11 papers
Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop
Yuqi Zhou, Sunhao Dai, Liang Pang +4
Recommender systems are essential for information access, allowing users to present their content for recommendation. With the rise of large language models (LLMs), AI-generated co…
NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search
Sunhao Dai, Wenjie Wang, Liang Pang +4
Generative AI search is reshaping information retrieval by offering end-to-end answers to complex queries, reducing users' reliance on manually browsing and summarizing multiple we…
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…
Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents
Haoyu Wang, Sunhao Dai, Haiyuan Zhao +6
Previous studies have found that PLM-based retrieval models exhibit a preference for LLM-generated content, assigning higher relevance scores to these documents even when their sem…
Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation
Chen Xu, Yuxin Li, Wenjie Wang +3
Group max-min fairness (MMF) is commonly used in fairness-aware recommender systems (RS) as an optimization objective, as it aims to protect marginalized item groups and ensures a…
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…