让大模型读懂用户评论，推荐更懂人心。
Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback
- 用用户评论等显式反馈优化大模型推荐
- 提升推荐个性化与可解释性
- 适合关注推荐透明度的平台开发者
传统推荐系统主要依赖点击、观看、购买等隐式行为信号推断用户偏好，忽略了用户通过文字评论、评价等显式上下文反馈所传递的深层动机。这些显式反馈蕴含了用户决策的语义细节，有助于实现更精准的偏好对齐与可解释推荐。忽视此类信号易导致推荐偏差并加剧信息茧房。尽管大语言模型（LLMs）为利用用户生成内容提供了新可能，但现有基于大模型的推荐仍侧重物品元数据，未充分挖掘显式反馈价值。本文倡导下一代大模型推荐系统应优先考虑显式上下文反馈，回顾推荐范式演进，强调上下文丰富反馈的价值，呼吁建立新基准与评估指标，并提出可扩展框架以整合用户显式信号，旨在构建更个性化、透明、可解释的推荐系统。
原文摘要 · Abstract (English)
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 Language Models (LLMs) present new opportunities to harness user-generated content for more accurate and diverse recommendations, yet current LLM-based recommendations still focus on using item meta-data and underutilize this resource. In this paper, we advocate for prioritizing explicit context feedback in the next generation of LLM-based RecSys. We review the evolution of recommendation paradigms, highlight the value of context-rich feedback, call for new benchmarks and metrics, and introduce frameworks for integrating explicit user signals into scalable LLM-driven RecSys. Centering on user-preference modeling, we aim to foster more personalized, transparent, and explainable RecSys online platforms.
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