Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback

ArXi:2605.29141v1 Announce Type: cross Traditional recommender systems (RecSys) primarily infer user preferences from implicit signals (such as clicks, watches, and purchases), often neglecting the rich explicit contextual feedback users provide through verbal text, like comments and reviews. This explicit context feedback captures the nuanced reasons behind user decisions regarding their preferences. In addition, it offers critical heterogeneous information for user preference alignment and explainable recommendations.