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. Overlooking such signals can lead to misaligned user preferences and further reinforce filter bubbles, as algorithms fail to understand the "semantic context" behind user choices. Recent advances in Large ...
| Authors | Weizhi Zhang, Wooseong Yang, Yuxin Cui et al. |
|---|---|
| Primary category | cs.IR |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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