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.

翻译：传统推荐系统主要从隐式信号（如点击、观看和购买行为）中推断用户偏好，往往忽略了用户通过评论等文本反馈提供的丰富显式上下文信息。这种显式上下文反馈不仅捕捉了用户决策背后关于偏好的细微原因，还为用户偏好对齐和更具可解释性的推荐提供了关键的异构信息。忽视此类信号会导致用户偏好错位，并进一步强化信息茧房效应——因为算法无法理解用户选择背后的“语义上下文”。大语言模型的最新进展为利用用户生成内容实现更精准、多样化的推荐带来了新机遇，但当前基于大语言模型的推荐仍主要聚焦于使用物品元数据，未能充分利用这一资源。本文主张在下一代基于大语言模型的推荐系统中优先考虑显式上下文反馈。我们回顾了推荐范式的演变历程，强调了富含上下文信息的反馈价值，呼吁建立新的基准测试与评估指标，并提出了将显式用户信号整合到可扩展的大语言模型驱动推荐系统中的框架。以用户偏好建模为核心，我们旨在推动在线推荐平台向更个性化、更透明、更具可解释性的方向发展。