7 papers · 1 filter
Precise Zero-Shot Pointwise Ranking with LLMs through Post-Aggregated Global Context Information
Kehan Long, Shasha Li, Chen Xu +2
Recent advancements have successfully harnessed the power of Large Language Models (LLMs) for zero-shot document ranking, exploring a variety of prompting strategies. Comparative a…
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…
Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions
Jia Chen, Qian Dong, Haitao Li +9
User-generated content (UGC) communities, especially those featuring multimodal content, improve user experiences by integrating visual and textual information into results (or ite…
A Study of Implicit Ranking Unfairness in Large Language Models
Chen Xu, Wenjie Wang, Yuxin Li +3
Recently, Large Language Models (LLMs) have demonstrated a superior ability to serve as ranking models. However, concerns have arisen as LLMs will exhibit discriminatory ranking be…
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…
Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era
Sunhao Dai, Chen Xu, Shicheng Xu +3
With the rapid advancements of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradi…