7 citations · 7 across the 5 of their papers we have counts for
5 papers · 1 filter
LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation
Hongchen Li, Bohao Wang, Jingbang Chen +5
Large language models (LLMs) have recently emerged as powerful backbones for recommender systems by reformulating recommendation as a token-level generation task. Despite their pro…
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…
Proactive Recommendation in Social Networks: Steering User Interest with Causal Inference
Hang Pan, Shuxian Bi, Wenjie Wang +3
Recommending items that solely cater to users' historical interests narrows users' horizons. Recent works have considered steering target users beyond their historical interests by…
Debias Can be Unreliable: Mitigating Bias Issue in Evaluating Debiasing Recommendation
Chengbing Wang, Wentao Shi, Jizhi Zhang +3
Recent work has improved recommendation models remarkably by equipping them with debiasing methods. Due to the unavailability of fully-exposed datasets, most existing approaches re…
Proactive Recommendation with Iterative Preference Guidance
Shuxian Bi, Wenjie Wang, Hang Pan +2
Recommender systems mainly tailor personalized recommendations according to user interests learned from user feedback. However, such recommender systems passively cater to user int…