Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback
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 more explainable recommendations. Overlook
Record details
Published: 27 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Transparency
Retrieved: 14 July 2026
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ethics.ai (27 May 2026), “Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback,” evidence record 3554, https://ethics.ai/record/3554 (originally published by arXiv).
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