10 papers
Learning to Retrieve from Agent Trajectories
Yuqi Zhou, Sunhao Dai, Changle Qu +3
Information retrieval (IR) systems have traditionally been designed and trained for human users, with learning-to-rank methods relying heavily on large-scale human interaction logs…
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…
Modeling Domain and Feedback Transitions for Cross-Domain Sequential Recommendation
Changshuo Zhang, Teng Shi, Xiao Zhang +4
Nowadays, many recommender systems encompass various domains to cater to users' diverse needs, leading to user behaviors transitioning across different domains. In fact, user behav…
IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History
Yi Xu, Weiran Shen, Xiao Zhang +1
Traditional imitation learning focuses on modeling the behavioral mechanisms of experts, which requires a large amount of interaction history generated by some fixed expert. Howeve…
Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation
Changshuo Zhang, Xiao Zhang, Teng Shi +2
Sequential recommendation is essential in modern recommender systems, aiming to predict the next item a user may interact with based on their historical behaviors. However, real-wo…