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cs.IR2026
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
Bohao Wang, Yu Cui, Zhenxiang Xu +13
The field of recommender systems (RS) is currently undergoing two profound paradigm shifts. From the perspective of objectives, the goal has shifted beyond mere recommendation accu…
cs.IR2026
Beyond Static Best-of-N: Bayesian List-wise Alignment for LLM-based Recommendation
Ruijun Chen, Chongming Gao, Jiawei Chen +2
Large Language Models have revolutionized recommender systems (LLM4Rec) by leveraging their generative capabilities to model complex user preferences. However, existing LLM4Rec met…
cs.IR2024
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems
Yuanqing Yu, Chongming Gao, Jiawei Chen +5
Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user engagement. However, research in this field f…