{
  "id": 3554,
  "url": "https://arxiv.org/abs/2605.29141v1",
  "title": "Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback",
  "summary": "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",
  "authors": "Weizhi Zhang, Wooseong Yang, Yuxin Cui, Zhaohui Guo, Hins Hu, Liangwei Yang et al.",
  "category": "research",
  "topics": "safety-alignment,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-27T22:10:33.000Z",
  "fetched_at": "2026-07-14T16:30:18.858Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3554",
  "original_url": "https://arxiv.org/abs/2605.29141v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}