LLM智能体让推荐系统更懂用户，还能解释推荐理由。
A Survey on LLM-powered Agents for Recommender Systems
- 用大语言模型构建智能体，提升推荐逻辑的可理解性。
- 分三类：增强推荐机制、自然对话互动、多智能体模拟用户行为。
- 适合对可解释推荐和人机交互感兴趣的科研人员。
推荐系统是众多在线平台的核心组件，但传统方法仍难以理解复杂用户偏好并提供可解释的推荐。大语言模型（LLM）驱动的智能体通过支持自然语言交互和可解释推理，为推荐系统研究带来新机遇。本文系统梳理了LLM智能体在推荐系统中的新兴应用，识别并分析三大研究范式：(1) 推荐导向型方法，利用智能体强化基础推荐机制；(2) 交互导向型方法，通过自然对话与可解释建议实现动态用户参与；(3) 模拟导向型方法，采用多智能体框架建模复杂的用户-物品交互与系统动态。除范式分类外，还剖析了智能体架构基础：用户画像构建、记忆管理、策略规划与动作执行。进一步考察该领域的基准数据集与评估框架。本系统性分析不仅揭示当前研究现状，也指明关键挑战与未来方向。
原文摘要 · Abstract (English)
Recommender systems are essential components of many online platforms, yet traditional approaches still struggle with understanding complex user preferences and providing explainable recommendations. The emergence of Large Language Model (LLM)-powered agents offers a promising approach by enabling natural language interactions and interpretable reasoning, potentially transforming research in recommender systems. This survey provides a systematic review of the emerging applications of LLM-powered agents in recommender systems. We identify and analyze three key paradigms in current research: (1) Recommender-oriented approaches, which leverage intelligent agents to enhance the fundamental recommendation mechanisms; (2) Interaction-oriented approaches, which facilitate dynamic user engagement through natural dialogue and interpretable suggestions; and (3) Simulation-oriented approaches, which employ multi-agent frameworks to model complex user-item interactions and system dynamics. Beyond paradigm categorization, we analyze the architectural foundations of LLM-powered recommendation agents, examining their essential components: profile construction, memory management, strategic planning, and action execution. Our investigation extends to a comprehensive analysis of benchmark datasets and evaluation frameworks in this domain. This systematic examination not only illuminates the current state of LLM-powered agent recommender systems but also charts critical challenges and promising research directions in this transformative field.
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