1 citations · 1 across the 8 of their papers we have counts for
8 papers
Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems
Xinyu Lin, Yashar Deldjoo, Sunhao Dai +7
The rapid integration of large language model-based agents into recommender systems has driven a shift from static, ranking-based pipelines toward autonomous and interactive system…
Enhancing Long-Term Welfare in Recommender Systems: An Information Revelation Approach
Xu Zhao, Xiaopeng Ye, Chen Xu +2
Improving the long-term user welfare (e.g., sustained user engagement) has become a central objective of recommender systems (RS). In real-world platforms, the creation behaviors o…
Regret-aware Re-ranking for Guaranteeing Two-sided Fairness and Accuracy in Recommender Systems
Xiaopeng Ye, Chen Xu, Jun Xu +3
In multi-stakeholder recommender systems (RS), users and providers operate as two crucial and interdependent roles, whose interests must be well-balanced. Prior research, including…
FairDiverse: A Comprehensive Toolkit for Fair and Diverse Information Retrieval Algorithms
Chen Xu, Zhirui Deng, Clara Rus +6
In modern information retrieval (IR). achieving more than just accuracy is essential to sustaining a healthy ecosystem, especially when addressing fairness and diversity considerat…
CreAgent: Towards Long-Term Evaluation of Recommender System under Platform-Creator Information Asymmetry
Xiaopeng Ye, Chen Xu, Zhongxiang Sun +4
Ensuring the long-term sustainability of recommender systems (RS) emerges as a crucial issue. Traditional offline evaluation methods for RS typically focus on immediate user feedba…
Guaranteeing Accuracy and Fairness under Fluctuating User Traffic: A Bankruptcy-Inspired Re-ranking Approach
Xiaopeng Ye, Chen Xu, Jun Xu +3
Out of sustainable and economical considerations, two-sided recommendation platforms must satisfy the needs of both users and providers. Previous studies often show that the two si…
A Taxation Perspective for Fair Re-ranking
Chen Xu, Xiaopeng Ye, Wenjie Wang +3
Fair re-ranking aims to redistribute ranking slots among items more equitably to ensure responsibility and ethics. The exploration of redistribution problems has a long history in…
LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops
Chen Xu, Xiaopeng Ye, Jun Xu +3
Multi-stakeholder recommender systems involve various roles, such as users, and providers. Previous work pointed out that max-min fairness (MMF) is a better metric to support weak…