collaborators

6 papers

cs.IR2026

The Attention Market: Interpreting Online Fair Re-ranking as Manifold Optimization under Walrasian Equilibrium

Chen Xu, Wei Chu, Wenyu Hu +3

Fair re-ranking aims to promote long-tail items and enhance diversity within groups in information retrieval. While previous research on online fairness-aware re-ranking has shown…

cs.IR2026

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…

cs.IR2026

Unveiling and Simulating Short-Video Addiction Behaviors via Economic Addiction Theory

Chen Xu, Zhipeng Yi, Ruizi Wang +3

Short-video applications have attracted substantial user traffic. However, these platforms also foster problematic usage patterns, commonly referred to as short-video addiction, wh…

cs.IR2025

Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics

Chen Xu, Jujia Zhao, Wenjie Wang +4

Fairness is an increasingly important factor in re-ranking tasks. Prior work has identified a trade-off between ranking accuracy and item fairness. However, the underlying mechanis…

cs.IR2025

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…

cs.IR2025

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…