让推荐系统从单次打分升级为多智能体协作，提升推荐质量与可解释性。
The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
- 用状态化智能体替代传统打分，实现规划、调用工具和协同决策。
- 在亚马逊数据集上验证：高多样性历史下多智能体更优，单次打分仍具效率优势。
- 提出可衡量的推荐信号评估框架，指导智能体复杂度的合理部署。
大型语言模型正从被动文本生成转向具备规划、状态维持、工具调用和多智能体协作的自主系统。本文探讨这一转变对推荐系统（RS）的影响。定义了智能体推荐系统：由一个或多个状态化智能体组成，通过观察、规划、调用工具和验证来完成推荐，而非单次打分。其价值体现在能显著提升相关性、约束满足、组合一致性、事实性、解释可信度或用户操作成本等推荐层指标，而不仅因引入了LLM或多个模块。提出针对推荐系统的专用形式化框架，将智能体建模为包含用户状态、上下文、历史、候选集的状态，结合推理核心、工具、分层记忆和显式策略约束，并将多智能体推荐系统视为三元组——多个智能体、共享环境与通信协议。在此框架下构建四类代表性任务，建立五大挑战与可测量推荐信号的关联。最后，在共享历史、候选集、提示和指标下，对比单次打分与多智能体流水线的控制实验。基于Amazon-2023的预研次项推荐研究显示：多智能体并非始终更优；在代表性样本中，单次打分基线具有帕累托效率，而分解与集成智能体主要在高多样性历史中表现更好。这支持一种条件设计原则：智能体复杂度应仅用于边际质量提升足以抵消延迟、成本与治理风险的场景。代码已开源。
原文摘要 · Abstract (English)
Large language models (LLMs) are evolving from passive text generators into agentic systems that can plan, maintain state, invoke tools, and coordinate with other agents. This perspective paper examines what this shift means for recommender systems (RS). We define agentic recommender systems as pipelines in which one or more stateful agents observe, plan, call tools, and verify, rather than score in a single shot, while operating over users, item catalogs, candidate sets, and recommendation objectives. Their value is strongest when this machinery measurably improves recommendation-layer outcomes such as relevance, constraint satisfaction, bundle coherence, grounding, explanation faithfulness, or user effort, rather than merely because a pipeline contains an LLM or several modules. We introduce a recommender-specific formalism that models an agent by its state (user, context, history, candidate set), a reasoning core, tools, a hierarchical memory, and explicit policy constraints, and casts a multi-agent recommender as a triple of agents, a shared environment, and a communication protocol. Within this framework we develop four representative task families and an agenda tying five recurring challenge families to measurable RS signals. Finally, we run a controlled study comparing single-shot and multi-agent pipelines under shared histories, candidate sets, prompts, and metrics. A pilot next-item ranking study on Amazon-2023 shows multi-agent systems are not uniformly superior: on representative samples the single-shot baseline is Pareto-efficient, whereas decomposition and ensemble agents help mainly on high-diversity histories. This supports a conditional design principle: agentic complexity should be routed to cases where its marginal quality gain justifies the added latency, cost, and governance risk. Code: https://github.com/RezaYM/agenticrecsys.git
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