3 papers
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.IR2025
Future-Conditioned Recommendations with Multi-Objective Controllable Decision Transformer
Chongming Gao, Kexin Huang, Ziang Fei +6
Securing long-term success is the ultimate aim of recommender systems, demanding strategies capable of foreseeing and shaping the impact of decisions on future user satisfaction. C…