Dixon; Ben Wedin; |
| 344 | Retrieval-Enhanced Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We describe a generic retrieval-enhanced machine learning (REML) framework, which includes a number of existing models as special cases. |
Hamed Zamani; Fernando Diaz; Mostafa Dehghani; Donald Metzler; Michael Bendersky; |
| 345 | MET-Meme: A Multimodal Meme Dataset Rich in Metaphors Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the existing researches ignore this key feature. Therefore, to incorporate informative metaphors into the meme analysis, we introduce a novel multimodal meme dataset called MET-Meme, which is rich in metaphorical features. |
Bo Xu; Tingting Li; Junzhe Zheng; Mehdi Naseriparsa; Zhehuan Zhao; Hongfei Lin; Feng Xia; |
| 346 | Revisiting Bundle Recommendation: Datasets, Tasks, Challenges and Opportunities for Intent-aware Product Bundling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to take a step back and consider the process of bundle recommendation from a holistic user experience perspective. |
Zhu Sun; Jie Yang; Kaidong Feng; Hui Fang; Xinghua Qu; Yew Soon Ong; |
| 347 | BARS: Towards Open Benchmarking for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: This largely limits the credibility and practical value of research results in this field. To tackle these issues, we present an initiative project aimed for open benchmarking for recommender systems. |
Jieming Zhu; Quanyu Dai; Liangcai Su; Rong Ma; Jinyang Liu; Guohao Cai; Xi Xiao; Rui Zhang; |
| 348 | OVQA: A Clinically Generated Visual Question Answering Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the challenge, we present a new dataset, denoted by OVQA, which is generated from electronic medical records. |
Yefan Huang; Xiaoli Wang; Feiyan Liu; Guofeng Huang; |
| 349 | Fostering Coopetition While Plugging Leaks: The Design and Implementation of The MS MARCO Leaderboards Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: These "leaks", accumulated over long periods of time, threaten the validity of the insights that can be derived from the leaderboards. In this paper, we share our experiences grappling with this issue over the past few years and how our considerations are operationalized into a coherent submission policy. |
Jimmy Lin; Daniel Campos; Nick Craswell; Bhaskar Mitra; Emine Yilmaz; |
| 350 | Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Especially in the current COVID-19 era, we witness an unprecedented growth of medical misinformation. To help tackle this problem with machine learning approaches, we are publishing a feature-rich dataset of approx. 317k medical news articles/blogs and 3.5k fact-checked claims. |
Ivan Srba; Branislav Pecher; Matus Tomlein; Robert Moro; Elena Stefancova; Jakub Simko; Maria Bielikova; |
| 351 | A Dataset for Sentence Retrieval for Open-Ended Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To adapt neural models to the types of dialogues in the dataset, we explored an approach to induce a large-scale weakly supervised training data from Reddit. |
Itay Harel; Hagai Taitelbaum; Idan Szpektor; Oren Kurland; |
| 352 | Too Many Relevants: Whither Cranfield Test Collections? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents the lessons regarding the construction and use of large Cranfield-style test collections learned from the TREC 2021 Deep Learning track. |
Ellen M. Voorhees; Nick Craswell; Jimmy Lin; |
| 353 | ACORDAR: A Test Collection for Ad Hoc Content-Based (RDF) Dataset Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we build and release the first test collection for ad hoc content-based dataset retrieval, where content-oriented dataset queries and content-based relevance judgments are annotated by human experts who are assisted with a dashboard designed specifically for comprehensively and conveniently browsing both the metadata and data of a dataset. |
Tengteng Lin; Qiaosheng Chen; Gong Cheng; Ahmet Soylu; Basil Ell; Ruoqi Zhao; Qing Shi; Xiaxia Wang; Yu Gu; Evgeny Kharlamov; |
| 354 | RELISON: A Framework for Link Recommendation in Social Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present RELISON, an extensible framework for running link recommendation experiments. |
Javier Sanz-Cruzado; Pablo Castells; |
| 355 | Wikimarks: Harvesting Relevance Benchmarks from Wikipedia Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We provide a resource for automatically harvesting relevance benchmarks from Wikipedia — which we refer to as "Wikimarks" to differentiate them from manually created benchmarks. |
Laura Dietz; Shubham Chatterjee; Connor Lennox; Sumanta Kashyapi; Pooja Oza; Ben Gamari; |
| 356 | ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: \endenumerate* In this paper, we present \acsReMeDi, a set of \aclReMeDi \acusedReMeDi. |
Guojun Yan; Jiahuan Pei; Pengjie Ren; Zhaochun Ren; Xin Xin; Huasheng Liang; Maarten de Rijke; Zhumin Chen; |
| 357 | ArchivalQA: A Large-scale Benchmark Dataset for Open-Domain Question Answering Over Historical News Collections Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To foster the research in the field of ODQA on such historical collections, we present ArchivalQA, a large question answering dataset consisting of 532,444 question-answer pairs which is designed for temporal news QA. |
Jiexin Wang; Adam Jatowt; Masatoshi Yoshikawa; |
| 358 | SoChainDB: A Database for Storing and Retrieving Blockchain-Powered Social Network Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, accessing and collecting data from these social networks is not easy because it requires strong blockchain knowledge, which is not the main focus of computer science and social science researchers. Hence, our work proposes the SoChainDB framework that facilitates obtaining data from these new social networks. |
Hoang H. Nguyen; Dmytro Bozhkov; Zahra Ahmadi; Nhat-Minh Nguyen; Thanh-Nam Doan; |
| 359 | Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, in the Chinese field, especially for specific domains, passage retrieval systems are still immature due to quality-annotated dataset being limited by scale. Therefore, in this paper, we present a novel multi-domain Chinese dataset for passage retrieval (Multi-CPR). |
Dingkun Long; Qiong Gao; Kuan Zou; Guangwei Xu; Pengjun Xie; Ruijie Guo; Jian Xu; Guanjun Jiang; Luxi Xing; Ping Yang; |
| 360 | ORCAS-I: Queries Annotated with Intent Using Weak Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we introduce a revised taxonomy of user intent. |
Daria Alexander; Wojciech Kusa; Arjen P. de Vries; |
| 361 | CODEC: Complex Document and Entity Collection Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We target essay-style information needs of social science researchers, i.e. "How has the UK’s Open Banking Regulation benefited Challenger Banks". |
Iain Mackie; Paul Owoicho; Carlos Gemmell; Sophie Fischer; Sean MacAvaney; Jeffrey Dalton; |
| 362 | Ir_metadata: An Extensible Metadata Schema for IR Experiments Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we introduce \textttir\_metadata – an extensible metadata schema for TREC run files based on the PRIMAD model. |
Timo Breuer; Jüri Keller; Philipp Schaer; |
| 363 | Would You Ask It That Way?: Measuring and Improving Question Naturalness for Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the resulting datasets often fall short of representing genuinely natural and fluent language. In the present work, we investigate ways to characterize and remedy these shortcomings. |
Trond Linjordet; Krisztian Balog; |
| 364 | The Istella22 Dataset: Bridging Traditional and Neural Learning to Rank Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: On the other hand, the benchmarks often used for evaluating neural models, e.g., MS MARCO, TREC Robust, etc., provide text but do not provide query-document feature vectors. In this paper, we present Istella22, a new dataset that enables such comparisons by providing both query/document text and strong query-document feature vectors used by an industrial search engine. |
Domenico Dato; Sean MacAvaney; Franco Maria Nardini; Raffaele Perego; Nicola Tonellotto; |
| 365 | ViQuAE, A Dataset for Knowledge-based Visual Question Answering About Named Entities Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this context, we are interested in answering questions about named entities grounded in a visual context using a Knowledge Base (KB). |
Paul Lerner; Olivier Ferret; Camille Guinaudeau; Hervé Le Borgne; Romaric Besançon; Jose G. Moreno; Jesús Lovón Melgarejo; |
| 366 | Biographical Semi-Supervised Relation Extraction Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata. |
Alistair Plum; Tharindu Ranasinghe; Spencer Jones; Constantin Orasan; Ruslan Mitkov; |
| 367 | Axiomatic Retrieval Experimentation with Ir_axioms Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, recent open-source retrieval frameworks like PyTerrier and Pyserini, which made it easy to experiment with sparse and dense retrieval models, have not included any retrieval axiom support so far. To fill this gap, we propose ir_axioms, an open-source Python framework that integrates retrieval axioms with common retrieval frameworks. |
Alexander Bondarenko; Maik Fröbe; Jan Heinrich Reimer; Benno Stein; Michael Völske; Matthias Hagen; |
| 368 | MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: However, datasets in the field of automatic misinformation detection are predominantly monolingual, include a limited amount of modalities and are not of sufficient scale and quality. Addressing this, we develop a data collection and linking system (MuMiN-trawl), to build a public misinformation graph dataset (MuMiN), containing rich social media data (tweets, replies, users, images, articles, hashtags) spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages, spanning more than a decade. |
Dan S. Nielsen; Ryan McConville; |
| 369 | CAVES: A Dataset to Facilitate Explainable Classification and Summarization of Concerns Towards COVID Vaccines Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this paper, we have curated CAVES, the first large-scale dataset containing about 10k COVID-19 anti-vaccine tweets labelled into various specific anti-vaccine concerns in a multi-label setting. |
Soham Poddar; Azlaan Mustafa Samad; Rajdeep Mukherjee; Niloy Ganguly; Saptarshi Ghosh; |
| 370 | IRec: An Interactive Recommendation Framework Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, this work proposes an interactive RS framework named iRec. |
Thiago Silva; Nícollas Silva; Heitor Werneck; Carlos Mito; Adriano C.M. Pereira; Leonardo Rocha; |
| 371 | From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: To support our collection, we provide an analysis, previously not possible, on how seed studies impact retrieval and perform several experiments using seed study based methods to compare the effectiveness of using seed studies versus pseudo seed studies. |
Shuai Wang; Harrisen Scells; Justin Clark; Bevan Koopman; Guido Zuccon; |
| 372 | Document Expansion Baselines and Learned Sparse Lexical Representations for MS MARCO V1 and V2 Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work describes a number of resources that support competitive, reproducible baselines for both the MS MARCO V1 and V2 test collections using our Anserini and Pyserini IR toolkits. |
Xueguang Ma; Ronak Pradeep; Rodrigo Nogueira; Jimmy Lin; |
| 373 | MIMICS-Duo: Offline & Online Evaluation of Search Clarification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Asking clarification questions is an active area of research; however, resources for training and evaluating search clarification methods are not sufficient. To address this issue, we describe MIMICS-Duo, a new freely available dataset of 306 search queries with multiple clarifications (a total of 1,034 query-clarification pairs). |
Leila Tavakoli; Johanne R. Trippas; Hamed Zamani; Falk Scholer; Mark Sanderson; |
| 374 | Knowledge Graph Question Answering Datasets and Their Generalizability: Are They Enough for Future Research? Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this work, we propose a mitigation method for re-splitting available KGQA datasets to enable their applicability to evaluate generalization, without any cost and manual effort. |
Longquan Jiang; Ricardo Usbeck; |
| 375 | SparCAssist: A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce SparCAssist, a general-purpose risk assessment tool for the machine learning models trained for language tasks. |
Zijian Zhang; Vinay Setty; Avishek Anand; |
| 376 | RecDelta: An Interactive Dashboard on Top-k Recommendation for Cross-model Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this demonstration, we present RecDelta, an interactive tool for the cross-model evaluation of top-k recommendation. |
Yi-Shyuan Chiang; Yu-Ze Liu; Chen-Feng Tsai; Jing-Kai Lou; Ming-Feng Tsai; Chuan-Ju Wang; |
| 377 | A Common Framework for Exploring Document-at-a-Time and Score-at-a-Time Retrieval Methods Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our contribution is a framework that enables future research comparing DaaT and SaaT methods in the context of modern neural retrieval models. |
Andrew Trotman; Joel Mackenzie; Pradeesh Parameswaran; Jimmy Lin; |
| 378 | Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints During Training Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Thus, a naïve use of validation loops during training will significantly increase training time. To address this issue, we propose Asyncval: a Python-based toolkit for efficiently validating DR checkpoints during training. |
Shengyao Zhuang; Guido Zuccon; |
| 379 | TaskMAD: A Platform for Multimodal Task-Centric Knowledge-Grounded Conversational Experimentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this demo, we introduce Task-oriented Multimodal Agent Dialogue (TaskMAD), a new platform that supports the creation of interactive multimodal and task-centric datasets in a Wizard-of-Oz experimental setup. |
Alessandro Speggiorin; Jeffrey Dalton; Anton Leuski; |
| 380 | Golden Retriever: A Real-Time Multi-Modal Text-Image Retrieval System with The Ability to Focus Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present the Golden Retriever, a system leveraging state-of-the-art visio-linguistic models (VLMs) for real-time text-image retrieval. |
Florian Schneider; Chris Biemann; |
| 381 | BiTe-REx: An Explainable Bilingual Text Retrieval System in The Automotive Domain Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work presents the first-of-its-kind Bilingual Text Retrieval Explanations (BiTe-REx) aimed at users performing competitor or wage analysis in the automotive domain. |
Viju Sudhi; Sabine Wehnert; Norbert Michael Homner; Sebastian Ernst; Mark Gonter; Andreas Krug; Ernesto William De Luca; |
| 382 | TARexp: A Python Framework for Technology-Assisted Review Experiments Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: Drawing on past open source TAR efforts, as well as design patterns from the IR and ML open source software, we present an open source Python framework for conducting experiments on TAR algorithms. |
Eugene Yang; David D. Lewis; |
| 383 | ZeroMatcher: A Cost-Off Entity Matching System Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The existing EM techniques can be either costly or tailored for a specific data type. We present ZeroMatcher, a cost-off entity matching system, which supports (i) handling EM tasks with different data types, including relational tables and knowledge graphs; (ii) keeping its EM performance always competitive by enabling the sub-modules to be updated in a lightweight manner, thus reducing development costs; and (iii) performing EM without human annotations to further slash the labor costs. |
Congcong Ge; Xiaocan Zeng; Lu Chen; Yunjun Gao; |
| 384 | Table Enrichment System for Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a table enrichment system that enriches a query table by adding external attributes (columns) from data lakes and improves the accuracy of machine learning predictive models. |
Yuyang Dong; Masafumi Oyamada; |
| 385 | QFinder: A Framework for Quantity-centric Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we demonstrate QFinder, our quantity-centric framework for ranking search results for queries with quantity constraints. |
Satya Almasian; Milena Bruseva; Michael Gertz; |
| 386 | ROGUE: A System for Exploratory Search of GANs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this article, we present ROGUE, a system to support exploratory search of images generated from GANs. |
Yang Liu; Alan Medlar; Dorota Glowacka; |
| 387 | CHERCHE: A New Tool to Rapidly Implement Pipelines in Information Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this demo paper, we present a new open-source python module for building information retrieval pipelines with transformers namely CHERCHE. |
Raphaël Sourty; Jose G. Moreno; Lynda Tamine; Francois-Paul Servant; |
| 388 | Online DATEing: A Web Interface for Temporal Annotations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we aim to increase the accessibility of temporal tagging systems by presenting an intuitive web interface, called "Online DATEing", which simplifies the interaction with existing temporal annotation frameworks. |
Dennis Aumiller; Satya Almasian; David Pohl; Michael Gertz; |
| 389 | Are Taylor’s Posts Risky? Evaluating Cumulative Revelations in Online Personal Data: A Persona-based Tool for Evaluating Awareness of Online Risks and Harms Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this demonstration paper, we present a tool designed to investigate how people assess and judge the relevance and potential risks ofsmall, apparently innocuous pieces of information associated with fictitious personas, such as Taylor Addison, when searching and browsing online profiles and social media. |
Leif Azzopardi; Jo Briggs; Melissa Duheric; Callum Nash; Emma Nicol; Wendy Moncur; Burkhard Schafer; |
| 390 | LawNet-Viz: A Web-based System to Visually Explore Networks of Law Article References Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present LawNet-Viz, a web-based tool for the modeling, analysis and visualization of law reference networks extracted from a statute law corpus. |
Lucio La Cava; Andrea Simeri; Andrea Tagarelli; |
| 391 | SpaceQA: Answering Questions About The Design of Space Missions and Space Craft Concepts Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: We present SpaceQA, to the best of our knowledge the first open-domain QA system in Space mission design. |
Andres Garcia-Silva; Cristian Berrio; Jose Manuel Gomez-Perez; Jose Antonio Martínez-Heras; Alessandro Donati; Ilaria Roma; |
| 392 | DIANES: A DEI Audit Toolkit for News Sources Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present DIANES, a DEI Audit Toolkit for News Sources. |
Xiaoxiao Shang; Zhiyuan Peng; Qiming Yuan; Sabiq Khan; Lauren Xie; Yi Fang; Subramaniam Vincent; |
| 393 | A2A-API: A Prototype for Biomedical Information Retrieval Research and Benchmarking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Finally, many of the existing baselines and systems are difficult to reproduce. We aim to alleviate all three of these bottlenecks with the launch of A2A-API. |
Maciej Rybinski; Liam Watts; Sarvnaz Karimi; |
| 394 | NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. |
Wen Zhang; Xiangnan Chen; Zhen Yao; Mingyang Chen; Yushan Zhu; Hongtao Yu; Yufeng Huang; Yajing Xu; Ningyu Zhang; Zezhong Xu; Zonggang Yuan; Feiyu Xiong; Huajun Chen; |
| 395 | IRVILAB: Gamified Searching on Multilingual Wikipedia Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper introduces a learning environment based on gamification of query construction for document retrieval, called IRVILAB (Information Retrieval Virtual Lab). |
Paavo Arvola; Tuulikki Alamettälä; |
| 396 | PKG: A Personal Knowledge Graph for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we demonstrate a novel system for integrating the data of a user from different sources into a Personal Knowledge Graph, i.e., PKG. |
Yu Yang; Jiangxu Lin; Xiaolian Zhang; Meng Wang; |
| 397 | A Python Interface to PISA! Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we demonstrate a new tool that provides a native Python wrapper around PISA. |
Sean MacAvaney; Craig Macdonald; |
| 398 | Arm: Efficient Learning of Neural Retrieval Models with Desired Accuracy By Automatic Knowledge Amalgamation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper will demonstrate the major workflow of Arm and present the produced student models to users. |
Linzhu Yu; Dawei Jiang; Ke Chen; Lidan Shou; |
| 399 | Quote Erat Demonstrandum: A Web Interface for Exploring The Quotebank Corpus Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here, we present an adaptive web interface for searching Quotebank, a massive collection of quotes from the news, which we make available at https://quotebank.dlab.tools. |
Vuk Vuković; Akhil Arora; Huan-Cheng Chang; Andreas Spitz; Robert West; |
| 400 | DDEN: A Heterogeneous Learning-to-Rank Approach with Deep Debiasing Experts Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: New challenges are faced when conducting heterogeneous ranking, including inconsistent feature space and more serious position bias caused by distinct representation spaces. Therefore, we propose Deep Debiasing Experts Network (DDEN), a novel heterogeneous LTR approach based on Mixture-of-Experts architecture and gating network, to deal with the inconsistent feature space of documents in ranking system. |
Wenchao Xiu; Yiran Wang; Taofeng Xue; Kai Zhang; Qin Zhang; Zhonghuo Wu; Yifan Yang; Gong Zhang; |
| 401 | ClueWeb22: 10 Billion Web Documents with Rich Information Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Several aspects of ClueWeb22 are available to the research community for the first time at this scale, for example, visual representations of rendered web pages, parsed structured information from the HTML document, and the alignment of document distributions (domains, languages, and topics) to commercial web search. This talk shares the design and construction of ClueWeb22, and discusses its new features. |
Arnold Overwijk; Chenyan Xiong; Jamie Callan; |
| 402 | An Auto Encoder-based Dimensionality Reduction Technique for Efficient Entity Linking in Business Phone Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present an entity linking system that leverages a transformer-based BERT encoder (the BLINK model) to connect the product and organization type entities in business phone conversations to their corresponding Wikipedia entries. |
Md Tahmid Rahman Laskar; Cheng Chen; Jonathan Johnston; Xue-Yong Fu; Shashi Bhushan TN; Simon Corston-Oliver; |
| 403 | An Intelligent Advertisement Short Video Production System Via Multi-Modal Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: paper proposes an intelligent advertising video production system driven by multi-modal retrieval, which only requires the input of descriptive copy. |
Yanheng Wei; Lianghua Huang; Yanhao Zhang; Yun Zheng; Pan Pan; |
| 404 | Applications and Future of Dense Retrieval in Industry Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this panel, we bring together experts in dense retrieval across multiple industry applications, including web search, enterprise and personal search, e-commerce, and out-of-domain retrieval. |
Yubin Kim; |
| 405 | Scalable User Interface Optimization Using Combinatorial Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we demonstrate how we deal with these issues using Combinatorial Bandits, an extension of Multi-Armed Bandits (MAB) where the agent selects not only one but multiple arms at the same time. |
Ioannis Kangas; Maud Schwoerer; Lucas Bernardi; |
| 406 | Flipping The Script: Inverse Information Seeking Dialogues for Market Research Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we introduce and provide a formal definition of an inverse information seeking agent, outline some of its unique challenges, and propose our novel framework to tackle this problem based on techniques from natural language processing (NLP) and IIR. |
Josh Seltzer; Kathy Cheng; Shi Zong; Jimmy Lin; |
| 407 | Information Ecosystem Threats in Minoritized Communities: Challenges, Open Problems and Research Directions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this panel, we will present and discuss the challenges and open problems facing such communities and the researchers hoping to serve them. |
Shiri Dori-Hacohen; Scott A. Hale; |
| 408 | Extractive Search for Analysis of Biomedical Texts Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a two-stage system targeted towards biomedical texts. |
Daniel Clothiaux; Ravi Starzl; |
| 409 | Organizing Portuguese Legal Documents Through Topic Discovery Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Using a dataset partially curated by the Jusbrasil legal team, we explore topic modeling solutions using state of the art language models, trained with legal Portuguese documents, to automatically organize and summarize this complex collection of documents. |
Daniela Vianna; Edleno Silva de Moura; |
| 410 | Query Facet Mapping and Its Applications in Streaming Services: The Netflix Case Study Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a high level overview of a Query Facet Mapping system that we have developed at Netflix, describe its main components, provide evaluation results with real-world data, and outline several potential applications. |
Sudeep Das; Ivan Provalov; Vickie Zhang; Weidong Zhang; |
| 411 | A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services As Example Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A framework is presented to automatically construct TaskFlow from large-scale dialogues and deploy online. |
Xiangyu Xi; Chenxu Lv; Yuncheng Hua; Wei Ye; Chaobo Sun; Shuaipeng Liu; Fan Yang; Guanglu Wan; |
| 412 | An Industrial Framework for Cold-Start Recommendation in Zero-Shot Scenarios Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this applied paper, we present an industrial framework recently deployed on Alipay to address the item cold-start problem in zero-shot scenarios. |
Zhaoxin Huan; Gongduo Zhang; Xiaolu Zhang; Jun Zhou; Qintong Wu; Lihong Gu; Jinjie Gu; Yong He; Yue Zhu; Linjian Mo; |
| 413 | Unsupervised Product Offering Title Quality Scores Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The focus of this work is to show how it is possible to assign a score that indicates the descriptive quality of product offers in an e-commerce marketplace environment using unsupervised methods. |
Henry S. Vieira; |
| 414 | Learning to Rank Instant Search Results with Multiple Indices: A Case Study in Search Aggregation for Entertainment Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Since results can be based on lexical matches, semantic matches, item-to-item similarity matches, or a variety of business logic driven sources, a key challenge is how to combine results into a single list. To accomplish this, we propose merging the lists via a Learning to Rank (LTR) neural model which takes into account the search query. |
Scott Rome; Sardar Hamidian; Richard Walsh; Kevin Foley; Ferhan Ture; |
| 415 | Recent Advances in Retrieval-Augmented Text Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Recently retrieval-augmented text generation has achieved state-of-the-art performance in many NLP tasks and has attracted increasing attention of the NLP and IR community, this tutorial thereby aims to present recent advances in retrieval-augmented text generation comprehensively and comparatively. |
Deng Cai; Yan Wang; Lemao Liu; Shuming Shi; |
| 416 | Retrieval and Recommendation Systems at The Crossroads of Artificial Intelligence, Ethics, and Regulation Related Papers Related Patents Related Grants Related Orgs Related Experts View Abstract: This tutorial aims at providing its audience an interdisciplinary overview about the topics of fairness and non-discrimination, diversity, and transparency of AI systems, tailored … |
Markus Schedl; Emilia Gómez; Elisabeth Lex; |
| 417 | Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: There is a lack of work to provide a comprehensive and systematic demonstration of the data characteristics, key challenges, most representative and state-of-the-art approaches, typical real- world applications and important future research directions in the area. This work aims to fill in these gaps so as to facilitate further research in this exciting and vibrant area. |
Shoujin Wang; Qi Zhang; Liang Hu; Xiuzhen Zhang; Yan Wang; Charu Aggarwal; |
| 418 | Continual Learning Dialogue Systems – Learning During Conversation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this tutorial, we introduce and discuss methods to give chatbots the ability to continuously and interactively learn new knowledge during conversation, i.e. "on-the-job" by themselves so that as the systems chat more and more with users, they become more and more knowledgeable and improve their performance over time. |
Sahisnu Mazumder; Bing Liu; |
| 419 | Improving Efficiency and Robustness of Transformer-based Information Retrieval Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This tutorial focuses on both theoretical and practical aspects of improving the efficiency and robustness of transformer-based approaches, so that these can be effectively used in practical, high-scale, and high-volume information retrieval (IR) scenarios. |
Edmon Begoli; Sudarshan Srinivasan; Maria Mahbub; |
| 420 | Gender Fairness in Information Retrieval Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this tutorial, we inform the audience of various studies that have systematically reported the presence of stereotypical gender biases in Information Retrieval (IR) systems. |
Amin Bigdeli; Negar Arabzadeh; Shirin SeyedSalehi; Morteza Zihayat; Ebrahim Bagheri; |
| 421 | Self-Supervised Learning for Recommender System Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this tutorial, we aim to provide a systemic review of existing self-supervised learning frameworks and analyze the corresponding challenges for various recommendation scenarios, such as general collaborative filtering paradigm, social recommendation, sequential recommendation, and multi-behavior recommendation. |
Chao Huang; Xiang Wang; Xiangnan He; Dawei Yin; |
| 422 | What The Actual…Examining User Behaviour in Information Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this tutorial we will address the main strategic and tactical choices for engaging with, designing and executing user studies, considering both evaluation and formative investigation. |
George Buchanan; Dana Mckay; |
| 423 | Beyond Opinion Mining: Summarizing Opinions of Customer Reviews Related Papers Related Patents Related Grants Related Orgs Related Experts Related Code View Highlight: In this tutorial, we present various aspects of opinion summarization that are useful for researchers and practitioners. |
Reinald Kim Amplayo; Arthur Brazinskas; Yoshi Suhara; Xiaolan Wang; Bing Liu; |
| 424 | Deep Knowledge Graph Representation Learning for Completion, Alignment, and Question Answering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The goal of this tutorial is to give IR researchers a thorough update on the best practices of neural KG representation and inference from AI, ML and NLP communities, and then explore how KG representation research in the IR community can be better driven by the needs of search, passage retrieval, and QA. |
Soumen Chakrabarti; |
| 425 | Conversational Information Seeking: Theory and Application Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This tutorial follows the content of the recent Conversational Information Seeking book authored by several of the tutorial presenters. |
Jeffrey Dalton; Sophie Fischer; Paul Owoicho; Filip Radlinski; Federico Rossetto; Johanne R. Trippas; Hamed Zamani; |
| 426 | Towards Reproducible Machine Learning Research in Information Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose this tutorial as a gentle introduction to help ensure reproducible research in IR, with a specific emphasis on ML aspects of IR research. |
Ana Lucic; Maurits Bleeker; Maarten de Rijke; Koustuv Sinha; Sami Jullien; Robert Stojnic; |