3 papers
cs.IR2026
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…
cs.IR2025
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…
cs.IR2025
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…