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cs.IR2026
Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback
Weizhi Zhang, Wooseong Yang, Yuxin Cui +9
Traditional recommender systems (RecSys) primarily infer user preferences from implicit signals (such as clicks, watches, and purchases), often neglecting the rich explicit context…
cs.IR2025
RAU: Towards Regularized Alignment and Uniformity for Representation Learning in Recommendation
Xi Wu, Dan Zhang, Chao Zhou +3
Recommender systems (RecSys) have become essential in modern society, driving user engagement and satisfaction across diverse online platforms. Most RecSys focuses on designing a p…