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- research-articleAugust 2024
Modeling User Retention through Generative Flow Networks
- Ziru Liu,
- Shuchang Liu,
- Bin Yang,
- Zhenghai Xue,
- Qingpeng Cai,
- Xiangyu Zhao,
- Zijian Zhang,
- Lantao Hu,
- Han Li,
- Peng Jiang
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 5497–5508https://doi.org/10.1145/3637528.3671531Recommender systems aim to fulfill the user's daily demands. While most existing research focuses on maximizing the user's engagement with the system, it has recently been pointed out that how frequently the users come back for the service also reflects ...
- research-articleAugust 2024
Future Impact Decomposition in Request-level Recommendations
- Xiaobei Wang,
- Shuchang Liu,
- Xueliang Wang,
- Qingpeng Cai,
- Lantao Hu,
- Han Li,
- Peng Jiang,
- Kun Gai,
- Guangming Xie
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 5905–5916https://doi.org/10.1145/3637528.3671506In recommender systems, reinforcement learning solutions have shown promising results in optimizing the interaction sequence between users and the system over the long-term performance. For practical reasons, the policy's actions are typically designed ...
- research-articleJuly 2024
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
- Ziru Liu,
- Shuchang Liu,
- Zijian Zhang,
- Qingpeng Cai,
- Xiangyu Zhao,
- Kesen Zhao,
- Lantao Hu,
- Peng Jiang,
- Kun Gai
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 1872–1882https://doi.org/10.1145/3626772.3657829In Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards. Nevertheless, it suffers from instability in the learning process, stemming ...
- research-articleJuly 2024
M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework
- Zijian Zhang,
- Shuchang Liu,
- Jiaao Yu,
- Qingpeng Cai,
- Xiangyu Zhao,
- Chunxu Zhang,
- Ziru Liu,
- Qidong Liu,
- Hongwei Zhao,
- Lantao Hu,
- Peng Jiang,
- Kun Gai
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 893–902https://doi.org/10.1145/3626772.3657686Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehensive user modeling. Nonetheless, the practical recommendation usually ...
- research-articleApril 2024JUST ACCEPTED
A Survey on Trustworthy Recommender Systems
- Yingqiang Ge,
- Shuchang Liu,
- Zuohui Fu,
- Juntao Tan,
- Zelong Li,
- Shuyuan Xu,
- Yunqi Li,
- Yikun Xian,
- Yongfeng Zhang
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead ...
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- research-articleMay 2024
KuaiSim: a comprehensive simulator for recommender systems
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing SystemsArticle No.: 1945, Pages 44880–44897Reinforcement Learning (RL)-based recommender systems (RSs) have garnered considerable attention due to their ability to learn optimal recommendation policies and maximize long-term user rewards. However, deploying RL models directly in online ...
- research-articleMay 2024
State regularized policy optimization on data with dynamics shift
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing SystemsArticle No.: 1427, Pages 32926–32937In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of current methods address such issue by training context encoders to identify ...
- surveyOctober 2023
Fairness in Recommendation: Foundations, Methods, and Applications
ACM Transactions on Intelligent Systems and Technology (TIST), Volume 14, Issue 5Article No.: 95, Pages 1–48https://doi.org/10.1145/3610302As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision-making. The satisfaction of users and the interests of platforms are closely related to the quality of the ...
- research-articleAugust 2023
PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 2874–2884https://doi.org/10.1145/3580305.3599473Current advances in recommender systems have been remarkably successful in optimizing immediate engagement. However, long-term user engagement, a more desirable performance metric, remains difficult to improve. Meanwhile, recent reinforcement learning (...
- research-articleAugust 2023
Generative Flow Network for Listwise Recommendation
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 1524–1534https://doi.org/10.1145/3580305.3599364Personalized recommender systems fulfill the daily demands of customers and boost online businesses. The goal is to learn a policy that can generate a list of items that matches the user's demand or interest. While most existing methods learn a ...
- short-paperApril 2023
Reinforcing User Retention in a Billion Scale Short Video Recommender System
- Qingpeng Cai,
- Shuchang Liu,
- Xueliang Wang,
- Tianyou Zuo,
- Wentao Xie,
- Bin Yang,
- Dong Zheng,
- Peng Jiang,
- Kun Gai
WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023Pages 421–426https://doi.org/10.1145/3543873.3584640Recently, short video platforms have achieved rapid user growth by recommending interesting content to users. The objective of the recommendation is to optimize user retention, thereby driving the growth of DAU (Daily Active Users). Retention is a long-...
- research-articleApril 2023
Multi-Task Recommendations with Reinforcement Learning
- Ziru Liu,
- Jiejie Tian,
- Qingpeng Cai,
- Xiangyu Zhao,
- Jingtong Gao,
- Shuchang Liu,
- Dayou Chen,
- Tonghao He,
- Dong Zheng,
- Peng Jiang,
- Kun Gai
WWW '23: Proceedings of the ACM Web Conference 2023Pages 1273–1282https://doi.org/10.1145/3543507.3583467In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications [40]. However, current MTL-based recommendation models tend to disregard the session-wise patterns of user-item interactions because they are ...
- research-articleApril 2023
Two-Stage Constrained Actor-Critic for Short Video Recommendation
- Qingpeng Cai,
- Zhenghai Xue,
- Chi Zhang,
- Wanqi Xue,
- Shuchang Liu,
- Ruohan Zhan,
- Xueliang Wang,
- Tianyou Zuo,
- Wentao Xie,
- Dong Zheng,
- Peng Jiang,
- Kun Gai
WWW '23: Proceedings of the ACM Web Conference 2023Pages 865–875https://doi.org/10.1145/3543507.3583259The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users sequentially interact with the system and provide complex and multi-faceted responses, ...
- research-articleApril 2023
Exploration and Regularization of the Latent Action Space in Recommendation
- Shuchang Liu,
- Qingpeng Cai,
- Bowen Sun,
- Yuhao Wang,
- Ji Jiang,
- Dong Zheng,
- Peng Jiang,
- Kun Gai,
- Xiangyu Zhao,
- Yongfeng Zhang
WWW '23: Proceedings of the ACM Web Conference 2023Pages 833–844https://doi.org/10.1145/3543507.3583244In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction. However, the action space of the recommendation policy is a list of items,...
- research-articleSeptember 2022
Fairness-aware Federated Matrix Factorization
RecSys '22: Proceedings of the 16th ACM Conference on Recommender SystemsPages 168–178https://doi.org/10.1145/3523227.3546771Achieving fairness over different user groups in recommender systems is an important problem. The majority of existing works achieve fairness through constrained optimization that combines the recommendation loss and the fairness constraint. To achieve ...
- research-articleSeptember 2022
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
RecSys '22: Proceedings of the 16th ACM Conference on Recommender SystemsPages 299–315https://doi.org/10.1145/3523227.3546767For a long time, different recommendation tasks require designing task-specific architectures and training objectives. As a result, it is hard to transfer the knowledge and representations from one task to another, thus restricting the generalization ...
- research-articleJuly 2022
Explainable Fairness in Recommendation
- Yingqiang Ge,
- Juntao Tan,
- Yan Zhu,
- Yinglong Xia,
- Jiebo Luo,
- Shuchang Liu,
- Zuohui Fu,
- Shijie Geng,
- Zelong Li,
- Yongfeng Zhang
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 681–691https://doi.org/10.1145/3477495.3531973Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies a more substantial problem--identifying the underlying reason of model ...
- research-articleJune 2022
A Multi–Model Based Adaptive Reconfiguration Control Scheme for an Electro–Hydraulic Position Servo System
International Journal of Applied Mathematics and Computer Science (IJAMCS), Volume 32, Issue 2Pages 185–196https://doi.org/10.34768/amcs-2022-0014AbstractReliability and safety of an electro-hydraulic position servo system (EHPSS) can be greatly reduced for potential sensor and actuator faults. This paper proposes a novel reconfiguration control (RC) scheme that combines multi-model and adaptive ...
- research-articleFebruary 2022
Graph Collaborative Reasoning
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data MiningPages 75–84https://doi.org/10.1145/3488560.3498410Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering. However, most of the graph-structured data in practice suffer from ...
- doctoral_thesisJanuary 2022
Multi-Dimensional Federated Learning in Recommender Systems
AbstractA wide range of web services like e-commerce, job-searching, and target advertising heavily rely on recommender systems that find products of interest to fulfill users' diverse and complicated demands. To better model the user preferences and ...