用大模型打造能主动思考的智能推荐系统，让推荐更懂用户、更会协作。
Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models
- 让推荐系统具备规划、记忆和多模态推理能力，像人一样思考决策。
- 通过多模态感知与外部工具调用，实现动态情境下的主动推荐。
- 适合关注下一代个性化服务、AI自主交互的研究者与开发者。
大语言模型（LLM）的突破催生了具备自主性的智能体系统，它们能感知环境、融合多模态信息并调用工具，展现出更强的适应性。这一演进为推荐系统带来新机遇：基于LLM的智能体推荐系统（LLM-ARS）可提供更交互、更上下文感知、更主动的推荐，有望重塑用户体验并拓展应用场景。尽管初步成果令人鼓舞，仍面临关键挑战，包括如何有效融入外部知识、平衡自主性与可控性，以及在动态多模态环境中的评估。本文系统分析了LLM-ARS的核心概念与架构，阐明规划、记忆与多模态推理等能力如何提升推荐质量，并提出安全、效率与终身个性化等核心研究问题。我们展望未来，认为LLM-ARS将推动推荐系统进入智能化、自主化与协同化的新阶段，更贴近用户复杂决策需求。
原文摘要 · Abstract (English)
Recent breakthroughs in Large Language Models (LLMs) have led to the emergence of agentic AI systems that extend beyond the capabilities of standalone models. By empowering LLMs to perceive external environments, integrate multimodal information, and interact with various tools, these agentic systems exhibit greater autonomy and adaptability across complex tasks. This evolution brings new opportunities to recommender systems (RS): LLM-based Agentic RS (LLM-ARS) can offer more interactive, context-aware, and proactive recommendations, potentially reshaping the user experience and broadening the application scope of RS. Despite promising early results, fundamental challenges remain, including how to effectively incorporate external knowledge, balance autonomy with controllability, and evaluate performance in dynamic, multimodal settings. In this perspective paper, we first present a systematic analysis of LLM-ARS: (1) clarifying core concepts and architectures; (2) highlighting how agentic capabilities -- such as planning, memory, and multimodal reasoning -- can enhance recommendation quality; and (3) outlining key research questions in areas such as safety, efficiency, and lifelong personalization. We also discuss open problems and future directions, arguing that LLM-ARS will drive the next wave of RS innovation. Ultimately, we foresee a paradigm shift toward intelligent, autonomous, and collaborative recommendation experiences that more closely align with users' evolving needs and complex decision-making processes.
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