thinking inside the box learning hypercube representations for group recommendation

Thinking inside The Box: Learning Hypercube Representations for Group Recommendation

• Yin, Hongzhi
• Nguyen, Quoc Viet Hung

As a step beyond traditional personalized recommendation, group recommendation is the task of suggesting items that can satisfy a group of users. In group recommendation, the core is to design preference aggregation functions to obtain a quality summary of all group members' preferences. Such user and group preferences are commonly represented as points in the vector space (i.e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs. However, the resulted group representations, as points, lack adequate flexibility and capacity to account for the multi-faceted user preferences. Also, the point embedding-based preference aggregation is a less faithful reflection of a group's decision-making process, where all users have to agree on a certain value in each embedding dimension instead of a negotiable interval. 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. Specifically, we design the hypercube recommender (CubeRec) to adaptively learn group hypercubes from user embeddings with minimal information loss during preference aggregation, and to leverage a revamped distance metric to measure the affinity between group hypercubes and item points. Moreover, to counteract the long-standing issue of data sparsity in group recommendation, we make full use of the geometric expressiveness of hypercubes and innovatively incorporate self-supervision by intersecting two groups. Experiments on four real-world datasets have validated the superiority of CubeRec over state-of-the-art baselines.

• Computer Science - Information Retrieval

Thinking inside The Box: Learning Hypercube Representations for Group Recommendation

As a step beyond traditional personalized recommendation, group recommendation is the task of suggesting items that can satisfy a group of users. In group recommendation, the core is to design preference aggregation functions to obtain a quality summary of all group members' preferences. Such user and group preferences are commonly represented as points in the vector space (i.e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs. However, the resulted group representations, as points, lack adequate flexibility and capacity to account for the multi-faceted user preferences. Also, the point embedding-based preference aggregation is a less faithful reflection of a group's decision-making process, where all users have to agree on a certain value in each embedding dimension instead of a negotiable interval. 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. Specifically, we design the hypercube recommender (CubeRec) to adaptively learn group hypercubes from user embeddings with minimal information loss during preference aggregation, and to leverage a revamped distance metric to measure the affinity between group hypercubes and item points. Moreover, to counteract the long-standing issue of data sparsity in group recommendation, we make full use of the geometric expressiveness of hypercubes and innovatively incorporate self-supervision by intersecting two groups. Experiments on four real-world datasets have validated the superiority of CubeRec over state-of-the-art baselines.

Hongzhi Yin

Quoc Viet Hung Nguyen

Related Research

Hierarchical hyperedge embedding-based representation learning for group recommendation, groupim: a mutual information maximization framework for neural group recommendation, neural group recommendation based on a probabilistic semantic aggregation, predicting group choices from group profiles, saga: a submodular greedy algorithm for group recommendation, double-scale self-supervised hypergraph learning for group recommendation, personalized elastic embedding learning for on-device recommendation.

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Thinking inside The Box: Learning Hypercube Representations for Group Recommendation

2022, ArXiv

As a step beyond traditional personalized recommendation, group recommendation is the task of suggesting items that can satisfy a group of users. In group recommendation, the core is to design preference aggregation functions to obtain a quality summary of all group members’ preferences. Such user and group preferences are commonly represented as points in the vector space (i.e., embed-dings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs. However, the resulted group representations, as points, lack adequate flexibility and capacity to account for the multi-faceted user preferences. Also, the point embedding-based preference aggregation is a less faithful reflection of a group’s decision-making process, where all users have to agree on a certain value in each embedding dimension instead of a negotiable interval. In this paper, we propose a novel representation of groups via the notion of hypercubes, which are subspaces containing i...

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Frequent group activities of human beings have become an indispensable part in their daily life. Group recommendation can recommend satisfactory activities to group members in the recommender systems, and the key issue is how to aggregate preferences in different group members. Most existing group recommendation employed the predefined static aggregation strategies to aggregate the preferences of different group members, but these static strategies cannot simulate the dynamic group decision-making. Meanwhile, most of these methods depend on intuitions or assumptions to analyze the influence of group members and lack of convincing theoretical support. We argue that the influence of group members plays a particularly important role in group decision-making and it can better assist group profile modeling and perform more accurate group recommendation. To tackle the issue of preference aggregation for group recommendation, we propose a novel attentive aggregation representation learning...

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Providing useful resources to patients is essential in achieving the vision of participatory medicine. However, the problem of identifying pertinent content for a group of patients is even more difficult than identifying information for just one. Nevertheless, studies suggest that the group dynamics-based principles of behavior change have a positive effect on the patients’ welfare. Along these lines, in this paper, we present a multidimensional recommendation model in the health domain using collaborative filtering. We propose a novel semantic similarity function between users, going beyond patient medical problems, considering additional dimensions such as the education level, the health literacy, and the psycho-emotional status of the patients. Exploiting those dimensions, we are interested in providing recommendations that are both high relevant and fair to groups of patients. Consequently, we introduce the notion of fairness and we present a new aggregation method, accumulating...

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Thinking inside The Box: Learning Hypercube Representations for Group Recommendation

Tong Chen , Hongzhi Yin , Jing Long , Quoc Viet Hung Nguyen , Yang Wang , Meng Wang . Thinking inside The Box: Learning Hypercube Representations for Group Recommendation . In Enrique Amigó , Pablo Castells , Julio Gonzalo , Ben Carterette , J. Shane Culpepper , Gabriella Kazai , editors, SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022 . pages 1664-1673 , ACM, 2022. [doi]

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Join the community, search results for author: hongzhi yin, found 154 papers, 52 papers with code, poisoning attacks and defenses in recommender systems: a survey.

1 code implementation • 3 Jun 2024 • Zongwei Wang , Junliang Yu , Min Gao , Wei Yuan , Guanhua Ye , Shazia Sadiq , Hongzhi Yin

Modern recommender systems (RS) have profoundly enhanced user experience across digital platforms, yet they face significant threats from poisoning attacks.

Graph Condensation for Open-World Graph Learning

no code implementations • 27 May 2024 • Xinyi Gao , Tong Chen , Wentao Zhang , Yayong Li , Xiangguo Sun , Hongzhi Yin

Consequently, due to the limited generalization capacity of condensed graphs, applications that employ GC for efficient GNN training end up with sub-optimal GNNs when confronted with evolving graph structures and distributions in dynamic real-world situations.

Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition

no code implementations • 22 May 2024 • Xinyi Gao , Tong Chen , Wentao Zhang , Junliang Yu , Guanhua Ye , Quoc Viet Hung Nguyen , Hongzhi Yin

The increasing prevalence of large-scale graphs poses a significant challenge for graph neural network training, attributed to their substantial computational requirements.

Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations

no code implementations • 22 May 2024 • Jing Long , Guanhua Ye , Tong Chen , Yang Wang , Meng Wang , Hongzhi Yin

The rapid expansion of Location-Based Social Networks (LBSNs) has highlighted the importance of effective next Point-of-Interest (POI) recommendations, which leverage historical check-in data to predict users' next POIs to visit.

A Survey of Generative Techniques for Spatial-Temporal Data Mining

no code implementations • 15 May 2024 • Qianru Zhang , Haixin Wang , Cheng Long , Liangcai Su , Xingwei He , Jianlong Chang , Tailin Wu , Hongzhi Yin , Siu-Ming Yiu , Qi Tian , Christian S. Jensen

By integrating generative techniques and providing a standardized framework, the paper contributes to advancing the field and encourages researchers to explore the vast potential of generative techniques in spatial-temporal data mining.

Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures

1 code implementation • 23 Apr 2024 • Thanh Toan Nguyen , Quoc Viet Hung Nguyen , Thanh Tam Nguyen , Thanh Trung Huynh , Thanh Thi Nguyen , Matthias Weidlich , Hongzhi Yin

This survey aims to fill this gap by primarily focusing on poisoning attacks and their countermeasures.

Automated Similarity Metric Generation for Recommendation

no code implementations • 18 Apr 2024 • Liang Qu , Yun Lin , Wei Yuan , Xiaojun Wan , Yuhui Shi , Hongzhi Yin

Given the critical role of similarity metrics in recommender systems, existing methods mainly employ handcrafted similarity metrics to capture the complex characteristics of user-item interactions.

Poisoning Decentralized Collaborative Recommender System and Its Countermeasures

no code implementations • 1 Apr 2024 • Ruiqi Zheng , Liang Qu , Tong Chen , Kai Zheng , Yuhui Shi , Hongzhi Yin

Knowledge sharing also opens a backdoor for model poisoning attacks, where adversaries disguise themselves as benign clients and disseminate polluted knowledge to achieve malicious goals like promoting an item's exposure rate.

A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures

1 code implementation • 31 Mar 2024 • Thanh Tam Nguyen , Thanh Trung Huynh , Zhao Ren , Thanh Toan Nguyen , Phi Le Nguyen , Hongzhi Yin , Quoc Viet Hung Nguyen

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies.

Robust Federated Contrastive Recommender System against Model Poisoning Attack

no code implementations • 29 Mar 2024 • Wei Yuan , Chaoqun Yang , Liang Qu , Guanhua Ye , Quoc Viet Hung Nguyen , Hongzhi Yin

In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec.

Lightweight Embeddings for Graph Collaborative Filtering

1 code implementation • 27 Mar 2024 • Xurong Liang , Tong Chen , Lizhen Cui , Yang Wang , Meng Wang , Hongzhi Yin

Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods.

CaseLink: Inductive Graph Learning for Legal Case Retrieval

1 code implementation • 26 Mar 2024 • Yanran Tang , Ruihong Qiu , Hongzhi Yin , Xue Li , Zi Huang

In a case pool, there are three types of case connectivity relationships: the case reference relationship, the case semantic relationship, and the case legal charge relationship.

Open-World Semi-Supervised Learning for Node Classification

1 code implementation • 18 Mar 2024 • Yanling Wang , Jing Zhang , Lingxi Zhang , Lixin Liu , Yuxiao Dong , Cuiping Li , Hong Chen , Hongzhi Yin

Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community.

Spatial-temporal Memories Enhanced Graph Autoencoder for Anomaly Detection in Dynamic Graphs

no code implementations • 14 Mar 2024 • Jie Liu , Xuequn Shang , Xiaolin Han , Wentao Zhang , Hongzhi Yin

Then STRIPE incorporates separate spatial and temporal memory networks, which capture and store prototypes of normal patterns, thereby preserving the uniqueness of spatial and temporal normality.

Distribution-Aware Data Expansion with Diffusion Models

1 code implementation • 11 Mar 2024 • Haowei Zhu , Ling Yang , Jun-Hai Yong , Hongzhi Yin , Jiawei Jiang , Meng Xiao , Wentao Zhang , Bin Wang

In this paper, we propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model.

BiVRec: Bidirectional View-based Multimodal Sequential Recommendation

no code implementations • 27 Feb 2024 • Jiaxi Hu , Jingtong Gao , Xiangyu Zhao , Yuehong Hu , Yuxuan Liang , Yiqi Wang , Ming He , Zitao Liu , Hongzhi Yin

The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research.

EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems

no code implementations • 1 Feb 2024 • Sheng Zhang , Maolin Wang , Yao Zhao , Chenyi Zhuang , Jinjie Gu , Ruocheng Guo , Xiangyu Zhao , Zijian Zhang , Hongzhi Yin

Our research addresses the computational and resource inefficiencies that current Sequential Recommender Systems (SRSs) suffer from.

Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation

no code implementations • 31 Jan 2024 • Liang Qu , Wei Yuan , Ruiqi Zheng , Lizhen Cui , Yuhui Shi , Hongzhi Yin

To bridge this gap, this paper explores a user-governed data contribution federated recommendation architecture where users are free to take control of whether they share data and the proportion of data they share to the server.

OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for Medication Recommendation

no code implementations • 29 Jan 2024 • Weicong Tan , Weiqing Wang , Xin Zhou , Wray Buntine , Gordon Bingham , Hongzhi Yin

Most existing medication recommendation models learn representations for medical concepts based on electronic health records (EHRs) and make recommendations with learnt representations.

Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation

1 code implementation • 26 Jan 2024 • Lei Guo , Ziang Lu , Junliang Yu , Nguyen Quoc Viet Hung , Hongzhi Yin

For Limitation 2, we model items in a universal feature space by their description texts.

Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation

no code implementations • 26 Jan 2024 • Jing Long , Tong Chen , Guanhua Ye , Kai Zheng , Nguyen Quoc Viet Hung , Hongzhi Yin

Empirical results demonstrate that PTIA poses a significant threat to users' historical trajectories.

Challenging Low Homophily in Social Recommendation

no code implementations • 26 Jan 2024 • Wei Jiang , Xinyi Gao , Guandong Xu , Tong Chen , Hongzhi Yin

To comprehensively extract preference-aware homophily information latent in the social graph, we propose Social Heterophily-alleviating Rewiring (SHaRe), a data-centric framework for enhancing existing graph-based social recommendation models.

Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation

no code implementations • 24 Jan 2024 • Ruiqi Zheng , Liang Qu , Tong Chen , Lizhen Cui , Yuhui Shi , Hongzhi Yin

Collaborative Learning (CL) emerges to promote model sharing among users, where reference data is an intermediary that allows users to exchange their soft decisions without directly sharing their private data or parameters, ensuring privacy and benefiting from collaboration.

Graph Condensation: A Survey

no code implementations • 22 Jan 2024 • Xinyi Gao , Junliang Yu , Wei Jiang , Tong Chen , Wentao Zhang , Hongzhi Yin

The burgeoning volume of graph data poses significant challenges in storage, transmission, and particularly the training of graph neural networks (GNNs).

On-Device Recommender Systems: A Comprehensive Survey

no code implementations • 21 Jan 2024 • Hongzhi Yin , Liang Qu , Tong Chen , Wei Yuan , Ruiqi Zheng , Jing Long , Xin Xia , Yuhui Shi , Chengqi Zhang

Recently, driven by the advances in storage, communication, and computation capabilities of edge devices, there has been a shift of focus from CloudRSs to on-device recommender systems (DeviceRSs), which leverage the capabilities of edge devices to minimize centralized data storage requirements, reduce the response latency caused by communication overheads, and enhance user privacy and security by localizing data processing and model training.

MCRPL: A Pretrain, Prompt & Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain Recommendation

no code implementations • 16 Jan 2024 • Hao liu , Lei Guo , Lei Zhu , Yongqiang Jiang , Min Gao , Hongzhi Yin

To overcome the above challenges, we focus on NMCR, and devise MCRPL as our solution.

ROIC-DM: Robust Text Inference and Classification via Diffusion Model

no code implementations • 7 Jan 2024 • Shilong Yuan , Wei Yuan , Hongzhi Yin , Tieke He

While language models have made many milestones in text inference and classification tasks, they remain susceptible to adversarial attacks that can lead to unforeseen outcomes.

Poisoning Attacks against Recommender Systems: A Survey

1 code implementation • 3 Jan 2024 • Zongwei Wang , Min Gao , Junliang Yu , Hao Ma , Hongzhi Yin , Shazia Sadiq

This survey paper provides a systematic and up-to-date review of the research landscape on Poisoning Attacks against Recommendation (PAR).

Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion

no code implementations • 25 Dec 2023 • Lijian Chen , Wei Yuan , Tong Chen , Guanhua Ye , Quoc Viet Hung Nguyen , Hongzhi Yin

Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences.

PUMA: Efficient Continual Graph Learning with Graph Condensation

1 code implementation • 22 Dec 2023 • Yilun Liu , Ruihong Qiu , Yanran Tang , Hongzhi Yin , Zi Huang

Our prior work, CaT is a replay-based framework with a balanced continual learning procedure, which designs a small yet effective memory bank for replaying data by condensing incoming graphs.

On-Device Recommender Systems: A Tutorial on The New-Generation Recommendation Paradigm

no code implementations • 18 Dec 2023 • Hongzhi Yin , Tong Chen , Liang Qu , Bin Cui

Given the sheer volume of contemporary e-commerce applications, recommender systems (RSs) have gained significant attention in both academia and industry.

Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks

1 code implementation • 30 Nov 2023 • Zongwei Wang , Junliang Yu , Min Gao , Hongzhi Yin , Bin Cui , Shazia Sadiq

Our analysis indicates that this vulnerability is attributed to the uniform spread of representations caused by the InfoNCE loss.

Hide Your Model: A Parameter Transmission-free Federated Recommender System

1 code implementation • 25 Nov 2023 • Wei Yuan , Chaoqun Yang , Liang Qu , Quoc Viet Hung Nguyen , JianXin Li , Hongzhi Yin

Existing FedRecs generally adhere to a learning protocol in which a central server shares a global recommendation model with clients, and participants achieve collaborative learning by frequently communicating the model's public parameters.

Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution

1 code implementation • 19 Nov 2023 • Yuting Sun , Guansong Pang , Guanhua Ye , Tong Chen , Xia Hu , Hongzhi Yin

The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient solution.

Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation

no code implementations • 1 Nov 2023 • Jiangnan Xia , Yu Yang , Senzhang Wang , Hongzhi Yin , Jiannong Cao , Philip S. Yu

To this end, we investigate a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network.

Defense Against Model Extraction Attacks on Recommender Systems

1 code implementation • 25 Oct 2023 • Sixiao Zhang , Hongzhi Yin , Hongxu Chen , Cheng Long

These gradients are used to compute a swap loss, which maximizes the loss of the student model.

Budgeted Embedding Table For Recommender Systems

no code implementations • 23 Oct 2023 • Yunke Qu , Tong Chen , Quoc Viet Hung Nguyen , Hongzhi Yin

Experiments have shown state-of-the-art performance on two real-world datasets when BET is paired with three popular recommender models under different memory budgets.

Motif-Based Prompt Learning for Universal Cross-Domain Recommendation

no code implementations • 20 Oct 2023 • Bowen Hao , Chaoqun Yang , Lei Guo , Junliang Yu , Hongzhi Yin

By unifying pre-training and recommendation tasks as a common motif-based similarity learning task and integrating adaptable prompt parameters to guide the model in downstream recommendation tasks, MOP excels in transferring domain knowledge effectively.

Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation

no code implementations • 17 Oct 2023 • Xinyi Gao , Wentao Zhang , Junliang Yu , Yingxia Shao , Quoc Viet Hung Nguyen , Bin Cui , Hongzhi Yin

To further accelerate Scalable GNNs inference in this inductive setting, we propose an online propagation framework and two novel node-adaptive propagation methods that can customize the optimal propagation depth for each node based on its topological information and thereby avoid redundant feature propagation.

To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data

1 code implementation • 15 Sep 2023 • Hechuan Wen , Tong Chen , Li Kheng Chai , Shazia Sadiq , Kai Zheng , Hongzhi Yin

Due to the imbalanced nature of networked observational data, the causal effect predictions for some individuals can severely violate the positivity/overlap assumption, rendering unreliable estimations.

Learning Compact Compositional Embeddings via Regularized Pruning for Recommendation

1 code implementation • 7 Sep 2023 • Xurong Liang , Tong Chen , Quoc Viet Hung Nguyen , JianXin Li , Hongzhi Yin

In addition, we innovatively design a regularized pruning mechanism in CERP, such that the two sparsified meta-embedding tables are encouraged to encode information that is mutually complementary.

Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation

no code implementations • 25 Aug 2023 • Guanhua Ye , Tong Chen , Quoc Viet Hung Nguyen , Hongzhi Yin

As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalized IoT service providers have to put unlearning functionality into their consideration.

Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation

1 code implementation • 24 Aug 2023 • Xin Xia , Junliang Yu , Guandong Xu , Hongzhi Yin

On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy.

Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph

no code implementations • 15 Aug 2023 • Yi Liu , Hongrui Xuan , Bohan Li , Meng Wang , Tong Chen , Hongzhi Yin

However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement.

Graph Condensation for Inductive Node Representation Learning

no code implementations • 29 Jul 2023 • Xinyi Gao , Tong Chen , Yilong Zang , Wentao Zhang , Quoc Viet Hung Nguyen , Kai Zheng , Hongzhi Yin

To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning.

BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection

no code implementations • 28 Jul 2023 • Jie Liu , Mengting He , Xuequn Shang , Jieming Shi , Bin Cui , Hongzhi Yin

By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies.

HeteFedRec: Federated Recommender Systems with Model Heterogeneity

no code implementations • 24 Jul 2023 • Wei Yuan , Liang Qu , Lizhen Cui , Yongxin Tong , Xiaofang Zhou , Hongzhi Yin

Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems.

Variational Counterfactual Prediction under Runtime Domain Corruption

no code implementations • 23 Jun 2023 • Hechuan Wen , Tong Chen , Li Kheng Chai , Shazia Sadiq , Junbin Gao , Hongzhi Yin

We term the co-occurrence of domain shift and inaccessible variables runtime domain corruption, which seriously impairs the generalizability of a trained counterfactual predictor.

Personalized Elastic Embedding Learning for On-Device Recommendation

no code implementations • 18 Jun 2023 • Ruiqi Zheng , Liang Qu , Tong Chen , Kai Zheng , Yuhui Shi , Hongzhi Yin

Given a memory budget, PEEL efficiently generates PEEs by selecting embedding blocks with the largest weights, making it adaptable to dynamic memory budgets on devices.

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no code implementations • 17 Jun 2023 • Shangfei Zheng , Hongzhi Yin , Tong Chen , Quoc Viet Hung Nguyen , Wei Chen , Lei Zhao

This paper proposes a model called TMR to mine valuable information from simulated data environments.

Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs

1 code implementation • 18 May 2023 • Jintang Li , Sheng Tian , Ruofan Wu , Liang Zhu , Welong Zhao , Changhua Meng , Liang Chen , Zibin Zheng , Hongzhi Yin

We approach the problem by our proposed STEP, a self-supervised temporal pruning framework that learns to remove potentially redundant edges from input dynamic graphs.

Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures

no code implementations • 14 May 2023 • Wei Yuan , Shilong Yuan , Chaoqun Yang , Quoc Viet Hung Nguyen , Hongzhi Yin

Therefore, when incorporating visual information in FedRecs, all existing model poisoning attacks' effectiveness becomes questionable.

KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment

1 code implementation • 11 May 2023 • Lingzhi Wang , Tong Chen , Wei Yuan , Xingshan Zeng , Kam-Fai Wong , Hongzhi Yin

Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific training instances as if they have never existed in the training set.

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no code implementations • 1 May 2023 • Xuhui Ren , Tong Chen , Quoc Viet Hung Nguyen , Lizhen Cui , Zi Huang , Hongzhi Yin

Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions.

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no code implementations • 28 Apr 2023 • Jie Liu , Mengting He , Guangtao Wang , Nguyen Quoc Viet Hung , Xuequn Shang , Hongzhi Yin

minority classes to balance the label and topology distribution.

Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling

no code implementations • 24 Apr 2023 • Xuhui Ren , Wei Yuan , Tong Chen , Chaoqun Yang , Quoc Viet Hung Nguyen , Hongzhi Yin

Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences.

Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation

no code implementations • 9 Apr 2023 • Lei Guo , Chunxiao Wang , Xinhua Wang , Lei Zhu , Hongzhi Yin

Cross-domain Recommendation (CR) has been extensively studied in recent years to alleviate the data sparsity issue in recommender systems by utilizing different domain information.

Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation

no code implementations • 8 Apr 2023 • Jing Long , Tong Chen , Nguyen Quoc Viet Hung , Guandong Xu , Kai Zheng , Hongzhi Yin

In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e. g., dimension \& number of hidden layers).

DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning

no code implementations • 8 Apr 2023 • Shangfei Zheng , Hongzhi Yin , Tong Chen , Quoc Viet Hung Nguyen , Wei Chen , Lei Zhao

Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack explainability.

Continuous Input Embedding Size Search For Recommender Systems

no code implementations • 7 Apr 2023 • Yunke Qu , Tong Chen , Xiangyu Zhao , Lizhen Cui , Kai Zheng , Hongzhi Yin

Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance.

Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures

no code implementations • 6 Apr 2023 • Wei Yuan , Quoc Viet Hung Nguyen , Tieke He , Liang Chen , Hongzhi Yin

To reveal the real vulnerability of FedRecs, in this paper, we present a new poisoning attack method to manipulate target items' ranks and exposure rates effectively in the top-$K$ recommendation without relying on any prior knowledge.

TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT

no code implementations • 7 Mar 2023 • Yuting Sun , Tong Chen , Quoc Viet Hung Nguyen , Hongzhi Yin

With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices.

Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks

no code implementations • 27 Feb 2023 • Xinyi Gao , Wentao Zhang , Tong Chen , Junliang Yu , Hung Quoc Viet Nguyen , Hongzhi Yin

To tackle the imbalance of minority classes and supplement their inadequate semantics, we present the first method for the semantic imbalance problem in imbalanced HINs named Semantic-aware Node Synthesis (SNS).

Semi-decentralized Federated Ego Graph Learning for Recommendation

no code implementations • 10 Feb 2023 • Liang Qu , Ningzhi Tang , Ruiqi Zheng , Quoc Viet Hung Nguyen , Zi Huang , Yuhui Shi , Hongzhi Yin

In light of this, we propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner.

Interaction-level Membership Inference Attack Against Federated Recommender Systems

no code implementations • 26 Jan 2023 • Wei Yuan , Chaoqun Yang , Quoc Viet Hung Nguyen , Lizhen Cui , Tieke He , Hongzhi Yin

An interaction-level membership inference attacker is first designed, and then the classical privacy protection mechanism, Local Differential Privacy (LDP), is adopted to defend against the membership inference attack.

Knowledge Enhancement for Contrastive Multi-Behavior Recommendation

no code implementations • 13 Jan 2023 • Hongrui Xuan , Yi Liu , Bohan Li , Hongzhi Yin

In particular, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement, and utilize knowledge graph in the knowledge enhancement module to derive more robust knowledge-aware representations for items.

HiTSKT: A Hierarchical Transformer Model for Session-Aware Knowledge Tracing

no code implementations • 23 Dec 2022 • Fucai Ke , Weiqing Wang , Weicong Tan , Lan Du , Yuan Jin , Yujin Huang , Hongzhi Yin

Knowledge tracing (KT) aims to leverage students' learning histories to estimate their mastery levels on a set of pre-defined skills, based on which the corresponding future performance can be accurately predicted.

Efficient Graph Neural Network Inference at Large Scale

no code implementations • 1 Nov 2022 • Xinyi Gao , Wentao Zhang , Yingxia Shao , Quoc Viet Hung Nguyen , Bin Cui , Hongzhi Yin

Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications.

Self-supervised Graph-based Point-of-interest Recommendation

no code implementations • 22 Oct 2022 • Yang Li , Tong Chen , Peng-Fei Zhang , Zi Huang , Hongzhi Yin

In order to counteract the scarcity and incompleteness of POI check-ins, we propose a novel self-supervised learning paradigm in \ssgrec, where the trajectory representations are contrastively learned from two augmented views on geolocations and temporal transitions.

Federated Unlearning for On-Device Recommendation

no code implementations • 20 Oct 2022 • Wei Yuan , Hongzhi Yin , Fangzhao Wu , Shijie Zhang , Tieke He , Hao Wang

It removes a user's contribution by rolling back and calibrating the historical parameter updates and then uses these updates to speed up federated recommender reconstruction.

Efficient Bi-Level Optimization for Recommendation Denoising

2 code implementations • 19 Oct 2022 • Zongwei Wang , Min Gao , Wentao Li , Junliang Yu , Linxin Guo , Hongzhi Yin

To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time.

Efficient On-Device Session-Based Recommendation

1 code implementation • 27 Sep 2022 • Xin Xia , Junliang Yu , Qinyong Wang , Chaoqun Yang , Quoc Viet Hung Nguyen , Hongzhi Yin

Specifically, each item is represented by a compositional code that consists of several codewords, and we learn embedding vectors to represent each codeword instead of each item.

Switchable Online Knowledge Distillation

1 code implementation • 12 Sep 2022 • Biao Qian , Yang Wang , Hongzhi Yin , Richang Hong , Meng Wang

Instead of focusing on the accuracy gap at test phase by the existing arts, the core idea of SwitOKD is to adaptively calibrate the gap at training phase, namely distillation gap, via a switching strategy between two modes -- expert mode (pause the teacher while keep the student learning) and learning mode (restart the teacher).

Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation

1 code implementation • 8 Sep 2022 • Ruihong Qiu , Zi Huang , Hongzhi Yin

In this paper, we propose the Overparameterised Recommender (OverRec), which utilises a recurrent neural tangent kernel (RNTK) as a similarity measurement for user sequences to successfully bypass the restriction of hardware for huge models.

A Survey of Machine Unlearning

1 code implementation • 6 Sep 2022 • Thanh Tam Nguyen , Thanh Trung Huynh , Phi Le Nguyen , Alan Wee-Chung Liew , Hongzhi Yin , Quoc Viet Hung Nguyen

Specifically, as a category collection of cutting-edge studies, the intention behind this article is to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine unlearning and its formulations, design criteria, removal requests, algorithms, and applications.

XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation

1 code implementation • 6 Sep 2022 • Junliang Yu , Xin Xia , Tong Chen , Lizhen Cui , Nguyen Quoc Viet Hung , Hongzhi Yin

Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance.

Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction

1 code implementation • 3 Sep 2022 • Yufeng Zhang , Weiqing Wang , Hongzhi Yin , Pengpeng Zhao , Wei Chen , Lei Zhao

A more challenging scenario is that emerging KGs consist of only unseen entities, called as disconnected emerging KGs (DEKGs).

MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning

no code implementations • 3 Sep 2022 • Shangfei Zheng , Weiqing Wang , Jianfeng Qu , Hongzhi Yin , Wei Chen , Lei Zhao

Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i. e., texts and images), which enhance the diversity of knowledge.

ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences

no code implementations • 28 Jul 2022 • Mubashir Imran , Hongzhi Yin , Tong Chen , Nguyen Quoc Viet Hung , Alexander Zhou , Kai Zheng

Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs

no code implementations • 17 Jul 2022 • Thanh Tam Nguyen , Thanh Cong Phan , Minh Hieu Nguyen , Matthias Weidlich , Hongzhi Yin , Jun Jo , Quoc Viet Hung Nguyen

Since the spread of rumours in social media is commonly modelled using feature-annotated graphs, we propose a query-by-example approach that, given a rumour graph, extracts the $k$ most similar and diverse subgraphs from past rumours.

Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution

no code implementations • 1 Jul 2022 • Yu Yang , Hongzhi Yin , Jiannong Cao , Tong Chen , Quoc Viet Hung Nguyen , Xiaofang Zhou , Lei Chen

Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information.

Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation

1 code implementation • 16 Jun 2022 • Lei Guo , Jinyu Zhang , Li Tang , Tong Chen , Lei Zhu , Hongzhi Yin

Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains.

Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation

1 code implementation • 16 Jun 2022 • Lei Guo , Jinyu Zhang , Tong Chen , Xinhua Wang , Hongzhi Yin

Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet challenging task that simultaneously considers the shared-account and cross-domain characteristics in the sequential recommendation.

Comprehensive Privacy Analysis on Federated Recommender System against Attribute Inference Attacks

no code implementations • 24 May 2022 • Shijie Zhang , Wei Yuan , Hongzhi Yin

In this paper, we first design a novel attribute inference attacker to perform a comprehensive privacy analysis of the state-of-the-art federated recommender models.

Detecting Rumours with Latency Guarantees using Massive Streaming Data

no code implementations • 13 May 2022 • Thanh Tam Nguyen , Thanh Trung Huynh , Hongzhi Yin , Matthias Weidlich , Thanh Thi Nguyen , Thai Son Mai , Quoc Viet Hung Nguyen

Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate.

Spatial-Temporal Meta-path Guided Explainable Crime Prediction

no code implementations • 4 May 2022 • Yuting Sun , Tong Chen , Hongzhi Yin

Exposure to crime and violence can harm individuals' quality of life and the economic growth of communities.

On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation

1 code implementation • 23 Apr 2022 • Xin Xia , Hongzhi Yin , Junliang Yu , Qinyong Wang , Guandong Xu , Nguyen Quoc Viet Hung

Meanwhile, to compensate for the capacity loss caused by compression, we develop a self-supervised knowledge distillation framework which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher).

Single-shot Embedding Dimension Search in Recommender System

no code implementations • 7 Apr 2022 • Liang Qu , Yonghong Ye , Ningzhi Tang , Lixin Zhang , Yuhui Shi , Hongzhi Yin

In order to alleviate the above issues, some works focus on automated embedding dimension search by formulating it as hyper-parameter optimization or embedding pruning problems.

Thinking inside The Box: Learning Hypercube Representations for Group Recommendation

1 code implementation • 6 Apr 2022 • Tong Chen , Hongzhi Yin , Jing Long , Quoc Viet Hung Nguyen , Yang Wang , Meng Wang

Such user and group preferences are commonly represented as points in the vector space (i. e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs.

Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations

no code implementations • 1 Apr 2022 • Yan Zhang , Changyu Li , Ivor W. Tsang , Hui Xu , Lixin Duan , Hongzhi Yin , Wen Li , Jie Shao

Motivated by the idea of meta-augmentation, in this paper, by treating a user's preference over items as a task, we propose a so-called Diverse Preference Augmentation framework with multiple source domains based on meta-learning (referred to as MetaDPA) to i) generate diverse ratings in a new domain of interest (known as target domain) to handle overfitting on the case of sparse interactions, and to ii) learn a preference model in the target domain via a meta-learning scheme to alleviate cold-start issues.

Decentralized Collaborative Learning Framework for Next POI Recommendation

no code implementations • 30 Mar 2022 • Jing Long , Tong Chen , Nguyen Quoc Viet Hung , Hongzhi Yin

On this basis, we propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner.

Self-Supervised Learning for Recommender Systems: A Survey

1 code implementation • 29 Mar 2022 • Junliang Yu , Hongzhi Yin , Xin Xia , Tong Chen , Jundong Li , Zi Huang

In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data.

AutoML for Deep Recommender Systems: A Survey

no code implementations • 25 Mar 2022 • Ruiqi Zheng , Liang Qu , Bin Cui , Yuhui Shi , Hongzhi Yin

To tackle this problem, Automated Machine Learning (AutoML) is introduced to automatically search for the proper candidates for different parts of deep recommender systems.

Towards Revenue Maximization with Popular and Profitable Products

no code implementations • 26 Feb 2022 • Wensheng Gan , Guoting Chen , Hongzhi Yin , Philippe Fournier-Viger , Chien-Ming Chen , Philip S. Yu

To fulfill this gap, in this paper, we first propose a general profit-oriented framework to address the problem of revenue maximization based on economic behavior, and compute the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.

Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion

no code implementations • 19 Feb 2022 • Shiqi Wang , Chongming Gao , Min Gao , Junliang Yu , Zongwei Wang , Hongzhi Yin

By providing users with opportunities to experience goods without charge, a free trial makes adopters know more about products and thus encourages their willingness to buy.

Unified Question Generation with Continual Lifelong Learning

no code implementations • 24 Jan 2022 • Wei Yuan , Hongzhi Yin , Tieke He , Tong Chen , Qiufeng Wang , Lizhen Cui

To solve the problems, we propose a model named Unified-QG based on lifelong learning techniques, which can continually learn QG tasks across different datasets and formats.

DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks

no code implementations • 8 Jan 2022 • Mubashir Imran , Hongzhi Yin , Tong Chen , Zi Huang , Kai Zheng

Such heterogeneous network embedding (HNE) methods effectively harness the heterogeneity of small-scale HINs.

LECF: Recommendation via Learnable Edge Collaborative Filtering

1 code implementation • Science China Information Sciences 2021 • Shitao Xiao , Yingxia Shao , Yawen Li , Hongzhi Yin , Yanyan Shen & Bin Cui

In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering (LECF).

Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent

no code implementations • 17 Dec 2021 • Guanhua Ye , Hongzhi Yin , Tong Chen , Miao Xu , Quoc Viet Hung Nguyen , Jiangning Song

Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating.

Incomplete Knowledge Graph Alignment

no code implementations • 17 Dec 2021 • Vinh Van Tong , Thanh Trung Huynh , Thanh Tam Nguyen , Hongzhi Yin , Quoc Viet Hung Nguyen , Quyet Thang Huynh

Our KG embedding framework exploits two feature channels: transitivity-based and proximity-based.

Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation

1 code implementation • 16 Dec 2021 • Junliang Yu , Hongzhi Yin , Xin Xia , Tong Chen , Lizhen Cui , Quoc Viet Hung Nguyen

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling the data sparsity issue.

Self-supervised Graph Learning for Occasional Group Recommendation

no code implementations • 4 Dec 2021 • Bowen Hao , Hongzhi Yin , Cuiping Li , Hong Chen

As each occasional group has extremely sparse interactions with items, traditional group recommendation methods can not learn high-quality group representations.

A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation

no code implementations • 4 Dec 2021 • Bowen Hao , Hongzhi Yin , Jing Zhang , Cuiping Li , Hong Chen

In terms of the pretext task, in addition to considering the intra-correlations of users and items by the embedding reconstruction task, we add embedding contrastive learning task to capture inter-correlations of users and items.

A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures

1 code implementation • 25 Nov 2021 • Minh Tam Pham , Thanh Trung Huynh , Van Vinh Tong , Thanh Tam Nguyen , Thanh Thi Nguyen , Hongzhi Yin , Quoc Viet Hung Nguyen

In recent years, visual forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security.

PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion

no code implementations • 21 Oct 2021 • Shijie Zhang , Hongzhi Yin , Tong Chen , Zi Huang , Quoc Viet Hung Nguyen , Lizhen Cui

Evaluations on two real-world datasets show that 1) our attack model significantly boosts the exposure rate of the target item in a stealthy way, without harming the accuracy of the poisoned recommender; and 2) existing defenses are not effective enough, highlighting the need for new defenses against our local model poisoning attacks to federated recommender systems.

Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation

2 code implementations • 12 Oct 2021 • Ruihong Qiu , Zi Huang , Hongzhi Yin , Zijian Wang

In this paper, both empirical and theoretical investigations of this representation degeneration problem are first provided, based on which a novel recommender model DuoRec is proposed to improve the item embeddings distribution.

Secure Your Ride: Real-time Matching Success Rate Prediction for Passenger-Driver Pairs

no code implementations • 14 Sep 2021 • Yuandong Wang , Hongzhi Yin , Lian Wu , Tong Chen , Chunyang Liu

In recent years, online ride-hailing platforms have become an indispensable part of urban transportation.

Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation

1 code implementation • 9 Sep 2021 • Junwei Zhang , Min Gao , Junliang Yu , Lei Guo , Jundong Li , Hongzhi Yin

Technically, for (1), a hierarchical hypergraph convolutional network based on the user- and group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups.

Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation

2 code implementations • 1 Sep 2021 • Ruihong Qiu , Zi Huang , Hongzhi Yin

In this paper, we propose a novel sequential recommendation framework to overcome these challenges based on a memory augmented multi-instance contrastive predictive coding scheme, denoted as MMInfoRec.

Lightweight Self-Attentive Sequential Recommendation

no code implementations • 25 Aug 2021 • Yang Li , Tong Chen , Peng-Fei Zhang , Hongzhi Yin

Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various sequential recommendation tasks.

Self-Supervised Graph Co-Training for Session-based Recommendation

2 code implementations • 24 Aug 2021 • Xin Xia , Hongzhi Yin , Junliang Yu , Yingxia Shao , Lizhen Cui

In this paper, for informative session-based data augmentation, we combine self-supervised learning with co-training, and then develop a framework to enhance session-based recommendation.

CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation

1 code implementation • 6 Jul 2021 • Ruihong Qiu , Sen Wang , Zhi Chen , Hongzhi Yin , Zi Huang

Existing visually-aware models make use of the visual features as a separate collaborative signal similarly to other features to directly predict the user's preference without considering a potential bias, which gives rise to a visually biased recommendation.

Exploiting Positional Information for Session-based Recommendation

no code implementations • 2 Jul 2021 • Ruihong Qiu , Zi Huang , Tong Chen , Hongzhi Yin

According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session.

Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks

no code implementations • 2 Jul 2021 • Ruihong Qiu , Zi Huang , Jingjing Li , Hongzhi Yin

Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i. e., a sequence of interactions between a user and multiple items within a period, to preserve the user's recent interest.

Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation

no code implementations • 30 Jun 2021 • Yang Li , Tong Chen , Yadan Luo , Hongzhi Yin , Zi Huang

Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation.

Socially-Aware Self-Supervised Tri-Training for Recommendation

1 code implementation • 7 Jun 2021 • Junliang Yu , Hongzhi Yin , Min Gao , Xin Xia , Xiangliang Zhang , Nguyen Quoc Viet Hung

Under this scheme, only a bijective mapping is built between nodes in two different views, which means that the self-supervision signals from other nodes are being neglected.

ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks

1 code implementation • 5 Jun 2021 • Liang Qu , Huaisheng Zhu , Ruiqi Zheng , Yuhui Shi , Hongzhi Yin

Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection.

Learning Elastic Embeddings for Customizing On-Device Recommenders

no code implementations • 4 Jun 2021 • Tong Chen , Hongzhi Yin , Yujia Zheng , Zi Huang , Yang Wang , Meng Wang

The core idea is to compose elastic embeddings for each item, where an elastic embedding is the concatenation of a set of embedding blocks that are carefully chosen by an automated search function.

Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems

1 code implementation • 19 May 2021 • Sixiao Zhang , Hongxu Chen , Xiao Ming , Lizhen Cui , Hongzhi Yin , Guandong Xu

Hyperbolic space and hyperbolic embeddings are becoming a popular research field for recommender systems.

Learning to Ask Appropriate Questions in Conversational Recommendation

no code implementations • 11 May 2021 • Xuhui Ren , Hongzhi Yin , Tong Chen , Hao Wang , Zi Huang , Kai Zheng

Hence, the ability to generate suitable clarifying questions is the key to timely tracing users' dynamic preferences and achieving successful recommendations.

DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation

no code implementations • 7 May 2021 • Lei Guo , Li Tang , Tong Chen , Lei Zhu , Quoc Viet Hung Nguyen , Hongzhi Yin

Shared-account Cross-domain Sequential recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains.

Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling

no code implementations • 5 Apr 2021 • Tong Chen , Hongzhi Yin , Xiangliang Zhang , Zi Huang , Yang Wang , Meng Wang

As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering.

Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning

no code implementations • 4 Apr 2021 • Tong Chen , Hongzhi Yin , Jie Ren , Zi Huang , Xiangliang Zhang , Hao Wang

In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes.

Fast-adapting and Privacy-preserving Federated Recommender System

no code implementations • 2 Apr 2021 • Qinyong Wang , Hongzhi Yin , Tong Chen , Junliang Yu , Alexander Zhou , Xiangliang Zhang

In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem.

Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation

no code implementations • 24 Mar 2021 • Lei Guo , Hongzhi Yin , Tong Chen , Xiangliang Zhang , Kai Zheng

However, the representation learning for a group is most complex beyond the fusion of group member representation, as the personal preferences and group preferences may be in different spaces.

Graph Embedding for Recommendation against Attribute Inference Attacks

no code implementations • 29 Jan 2021 • Shijie Zhang , Hongzhi Yin , Tong Chen , Zi Huang , Lizhen Cui , Xiangliang Zhang

Specifically, in GERAI, we bind the information perturbation mechanism in differential privacy with the recommendation capability of graph convolutional networks.

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

4 code implementations • 16 Jan 2021 • Junliang Yu , Hongzhi Yin , Jundong Li , Qinyong Wang , Nguyen Quoc Viet Hung , Xiangliang Zhang

In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations.

FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection

no code implementations • 8 Jan 2021 • Guanhua Ye , Hongzhi Yin , Tong Chen , Hongxu Chen , Lizhen Cui , Xiangliang Zhang

Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings.

Temporal Meta-path Guided Explainable Recommendation

1 code implementation • 5 Jan 2021 • Hongxu Chen , Yicong Li , Xiangguo Sun , Guandong Xu , Hongzhi Yin

This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations.

Social and Information Networks

Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph

no code implementations • 4 Jan 2021 • Yuandong Wang , Hongzhi Yin , Tong Chen , Chunyang Liu , Ben Wang , Tianyu Wo , Jie Xu

Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i. e., weights) of passenger demands between two connected regions.

Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach

1 code implementation • 28 Dec 2020 • Bowen Hao , Jing Zhang , Cuiping Li , Hong Chen , Hongzhi Yin

On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models.

Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation

1 code implementation • 13 Dec 2020 • Bowen Hao , Jing Zhang , Hongzhi Yin , Cuiping Li , Hong Chen

Cold-start problem is a fundamental challenge for recommendation tasks.

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

2 code implementations • 12 Dec 2020 • Xin Xia , Hongzhi Yin , Junliang Yu , Qinyong Wang , Lizhen Cui , Xiangliang Zhang

Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task.

Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks

1 code implementation • 17 Nov 2020 • Tam Thanh Nguyen , Thanh Trung Huynh , Hongzhi Yin , Vinh Van Tong , Darnbi Sakong , Bolong Zheng , Quoc Viet Hung Nguyen

Knowledge graphs (KGs) have become popular structures for unifying real-world entities by modelling the relationships between them and their attributes.

Deep Pairwise Hashing for Cold-start Recommendation

no code implementations • 2 Nov 2020 • Yan Zhang , Ivor W. Tsang , Hongzhi Yin , Guowu Yang , Defu Lian , Jingjing Li

Specifically, we first pre-train robust item representation from item content data by a Denoising Auto-encoder instead of other deterministic deep learning frameworks; then we finetune the entire framework by adding a pairwise loss objective with discrete constraints; moreover, DPH aims to minimize a pairwise ranking loss that is consistent with the ultimate goal of recommendation.

Overcoming Data Sparsity in Group Recommendation

no code implementations • 2 Oct 2020 • Hongzhi Yin , Qinyong Wang , Kai Zheng , Zhixu Li , Xiaofang Zhou

Specifically, we first extend BGEM to model group-item interactions, and then in order to overcome the limitation and sparsity of the interaction data generated by occasional groups, we propose a self-attentive mechanism to represent groups based on the group members.

GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation

1 code implementation • 6 Jul 2020 • Ruihong Qiu , Hongzhi Yin , Zi Huang , Tong Chen

On one hand, when a new session arrives, a session graph with a global attribute is constructed based on the current session and its associate user.

EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors

no code implementations • 6 Jun 2020 • Yu Yang , Zhiyuan Wen , Jiannong Cao , Jiaxing Shen , Hongzhi Yin , Xiaofang Zhou

We propose a novel algorithm (EPARS) that could early predict STAR in a semester by modeling online and offline learning behaviors.

Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

no code implementations • 2 Jun 2020 • Hongxu Chen , Hongzhi Yin , Xiangguo Sun , Tong Chen , Bogdan Gabrys , Katarzyna Musial

Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning based parallel training and account matching across different social networks.

GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection

1 code implementation • 20 May 2020 • Shijie Zhang , Hongzhi Yin , Tong Chen , Quoc Viet Nguyen Hung , Zi Huang , Lizhen Cui

Therefore, it is of great practical significance to construct a robust recommender system that is able to generate stable recommendations even in the presence of shilling attacks.

Try This Instead: Personalized and Interpretable Substitute Recommendation

no code implementations • 19 May 2020 • Tong Chen , Hongzhi Yin , Guanhua Ye , Zi Huang , Yang Wang , Meng Wang

Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i. e., personalization) and item-item relationships (i. e., substitution) for recommendation.

Enhancing Social Recommendation with Adversarial Graph Convolutional Networks

no code implementations • 5 Apr 2020 • Junliang Yu , Hongzhi Yin , Jundong Li , Min Gao , Zi Huang , Lizhen Cui

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data.

A Block-based Generative Model for Attributed Networks Embedding

no code implementations • 6 Jan 2020 • Xueyan Liu , Bo Yang , Wenzhuo Song , Katarzyna Musial , Wanli Zuo , Hongxu Chen , Hongzhi Yin

To preserve the attribute information, we assume that each node has a hidden embedding related to its assigned block.

Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks

1 code implementation • 27 Nov 2019 • Ruihong Qiu , Jingjing Li , Zi Huang , Hongzhi Yin

In this paper, therefore, we study the item transition pattern by constructing a session graph and propose a novel model which collaboratively considers the sequence order and the latent order in the session graph for a session-based recommender system.

Sequence-Aware Factorization Machines for Temporal Predictive Analytics

no code implementations • 7 Nov 2019 • Tong Chen , Hongzhi Yin , Quoc Viet Hung Nguyen , Wen-Chih Peng , Xue Li , Xiaofang Zhou

As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics.

Unsupervised Learning of Node Embeddings by Detecting Communities

no code implementations • 25 Sep 2019 • Chi Thang Duong , Dung Hoang , Truong Giang Le Ba , Thanh Le Cong , Hongzhi Yin , Matthias Weidlich , Quoc Viet Hung Nguyen , Karl Aberer

We provide empirical evidence that the communities learned by DMC are meaningful and that the node embeddings are competitive in different node classification benchmarks.

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

no code implementations • 8 Sep 2019 • Junliang Yu , Min Gao , Hongzhi Yin , Jundong Li , Chongming Gao , Qinyong Wang

Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems.

Parallel Computation of Graph Embeddings

no code implementations • 6 Sep 2019 • Chi Thang Duong , Hongzhi Yin , Thanh Dat Hoang , Truong Giang Le Ba , Matthias Weidlich , Quoc Viet Hung Nguyen , Karl Aberer

We therefore propose a framework for parallel computation of a graph embedding using a cluster of compute nodes with resource constraints.

TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs

no code implementations • 6 Jul 2019 • Saeid Hosseini , Saeed Najafipour , Ngai-Man Cheung , Hongzhi Yin , Mohammad Reza Kangavari , Xiaofang Zhou

We can use the temporal and textual data of the nodes to compute the edge weights and then generate subgraphs with highly relevant nodes.

Few-Shot Deep Adversarial Learning for Video-based Person Re-identification

no code implementations • 29 Mar 2019 • Lin Wu , Yang Wang , Hongzhi Yin , Meng Wang , Ling Shao

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages.

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

no code implementations • 13 Dec 2018 • Mingyue Shang , Zhenxin Fu , Hongzhi Yin , Bo Tang , Dongyan Zhao , Rui Yan

In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT).

Look Deeper See Richer: Depth-aware Image Paragraph Captioning

no code implementations • ACM International Conference on Multimedia 2018 • Ziwei Wang , Yadan Luo , Yang Li , Zi Huang , Hongzhi Yin

Existing image paragraph captioning methods give a series of sentences to represent the objects and regions of interests, where the descriptions are essentially generated by feeding the image fragments containing objects and regions into conventional image single-sentence captioning models.

Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection

no code implementations • 20 Apr 2017 • Tong Chen , Lin Wu , Xue Li , Jun Zhang , Hongzhi Yin , Yang Wang

The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time.

Personalized Video Recommendation Using Rich Contents from Videos

1 code implementation • 21 Dec 2016 • Xingzhong Du , Hongzhi Yin , Ling Chen , Yang Wang , Yi Yang , Xiaofang Zhou

In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features.

Accepted papers

Full Papers

Perspectives papers, reproducibility papers, short papers, resource papers, sirip papers.

Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion Xiang Chen, Ningyu Zhang, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si and Huajun Chen

Decoupled Side Information Fusion for Sequential Recommendation Yueqi Xie, Peilin Zhou and Sunghun Kim

A Robust Computerized Adaptive Testing Approach in Educational Question Retrieval Yan Zhuang, Qi Liu, Zhenya Huang, Zhi Li, Binbin Jin, Haoyang Bi, Enhong Chen and Shijin Wang

Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation Nicholas Lim, Bryan Hooi, See-Kiong Ng, Yong Liang Goh, Renrong Weng and Rui Tan

Curriculum Contrastive Context Denoising for Few-shot Conversational Dense Retrieval Kelong Mao, Zhicheng Dou and Hongjin Qian

Fairness of Exposure in Light of Incomplete Exposure Estimation Maria Heuss, Fatemeh Sarvi and Maarten de Rijke

Learn from Unlabeled Videos for Near-duplicate Video Retrieval Xiangteng He, Yulin Pan, Mingqian Tang, Yiliang Lv and Yuxin Peng

RankFlow: Joint Optimization of Multi-Stage Cascade Ranking Systems as Flows Jiarui Qin, Jiachen Zhu, Bo Chen, Zhirong Liu, Weiwen Liu, Ruiming Tang, Rui Zhang, Yong Yu and Weinan Zhang

Personalized Fashion Compatibility Modeling via Metapath-guided Heterogeneous Graph Learning Weili Guan, Fangkai Jiao, Xuemeng Song, Haokun Wen, Chung-Hsing Yeh and Xiaojun Chang

Interpolative Distillation for Unifying Biased and DebiasedRecommendation Sihao Ding, Fuli Feng, Xiangnan He, Jinqiu Jin, Wenjie Wang, Yong Liao and Yongdong Zhang

Locality-Sensitive State-Guided Experience Replay Optimization for Sparse-Reward in Online Recommendation Xiaocong Chen, Lina Yao, Julian Mcauley, Weili Guan, Xiaojun Chang and Xianzhi Wang

Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation Wanwei He, Yinpei Dai, Min Yang, Jian Sun, Fei Huang, Luo Si and Yongbin Li

Assessing Student's Dynamic Knowledge State by Exploring the Question Difficulty Effect Shuanghong Shen, Zhenya Huang, Qi Liu, Yu Su, Shijin Wang and Enhong Chen

Contrastive Learning with Hard Negative Entities for Entity Set Expansion Yinghui Li, Yangning Li, Yuxin He, Tianyu Yu, Ying Shen and Hai-Tao Zheng

HTKG: Deep Keyphrase Generation with Neural Hierarchical Topic Guidance Yuxiang Zhang, Tao Jiang, Tianyu Yang, Xiaoli Li and Suge Wang

Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing Hanshuang Tong, Zhen Wang, Qi Liu, Yun Zhou, Shiwei Tong and Wenyuan Han

Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities Jiandian Zeng, Tianyi Liu and Jiantao Zhou

Progressive Learning for Image Retrieval with Hybrid-Modality Queries Yida Zhao, Yuqing Song and Qin Jin

Unify Local and Global Information for Top-N Recommendation Xiaoming Liu, Shaocong Wu, Zhaohan Zhang and Chao Shen

Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction Weijie Yu, Zhongxiang Sun, Jun Xu, Zhenhua Dong, Xu Chen, Hongteng Xu and Ji-Rong Wen

You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos Xin Sun, Xuan Wang, Jialin Gao, Qiong Liu and Xi Zhou

Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders Zhihong Chen, Jiawei Wu, Chenliang Li, Jingxu Chen, Rong Xiao and Binqiang Zhao

User-Aware Multi-Interest Learning for Candidate Matching in Recommenders Zheng Chai, Zhihong Chen, Chenliang Li, Rong Xiao, Houyi Li, Jiawei Wu, Jingxu Chen and Haihong Tang

V2P: Vision-to-Prompt based Multi-Modal Product Summary Generation Xuemeng Song, Liqiang Jing, Dengtian Lin, Zhongzhou Zhao, Haiqing Chen and Liqiang Nie

Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations Giacomo Balloccu, Ludovico Boratto, Mirko Marras and Gianni Fenu

Multi-Level Interaction Reranking with User Behavior History Yunjia Xi, Weiwen Liu, Jieming Zhu, Xilong Zhao, Xinyi Dai, Ruiming Tang, Rui Zhang, Weinan Zhang and Yong Yu

Mutual Disentanglement Learning for Joint Fine-Grained Sentiment Classification and Controllable Text Generation Hao Fei, Chenliang Li, Donghong Ji and Fei Li

CenterCLIP: Token Clustering for Efficient Text-Video Retrieval Shuai Zhao, Linchao Zhu, Xiaohan Wang and Yi Yang

User-controllable Recommendation Against Filter Bubbles Wenjie Wang, Fuli Feng, Liqiang Nie and Tat-Seng Chua

DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation Jiangxia Cao, Xixun Lin, Xin Cong, Jing Ya, Tingwen Liu and Bin Wang

Thinking inside The Box: Learning Hypercube Representations for Group Recommendation Tong Chen, Hongzhi Yin, Jing Long, Nguyen Quoc Viet Hung, Yang Wang and Meng Wang

Multimodal Entity Linking with Gated Hierarchical Fusion and Contrastive Training Peng Wang, Jiangheng Wu and Xiaohang Chen

A Review-aware Graph Contrastive Learning Framework for Recommendation Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang and Yong Li

Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui and Quoc Viet Hung Nguyen

On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Guandong Xu and Quoc Viet Hung Nguyen

Unifying Cross-lingual Summarization and Machine Translation with Compression Rate Yu Bai, Heyan Huang, Kai Fan, Yang Gao, Yiming Zhu, Jiaao Zhan, Zewen Chi and Boxing Chen

Multi-Agent RL-based Information Selection Model for Sequential Recommendation Kaiyuan Li, Pengfei Wang and Chenliang Li

Structure and Semantics Preserving Document Representations Natraj Raman, Sameena Shah and Manuela Veloso

Knowledge Graph Contrastive Learning for Recommendation Yuhao Yang, Chao Huang, Lianghao Xia and Chenliang Li

Enhancing CTR Prediction with Context-Aware Feature Representation Learning Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang and Ning Gu

Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion Adam Block, Rahul Kidambi, Daniel Hill, Thorsten Joachims and Inderjit Dhillon

Joint Multisided Exposure Fairness for Recommendation Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz and Xue Liu

When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation Yu Tian, Jianxin Chang, Yanan Niu, Yang Song and Chenliang Li

Automatic Expert Selection for Multi-Scenario and Multi-Task Search Xinyu Zou, Zhi Hu, Yiming Zhao, Xuchu Ding, Zhongyi Liu, Chenliang Li and Aixin Sun

Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou and Zhaochun Ren

DGQAN: Dual Graph Question-Answer Attention Networks for Answer Selection Haitian Yang, Xuan Zhao, Yan Wang, Min Li, Wei Chen and Weiqing Huang

Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy Wenqiang Lei, Yao Zhang, Feifan Song, Hongru Liang, Jiaxin Mao, Jiancheng Lv, Zhenglu Yang and Tat-Seng Chua

Single-shot Embedding Dimension Search in Recommender System Liang Qu, Yonghong Ye, Ningzhi Tang, Lixin Zhang, Yuhui Shi and Hongzhi Yin

Learning to Infer User Implicit Preference in Conversational Recommendation Chenhao Hu, Shuhua Huang, Yansen Zhang and Yubao Liu

Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking Qian Dong, Yiding Liu, Suqi Cheng, Shuaiqiang Wang, Zhicong Cheng, Shuzi Niu and Dawei Yin

Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation Junda Wu, Zhihui Xie, Tong Yu, Handong Zhao, Ruiyi Zhang and Shuai Li

An Attribute-Driven Mirroring Graph Network for Session-based Recommendation Siqi Lai, Erli Meng, Fan Zhang, Chenliang Li, Bin Wang and Aixin Sun

IR Evaluation and Learning in the Presence of Forbidden Documents David Carmel, Nachshon Cohen, Amir Ingber and Elad Kravi

Target-aware Abstractive Related Work Generation with Contrastive Learning Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Rui Yan, Xin Gao and Xiangliang Zhang

Geometric Disentangled Collaborative Filtering Yiding Zhang, Chaozhuo Li, Xing Xie, Xiao Wang, Chuan Shi, Yuming Liu, Hao Sun, Liangjie Zhang, Weiwei Deng and Qi Zhang

Exploring Modular Task Decomposition in Cross-domain Named Entity Recognition Xinghua Zhang, Bowen Yu, Tingwen Liu, Yubin Wang, Taoyu Su and Hongbo Xu

CorED: Incorporating Type-level and Instance-level Correlations for Fine-grained Event Detection Jiawei Sheng, Rui Sun, Shu Guo, Shiyao Cui, Jiangxia Cao, Lihong Wang, Tingwen Liu and Hongbo Xu

Self-Augmented Recommendation with Hypergraph Contrastive Collaborative Filtering Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin and Jimmy Huang

Incorporating Retrieval Information into the Truncation of Ranking Lists in the Legal Domain Yixiao Ma, Qingyao Ai, Yueyue Wu, Yunqiu Shao, Yiqun Liu, Min Zhang and Shaoping Ma

Can clicks be both labels and features? Unbiased Behavior Feature Collection and Uncertainty-aware Learning to Rank Tao Yang, Chen Luo, Hanqing Lu, Parth Gupta, Bing Yin and Qingyao Ai

PTAU: Prompt Tuning for Attributing Unanswerable Questions Jinzhi Liao, Xiang Zhao, Jianming Zheng, Xinyi Li, Fei Cai and Jiuyang Tang

Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu and Xin Cao

User-Centric Conversational Recommendation with Multi-Aspect User Modeling Shuokai Li, Ruobing Xie, Yongchun Zhu, Xiang Ao, Fuzhen Zhuang and Qing He

MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations Xiangmeng Wang, Qian Li, Dianer Yu, Zhichao Wang, Hongxu Chen and Guandong Xu

Generating Clarifying Questions with Web Search Results Ziliang Zhao, Zhicheng Dou, Jiaxin Mao and Jirong Wen

HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction Zuowu Zheng, Changwang Zhang, Xiaofeng Gao and Guihai Chen

Webformer: Pre-training with Web Pages for Information Retrieval Yu Guo , Zhengyi Ma, Jiaxin Mao, Hongjin Qian, Xinyu Zhang, Hao Jiang, Zhao Cao and Zhicheng Dou

Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders Jinning Li, Huajie Shao, Dachun Sun, Ruijie Wang, Yuchen Yan, Jinyang Li, Shengzhong Liu, Hanghang Tong and Tarek Abdelzaher

Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach Shengyao Zhuang, Hang Li and Guido Zuccon

Why Don't You Click: Understanding Non-Click Results in Web Search with Brain Signals Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuancheng Li, Jiaji Li, Xuesong Chen, Min Zhang and Shaoping Ma

CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos Shengyao Zhuang and Guido Zuccon

Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction Qika Lin, Jun Liu, Fangzhi Xu, Yudai Pan, Yifan Zhu, Lingling Zhang and Tianzhe Zhao

Forest-based Deep Recommender Chao Feng, Defu Lian, Zheng Liu, Xing Xie, Le Wu and Enhong Chen

Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings Shitao Xiao, Zheng Liu, Weihao Han, Jianjin Zhang, Defu Lian, Yeyun Gong, Qi Chen, Fan Yang, Hao Sun, Yingxia Shao and Xing Xie

Bilateral Self-unbiased Recommender Learning from Biased Implicit Feedback Jae-woong Lee, Seongmin Park, Joonseok Lee and Jongwuk Lee

Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation Xiaokun Zhang, Bo Xu, Liang Yang, Chenliang Li, Fenglong Ma, Haifeng Liu and Hongfei Lin

Personalized Abstractive Opinion Tagging Mengxue Zhao, Yang Yang, Miao Li, Jingang Wang, Wei Wu, Pengjie Ren, Maarten de Rijke and Zhaochun Ren

Privacy-Preserving Synthetic Data Generation for Recommendation Fan Liu, Huilin Chen, Zhiyong Cheng, Yinwei Wei, Liqiang Nie and Mohan Kankanhalli

Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering Minghao Zhao, Le Wu, Yile Liang, Lei Chen, Jian Zhang, Qilin Deng, Kai Wang, Runze Wu, Xudong Shen and Tangjie Lv

CRET: Cross-Modal Retrieval Transformer for Efficient Text-Video Retrieval Kaixiang Ji, Jiajia Liu, Weixiang Hong, Liheng Zhong, Jian Wang, Jingdong Chen and Wei Chu

H-ERNIE: A Hierarchical Multi-Granularity Pre-Trained Language Model for Chinese Search Engine Xiaokai Chu, Jiashu Zhao, Lixin Zou and Dawei Yin

DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation based on Correlation Graph Wenwen Gong, Xuyun Zhang, Yifei Chen, Qiang He, Amin Beheshti, Xiaolong Xu, Chao Yan and Lianyong Qi

Variational Reasoning about User Preferences for Conversational Recommendation Zhi Tian, Zhaochun Ren, Dongdong Li, Pengjie Ren, Liu Yang, Xin Xin, Huasheng Liang, Maarten de Rijke and Zhumin Chen

Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator Shanlei Mu, Yaliang Li, Wayne Xin Zhao, Jingyuan Wang, Bolin Ding and Ji-Rong Wen

NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction Guanghui Zhu, Feng Cheng, Defu Lian, Chunfeng Yuan and Yihua Huang

Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation Weiming Liu, Xiaolin Zheng, Jiajie Su, Mengling Hu, Yanchao Tan and Chaochao Chen

Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems Shuo Zhang, Mu Chun Wang and Krisztian Balog

Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding Mingyang Chen, Wen Zhang, Yushan Zhu, Hongting Zhou, Zonggang Yuan, Changliang Xu and Huajun Chen

HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation Yuntao Du, Xinjun Zhu, Lu Chen, Baihua Zheng and Yunjun Gao

Self-Guided Learning to Denoise for Robust Recommendation Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang and Baihua Zheng

MetaCare++: Meta-Learning with Hierarchical Subtyping for Cold-Start Diagnosis Prediction in Healthcare Data Yanchao Tan, Carl Yang, Xiangyu Wei, Chaochao Chen, Weiming Liu, Longfei Li, Jun Zhou and Xiaolin Zheng

AutoGSR: Neural Architecture Search for Graph-based Session Recommendation Jingfan Chen, Guanghui Zhu, Haojun Hou, Chunfeng Yuan and Yihua Huang

Learning Graph-based Disentangled Representations for Next POI Recommendation Zhaobo Wang, Yanmin Zhu, Haobing Liu and Chunyang Wang

Interpreting Patient Descriptions using Distantly Supervised Similar Case Retrieval Israa Alghanmi, Luis Espinosa-Anke and Steven Schockaert

Risk-Sensitive Deep Neural Learning to Rank Pedro Henrique Silva Rodrigues, Daniel Xavier Sousa, Thierson Couto Rosa and Marcos Andre Goncalves

BERT-ER: Query-Specific BERT Entity Representations for Entity Ranking Shubham Chatterjee and Laura Dietz

Conversational Question Answering on Heterogeneous Sources Philipp Christmann, Rishiraj Saha Roy and Gerhard Weikum

Human preferences as dueling bandits Xinyi Yan, Chengxi Luo, Charles Clarke, Nick Craswell, Ellen Voorhees and Pablo Castells

A Non-Factoid Question-Answering Taxonomy Valeriia Bolotova, Vladislav Blinov, Falk Scholer, Bruce Croft and Mark Sanderson

Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification Kai Zhang, Qi Liu, Zhenya Huang, Mengdi Zhang, Kun Zhang, Cheng Mingyue, Wei Wu and Enhong Chen

Axiomatically Regularized Pre-training for Ad hoc Search Jia Chen, Yiqun Liu, Yan Fang, Jiaxin Mao, Hui Fang, Shenghao Yang, Xiaohui Xie, Min Zhang and Shaoping Ma

Bit-aware Semantic Transformer Hashing for Multi-modal Retrieval Wentao Tan, Lei Zhu, Weili Guan, Jingjing Li and Zhiyong Cheng

Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning Fangzhi Xu, Jun Liu, Qika Lin, Yudai Pan and Lingling Zhang

A Flexible Framework for Offline Effectiveness Metrics Alistair Moffat, Joel Mackenzie, Paul Thomas and Leif Azzopardi

GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation Song Yang, Jiamou Liu and Kaiqi Zhao

What Makes The Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation Li Lin, Yixin Cao, Lifu Huang, Shu'Ang Li, Xuming Hu, Lijie Wen and Jianmin Wang

Recognizing Medical Search Query Intent by Few-shot Learning Yaqing Wang, Song Wang, Li Yanyan and Dejing Dou

Ranking Interruptus: When Truncated Rankings Are Better and How to Measure That Enrique Amigo, Stefano Mizzaro and Damiano Spina

A Dual-Expert Framework for Event Argument Extraction Rui Li, Wenlin Zhao, Cheng Yang and Sen Su

Few-shot Node Classification on Attributed Networks with Graph Meta-learning Yonghao Liu, Mengyu Li, Ximing Li, Fausto Giunchiglia, Xiaoyue Feng and Renchu Guan

Adaptable Text Matching via Meta-Weight Regulator Bo Zhang, Chen Zhang, Fang Ma and Dawei Song

Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation Xinyan Fan, Jianxun Lian, Wayne Xin Zhao, Zheng Liu, Chaozhuo Li and Xing Xie

Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities Vishwa Vinay, Manoj Kilaru and David Arbour

Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering Changxin Tian, Yuexiang Xie, Yaliang Li, Nan Yang and Wayne Xin Zhao

A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning Ruichao Yang, Jing Ma, Hongzhan Lin and Wei Gao

INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering Yunfan Wu, Qi Cao, Huawei Shen, Shuchang Tao and Xueqi Cheng

Video Moment Retrieval from Text Queries via Single Frame Annotation Ran Cui, Tianwen Qian, Pai Peng, Elena Daskalaki, Jingjing Chen, Xiaowei Guo, Huyang Sun and Yu-Gang Jiang

ProFairRec: Provider Fairness-aware News Recommendation Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang and Xing Xie

Multi-Faceted Global Item Relation Learning for Session-Based Recommendation Qilong Han, Chi Zhang, Rui Chen, Riwei Lai, Hongtao Song and Li Li

ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping Mozhdeh Ariannezhad, Sami Jullien, Ming Li, Min Fang, Sebastian Schelter and Maarten de Rijke

Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Peilin Zhao, Luo Da, Kangyi Lin, Sophia Ananiadou and Junzhou Huang

COSPLAY: Concept Set Guided Personalized Dialogue System Chen Xu, Piji Li, Wei Wang, Haoran Yang, Siyun Wang and Chuangbai Xiao

CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users Haoran Xin, Xinjiang Lu, Nengjun Zhu, Tong Xu, Dejing Dou and Hui Xiong

LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback Yunchang Zhu, Liang Pang, Yanyan Lan, Huawei Shen and Xueqi Cheng

Improving Implicit Alternating Least Squares with Ring-based Regularization Rui Fan, Jin Chen, Jin Zhang, Defu Lian and Enhong Chen

Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking Ali Vardasbi, Fatemeh Sarvi and Maarten de Rijke

Offline Retrieval Evaluation Without Evaluation Metrics Fernando Diaz and Andres Ferraro

Multi-Behavior Sequential Transformer Recommender Enming Yuan, Wei Guo, Zhicheng He, Huifeng Guo, Chengkai Liu and Ruiming Tang

ADPL: Adversarial Prompt-based Domain Adaptation for Dialogue Summarization with Knowledge Disentanglement Lulu Zhao, Fujia Zheng, Weihao Zeng, Keqing He, Ruotong Geng, Huixing Jiang, Wei Wu and Weiran Xu

Aspect Feature Distillation and Enhancement Network for Aspect-based Sentiment Analysis Rui Liu, Jiahao Cao, Nannan Sun and Lei Jiang

Deployable and Continuable Meta-Learning-Based Recommender System with Fast User-Incremental Updates Renchu Guan, Haoyu Pang, Fausto Giunchiglia, Ximing Li, Xuefeng Yang and Xiaoyue Feng

Multimodal Disentanglement Variational AutoEncoders for Zero-Shot Cross-Modal Retrieval Jialin Tian, Kai Wang, Xing Xu, Zuo Cao, Fumin Shen and Heng Tao Shen

Explainable Fairness for Feature-aware Recommender Systems Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li and Yongfeng Zhang

Graph Trend Filtering Networks for Recommendation Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang and Qing Li

AutoLossGen: Automatic Loss Function Generation for Recommender Systems Zelong Li, Jianchao Ji, Yingqiang Ge and Yongfeng Zhang

Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison Amifa Raj and Michael Ekstrand

Entity-aware Transformers for Entity Search Emma Gerritse, Faegheh Hasibi and Arjen de Vries

Optimizing generalized Gini indices for fairness in rankings Virginie Do and Nicolas Usunier

Co-clustering Interactions via Attentive Hypergraph Neural Network Tianchi Yang, Cheng Yang, Luhao Zhang, Chuan Shi, Maodi Hu, Huaijun Liu, Tao Li and Dong Wang

A Study of Cross-Session Cross-Device Search within an Academic Digital Library Sebastian Gomes, Miriam Boon and Orland Hoeber

A Multitask Framework for Sentiment, Emotion and Sarcasm aware Cyberbullying Detection in Multi-modal Code-Mixed Memes Krishanu Maity, Prince Jha, Sriparna Saha and Pushpak Bhattacharyya

Scalable Exploration for Online Learning to Rank with Perturbed Click Feedback Yiling Jia and Hongning Wang

Determinantal Point Process Set Likelihood-Based Loss Functions for Sequential Recommendation Yuli Liu, Christian Walder and Lexing Xie

Bias Mitigation for Toxicity Detection via Sequential Decisions Lu Cheng, Ahmadreza Mosallanezhad, Yasin Silva, Deborah Hall and Huan Liu

Information Need Awareness: an EEG study Dominika Michalkova, Mario Parra and Yashar Moshfeghi

Towards Explainable Search Results: A Listwise Explanation Generator Puxuan Yu, Razieh Rahimi and James Allan

KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums Limeng Cui and Dongwon Lee

CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems Mohammadmehdi Naghiaei, Hossein A. Rahmani and Yashar Deldjoo

Pareto-Optimal Fairness-Utility Amortizations in Rankings with a DBN Exposure Model Till Kletti, Jean-Michel Renders and Patrick Loiseau

IAOTP: An Interactive End-to-End Solution for Aspect-Opinion Term Pairs Extraction Ambreen Nazir and Rao Yuan

Structure-Aware Semantic-Aligned Network for Universal Cross-Domain Retrieval Jialin Tian, Xing Xu, Kai Wang, Zuo Cao, Xunliang Cai and Heng Tao Shen

Few-Shot Stance Detection via Target-Aware Prompt Distillation Yan Jiang, Jinhua Gao, Huawei Shen and Xueqi Cheng

Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction Xinyu Ma, Jiafeng Guo, Ruqing Zhang, Yixing Fan and Xueqi Cheng

Re-thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective Ying Zhou, Xuanang Chen, Ben He, Zheng Ye and Le Sun

QUASER: Question Answering with Scalable Extractive Rationalization Asish Ghoshal, Srini Iyer, Bhargavi Paranjape, Kushal Lakhotia, Scott Yih and Yashar Mehdad

ESCM^2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation Hao Wang, Tai-Wei Chang, Tianqiao Liu, Jianmin Huang, Zhichao Chen, Chao Yu, Ruopeng Li and Wei Chu

PEVAE: A hierarchical VAE for personalized explainable recommendation. Zefeng Cai and Zerui Cai

Towards Suicide Ideation Detection Through Online Conversational Context Ramit Sawhney, Shivam Agarwal, Atula Tejaswi Neerkaje, Nikolaos Aletras, Preslav Nakov and Lucie Flek

Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation Shansan Gong and Kenny Zhu

Less is More: Reweighting Important Spectral Graph Features for Recommendation Shaowen Peng, Kazunari Sugiyama and Tsunenori Mine

Structured and Natural Responses Co-generation for Conversational Search Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji and Tat-Seng Chua

Is non-IID Data a Threat in Federated Online Learning to Rank? Shuyi Wang and Guido Zuccon

Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli and Paolo Cremonesi

Reduce, Reuse, Recycle: Green Information Retrieval Research Harrisen Scells, Shengyao Zhuang and Guido Zuccon

Where Do Queries Come From? Marwah Alaofi, Luke Gallagher, Dana Mckay, Lauren L. Saling, Mark Sanderson, Falk Scholer, Damiano Spina and Ryen W. White

Competitive Search Oren Kurland and Moshe Tennenholtz

On Natural Language User Profiles for Transparent and Scrutable Recommendation Filip Radlinski, Krisztian Balog, Fernando Diaz, Lucas Dixon and Ben Wedin

Retrieval-Enhanced Machine Learning Hamed Zamani, Fernando Diaz, Mostafa Dehghani, Donald Metzler and Michael Bendersky

Where Does the Performance Improvement Come From? - A Reproducibility Concern about Image-Text Retrieval Jun Rao, Fei Wang, Liang Ding, Shuhan Qi, Yibing Zhan, Weifeng Liu and Dacheng Tao

State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood and Maarten de Rijke

Another Look at Information Retrieval as Statistical Translation Yuqi Liu, Chengcheng Hu and Jimmy Lin

Experiments on Generalizability of User-Oriented Fairness in Recommender Systems Hossein A. Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan and Mohammad Aliannejadi

Users and Contemporary SERPs: A (Re-)Investigation Nirmal Roy, David Maxwell and Claudia Hauff

Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network Tianyu Zhao, Cheng Yang, Yibo Li, Quan Gan, Zhenyi Wang, Fengqi Liang, Huan Zhao, Yingxia Shao, Xiao Wang and Chuan Shi

An Inspection of the Reproducibility and Replicability of TCT-ColBERT Xiao Wang, Sean MacAvaney, Craig Macdonald and Iadh Ounis

Item Similarity Mining for Multi-Market Recommendation Jiangxia Cao, Xin Cong, Tingwen Liu and Bin Wang

MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios Xiaofeng Pan, Ming Li, Jing Zhang, Keren Yu, Luping Wang, Hong Wen, Chengjun Mao and Bo Cao

Towards Event-level Causal Relation Identification Chuang Fan, Daoxing Liu, Libo Qin, Yue Zhang and Ruifeng Xu

Exploring Heterogeneous Data Lake based on Unified Canonical Graphs Qin Yuan, Ye Yuan, Zhenyu Wen, He Wang, Chen Chen and Guoren Wang

Relation-Guided Few-Shot Relational Triple Extraction Xin Cong, Jiawei Sheng, Shiyao Cui, Bowen Yu, Tingwen Liu and Bin Wang

L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks Fangxin Liu, Haomin Li and Li Jiang

Neural Statistics for Click-Through Rate Prediction Yanhua Huang, Hangyu Wang, Yiyun Miao, Ruiwen Xu, Lei Zhang and Weinan Zhang

Adversarial Graph Perturbations for Recommendations at Scale Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Xia Hu, Fei Wang and Hao Yang

Graph Capsule Network with a Dual Adaptive Mechanism Xiangping Zheng, Xun Liang, Bo Wu, Yuhui Guo and Xuan Zhang

Constructing Better Evaluation Metrics by Incorporating theAnchoring Effect into the User Model Nuo Chen, Fan Zhang and Tetsuya Sakai

Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning Xiang Chen, Lei Li, Ningyu Zhang, Chuanqi Tan, Fei Huang, Luo Si and Huajun Chen

ReLoop: A Self-Correction Learning Loop for Recommender Systems Guohao Cai, Jieming Zhu, Quanyu Dai, Zhenhua Dong, Rui Zhang, Xiuqiang He and Ruiming Tang

Training Entire-Space Models for Target-oriented Opinion Words Extraction Yuncong Li, Fang Wang and Sheng-Hua Zhong

Zero-shot Query Contextualization for Conversational Search Antonios Minas Krasakis, Andrew Yates and Evangelos Kanoulas

EFLEC: Efficient Feature-LEakage Correction in GNN based Recommendation Systems Ishaan Kumar, Yaochen Hu and Yingxue Zhang

Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction Xiaoxiao Xu, Zhiwei Fang, Qian Yu, Ruoran Huang, Chaosheng Fan, Yong Li, Yang He, Changping Peng, Zhangang Lin and Jingping Shao

Preference Enhanced Social Infulence Modeling for Network-Aware Cascade Prediction Likang Wu, Hao Wang, Enhong Chen, Zhi Li, Hongke Zhao and Jianhui Ma

Enhancing Top-N Item Recommendations by Peer Collaboration Yang Sun, Fajie Yuan, Min Yang, Alexandros Karatzoglou, Li Shen and Xiaoyan Zhao

Faster Learned Sparse Retrieval with Guided Traversal Antonio Mallia, Joel Mackenzie, Torsten Suel and Nicola Tonellotto

On Extractive Summarization for Profile-centric Neural Expert Search in Academia Rennan Lima and Rodrygo Santos

Animating Images to transfer CLIP for Video-Text Retrieval Yu Liu, Huai Chen, Lianghua Huang, Di Chen, Bin Wang, Pan Pan and Lisheng Wang

IPR: Interaction-level Preference Ranking for explicit feedback Shih-Yang Liu, Hsien Hao Chen, Chih-Ming Chen, Ming-Feng Tsai and Chuan-Ju Wang

News Recommendation with Candidate-aware User Modeling Tao Qi, Fangzhao Wu, Chuhan Wu and Yongfeng Huang

PERD: Personalized Emoji Recommendation with Dynamic User Preference Xuanzhi Zheng, Guoshuai Zhao, Li Zhu and Xueming Qian

Socially-aware Dual Contrastive Learning for Cold-Start Recommendation Jing Du, Zesheng Ye, Lina Yao, Bin Guo and Zhiwen Yu

Hierarchical Task-aware Multi-Head Attention Network Jing Du, Lina Yao, Xianzhi Wang, Bin Guo and Zhiwen Yu

Constrained Sequence-to-Tree Generation for Hierarchical Text Classification Chao Yu, Yi Shen and Yue Mao

Image-Text Retrieval via Contrastive Learning with Auxiliary Generative Features and Support-set Regularization Lei Zhang, Min Yang, Chengming Li and Ruifeng Xu

Enhancing Event-Level Sentiment Analysis with Structured Arguments Qi Zhang, Jie Zhou, Qin Chen, Qingchun Bai and Liang He

Denoising Time Cycle Modeling for Recommendation Sicong Xie, Qunwei Li, Weidi Xu, Kaiming Shen, Shaohu Chen and Wenliang Zhong

P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning Xiaomeng Hu, Shi Yu, Chenyan Xiong, Zhenghao Liu, Zhiyuan Liu and Ge Yu

Towards Results-level Proportionality for Multi-objective Recommender Systems Ladislav Peska and Patrik Dokoupil

Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction Xiaochen Li, Jian Liang, Xialong Liu and Yu Zhang

Regulating Provider Groups Exposure in Recommendations Mirko Marras, Ludovico Boratto, Guilherme Ramos and Gianni Fenu

FUM: Fine-grained and Fast User Modeling for News Recommendation Tao Qi, Fangzhao Wu, Chuhan Wu and Yongfeng Huang

Curriculum Learning for Dense Retrieval Distillation Hansi Zeng, Hamed Zamani and Vishwa Vinay

Evaluation of Herd Behavior Caused by Population-scale Concept Drift in Collaborative Filtering Chenglong Ma, Yongli Ren, Pablo Castells and Mark Sanderson

Detecting Frozen Phrases in Open-Domain Question Answering Mostafa Yadegari, Ehsan Kamalloo and Davood Rafiei

Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation Yinfeng Li, Chen Gao, Hengliang Luo, Depeng Jin and Yong Li

Point Prompt Tuning for Temporally Language Grounding Yawen Zeng

Value Penalized Q-Learning for Recommender Systems Chengqian Gao, Ke Xu, Kuangqi Zhou, Lanqing Li, Xueqian Wang, Bo Yuan and Peilin Zhao

Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation Pengyang Li, Rong Chen, Quan Liu, Jian Xu and Bo Zheng

Understanding User Satisfaction with Task-Oriented Dialogue Systems Clemencia Siro, Mohammad Aliannejadi and Maarten de Rijke

Distilling Knowledge on Text Graph for Social Media Attribute Inference Quan Li, Xiaoting Li, Lingwei Chen and Dinghao Wu

Progressive Self-Attention Network with Unsymmetrical Positional Encoding for Sequential Recommendation Yuehua Zhu, Bo Huang, Shaohua Jiang, Muli Yang, Yanhua Yang and Wenliang Zhong

Empowering Next POI Recommendation with Multi-Relational Modeling Zheng Huang, Jing Ma, Yushun Dong, Natasha Foutz and Jundong Li

What Makes a Good Podcast Summary? Rezvaneh Rezapour, Sravana Reddy, Rosie Jones and Ian Soboroff

A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism Han Liu, Siyang Zhao, Xiaotong Zhang, Feng Zhang, Junjie Sun, Hong Yu and Xianchao Zhang

BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction Bisheng Li, Min Zhou, Zengfeng Huang, Shengzhong Zhang, Menglin Yang and Defu Lian

Analyzing the Support Level for Tips Extracted from Product Reviews Miriam Farber, David Carmel, Lital Kuchy and Avihai Mejer

DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation Liqi Yang, Linhao Luo, Xiaofeng Zhang, Fengxin Li, Xinni Zhang, Zelin Jiang and Shuai Tang

Enhancing Zero-Shot Stance Detection via Targeted Background Knowledge Qinglin Zhu, Bin Liang, Jingyi Sun, Jiachen Du, Lanjun Zhou and Xu Ruifeng

Translation-Based Implicit Annotation Projection for Zero-Shot Cross-Lingual Event Argument Extraction Chenwei Lou, Jun Gao, Changlong Yu, Wei Wang, Huan Zhao, Weiwei Tu and Ruifeng Xu

RESETBERT4Rec: A Pre-training Model Integrating Time And User Historical Behavior for Sequential Recommendation Qihang Zhao

PUM: Pre-training User Model with Contrastive Self-supervision Chuhan Wu, Fangzhao Wu, Tao Qi and Yongfeng Huang

Understanding Long Programming Languages with Structure-Aware Sparse Attention Tingting Liu, Chengyu Wang, Cen Chen, Ming Gao and Aoying Zhou

Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexity Harrie Oosterhuis

MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning Menghan Wang, Yuchen Guo, Zhenqi Zhao, Guangzheng Hu, Yuming Shen, Mingming Gong and Philip Torr

A Multi-Task Based Neural Model to Simulate Users in Goal Oriented Dialogue Systems To Eun Kim and Aldo Lipani

Conversational Recommendation via Hierarchical Information Modeling Quan Tu, Shen Gao, Yanran Li, Jianwei Cui, Bin Wang and Rui Yan

Generalizing to the Future: Mitigating Entity Bias in Fake News Detection Yongchun Zhu, Qiang Sheng, Juan Cao, Shuokai Li, Danding Wang and Fuzhen Zhuang

Dialogue Topic Segmentation via Parallel Extraction Network with Neighbor Smoothing Xia Jinxiong, Liu Cao, Chen Jiansong, Li Yuchen, Yang Fan, Cai Xunliang, Wan Guanglu and Wang Houfeng

Analysing the Robustness of Dual Encoders for Dense Retrieval Against Misspellings Georgios Sidiropoulos and Evangelos Kanoulas

From Cluster Ranking to Document Ranking Egor Markovskiy, Fiana Raiber, Shoham Sabach and Oren Kurland

Mitigating Consumer Biases in Recommendations with Adversarial Training Christian Ganhor, David Penz, Navid Rekabsaz, Oleg Lesota and Markus Schedl

A 'Pointwise-Query, Listwise-Document' based QPP Approach Suchana Datta, Sean MacAvaney, Debasis Ganguly and Derek Greene

How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval Hang Li, Ahmed Mourad, Bevan Koopman and Guido Zuccon

Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives Wei Wang, Liangzhu Ge, Jingqiao Zhang and Cheng Yang

Task-Oriented Dialogue System as Natural Language Generation Weizhi Wang, Zhirui Zhang, Junliang Guo, Yinpei Dai, Boxing Chen and Weihua Luo

Expression Syntax Information Bottleneck for Math Word Problems Jing Xiong, Chengming Li, Min Yang, Xiping Hu and Bin Hu

Masking and Generation: An Unsupervised Method for Sarcasm Detection Rui Wang, Qianlong Wang, Bin Liang, Yi Chen, Zhiyuan Wen, Bing Qin and Ruifeng Xu

Cross-Probe BERT for Fast Cross-Modal Search Tan Yu, Hongliang Fei and Ping Li

GERE: Generative Evidence Retrieval for Fact Verification Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Yixing Fan and Xueqi Cheng

DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations Jiadi Han, Qian Tao, Yufei Tang and Yuhan Xia

Clustering based Behavior Sampling with Long Sequential Data for CTR Prediction Yuren Zhang, Enhong Chen, Binbin Jin, Hao Wang, Min Hou, Wei Huang and Runlong Yu

CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space Yupeng Hou, Binbin Hu, Zhiqiang Zhang and Wayne Xin Zhao

Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh and Hsiang-Fu Yu

On the Role of Relevance in Natural Language Processing Tasks Artsiom Sauchuk, James Thorne, Alon Halevy, Nicola Tonellotto and Fabrizio Silvestri

An Efficiency Study for SPLADE Models Carlos Lassance and Stephane Clinchant

Tensor-based Graph Modularity for Text Data Clustering Rafika Boutalbi, Mira Ait-Saada, Anastasiia Iurshina, Steffen Staab and Mohamed Nadif

Learned Token Pruning in Contextualized Late Interaction over BERT (ColBERT) Carlos Lassance, Maroua Maachou, Joohee Park and Stephane Clinchant

AHP: Learning to Negative Sample for Hyperedge Prediction Hyunjin Hwang, Seungwoo Lee, Chanyoung Park and Kijung Shin

Re-weighting Negative Samples for Model-Agnostic Matching Jiazhen Lou, Hong Wen, Fuyu Lv, Jing Zhang, Tengfei Yuan and Zhao Li

GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment Junseok Lee, Yunhak Oh, Yeonjun In, Namkyeong Lee, Dongmin Hyun and Chanyoung Park

Item-Provider Co-learning for Sequential Recommendation Lei Chen, Jingtao Ding, Min Yang, Chengming Li, Chonggang Song and Lingling Yi

ILMART: Interpretable Ranking with Constrained LambdaMART Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego and Alberto Veneri

Modern baselines for SPARQL Semantic Parsing Debayan Banerjee, Pranav Ajit Nair, Jivat Neet Kaur, Ricardo Usbeck and Chris Biemann

Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization Mingyuan Cheng, Xinru Liao, Quan Liu, Bin Ma, Jian Xu and Bo Zheng

CTnoCVR: A Novelty Auxiliary Task Making the Lower-CTR-Higher-CVR Upper Dandan Zhang, Haotian Wu, Guanqi Zeng, Yao Yang, Weijiang Qiu, Yujie Chen and Haoyuan Hu

Learning to Rank Knowledge Sub-Graph Nodes for Entity Retrieval Parastoo Jafarzadeh, Zahra Amirmahani and Faezeh Ensan

Deep Multi-Representational Item Network for CTR Prediction Jihai Zhang, Fangquan Lin, Cheng Yang and Wei Wang

A New Sequential Prediction Framework with Spatial-temporal Embedding Jihai Zhang, Fangquan Lin, Cheng Yang and Wei Jiang

Rethinking Correlation-based Item-Item Similarities for Recommender Systems Katsuhiko Hayashi

Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation Guogang Liao, Xiaowen Shi, Ze Wang, Xiaoxu Wu, Chuheng Zhang, Yongkang Wang, Xingxing Wang and Dong Wang

GraphAD: A Graph Neural Network Model for Entity-Wise Multivariate Time-Series Anomaly Detection Xu Chen, Qiu Qiu, Changshan Li and Kunqing Xie

On Survivorship Bias in MS MARCO Prashansa Gupta and Sean MacAvaney

Counterfacutal Debiasing for Evidence-aware Fake News Detection Junfei Wu, Qiang Liu, Weizhi Xu and Shu Wu

DisenCTR: Dynamic Graph-based Disentangled Representation for Click-Through Rate Prediction Yifan Wang, Yifang Qin, Fang Sun, Bo Zhang, Xuyang Hou, Ke Hu, Jia Cheng, Jun Lei and Ming Zhang

Improving Conversational Recommender Systems via Transformer-based Sequential Modelling Jie Zou, Evangelos Kanoulas, Pengjie Ren, Zhaochun Ren, Aixin Sun and Cheng Long

Neural Query Synthesis and Domain-Specific Ranking Templates for Multi-Stage Clinical Trial Matching Ronak Pradeep, Yilin Li, Yuetong Wang and Jimmy Lin

Trainable CNNs based attention for user image behavior modeling with category prior Xin Chen, Qingtao Tang, Ke Hu, Yue Xu, Shihang Qiu, Jia Cheng and Jun Lei

Joint Optimization of Ad Ranking and Creative Selection Kaiyi Lin, Xiang Zhang, Feng Li, Pengjie Wang, Qingqing Long, Hongbo Deng, Jian Xu and Bo Zheng

BERT-based Dense Intra-ranking and Contextualized Late Interaction via Multi-task Learning for Long Document Retrieval Minghan Li and Eric Gaussier

From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective Thibault Formal, Carlos Lassance, Benjamin Piwowarski and Stephane Clinchant

Choosing The Right Teammate For Cooperative Text Generation Antoine Chaffin, Thomas Scialom, Sylvain Lamprier, Jacopo Staiano, Benjamin Piwowarski, Ewa Kijak and Vincent Claveau

When online meets offline: exploring periodicity for travel destination prediction Wanjie Tao, Liangyue Li, Chen Chen, Zulong Chen and Hong Wen

Long Document Re-ranking with Modular Re-ranker Luyu Gao and Jamie Callan

Improving Micro-video Recommendation via Contrastive Multiple Interests Beibei Li, Beihong Jin, Jiageng Song, Yisong Yu, Yiyuan Zheng and Wei Zhuo

Is News Recommendation a Sequential Recommendation Task? Chuhan Wu, Fangzhao Wu, Tao Qi, Chenliang Li and Yongfeng Huang

Unsupervised Dataset Generation for Information Retrieval Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee and Rodrigo Nogueira

Identifying Argumentative Questions in Web Search Logs Yamen Ajjour, Pavel Braslavski, Alexander Bondarenko and Benno Stein

Smooth-AUC: Smoothing the Path Towards Rank-based CTR Prediction Shuang Tang, Fangyuan Luo, Jun Wu and Zhuo Wang

Diversity vs Relevance: a practical multi-objective study in luxury fashion recommendations Joao Sa, Vanessa Queiroz Marinho, Ana Rita Magalhaes, Tiago Lacerda and Diogo Goncalves

Multi-modal Graph Contrastive Learning for Micro-video Recommendation Zixuan Yi, Xi Wang, Craig Macdonald and Iadh Ounis

Coarse-to-Fine Sparse Sequential Recommendation Jiacheng Li, Tong Zhao, Jin Li, Jim Chan, Christos Faloutsos, George Karypis, Soo-Min Pantel and Julian McAuley

Revisiting Two-tower Models for Unbiased Learning to Rank Le Yan, Zhen Qin, Honglei Zhuang, Xuanhui Wang, Michael Bendersky and Marc Najork

Answering Count Query with Explanatory Evidence Shrestha Ghosh, Simon Razniewski and Gerhard Weikum

Interactive query clarification and refinement via user simulation Pierre Erbacher, Ludovic Denoyer and Laure Soulier

Summarizing Legal Regulatory Documents using Transformers Svea Klaus, Ria Van Hecke, Kaweh Djafari Naini, Ismail Sengor Altingovde, Juan Bernabe-Moreno and Enrique Herrera-Viedma

On Optimizing Top-K Metrics for Neural Ranking Models Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky and Marc Najork

An MLP-based Algorithm for Efficient Contrastive Graph Recommendations Siwei Liu, Iadh Ounis and Craig Macdonald

Modeling User Behavior With Interaction Networks for Spam Detection Prabhat Agarwal, Manisha Srivastava, Vishwakarma Singh and Chuck Rosenburg

End-to-end Distantly Supervised Information Extraction with Retrieval Augmentation Yue Zhang, Hongliang Fei and Ping Li

DeSCoVeR: Debiased Semantic Context Prior for Venue Recommendation Sailaja Rajanala, Arghya Pal, Manish Singh, Raphael Phan and Wong Koksheik

Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval Revanth Gangi Reddy, Md Arafat Sultan, Martin Franz, Avirup Sil and Heng Ji

Assessing Scientific Research Papers with Knowledge Graphs Kexuan Sun, Zhiqiang Qiu, Abel Salinas, Yuzhong Huang, Dong-Ho Lee, Daniel Benjamin, Fred Morstatter, Xiang Ren, Kristina Lerman and Jay Pujara

Matching Search Result Diversity with User Diversity Acceptance in Web Search Sessions Jiqun Liu and Fangyuan Han

Topological Analysis of Contradictions in Text Xiangcheng Wu, Xi Niu and Ruhani Rahman

Addressing Gender-related Performance Disparities in Neural Rankers Shirin Seyedsalehi, Negar Arabzadeh, Amin Bigdeli, Morteza Zihayat and Ebrahim Bagheri

Alignment Rationale for Query-Document Youngwoo Kim, Negin Rahimi and James Allan

To interpolate or not to interpolate: PRF, Dense Retrievers and BM25 Hang Li, Shuai Wang, Shengyao Zhuang, Ahmed Mourad, Xueguang Ma, Jimmy Lin and Guido Zuccon

A Content Recommendation Policy for Gaining Subscribers Konstantinos Theocharidis, Manolis Terrovitis, Spiros Skiadopoulos and Panagiotis Karras

C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval Eugene Yang, Suraj Nair, Ramraj Chandradevan, Rebecca Iglesias-Flores and Douglas Oard

Dual Pseudo Supervision for Semi-Supervised Text Classification with a Reliable Teacher Shujie Li, Min Yang, Chengming Li and Ruifeng Xu

Learning Trustworthy Web Sources to Derive Correct Answers and Reduce Health Misinformation in Search Dake Zhang, Amir Vakili Tahami, Mustafa Abualsaud and Mark Smucker

Learning to Enrich Query Representation with Pseudo-Relevance Feedback for Cross-lingual Retrieval Ramraj Chandradevan, Eugene Yang, Mahsa Yarmohammadi and Eugene Agichtein

Mitigating the Filter Bubble while Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems Zhaolin Gao, Tianshu Shen, Zheda Mai, Mohamed Reda Bouadjenek, Isaac Waller, Ashton Anderson, Ron Bodkin and Scott Sanner

Mitigating bias in search results through set-based document reranking and neutrality regularization George Zerveas, Navid Rekabsaz, Daniel Cohen and Carsten Eickhoff

A Meta-learning Approach to Fair Ranking Yuan Wang, Zhiqiang Tao and Yi Fang

Can Users Predict Relative Query Effectiveness? Oleg Zendel, Melika Ebrahim, Shane Culpepper, Alistair Moffat and Falk Scholer

ELECRec: Training Sequential Recommenders as Discriminators Yongjun Chen, Jia Li and Caiming Xiong

Explainable Session-based Recommendation with Meta-Path Guided Instances and Self-Attention Mechanism Jiayin Zheng, Juanyun Mai and Yanlong Wen

MM-Rec: Visiolinguistic Model Empowered Multimodal News Recommendation Chuhan Wu, Fangzhao Wu, Tao Qi, Chao Zhang, Yongfeng Huang and Tong Xu

Generative Adversarial Framework for Cold-Start Item Recommendation Hao Chen, Zefan Wang, Feiran Huang, Xiao Huang, Yue Xu, Yishi Lin, Peng He and Zhoujun Li

Inconsistent Ranking Assumptions in Medical Search and Their Downstream Consequences Daniel Cohen, Kevin Du, Bhaskar Mitra, Laura Mercurio, Navid Rekabsaz and Carsten Eickhoff

Modality-Balanced Embedding for Video Retrieval Xun Wang, Bingqing Ke, Xuanping Li, Fangyu Liu, Mingyu Zhang, Xiao Liang and Qiushi Xiao

An Efficient Fusion Mechanism for Multimodal Low-resource Setting Dushyant Singh Chauhan, Asif Ekbal and Pushpak Bhattacharyya

QSG Transformer: Transformer with Query-Attentive Semantic Graph for Query-Focused Summarization Choongwon Park and Youngjoong Ko

Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder Xu Zhao, Yi Ren, Ying Du, Shenzheng Zhang and Nian Wang

PST: Measuring Skill Proficiency in Programming Exercise Process via Programming Skill Tracing Ruixin Li, Yu Yin, Le Dai, Shuanghong Shen, Xin Lin, Yu Su and Enhong Chen

Revisiting Interactive Recommender System with Reinforcement Learning Hojoon Lee, Dongyoon Hwang, Kyushik Min and Jaegul Choo

Next Point-of-Interest Recommendation with Auto-Correlation Enhanced Multi-Modal Transformer Network Yanjun Qin, Yuchen Fang, Haiyong Luo, Fang Zhao and Chenxing Wang

MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization Qianren Mao, Hongdong Zhu, Junnan Liu, Cheng Ji, Zheng Wang, Hao Peng, Jianxin Li and Lihong Wang

Neutralizing Popularity Bias in Recommendation Models Guipeng Xv, Chen Lin, Hui Li, Jinsong Su, Weiyao Ye and Yewang Chen

Lightweight Meta-Learning for Low-Resource Abstractive Summarization Taehun Huh and Youngjoong Ko

Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding Penghui Wei, Shaoguo Liu, Xuanhua Yang, Liang Wang and Bo Zheng

Exploiting Session Information in BERT-based Session-aware Sequential Recommendation Jinseok Seol, Youngrok Ko and Sang-Goo Lee

Posterior Probability Matters: Doubly-Adaptive Calibration for Neural Predictions in Online Advertising Penghui Wei, Weimin Zhang, Ruijie Hou, Jinquan Liu, Shaoguo Liu, Liang Wang and Bo Zheng

Towards Motivational and Empathetic Response Generation in Online Mental Health Support Tulika Saha, Vaibhav Gakhreja, Anindya Sundar Das, Souhitya Chakraborty and Sriparna Saha

Selective Fairness in Recommendation via Prompts Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin and Qing He

Multi-labels Masked Language Modeling on Zero-shot Code-switched Sentiment Analysis Zhi Li, Xing Gao, Ji Zhang and Yin Zhang

Expanded Lattice Embeddings for Spoken Document Retrieval on Informal Meetings Esau Villatoro-Tello, Srikanth Madikeri, Petr Motlicek, Aravind Ganapathiraju and Alexei V. Ivanov

Extractive Elementary Discourse Units for Improving Abstractive Summarization Ye Xiong, Teeradaj Racharak and Minh Le Nguyen

LightSGCN: Powering Signed Graph Convolution Network for Link Sign Prediction with Simplified Architecture Design Haoxin Liu

Dual Contrastive Network for Sequential Recommendation Guanyu Lin, Chen Gao, Yinfeng Li, Yu Zheng, Zhiheng Li, Depeng Jin and Yong Li

Revisiting Bundle Recommendation: Datasets, Tasks, Challenges and Opportunities for Intent-aware Product Bundling Zhu Sun, Jie Yang, Kaidong Feng, Hui Fang, Xinghua Qu and Yew Soon Ong

BARS: Towards Open-Benchmarking for Recommender Systems Jieming Zhu, Kelong Mao, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Zhicheng Dou, Xi Xiao and Rui Zhang

OVQA: A clinically generated visual question answering dataset Yefan Huang, Xiaoli Wang, Feiyan Liu and Guofeng Huang

Fostering Coopetition While Plugging Leaks: The Design and Implementation of the MS MARCO Leaderboards Jimmy Lin, Daniel Campos, Nick Craswell, Bhaskar Mitra and Emine Yilmaz

Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims Ivan Srba, Branislav Pecher, Matus Tomlein, Robert Moro, Elena Stefancova, Jakub Simko and Maria Bielikova

A Dataset for Sentence Retrieval for Open-Ended Dialogues Itay Harel, Hagai Taitelbaum, Idan Szpektor and Oren Kurland

Too Many Relevants: Whither Cranfield Test Collections? Ellen Voorhees, Nick Craswell and Jimmy Lin

ACORDAR: A Test Collection for Ad Hoc Content-Based (RDF) Dataset Retrieval Tengteng Lin, Qiaosheng Chen, Gong Cheng, Ahmet Soylu, Basil Ell, Ruoqi Zhao, Qing Shi, Xiaxia Wang, Yu Gu and Evgeny Kharlamov

ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues Guojun Yan, Jiahuan Pei, Pengjie Ren, Zhaochun Ren, Xin Xin, Huasheng Liang, Maarten de Rijke and Zhumin Chen

Wikimarks: Harvesting Relevance Benchmarks from Wikipedia Laura Dietz, Shubham Chatterjee, Connor Lennox, Sumanta Kashyapi, Pooja Oza and Ben Gamari

CODEC: Complex Document and Entity Collection Iain Mackie, Paul Owoicho, Carlos Gemmell, Sophie Fischer, Sean MacAvaney and Jeffrey Dalton

MET-Meme: A Multimodal Meme Dataset Rich in Metaphors Bo Xu, Tingting Li, Junzhe Zheng, Mehdi Naseriparsa, Zhehuan Zhao, Hongfei Lin and Feng Xia

ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collections Jiexin Wang, Adam Jatowt and Masatoshi Yoshikawa

SoChainDB: A Database for Storing and Retrieving Blockchain-Powered Social Network Data Hoang H. Nguyen, Dmytro Bozhkov, Zahra Ahmadi, Nhat-Minh Nguyen and Thanh-Nam Doan

Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval Dingkun Long, Qiong Gao, Kuan Zou, Guangwei Xu, Pengjun Xie, Ruijie Guo, Jian Xu, Guanjun Jiang, Luxi Xing and Ping Yang

ORCAS-I: Queries Annotated with Intent using Weak Supervision Daria Alexander, Wojciech Kusa and Arjen P. de Vries

ir_metadata: An Extensible Metadata Schema for IR Experiments Timo Breuer, Juri Keller and Philipp Schaer

Would You Ask it that Way? Measuring and Improving Question Naturalness for Knowledge Graph Question Answering Trond Linjordet and Krisztian Balog

The Istella22 Dataset: Bridging Traditional and Neural Learning to Rank Evaluation Domenico Dato, Sean MacAvaney, Franco Maria Nardini, Raffaele Perego and Nicola Tonellotto

Knowledge Graph Question Answering Datasets and their Generalizability: Are they enough for future research? Longquan Jiang and Ricardo Usbeck

ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities Paul Lerner, Olivier Ferret, Camille Guinaudeau, Herve Le Borgne, Romaric Besancon, Jose G Moreno and Jesus Lovon Melgarejo

Biographical Semi-Supervised Relation Extraction Dataset Alistair Plum, Tharindu Ranasinghe, Spencer Jones, Constantin Orasan and Ruslan Mitkov

Axiomatic Retrieval Experimentation with ir_axioms Alexander Bondarenko, Maik Frobe, Jan Heinrich Reimer, Benno Stein, Michael Volske and Matthias Hagen

MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset Dan Saattrup Nielsen and Ryan McConville

CAVES: A dataset to facilitate explainable classification and summarization of concerns towards COVID vaccines Soham Poddar, Azlaan Mustafa Samad, Rajdeep Mukherjee, Niloy Ganguly and Saptarshi Ghosh

RELISON: A Framework for Link Recommendation in Social Networks Javier Sanz-Cruzado and Pablo Castells

iRec: An Interactive Recommendation Framework Thiago Silva, Nicollas Silva, Heitor Werneck, Carlos Mito, Adriano Pereira and Leonardo Rocha

From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search Shuai Wang, Harrisen Scells, Justin Clark, Bevan Koopman and Guido Zuccon

Document Expansion Baselines and Learned Sparse Lexical Representations for MS MARCO V1 and V2 Xueguang Ma, Ronak Pradeep, Rodrigo Nogueira and Jimmy Lin

MIMICS-Duo: Offline & Online Evaluation of Search Clarification Leila Tavakoli, Johanne R. Trippas, Hamed Zamani, Falk Scholer and Mark Sanderson

RecDelta: An Interactive Dashboard on Top-k Recommendation for Cross-model Evaluation Yi-Shyuan Chiang, Yu-Ze Liu, Chen Feng Tsai, Jing-Kai Lou, Ming-Feng Tsai and Chuan-Ju Wang

SparCAssist: A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals Zijian Zijian, Vinay Setty and Avishek Anand

A Common Framework for Exploring Document-at-a-Time and Score-at-a-Time Retrieval Methods Andrew Trotman, Joel Mackenzie, Pradeesh Parameswaran and Jimmy Lin

Asyncval: A toolkit for asynchronously validating dense retriever checkpoints during training Shengyao Zhuang and Guido Zuccon

Golden Retriever: A Real-Time Multi-Modal Text-Image Retrieval System with the Ability to Focus Florian Schneider and Chris Biemann

BiTe-REx: An Explainable Bilingual Text Retrieval System in the Automotive Domain Viju Sudhi, Sabine Wehnert, Norbert Michael Homner, Sebastian Ernst, Mark Gonter, Andreas Krug and Ernesto W. De Luca

TARexp: A Python Framework for Technology-Assisted Review Experiments Eugene Yang and David Lewis

ZeroMatcher: A Cost-Off Entity Matching System Congcong Ge, Xiaocan Zeng, Lu Chen and Yunjun Gao

Table Enrichment System for Machine Learning Yuyang Dong and Masafumi Oyamada

QFinder: A Framework for Quantity-centric Ranking Satya Almasian, Milena Bruseva and Michael Gertz

ROGUE: A System for Exploratory Search of GANs Yang Liu, Alan Medlar and Dorota Glowacka

cherche: A new tool to rapidly implement pipelines in information retrieval Rapahel Sourty, Jose G Moreno, Lynda Tamine and Francois-Paul Servant

Online DATEing: A Web Interface for Temporal Annotations Dennis Aumiller, Satya Almasian, David Pohl and Michael Gertz

Are Taylor's Posts Risky? Evaluating Cumulative Revelations in Online Personal Data Leif Azzopardi, Jo Briggs, Melissa Duheric, Callum Nash, Emma Nicol, Wendy Moncur and Burkhard Schafer

LawNet-Viz: A Web-based System to Visually Explore Networks of Law Article References Lucio La Cava, Andrea Simeri and Andrea Tagarelli

SpaceQA: Answering Questions about the Design of Space Missions and Space Craft Concepts Andres Garcia-Silva, Cristian Camilo Berrio Aroca, Jose Manuel Gomez-Perez, Jose Antonio Martinez-Heras, Alessandro Donati and Ilaria Roma

TaskMAD: A Platform for Multimodal Task-Centric Knowledge-Grounded Conversational Experimentation Alessandro Speggiorin, Jeffrey Dalton and Anton Leuski

DIANES: A DEI Audit Toolkit for News Sources Xiaoxiao Shang, Zhiyuan Peng, Qiming Yuan, Sabiq Khan, Lauren Xie, Yi Fang and Subramaniam Vincent

A2A-API: A Prototype for Biomedical Information Retrieval Research and Benchmarking Maciej Rybinski, Liam Watts and Sarvnaz Karimi

NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs Wen Zhang, Xiangnan Chen, Zhen Yao, Mingyang Chen, Yushan Zhu, Hongtao Yu, Yufeng Huang, Zezhong Xu, Yajing Xu, Ningyu Zhang, Zonggang Yuan, Feiyu Xiong and Huajun Chen

IRVILAB: Gamified Searching on Multilingual Wikipedia Paavo Arvola and Tuulikki Alamettala

PKG: A Personal Knowledge Graph for Recommendation Yu Yang, Jiangxu Lin, Xiaolian Zhang and Meng Wang

A Python Interface to PISA! Sean MacAvaney and Craig Macdonald

Arm: Efficient Learning of Neural Retrieval Models with Desired Accuracy by Automatic Knowledge Amalgamation Linzhu Yu, Dawei Jiang, Ke Chen and Lidan Shou

Quote Erat Demonstrandum: A Web Interface for Exploring the Quotebank Corpus Vuk Vukovic, Akhil Arora, Huan-Cheng Chang, Andreas Spitz and Robert West

DDEN:A Heterogeneous Learning-to-Rank Approach with Deep Debiasing Experts Network Yiran Wang, Wenchao Xiu, Taofeng Xue, Qin Zhang, Kai Zhang, Zhonghuo Wu, Yifan Yang and Gong Zhang - Meituan (China) Qiaowen Tan - Tongji University (China)

Organizing Portuguese Legal Documents through Topic Discovery Daniela Vianna and Edleno Moura - Universidade Federal do Amazonas (Brazil) and Jusbrasil (Brazil)

Query Facet Mapping and its Applications in Streaming Services: the Netflix Case Study Sudeep Das, Ivan Provalov, Vickie Zhang and Weidong Zhang - Netflix Inc. (United States)

A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example Xiangyu Xi, Wei Ye - Peking University (China) Chenxu Lv, Yuncheng Hua, Chaobo Sun, Shuaipeng Liu, Fan Yang and Guanglu Wan - Meituan (China)

An Industrial Framework for Cold-Start Recommendation in Zero-Shot Scenarios Zhaoxin Huan, Gong-Duo Zhang, Xiaolu Zhang, Jun Zhou, Qintong Wu, Lihong Gu, Jinjie Gu, Yong He, Yue Zhu and Linjian Mo - Antgroup (China)

Unsupervised Product Offering Title Quality Scores Henry Vieira - Magalu Labs (Brazil)

Learning to Rank Instant Search Results with Multiple Indices: A Case Study in Search Aggregation for Entertainment Scott Rome, Sardar Hamidian, Richard Walsh, Kevin Foley and Ferhan Ture - Comcast (United States)

ClueWeb22: 10 Billion Web Documents with Rich Information Arnold Overwijk, Chenyan Xiong - Microsoft (United States) Jamie Callan - Carnegie Mellon University (United States)

An Auto Encoder-based Dimensionality Reduction Technique for Efficient Entity Linking in Business Phone Conversations Md Tahmid Rahman Laskar, Cheng Chen, Jonathan Johnston, Xue-Yong Fu, Shashi Bhushan Tn and Simon Corston-Oliver - Dialpad Canada Inc. (Canada)

An Intelligent Advertisement Short Video Production System via Multi-Modal Retrieval Yanheng Wei and Yanhao Zhang - Alibaba Group (China)

Scalable User Interface Optimization Using Combinatorial Bandits Ioannis Kangas, Maud Schwoerer and Lucas Bernardi - Booking.com (Netherlands)

Flipping the Script: Inverse Information Seeking Dialogues for Market Research Josh Seltzer, Kathy Cheng - Nexxt Intelligence (Canada) Shi Zong and Jimmy Lin. - University of Waterloo (Canada)

Extractive Search for Analysis of Biomedical Texts Daniel Clothiaux and Ravi Starz - Bioplx (United States)

Panel - Information Ecosystem Threats in Minoritized Communities: Challenges, Open Problems and Research Directions Shiri Dori-Hacohen - University of Connecticut (United States) Scott Hale - University of Oxford and Meedan (United Kingdom)

Panel - Applications and Future of Dense Retrieval in Industry Yubin Kim - Etsy, Inc. (United States)

Thinking inside The Box: Learning Hypercube Representations for Group Recommendation

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As a step beyond traditional personalized recommendation, group recommendation is the task of suggesting items that can satisfy a group of users. In group recommendation, the core is to design preference aggregation functions to obtain a quality summary of all group members' preferences. Such user and group preferences are commonly represented as points in the vector space (i.e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs. However, the resulted group representations, as points, lack adequate flexibility and capacity to account for the multi-faceted user preferences. Also, the point embedding-based preference aggregation is a less faithful reflection of a group's decision-making process, where all users have to agree on a certain value in each embedding dimension instead of a negotiable interval. 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. Specifically, we design the hypercube recommender (CubeRec) to adaptively learn group hypercubes from user embeddings with minimal information loss during preference aggregation, and to leverage a revamped distance metric to measure the affinity between group hypercubes and item points. Moreover, to counteract the long-standing issue of data sparsity in group recommendation, we make full use of the geometric expressiveness of hypercubes and innovatively incorporate self-supervision by intersecting two groups. Experiments on four real-world datasets have validated the superiority of CubeRec over state-of-the-art baselines.

115 Recommender-Systems Papers accepted at SIGIR 2022

The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval ( SIGIR 2022 ) released the list of accepted papers . It contains a total of 115 papers incl. posters etc. related to recommender-systems. I assessed the papers based on the title, so chances are, I missed a few and the actual number is even higher.

Here is the list:

Full Papers

Decoupled Side Information Fusion for Sequential Recommendation Yueqi Xie, Peilin Zhou and Sunghun Kim

Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation Nicholas Lim, Bryan Hooi, See-Kiong Ng, Yong Liang Goh, Renrong Weng and Rui Tan

Interpolative Distillation for Unifying Biased and DebiasedRecommendation Sihao Ding, Fuli Feng, Xiangnan He, Jinqiu Jin, Wenjie Wang, Yong Liao and Yongdong Zhang

Locality-Sensitive State-Guided Experience Replay Optimization for Sparse-Reward in Online Recommendation Xiaocong Chen, Lina Yao, Julian Mcauley, Weili Guan, Xiaojun Chang and Xianzhi Wang

Unify Local and Global Information for Top-N Recommendation Xiaoming Liu, Shaocong Wu, Zhaohan Zhang and Chao Shen

Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders Zhihong Chen, Jiawei Wu, Chenliang Li, Jingxu Chen, Rong Xiao and Binqiang Zhao

User-Aware Multi-Interest Learning for Candidate Matching in Recommenders Zheng Chai, Zhihong Chen, Chenliang Li, Rong Xiao, Houyi Li, Jiawei Wu, Jingxu Chen and Haihong Tang

Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations Giacomo Balloccu, Ludovico Boratto, Mirko Marras and Gianni Fenu

User-controllable Recommendation Against Filter Bubbles Wenjie Wang, Fuli Feng, Liqiang Nie and Tat-Seng Chua

DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation Jiangxia Cao, Xixun Lin, Xin Cong, Jing Ya, Tingwen Liu and Bin Wang

Thinking inside The Box: Learning Hypercube Representations for Group Recommendation Tong Chen, Hongzhi Yin, Jing Long, Nguyen Quoc Viet Hung, Yang Wang and Meng Wang

A Review-aware Graph Contrastive Learning Framework for Recommendation Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang and Yong Li

Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui and Quoc Viet Hung Nguyen

On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Guandong Xu and Quoc Viet Hung Nguyen

Multi-Agent RL-based Information Selection Model for Sequential Recommendation Kaiyuan Li, Pengfei Wang and Chenliang Li

Knowledge Graph Contrastive Learning for Recommendation Yuhao Yang, Chao Huang, Lianghao Xia and Chenliang Li

Joint Multisided Exposure Fairness for Recommendation Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz and Xue Liu

When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation Yu Tian, Jianxin Chang, Yanan Niu, Yang Song and Chenliang Li

Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou and Zhaochun Ren

Single-shot Embedding Dimension Search in Recommender System Liang Qu, Yonghong Ye, Ningzhi Tang, Lixin Zhang, Yuhui Shi and Hongzhi Yin

Learning to Infer User Implicit Preference in Conversational Recommendation Chenhao Hu, Shuhua Huang, Yansen Zhang and Yubao Liu

Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation Junda Wu, Zhihui Xie, Tong Yu, Handong Zhao, Ruiyi Zhang and Shuai Li

An Attribute-Driven Mirroring Graph Network for Session-based Recommendation Siqi Lai, Erli Meng, Fan Zhang, Chenliang Li, Bin Wang and Aixin Sun

Self-Augmented Recommendation with Hypergraph Contrastive Collaborative Filtering Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin and Jimmy Huang

Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu and Xin Cao

User-Centric Conversational Recommendation with Multi-Aspect User Modeling Shuokai Li, Ruobing Xie, Yongchun Zhu, Xiang Ao, Fuzhen Zhuang and Qing He

MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations Xiangmeng Wang, Qian Li, Dianer Yu, Zhichao Wang, Hongxu Chen and Guandong Xu

Forest-based Deep Recommender Chao Feng, Defu Lian, Zheng Liu, Xing Xie, Le Wu and Enhong Chen

Bilateral Self-unbiased Recommender Learning from Biased Implicit Feedback Jae-woong Lee, Seongmin Park, Joonseok Lee and Jongwuk Lee

Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation Xiaokun Zhang, Bo Xu, Liang Yang, Chenliang Li, Fenglong Ma, Haifeng Liu and Hongfei Lin

Privacy-Preserving Synthetic Data Generation for Recommendation Fan Liu, Huilin Chen, Zhiyong Cheng, Yinwei Wei, Liqiang Nie and Mohan Kankanhalli

DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation based on Correlation Graph Wenwen Gong, Xuyun Zhang, Yifei Chen, Qiang He, Amin Beheshti, Xiaolong Xu, Chao Yan and Lianyong Qi

Variational Reasoning about User Preferences for Conversational Recommendation Zhi Tian, Zhaochun Ren, Dongdong Li, Pengjie Ren, Liu Yang, Xin Xin, Huasheng Liang, Maarten de Rijke and Zhumin Chen

Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator Shanlei Mu, Yaliang Li, Wayne Xin Zhao, Jingyuan Wang, Bolin Ding and Ji-Rong Wen

Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation Weiming Liu, Xiaolin Zheng, Jiajie Su, Mengling Hu, Yanchao Tan and Chaochao Chen

Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems Shuo Zhang, Mu Chun Wang and Krisztian Balog

HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation Yuntao Du, Xinjun Zhu, Lu Chen, Baihua Zheng and Yunjun Gao

Self-Guided Learning to Denoise for Robust Recommendation Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang and Baihua Zheng

AutoGSR: Neural Architecture Search for Graph-based Session Recommendation Jingfan Chen, Guanghui Zhu, Haojun Hou, Chunfeng Yuan and Yihua Huang

Learning Graph-based Disentangled Representations for Next POI Recommendation Zhaobo Wang, Yanmin Zhu, Haobing Liu and Chunyang Wang

GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation Song Yang, Jiamou Liu and Kaiqi Zhao

Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation Xinyan Fan, Jianxun Lian, Wayne Xin Zhao, Zheng Liu, Chaozhuo Li and Xing Xie

ProFairRec: Provider Fairness-aware News Recommendation Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang and Xing Xie

Multi-Faceted Global Item Relation Learning for Session-Based Recommendation Qilong Han, Chi Zhang, Rui Chen, Riwei Lai, Hongtao Song and Li Li

ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping Mozhdeh Ariannezhad, Sami Jullien, Ming Li, Min Fang, Sebastian Schelter and Maarten de Rijke

CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users Haoran Xin, Xinjiang Lu, Nengjun Zhu, Tong Xu, Dejing Dou and Hui Xiong

Multi-Behavior Sequential Transformer Recommender Enming Yuan, Wei Guo, Zhicheng He, Huifeng Guo, Chengkai Liu and Ruiming Tang

Deployable and Continuable Meta-Learning-Based Recommender System with Fast User-Incremental Updates Renchu Guan, Haoyu Pang, Fausto Giunchiglia, Ximing Li, Xuefeng Yang and Xiaoyue Feng

Explainable Fairness for Feature-aware Recommender Systems Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li and Yongfeng Zhang

Graph Trend Filtering Networks for Recommendation Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang and Qing Li

AutoLossGen: Automatic Loss Function Generation for Recommender Systems Zelong Li, Jianchao Ji, Yingqiang Ge and Yongfeng Zhang

Determinantal Point Process Set Likelihood-Based Loss Functions for Sequential Recommendation Yuli Liu, Christian Walder and Lexing Xie

KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums Limeng Cui and Dongwon Lee

CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems Mohammadmehdi Naghiaei, Hossein A. Rahmani and Yashar Deldjoo

PEVAE: A hierarchical VAE for personalized explainable recommendation. Zefeng Cai and Zerui Cai

Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation Shansan Gong and Kenny Zhu

Less is More: Reweighting Important Spectral Graph Features for Recommendation Shaowen Peng, Kazunari Sugiyama and Tsunenori Mine

Short Papers

Item Similarity Mining for Multi-Market Recommendation Jiangxia Cao, Xin Cong, Tingwen Liu and Bin Wang

MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios Xiaofeng Pan, Ming Li, Jing Zhang, Keren Yu, Luping Wang, Hong Wen, Chengjun Mao and Bo Cao

Adversarial Graph Perturbations for Recommendations at Scale Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Xia Hu, Fei Wang and Hao Yang

ReLoop: A Self-Correction Learning Loop for Recommender Systems Guohao Cai, Jieming Zhu, Quanyu Dai, Zhenhua Dong, Rui Zhang, Xiuqiang He and Ruiming Tang

EFLEC: Efficient Feature-LEakage Correction in GNN based Recommendation Systems Ishaan Kumar, Yaochen Hu and Yingxue Zhang

Enhancing Top-N Item Recommendations by Peer Collaboration Yang Sun, Fajie Yuan, Min Yang, Alexandros Karatzoglou, Li Shen and Xiaoyan Zhao

News Recommendation with Candidate-aware User Modeling Tao Qi, Fangzhao Wu, Chuhan Wu and Yongfeng Huang

PERD: Personalized Emoji Recommendation with Dynamic User Preference Xuanzhi Zheng, Guoshuai Zhao, Li Zhu and Xueming Qian

Denoising Time Cycle Modeling for Recommendation Sicong Xie, Qunwei Li, Weidi Xu, Kaiming Shen, Shaohu Chen and Wenliang Zhong

Towards Results-level Proportionality for Multi-objective Recommender Systems Ladislav Peska and Patrik Dokoupil

Regulating Provider Groups Exposure in Recommendations Mirko Marras, Ludovico Boratto, Guilherme Ramos and Gianni Fenu

FUM: Fine-grained and Fast User Modeling for News Recommendation Tao Qi, Fangzhao Wu, Chuhan Wu and Yongfeng Huang

Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation Yinfeng Li, Chen Gao, Hengliang Luo, Depeng Jin and Yong Li

Value Penalized Q-Learning for Recommender Systems Chengqian Gao, Ke Xu, Kuangqi Zhou, Lanqing Li, Xueqian Wang, Bo Yuan and Peilin Zhao

Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation Pengyang Li, Rong Chen, Quan Liu, Jian Xu and Bo Zheng

Progressive Self-Attention Network with Unsymmetrical Positional Encoding for Sequential Recommendation Yuehua Zhu, Bo Huang, Shaohua Jiang, Muli Yang, Yanhua Yang and Wenliang Zhong

Empowering Next POI Recommendation with Multi-Relational Modeling Zheng Huang, Jing Ma, Yushun Dong, Natasha Foutz and Jundong Li

DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation Liqi Yang, Linhao Luo, Xiaofeng Zhang, Fengxin Li, Xinni Zhang, Zelin Jiang and Shuai Tang

RESETBERT4Rec: A Pre-training Model Integrating Time And User Historical Behavior for Sequential Recommendation Qihang Zhao

MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning Menghan Wang, Yuchen Guo, Zhenqi Zhao, Guangzheng Hu, Yuming Shen, Mingming Gong and Philip Torr

Conversational Recommendation via Hierarchical Information Modeling Quan Tu, Shen Gao, Yanran Li, Jianwei Cui, Bin Wang and Rui Yan

Mitigating Consumer Biases in Recommendations with Adversarial Training Christian Ganhor, David Penz, Navid Rekabsaz, Oleg Lesota and Markus Schedl

DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations Jiadi Han, Qian Tao, Yufei Tang and Yuhan Xia

CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space Yupeng Hou, Binbin Hu, Zhiqiang Zhang and Wayne Xin Zhao

Item-Provider Co-learning for Sequential Recommendation Lei Chen, Jingtao Ding, Min Yang, Chengming Li, Chonggang Song and Lingling Yi

Rethinking Correlation-based Item-Item Similarities for Recommender Systems Katsuhiko Hayashi

Improving Conversational Recommender Systems via Transformer-based Sequential Modelling Jie Zou, Evangelos Kanoulas, Pengjie Ren, Zhaochun Ren, Aixin Sun and Cheng Long

Improving Micro-video Recommendation via Contrastive Multiple Interests Beibei Li, Beihong Jin, Jiageng Song, Yisong Yu, Yiyuan Zheng and Wei Zhuo

Is News Recommendation a Sequential Recommendation Task? Chuhan Wu, Fangzhao Wu, Tao Qi, Chenliang Li and Yongfeng Huang

Diversity vs Relevance: a practical multi-objective study in luxury fashion recommendations Joao Sa, Vanessa Queiroz Marinho, Ana Rita Magalhaes, Tiago Lacerda and Diogo Goncalves

Multi-modal Graph Contrastive Learning for Micro-video Recommendation Zixuan Yi, Xi Wang, Craig Macdonald and Iadh Ounis

Coarse-to-Fine Sparse Sequential Recommendation Jiacheng Li, Tong Zhao, Jin Li, Jim Chan, Christos Faloutsos, George Karypis, Soo-Min Pantel and Julian McAuley

An MLP-based Algorithm for Efficient Contrastive Graph Recommendations Siwei Liu, Iadh Ounis and Craig Macdonald

DeSCoVeR: Debiased Semantic Context Prior for Venue Recommendation Sailaja Rajanala, Arghya Pal, Manish Singh, Raphael Phan and Wong Koksheik

A Content Recommendation Policy for Gaining Subscribers Konstantinos Theocharidis, Manolis Terrovitis, Spiros Skiadopoulos and Panagiotis Karras

Mitigating the Filter Bubble while Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems Zhaolin Gao, Tianshu Shen, Zheda Mai, Mohamed Reda Bouadjenek, Isaac Waller, Ashton Anderson, Ron Bodkin and Scott Sanner

ELECRec: Training Sequential Recommenders as Discriminators Yongjun Chen, Jia Li and Caiming Xiong

Explainable Session-based Recommendation with Meta-Path Guided Instances and Self-Attention Mechanism Jiayin Zheng, Juanyun Mai and Yanlong Wen

MM-Rec: Visiolinguistic Model Empowered Multimodal News Recommendation Chuhan Wu, Fangzhao Wu, Tao Qi, Chao Zhang, Yongfeng Huang and Tong Xu

Generative Adversarial Framework for Cold-Start Item Recommendation Hao Chen, Zefan Wang, Feiran Huang, Xiao Huang, Yue Xu, Yishi Lin, Peng He and Zhoujun Li

Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder Xu Zhao, Yi Ren, Ying Du, Shenzheng Zhang and Nian Wang

Revisiting Interactive Recommender System with Reinforcement Learning Hojoon Lee, Dongyoon Hwang, Kyushik Min and Jaegul Choo

Next Point-of-Interest Recommendation with Auto-Correlation Enhanced Multi-Modal Transformer Network Yanjun Qin, Yuchen Fang, Haiyong Luo, Fang Zhao and Chenxing Wang

Neutralizing Popularity Bias in Recommendation Models Guipeng Xv, Chen Lin, Hui Li, Jinsong Su, Weiyao Ye and Yewang Chen

Exploiting Session Information in BERT-based Session-aware Sequential Recommendation Jinseok Seol, Youngrok Ko and Sang-Goo Lee

Selective Fairness in Recommendation via Prompts Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin and Qing He

Dual Contrastive Network for Sequential Recommendation Guanyu Lin, Chen Gao, Yinfeng Li, Yu Zheng, Zhiheng Li, Depeng Jin and Yong Li

Resource Papers

Revisiting Bundle Recommendation: Datasets, Tasks, Challenges and Opportunities for Intent-aware Product Bundling Zhu Sun, Jie Yang, Kaidong Feng, Hui Fang, Xinghua Qu and Yew Soon Ong

BARS: Towards Open-Benchmarking for Recommender Systems Jieming Zhu, Kelong Mao, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Zhicheng Dou, Xi Xiao and Rui Zhang

RELISON: A Framework for Link Recommendation in Social Networks Javier Sanz-Cruzado and Pablo Castells

iRec: An Interactive Recommendation Framework Thiago Silva, Nicollas Silva, Heitor Werneck, Carlos Mito, Adriano Pereira and Leonardo Rocha

RecDelta: An Interactive Dashboard on Top-k Recommendation for Cross-model Evaluation Yi-Shyuan Chiang, Yu-Ze Liu, Chen Feng Tsai, Jing-Kai Lou, Ming-Feng Tsai and Chuan-Ju Wang

PKG: A Personal Knowledge Graph for Recommendation Yu Yang, Jiangxu Lin, Xiaolian Zhang and Meng Wang

Perspectives Papers

Reproducibility papers.

Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion Xiang Chen, Ningyu Zhang, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si and Huajun Chen

A Robust Computerized Adaptive Testing Approach in Educational Question Retrieval Yan Zhuang, Qi Liu, Zhenya Huang, Zhi Li, Binbin Jin, Haoyang Bi, Enhong Chen and Shijin Wang

Curriculum Contrastive Context Denoising for Few-shot Conversational Dense Retrieval Kelong Mao, Zhicheng Dou and Hongjin Qian

Fairness of Exposure in Light of Incomplete Exposure Estimation Maria Heuss, Fatemeh Sarvi and Maarten de Rijke

Learn from Unlabeled Videos for Near-duplicate Video Retrieval Xiangteng He, Yulin Pan, Mingqian Tang, Yiliang Lv and Yuxin Peng

RankFlow: Joint Optimization of Multi-Stage Cascade Ranking Systems as Flows Jiarui Qin, Jiachen Zhu, Bo Chen, Zhirong Liu, Weiwen Liu, Ruiming Tang, Rui Zhang, Yong Yu and Weinan Zhang

Personalized Fashion Compatibility Modeling via Metapath-guided Heterogeneous Graph Learning Weili Guan, Fangkai Jiao, Xuemeng Song, Haokun Wen, Chung-Hsing Yeh and Xiaojun Chang

Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation Wanwei He, Yinpei Dai, Min Yang, Jian Sun, Fei Huang, Luo Si and Yongbin Li

Assessing Student’s Dynamic Knowledge State by Exploring the Question Difficulty Effect Shuanghong Shen, Zhenya Huang, Qi Liu, Yu Su, Shijin Wang and Enhong Chen

Contrastive Learning with Hard Negative Entities for Entity Set Expansion Yinghui Li, Yangning Li, Yuxin He, Tianyu Yu, Ying Shen and Hai-Tao Zheng

HTKG: Deep Keyphrase Generation with Neural Hierarchical Topic Guidance Yuxiang Zhang, Tao Jiang, Tianyu Yang, Xiaoli Li and Suge Wang

Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing Hanshuang Tong, Zhen Wang, Qi Liu, Yun Zhou, Shiwei Tong and Wenyuan Han

Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities Jiandian Zeng, Tianyi Liu and Jiantao Zhou

Progressive Learning for Image Retrieval with Hybrid-Modality Queries Yida Zhao, Yuqing Song and Qin Jin

Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction Weijie Yu, Zhongxiang Sun, Jun Xu, Zhenhua Dong, Xu Chen, Hongteng Xu and Ji-Rong Wen

You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos Xin Sun, Xuan Wang, Jialin Gao, Qiong Liu and Xi Zhou

V2P: Vision-to-Prompt based Multi-Modal Product Summary Generation Xuemeng Song, Liqiang Jing, Dengtian Lin, Zhongzhou Zhao, Haiqing Chen and Liqiang Nie

Multi-Level Interaction Reranking with User Behavior History Yunjia Xi, Weiwen Liu, Jieming Zhu, Xilong Zhao, Xinyi Dai, Ruiming Tang, Rui Zhang, Weinan Zhang and Yong Yu

Mutual Disentanglement Learning for Joint Fine-Grained Sentiment Classification and Controllable Text Generation Hao Fei, Chenliang Li, Donghong Ji and Fei Li

CenterCLIP: Token Clustering for Efficient Text-Video Retrieval Shuai Zhao, Linchao Zhu, Xiaohan Wang and Yi Yang

Multimodal Entity Linking with Gated Hierarchical Fusion and Contrastive Training Peng Wang, Jiangheng Wu and Xiaohang Chen

Unifying Cross-lingual Summarization and Machine Translation with Compression Rate Yu Bai, Heyan Huang, Kai Fan, Yang Gao, Yiming Zhu, Jiaao Zhan, Zewen Chi and Boxing Chen

Structure and Semantics Preserving Document Representations Natraj Raman, Sameena Shah and Manuela Veloso

Enhancing CTR Prediction with Context-Aware Feature Representation Learning Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang and Ning Gu

Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion Adam Block, Rahul Kidambi, Daniel Hill, Thorsten Joachims and Inderjit Dhillon

Automatic Expert Selection for Multi-Scenario and Multi-Task Search Xinyu Zou, Zhi Hu, Yiming Zhao, Xuchu Ding, Zhongyi Liu, Chenliang Li and Aixin Sun

DGQAN: Dual Graph Question-Answer Attention Networks for Answer Selection Haitian Yang, Xuan Zhao, Yan Wang, Min Li, Wei Chen and Weiqing Huang

Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy Wenqiang Lei, Yao Zhang, Feifan Song, Hongru Liang, Jiaxin Mao, Jiancheng Lv, Zhenglu Yang and Tat-Seng Chua

Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking Qian Dong, Yiding Liu, Suqi Cheng, Shuaiqiang Wang, Zhicong Cheng, Shuzi Niu and Dawei Yin

IR Evaluation and Learning in the Presence of Forbidden Documents David Carmel, Nachshon Cohen, Amir Ingber and Elad Kravi

Target-aware Abstractive Related Work Generation with Contrastive Learning Xiuying Chen, Hind Alamro, Mingzhe Li, Shen Gao, Rui Yan, Xin Gao and Xiangliang Zhang

Geometric Disentangled Collaborative Filtering Yiding Zhang, Chaozhuo Li, Xing Xie, Xiao Wang, Chuan Shi, Yuming Liu, Hao Sun, Liangjie Zhang, Weiwei Deng and Qi Zhang

Exploring Modular Task Decomposition in Cross-domain Named Entity Recognition Xinghua Zhang, Bowen Yu, Tingwen Liu, Yubin Wang, Taoyu Su and Hongbo Xu

CorED: Incorporating Type-level and Instance-level Correlations for Fine-grained Event Detection Jiawei Sheng, Rui Sun, Shu Guo, Shiyao Cui, Jiangxia Cao, Lihong Wang, Tingwen Liu and Hongbo Xu

Incorporating Retrieval Information into the Truncation of Ranking Lists in the Legal Domain Yixiao Ma, Qingyao Ai, Yueyue Wu, Yunqiu Shao, Yiqun Liu, Min Zhang and Shaoping Ma

Can clicks be both labels and features? Unbiased Behavior Feature Collection and Uncertainty-aware Learning to Rank Tao Yang, Chen Luo, Hanqing Lu, Parth Gupta, Bing Yin and Qingyao Ai

PTAU: Prompt Tuning for Attributing Unanswerable Questions Jinzhi Liao, Xiang Zhao, Jianming Zheng, Xinyi Li, Fei Cai and Jiuyang Tang

Generating Clarifying Questions with Web Search Results Ziliang Zhao, Zhicheng Dou, Jiaxin Mao and Jirong Wen

HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction Zuowu Zheng, Changwang Zhang, Xiaofeng Gao and Guihai Chen

Webformer: Pre-training with Web Pages for Information Retrieval Yu Guo , Zhengyi Ma, Jiaxin Mao, Hongjin Qian, Xinyu Zhang, Hao Jiang, Zhao Cao and Zhicheng Dou

Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders Jinning Li, Huajie Shao, Dachun Sun, Ruijie Wang, Yuchen Yan, Jinyang Li, Shengzhong Liu, Hanghang Tong and Tarek Abdelzaher

Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach Shengyao Zhuang, Hang Li and Guido Zuccon

Why Don’t You Click: Understanding Non-Click Results in Web Search with Brain Signals Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuancheng Li, Jiaji Li, Xuesong Chen, Min Zhang and Shaoping Ma

CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos Shengyao Zhuang and Guido Zuccon

Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction Qika Lin, Jun Liu, Fangzhi Xu, Yudai Pan, Yifan Zhu, Lingling Zhang and Tianzhe Zhao

Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings Shitao Xiao, Zheng Liu, Weihao Han, Jianjin Zhang, Defu Lian, Yeyun Gong, Qi Chen, Fan Yang, Hao Sun, Yingxia Shao and Xing Xie

Personalized Abstractive Opinion Tagging Mengxue Zhao, Yang Yang, Miao Li, Jingang Wang, Wei Wu, Pengjie Ren, Maarten de Rijke and Zhaochun Ren

Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering Minghao Zhao, Le Wu, Yile Liang, Lei Chen, Jian Zhang, Qilin Deng, Kai Wang, Runze Wu, Xudong Shen and Tangjie Lv

CRET: Cross-Modal Retrieval Transformer for Efficient Text-Video Retrieval Kaixiang Ji, Jiajia Liu, Weixiang Hong, Liheng Zhong, Jian Wang, Jingdong Chen and Wei Chu

H-ERNIE: A Hierarchical Multi-Granularity Pre-Trained Language Model for Chinese Search Engine Xiaokai Chu, Jiashu Zhao, Lixin Zou and Dawei Yin

NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction Guanghui Zhu, Feng Cheng, Defu Lian, Chunfeng Yuan and Yihua Huang

Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding Mingyang Chen, Wen Zhang, Yushan Zhu, Hongting Zhou, Zonggang Yuan, Changliang Xu and Huajun Chen

MetaCare++: Meta-Learning with Hierarchical Subtyping for Cold-Start Diagnosis Prediction in Healthcare Data Yanchao Tan, Carl Yang, Xiangyu Wei, Chaochao Chen, Weiming Liu, Longfei Li, Jun Zhou and Xiaolin Zheng

Interpreting Patient Descriptions using Distantly Supervised Similar Case Retrieval Israa Alghanmi, Luis Espinosa-Anke and Steven Schockaert

Risk-Sensitive Deep Neural Learning to Rank Pedro Henrique Silva Rodrigues, Daniel Xavier Sousa, Thierson Couto Rosa and Marcos Andre Goncalves

BERT-ER: Query-Specific BERT Entity Representations for Entity Ranking Shubham Chatterjee and Laura Dietz

Conversational Question Answering on Heterogeneous Sources Philipp Christmann, Rishiraj Saha Roy and Gerhard Weikum

Human preferences as dueling bandits Xinyi Yan, Chengxi Luo, Charles Clarke, Nick Craswell, Ellen Voorhees and Pablo Castells

A Non-Factoid Question-Answering Taxonomy Valeriia Bolotova, Vladislav Blinov, Falk Scholer, Bruce Croft and Mark Sanderson

Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification Kai Zhang, Qi Liu, Zhenya Huang, Mengdi Zhang, Kun Zhang, Cheng Mingyue, Wei Wu and Enhong Chen

Axiomatically Regularized Pre-training for Ad hoc Search Jia Chen, Yiqun Liu, Yan Fang, Jiaxin Mao, Hui Fang, Shenghao Yang, Xiaohui Xie, Min Zhang and Shaoping Ma

Bit-aware Semantic Transformer Hashing for Multi-modal Retrieval Wentao Tan, Lei Zhu, Weili Guan, Jingjing Li and Zhiyong Cheng

Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning Fangzhi Xu, Jun Liu, Qika Lin, Yudai Pan and Lingling Zhang

A Flexible Framework for Offline Effectiveness Metrics Alistair Moffat, Joel Mackenzie, Paul Thomas and Leif Azzopardi

What Makes The Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation Li Lin, Yixin Cao, Lifu Huang, Shu’Ang Li, Xuming Hu, Lijie Wen and Jianmin Wang

Recognizing Medical Search Query Intent by Few-shot Learning Yaqing Wang, Song Wang, Li Yanyan and Dejing Dou

Ranking Interruptus: When Truncated Rankings Are Better and How to Measure That Enrique Amigo, Stefano Mizzaro and Damiano Spina

A Dual-Expert Framework for Event Argument Extraction Rui Li, Wenlin Zhao, Cheng Yang and Sen Su

Few-shot Node Classification on Attributed Networks with Graph Meta-learning Yonghao Liu, Mengyu Li, Ximing Li, Fausto Giunchiglia, Xiaoyue Feng and Renchu Guan

Adaptable Text Matching via Meta-Weight Regulator Bo Zhang, Chen Zhang, Fang Ma and Dawei Song

Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities Vishwa Vinay, Manoj Kilaru and David Arbour

Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering Changxin Tian, Yuexiang Xie, Yaliang Li, Nan Yang and Wayne Xin Zhao

A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning Ruichao Yang, Jing Ma, Hongzhan Lin and Wei Gao

INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering Yunfan Wu, Qi Cao, Huawei Shen, Shuchang Tao and Xueqi Cheng

Video Moment Retrieval from Text Queries via Single Frame Annotation Ran Cui, Tianwen Qian, Pai Peng, Elena Daskalaki, Jingjing Chen, Xiaowei Guo, Huyang Sun and Yu-Gang Jiang

Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Peilin Zhao, Luo Da, Kangyi Lin, Sophia Ananiadou and Junzhou Huang

COSPLAY: Concept Set Guided Personalized Dialogue System Chen Xu, Piji Li, Wei Wang, Haoran Yang, Siyun Wang and Chuangbai Xiao

LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback Yunchang Zhu, Liang Pang, Yanyan Lan, Huawei Shen and Xueqi Cheng

Improving Implicit Alternating Least Squares with Ring-based Regularization Rui Fan, Jin Chen, Jin Zhang, Defu Lian and Enhong Chen

Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking Ali Vardasbi, Fatemeh Sarvi and Maarten de Rijke

Offline Retrieval Evaluation Without Evaluation Metrics Fernando Diaz and Andres Ferraro

ADPL: Adversarial Prompt-based Domain Adaptation for Dialogue Summarization with Knowledge Disentanglement Lulu Zhao, Fujia Zheng, Weihao Zeng, Keqing He, Ruotong Geng, Huixing Jiang, Wei Wu and Weiran Xu

Aspect Feature Distillation and Enhancement Network for Aspect-based Sentiment Analysis Rui Liu, Jiahao Cao, Nannan Sun and Lei Jiang

Multimodal Disentanglement Variational AutoEncoders for Zero-Shot Cross-Modal Retrieval Jialin Tian, Kai Wang, Xing Xu, Zuo Cao, Fumin Shen and Heng Tao Shen

Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison Amifa Raj and Michael Ekstrand

Entity-aware Transformers for Entity Search Emma Gerritse, Faegheh Hasibi and Arjen de Vries

Optimizing generalized Gini indices for fairness in rankings Virginie Do and Nicolas Usunier

Co-clustering Interactions via Attentive Hypergraph Neural Network Tianchi Yang, Cheng Yang, Luhao Zhang, Chuan Shi, Maodi Hu, Huaijun Liu, Tao Li and Dong Wang

A Study of Cross-Session Cross-Device Search within an Academic Digital Library Sebastian Gomes, Miriam Boon and Orland Hoeber

A Multitask Framework for Sentiment, Emotion and Sarcasm aware Cyberbullying Detection in Multi-modal Code-Mixed Memes Krishanu Maity, Prince Jha, Sriparna Saha and Pushpak Bhattacharyya

Scalable Exploration for Online Learning to Rank with Perturbed Click Feedback Yiling Jia and Hongning Wang

Bias Mitigation for Toxicity Detection via Sequential Decisions Lu Cheng, Ahmadreza Mosallanezhad, Yasin Silva, Deborah Hall and Huan Liu

Information Need Awareness: an EEG study Dominika Michalkova, Mario Parra and Yashar Moshfeghi

Towards Explainable Search Results: A Listwise Explanation Generator Puxuan Yu, Razieh Rahimi and James Allan

Pareto-Optimal Fairness-Utility Amortizations in Rankings with a DBN Exposure Model Till Kletti, Jean-Michel Renders and Patrick Loiseau

IAOTP: An Interactive End-to-End Solution for Aspect-Opinion Term Pairs Extraction Ambreen Nazir and Rao Yuan

Structure-Aware Semantic-Aligned Network for Universal Cross-Domain Retrieval Jialin Tian, Xing Xu, Kai Wang, Zuo Cao, Xunliang Cai and Heng Tao Shen

Few-Shot Stance Detection via Target-Aware Prompt Distillation Yan Jiang, Jinhua Gao, Huawei Shen and Xueqi Cheng

Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction Xinyu Ma, Jiafeng Guo, Ruqing Zhang, Yixing Fan and Xueqi Cheng

Re-thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective Ying Zhou, Xuanang Chen, Ben He, Zheng Ye and Le Sun

QUASER: Question Answering with Scalable Extractive Rationalization Asish Ghoshal, Srini Iyer, Bhargavi Paranjape, Kushal Lakhotia, Scott Yih and Yashar Mehdad

ESCM^2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation Hao Wang, Tai-Wei Chang, Tianqiao Liu, Jianmin Huang, Zhichao Chen, Chao Yu, Ruopeng Li and Wei Chu

Towards Suicide Ideation Detection Through Online Conversational Context Ramit Sawhney, Shivam Agarwal, Atula Tejaswi Neerkaje, Nikolaos Aletras, Preslav Nakov and Lucie Flek

Structured and Natural Responses Co-generation for Conversational Search Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji and Tat-Seng Chua

Towards Event-level Causal Relation Identification Chuang Fan, Daoxing Liu, Libo Qin, Yue Zhang and Ruifeng Xu

Exploring Heterogeneous Data Lake based on Unified Canonical Graphs Qin Yuan, Ye Yuan, Zhenyu Wen, He Wang, Chen Chen and Guoren Wang

Relation-Guided Few-Shot Relational Triple Extraction Xin Cong, Jiawei Sheng, Shiyao Cui, Bowen Yu, Tingwen Liu and Bin Wang

L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks Fangxin Liu, Haomin Li and Li Jiang

Neural Statistics for Click-Through Rate Prediction Yanhua Huang, Hangyu Wang, Yiyun Miao, Ruiwen Xu, Lei Zhang and Weinan Zhang

Graph Capsule Network with a Dual Adaptive Mechanism Xiangping Zheng, Xun Liang, Bo Wu, Yuhui Guo and Xuan Zhang

Constructing Better Evaluation Metrics by Incorporating theAnchoring Effect into the User Model Nuo Chen, Fan Zhang and Tetsuya Sakai

Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning Xiang Chen, Lei Li, Ningyu Zhang, Chuanqi Tan, Fei Huang, Luo Si and Huajun Chen

Training Entire-Space Models for Target-oriented Opinion Words Extraction Yuncong Li, Fang Wang and Sheng-Hua Zhong

Zero-shot Query Contextualization for Conversational Search Antonios Minas Krasakis, Andrew Yates and Evangelos Kanoulas

Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction Xiaoxiao Xu, Zhiwei Fang, Qian Yu, Ruoran Huang, Chaosheng Fan, Yong Li, Yang He, Changping Peng, Zhangang Lin and Jingping Shao

Preference Enhanced Social Infulence Modeling for Network-Aware Cascade Prediction Likang Wu, Hao Wang, Enhong Chen, Zhi Li, Hongke Zhao and Jianhui Ma

Faster Learned Sparse Retrieval with Guided Traversal Antonio Mallia, Joel Mackenzie, Torsten Suel and Nicola Tonellotto

On Extractive Summarization for Profile-centric Neural Expert Search in Academia Rennan Lima and Rodrygo Santos

Animating Images to transfer CLIP for Video-Text Retrieval Yu Liu, Huai Chen, Lianghua Huang, Di Chen, Bin Wang, Pan Pan and Lisheng Wang

IPR: Interaction-level Preference Ranking for explicit feedback Shih-Yang Liu, Hsien Hao Chen, Chih-Ming Chen, Ming-Feng Tsai and Chuan-Ju Wang

Socially-aware Dual Contrastive Learning for Cold-Start Recommendation Jing Du, Zesheng Ye, Lina Yao, Bin Guo and Zhiwen Yu

Hierarchical Task-aware Multi-Head Attention Network Jing Du, Lina Yao, Xianzhi Wang, Bin Guo and Zhiwen Yu

Constrained Sequence-to-Tree Generation for Hierarchical Text Classification Chao Yu, Yi Shen and Yue Mao

Image-Text Retrieval via Contrastive Learning with Auxiliary Generative Features and Support-set Regularization Lei Zhang, Min Yang, Chengming Li and Ruifeng Xu

Enhancing Event-Level Sentiment Analysis with Structured Arguments Qi Zhang, Jie Zhou, Qin Chen, Qingchun Bai and Liang He

P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning Xiaomeng Hu, Shi Yu, Chenyan Xiong, Zhenghao Liu, Zhiyuan Liu and Ge Yu

Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction Xiaochen Li, Jian Liang, Xialong Liu and Yu Zhang

Curriculum Learning for Dense Retrieval Distillation Hansi Zeng, Hamed Zamani and Vishwa Vinay

Evaluation of Herd Behavior Caused by Population-scale Concept Drift in Collaborative Filtering Chenglong Ma, Yongli Ren, Pablo Castells and Mark Sanderson

Detecting Frozen Phrases in Open-Domain Question Answering Mostafa Yadegari, Ehsan Kamalloo and Davood Rafiei

Point Prompt Tuning for Temporally Language Grounding Yawen Zeng

Understanding User Satisfaction with Task-Oriented Dialogue Systems Clemencia Siro, Mohammad Aliannejadi and Maarten de Rijke

Distilling Knowledge on Text Graph for Social Media Attribute Inference Quan Li, Xiaoting Li, Lingwei Chen and Dinghao Wu

What Makes a Good Podcast Summary? Rezvaneh Rezapour, Sravana Reddy, Rosie Jones and Ian Soboroff

A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism Han Liu, Siyang Zhao, Xiaotong Zhang, Feng Zhang, Junjie Sun, Hong Yu and Xianchao Zhang

BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction Bisheng Li, Min Zhou, Zengfeng Huang, Shengzhong Zhang, Menglin Yang and Defu Lian

Analyzing the Support Level for Tips Extracted from Product Reviews Miriam Farber, David Carmel, Lital Kuchy and Avihai Mejer

Enhancing Zero-Shot Stance Detection via Targeted Background Knowledge Qinglin Zhu, Bin Liang, Jingyi Sun, Jiachen Du, Lanjun Zhou and Xu Ruifeng

Translation-Based Implicit Annotation Projection for Zero-Shot Cross-Lingual Event Argument Extraction Chenwei Lou, Jun Gao, Changlong Yu, Wei Wang, Huan Zhao, Weiwei Tu and Ruifeng Xu

PUM: Pre-training User Model with Contrastive Self-supervision Chuhan Wu, Fangzhao Wu, Tao Qi and Yongfeng Huang

Understanding Long Programming Languages with Structure-Aware Sparse Attention Tingting Liu, Chengyu Wang, Cen Chen, Ming Gao and Aoying Zhou

Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexity Harrie Oosterhuis

A Multi-Task Based Neural Model to Simulate Users in Goal Oriented Dialogue Systems To Eun Kim and Aldo Lipani

Generalizing to the Future: Mitigating Entity Bias in Fake News Detection Yongchun Zhu, Qiang Sheng, Juan Cao, Shuokai Li, Danding Wang and Fuzhen Zhuang

Dialogue Topic Segmentation via Parallel Extraction Network with Neighbor Smoothing Xia Jinxiong, Liu Cao, Chen Jiansong, Li Yuchen, Yang Fan, Cai Xunliang, Wan Guanglu and Wang Houfeng

Analysing the Robustness of Dual Encoders for Dense Retrieval Against Misspellings Georgios Sidiropoulos and Evangelos Kanoulas

From Cluster Ranking to Document Ranking Egor Markovskiy, Fiana Raiber, Shoham Sabach and Oren Kurland

A ‘Pointwise-Query, Listwise-Document’ based QPP Approach Suchana Datta, Sean MacAvaney, Debasis Ganguly and Derek Greene

How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval Hang Li, Ahmed Mourad, Bevan Koopman and Guido Zuccon

Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives Wei Wang, Liangzhu Ge, Jingqiao Zhang and Cheng Yang

Task-Oriented Dialogue System as Natural Language Generation Weizhi Wang, Zhirui Zhang, Junliang Guo, Yinpei Dai, Boxing Chen and Weihua Luo

Expression Syntax Information Bottleneck for Math Word Problems Jing Xiong, Chengming Li, Min Yang, Xiping Hu and Bin Hu

Masking and Generation: An Unsupervised Method for Sarcasm Detection Rui Wang, Qianlong Wang, Bin Liang, Yi Chen, Zhiyuan Wen, Bing Qin and Ruifeng Xu

Cross-Probe BERT for Fast Cross-Modal Search Tan Yu, Hongliang Fei and Ping Li

GERE: Generative Evidence Retrieval for Fact Verification Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Yixing Fan and Xueqi Cheng

Clustering based Behavior Sampling with Long Sequential Data for CTR Prediction Yuren Zhang, Enhong Chen, Binbin Jin, Hao Wang, Min Hou, Wei Huang and Runlong Yu

Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh and Hsiang-Fu Yu

On the Role of Relevance in Natural Language Processing Tasks Artsiom Sauchuk, James Thorne, Alon Halevy, Nicola Tonellotto and Fabrizio Silvestri

An Efficiency Study for SPLADE Models Carlos Lassance and Stephane Clinchant

Tensor-based Graph Modularity for Text Data Clustering Rafika Boutalbi, Mira Ait-Saada, Anastasiia Iurshina, Steffen Staab and Mohamed Nadif

Learned Token Pruning in Contextualized Late Interaction over BERT (ColBERT) Carlos Lassance, Maroua Maachou, Joohee Park and Stephane Clinchant

AHP: Learning to Negative Sample for Hyperedge Prediction Hyunjin Hwang, Seungwoo Lee, Chanyoung Park and Kijung Shin

Re-weighting Negative Samples for Model-Agnostic Matching Jiazhen Lou, Hong Wen, Fuyu Lv, Jing Zhang, Tengfei Yuan and Zhao Li

Semi-Supervised Graph Representation Learning with Few Labels via Non-Parametric Distribution Assignment Junseok Lee, Yunhak Oh, Yeonjun In, Namkyeong Lee, Dongmin Hyun and Chanyoung Park

ILMART: Interpretable Ranking with Constrained LambdaMART Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego and Alberto Veneri

Modern baselines for SPARQL Semantic Parsing Debayan Banerjee, Pranav Ajit Nair, Jivat Neet Kaur, Ricardo Usbeck and Chris Biemann

Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization Mingyuan Cheng, Xinru Liao, Quan Liu, Bin Ma, Jian Xu and Bo Zheng

CTnoCVR: A Novelty Auxiliary Task Making the Lower-CTR-Higher-CVR Upper Dandan Zhang, Haotian Wu, Guanqi Zeng, Yao Yang, Weijiang Qiu, Yujie Chen and Haoyuan Hu

Learning to Rank Knowledge Sub-Graph Nodes for Entity Retrieval Parastoo Jafarzadeh, Zahra Amirmahani and Faezeh Ensan

Deep Multi-Representational Item Network for CTR Prediction Jihai Zhang, Fangquan Lin, Cheng Yang and Wei Wang

A New Sequential Prediction Framework with Spatial-temporal Embedding Jihai Zhang, Fangquan Lin, Cheng Yang and Wei Jiang

Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation Guogang Liao, Xiaowen Shi, Ze Wang, Xiaoxu Wu, Chuheng Zhang, Yongkang Wang, Xingxing Wang and Dong Wang

GraphAD: A Graph Neural Network Model for Entity-Wise Multivariate Time-Series Anomaly Detection Xu Chen, Qiu Qiu, Changshan Li and Kunqing Xie

On Survivorship Bias in MS MARCO Prashansa Gupta and Sean MacAvaney

Counterfacutal Debiasing for Evidence-aware Fake News Detection Junfei Wu, Qiang Liu, Weizhi Xu and Shu Wu

DisenCTR: Dynamic Graph-based Disentangled Representation for Click-Through Rate Prediction Yifan Wang, Yifang Qin, Fang Sun, Bo Zhang, Xuyang Hou, Ke Hu, Jia Cheng, Jun Lei and Ming Zhang

Neural Query Synthesis and Domain-Specific Ranking Templates for Multi-Stage Clinical Trial Matching Ronak Pradeep, Yilin Li, Yuetong Wang and Jimmy Lin

Trainable CNNs based attention for user image behavior modeling with category prior Xin Chen, Qingtao Tang, Ke Hu, Yue Xu, Shihang Qiu, Jia Cheng and Jun Lei

Joint Optimization of Ad Ranking and Creative Selection Kaiyi Lin, Xiang Zhang, Feng Li, Pengjie Wang, Qingqing Long, Hongbo Deng, Jian Xu and Bo Zheng

BERT-based Dense Intra-ranking and Contextualized Late Interaction via Multi-task Learning for Long Document Retrieval Minghan Li and Eric Gaussier

From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective Thibault Formal, Carlos Lassance, Benjamin Piwowarski and Stephane Clinchant

Choosing The Right Teammate For Cooperative Text Generation Antoine Chaffin, Thomas Scialom, Sylvain Lamprier, Jacopo Staiano, Benjamin Piwowarski, Ewa Kijak and Vincent Claveau

When online meets offline: exploring periodicity for travel destination prediction Wanjie Tao, Liangyue Li, Chen Chen, Zulong Chen and Hong Wen

Long Document Re-ranking with Modular Re-ranker Luyu Gao and Jamie Callan

Unsupervised Dataset Generation for Information Retrieval Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee and Rodrigo Nogueira

Identifying Argumentative Questions in Web Search Logs Yamen Ajjour, Pavel Braslavski, Alexander Bondarenko and Benno Stein

Smooth-AUC: Smoothing the Path Towards Rank-based CTR Prediction Shuang Tang, Fangyuan Luo, Jun Wu and Zhuo Wang

Revisiting Two-tower Models for Unbiased Learning to Rank Le Yan, Zhen Qin, Honglei Zhuang, Xuanhui Wang, Michael Bendersky and Marc Najork

Answering Count Query with Explanatory Evidence Shrestha Ghosh, Simon Razniewski and Gerhard Weikum

Interactive query clarification and refinement via user simulation Pierre Erbacher, Ludovic Denoyer and Laure Soulier

Summarizing Legal Regulatory Documents using Transformers Svea Klaus, Ria Van Hecke, Kaweh Djafari Naini, Ismail Sengor Altingovde, Juan Bernabe-Moreno and Enrique Herrera-Viedma

On Optimizing Top-K Metrics for Neural Ranking Models Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky and Marc Najork

Modeling User Behavior With Interaction Networks for Spam Detection Prabhat Agarwal, Manisha Srivastava, Vishwakarma Singh and Chuck Rosenburg

End-to-end Distantly Supervised Information Extraction with Retrieval Augmentation Yue Zhang, Hongliang Fei and Ping Li

Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval Revanth Gangi Reddy, Md Arafat Sultan, Martin Franz, Avirup Sil and Heng Ji

Assessing Scientific Research Papers with Knowledge Graphs Kexuan Sun, Zhiqiang Qiu, Abel Salinas, Yuzhong Huang, Dong-Ho Lee, Daniel Benjamin, Fred Morstatter, Xiang Ren, Kristina Lerman and Jay Pujara

Matching Search Result Diversity with User Diversity Acceptance in Web Search Sessions Jiqun Liu and Fangyuan Han

Topological Analysis of Contradictions in Text Xiangcheng Wu, Xi Niu and Ruhani Rahman

Addressing Gender-related Performance Disparities in Neural Rankers Shirin Seyedsalehi, Negar Arabzadeh, Amin Bigdeli, Morteza Zihayat and Ebrahim Bagheri

Alignment Rationale for Query-Document Youngwoo Kim, Negin Rahimi and James Allan

To interpolate or not to interpolate: PRF, Dense Retrievers and BM25 Hang Li, Shuai Wang, Shengyao Zhuang, Ahmed Mourad, Xueguang Ma, Jimmy Lin and Guido Zuccon

C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval Eugene Yang, Suraj Nair, Ramraj Chandradevan, Rebecca Iglesias-Flores and Douglas Oard

Dual Pseudo Supervision for Semi-Supervised Text Classification with a Reliable Teacher Shujie Li, Min Yang, Chengming Li and Ruifeng Xu

Learning Trustworthy Web Sources to Derive Correct Answers and Reduce Health Misinformation in Search Dake Zhang, Amir Vakili Tahami, Mustafa Abualsaud and Mark Smucker

Learning to Enrich Query Representation with Pseudo-Relevance Feedback for Cross-lingual Retrieval Ramraj Chandradevan, Eugene Yang, Mahsa Yarmohammadi and Eugene Agichtein

Mitigating bias in search results through set-based document reranking and neutrality regularization George Zerveas, Navid Rekabsaz, Daniel Cohen and Carsten Eickhoff

A Meta-learning Approach to Fair Ranking Yuan Wang, Zhiqiang Tao and Yi Fang

Can Users Predict Relative Query Effectiveness? Oleg Zendel, Melika Ebrahim, Shane Culpepper, Alistair Moffat and Falk Scholer

Inconsistent Ranking Assumptions in Medical Search and Their Downstream Consequences Daniel Cohen, Kevin Du, Bhaskar Mitra, Laura Mercurio, Navid Rekabsaz and Carsten Eickhoff

Modality-Balanced Embedding for Video Retrieval Xun Wang, Bingqing Ke, Xuanping Li, Fangyu Liu, Mingyu Zhang, Xiao Liang and Qiushi Xiao

An Efficient Fusion Mechanism for Multimodal Low-resource Setting Dushyant Singh Chauhan, Asif Ekbal and Pushpak Bhattacharyya

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Joeran Beel

I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries. We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).

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ConsRec: Learning Consensus Behind Interactions for Group Recommendation

Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, China

University of Illinois at Urbana-Champaign, USA

IFM Lab, Department of Computer Science, University of California, Davis, USA

University of Illinois at Chicago, USA

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WWW '23: Proceedings of the ACM Web Conference 2023

Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task. Existing group recommendation methods usually infer groups’ preferences via aggregating diverse members’ interests. Actually, groups’ ultimate choice involves compromises between members, and finally, an agreement can be reached. However, existing individual information aggregation lacks a holistic group-level consideration, failing to capture the consensus information. Besides, their specific aggregation strategies either suffer from high computational costs or become too coarse-grained to make precise predictions.

To solve the aforementioned limitations, in this paper, we focus on exploring consensus behind group behavior data. To comprehensively capture the group consensus, we innovatively design three distinct views which provide mutually complementary information to enable multi-view learning, including member-level aggregation, item-level tastes, and group-level inherent preferences. To integrate and balance the multi-view information, an adaptive fusion component is further proposed. As to member-level aggregation, different from existing linear or attentive strategies, we design a novel hypergraph neural network that allows for efficient hypergraph convolutional operations to generate expressive member-level aggregation. We evaluate our ConsRec on two real-world datasets and experimental results show that our model outperforms state-of-the-art methods. An extensive case study also verifies the effectiveness of consensus modeling.

Index Terms

Computing methodologies

Machine learning

Machine learning approaches

Neural networks

Information systems

Information retrieval

Retrieval tasks and goals

Recommender systems

Recommendations

Personalized hybrid recommendation for group of users.

Novel group hybrid method combining collaborative and content-based recommendation.Proposed method improves the quality of recommended items ordering.Proposed method increases the recommendation precision for very Top-N results.Applicable for single ...

Attentive Group Recommendation

Due to the prevalence of group activities in people's daily life, recommending content to a group of users becomes an important task in many information systems. A fundamental problem in group recommendation is how to aggregate the preferences of group ...

Generating recommendations for consensus negotiation in group personalization services

There are increasingly many personalization services in ubiquitous computing environments that involve a group of users rather than individuals. Ubiquitous commerce is one example of these environments. Ubiquitous commerce research is highly related to ...

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• Published: 30 April 2023

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• Data Mining
• Graph Representation Learning
• Group Recommendation
• research-article
• Refereed limited

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• DOI: 10.1145/3589334.3645577
• Corpus ID: 269711661

Enhancing Recommendation Accuracy and Diversity with Box Embedding: A Universal Framework

• Cheng Wu , Shaoyun Shi , +6 authors Kun Gai
• Published in The Web Conference 13 May 2024
• Computer Science

34 References

Human–ai adaptive dynamics drives the emergence of information cocoons, contrastive box embedding for collaborative reasoning, multi-behavior self-supervised learning for recommendation, an exploratory study of information cocoon on short-form video platform, learning probabilistic box embeddings for effective and efficient ranking, thinking inside the box: learning hypercube representations for group recommendation, kuairec: a fully-observed dataset and insights for evaluating recommender systems, neural-answering logical queries on knowledge graphs, sequential recommendation with graph neural networks, dgcn: diversified recommendation with graph convolutional networks, related papers.

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