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- research-articleJune 2024
ID-SR: Privacy-Preserving Social Recommendation Based on Infinite Divisibility for Trustworthy AI
- Jingyi Cui,
- Guangquan Xu,
- Jian Liu,
- Shicheng Feng,
- Jianli Wang,
- Hao Peng,
- Shihui Fu,
- Zhaohua Zheng,
- Xi Zheng,
- Shaoying Liu
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 7Article No.: 161, Pages 1–25https://doi.org/10.1145/3639412Recommendation systems powered by artificial intelligence (AI) are widely used to improve user experience. However, AI inevitably raises privacy leakage and other security issues due to the utilization of extensive user data. Addressing these challenges ...
- research-articleJune 2024JUST ACCEPTED
Deconfounding User Preference in Recommendation Systems through Implicit and Explicit Feedback
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3673762Recommender systems are influenced by many confounding factors (i.e., confounders) which result in various biases (e.g., popularity biases) and inaccurate user preference. Existing approaches try to eliminate these biases by inference with causal graphs. ...
- research-articleJune 2024JUST ACCEPTED
Heterogeneous Meta-Path Graph Learning for Higher-order Social Recommendation
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3673658Recommendation systems have become an indispensable part of daily life. Social recommendation systems, which utilize social relationships and past behaviors to infer users’ preferences, have gained popularity in recent years. Exploring the inherent ...
- research-articleJune 2024JUST ACCEPTED
Utility-oriented Reranking with Counterfactual Context
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3671004As a critical task for large-scale commercial recommender systems, reranking rearranges items in the initial ranking lists from the previous ranking stage to better meet users’ demands. Foundational work in reranking has shown the potential of improving ...
- research-articleMay 2024JUST ACCEPTED
Improving Graph Collaborative Filtering with Directional Behavior Enhanced Contrastive Learning
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3663574Graph Collaborative Filtering is a widely adopted approach for recommendation, which captures similar behavior features through graph neural network. Recently, Contrastive Learning (CL) has been demonstrated as an effective method to enhance the ...
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- research-articleApril 2024
Dual Homogeneity Hypergraph Motifs with Cross-view Contrastive Learning for Multiple Social Recommendations
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 6Article No.: 158, Pages 1–24https://doi.org/10.1145/3653976Social relations are often used as auxiliary information to address data sparsity and cold-start issues in social recommendations. In the real world, social relations among users are complex and diverse. Widely used graph neural networks (GNNs) can only ...
- research-articleApril 2024
A Dual Perspective Framework of Knowledge-correlation for Cross-domain Recommendation
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 6Article No.: 152, Pages 1–28https://doi.org/10.1145/3652520Recommender System provides users with online services in a personalized way. The performance of traditional recommender systems may deteriorate because of problems such as cold-start and data sparsity. Cross-domain Recommendation System utilizes the ...
- research-articleApril 2024
MoMENt: Marked Point Processes with Memory-Enhanced Neural Networks for User Activity Modeling
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 6Article No.: 155, Pages 1–32https://doi.org/10.1145/3649504Marked temporal point process models (MTPPs) aim to model event sequences and event markers (associated features) in continuous time. These models have been applied to various application domains where capturing event dynamics in continuous time is ...
- research-articleApril 2024
Adaptive Content-Aware Influence Maximization via Online Learning to Rank
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 6Article No.: 146, Pages 1–35https://doi.org/10.1145/3651987How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence ...
- research-articleFebruary 2024
Attacking Click-through Rate Predictors via Generating Realistic Fake Samples
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 5Article No.: 110, Pages 1–24https://doi.org/10.1145/3643685How to construct imperceptible (realistic) fake samples is critical in adversarial attacks. Due to the sample feature diversity of a recommender system (containing both discrete and continuous features), traditional gradient-based adversarial attack ...
- research-articleFebruary 2024
Prerequisite-Enhanced Category-Aware Graph Neural Networks for Course Recommendation
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 5Article No.: 112, Pages 1–21https://doi.org/10.1145/3643644The rapid development of Massive Open Online Courses (MOOCs) platforms has created an urgent need for an efficient personalized course recommender system that can assist learners of all backgrounds and levels of knowledge in selecting appropriate courses. ...
- research-articleFebruary 2024
- research-articleDecember 2023
Adaptive Adversarial Contrastive Learning for Cross-Domain Recommendation
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 3Article No.: 57, Pages 1–34https://doi.org/10.1145/3630259Graph-based cross-domain recommendations (CDRs) are useful for suggesting appropriate items because of their promising ability to extract features from user–item interactions and transfer knowledge across domains. Thus, the model can effectively alleviate ...
- research-articleNovember 2023
Co-Training-Teaching: A Robust Semi-Supervised Framework for Review-Aware Rating Regression
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 2Article No.: 41, Pages 1–16https://doi.org/10.1145/3625391Review-aware Rating Regression (RaRR) suffers the severe challenge of extreme data sparsity as the multi-modality interactions of ratings accompanied by reviews are costly to obtain. Although some studies of semi-supervised rating regression are proposed ...
- research-articleOctober 2023
Modeling Users’ Curiosity in Recommender Systems
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 1Article No.: 26, Pages 1–23https://doi.org/10.1145/3617598Today’s recommender systems are criticized for recommending items that are too obvious to arouse users’ interests. Therefore, the research community has advocated some “beyond accuracy” evaluation metrics such as novelty, diversity, and serendipity with ...
- research-articleSeptember 2023
DeepCPR: Deep Path Reasoning Using Sequence of User-Preferred Attributes for Conversational Recommendation
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 1Article No.: 15, Pages 1–22https://doi.org/10.1145/3610775Conversational recommender systems (CRS) have garnered significant attention in academia and industry because of their ability to capture user preferences via system questions and user responses. Typically, in a CRS, reinforcement learning (RL) is ...
- research-articleAugust 2023
Discrete Listwise Content-aware Recommendation
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 1Article No.: 7, Pages 1–20https://doi.org/10.1145/3609334To perform online inference efficiently, hashing techniques, devoted to encoding model parameters as binary codes, play a key role in reducing the computational cost of content-aware recommendation (CAR), particularly on devices with limited computation ...
- research-articleAugust 2023
Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer Gate
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 1Article No.: 8, Pages 1–28https://doi.org/10.1145/3604615Cross-domain recommender systems could potentially improve the recommendation performance by means of transferring abundant knowledge from the auxiliary domain to the target domain. They could help address some key challenges in recommender systems, such ...
- tutorialAugust 2023
Fairness in Recommender Systems: Evaluation Approaches and Assurance Strategies
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 1Article No.: 10, Pages 1–37https://doi.org/10.1145/3604558With the wide application of recommender systems, the potential impacts of recommender systems on customers, item providers and other parties have attracted increasing attention. Fairness, which is the quality of treating people equally, is also becoming ...
- research-articleJuly 2023
Multifaceted Relation-aware Meta-learning with Dual Customization for User Cold-start Recommendation
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 17, Issue 9Article No.: 130, Pages 1–27https://doi.org/10.1145/3597458User cold-start scenarios pose great challenges to recommendation systems in accurately capturing user preferences with sparse interaction records. Besides incorporating auxiliary information to enrich user/item representations, recent studies under the ...