3 papers
cs.IR2026
Sequence-aware Large Language Models for Explainable Recommendation
Gangyi Zhang, Runzhe Teng, Chongming Gao
Large Language Models (LLMs) have shown strong potential in generating natural language explanations for recommender systems. However, existing methods often overlook the sequentia…
cs.IR2025
Vague Preference Policy Learning for Conversational Recommendation
Gangyi Zhang, Chongming Gao, Wenqiang Lei +6
Conversational recommendation systems (CRS) commonly assume users have clear preferences, leading to potential over-filtering of relevant alternatives. However, users often exhibit…
cs.IR2024
Reformulating Conversational Recommender Systems as Tri-Phase Offline Policy Learning
Gangyi Zhang, Chongming Gao, Hang Pan +2
Existing Conversational Recommender Systems (CRS) predominantly utilize user simulators for training and evaluating recommendation policies. These simulators often oversimplify the…