Agentic recommendations cast recommenders as large language model (LLM) agents that can plan, reason, use tools, and interact with users of varying preferences in web applications. However, most existing agentic recommender systems focus on generic single-agent plan-execute workflows or multi-agent task decomposition pipelines. Without recommendation-oriented design, they often underuse the collaborative signals in the user-item interaction history, leading to unsatisfying recommendation results. To address this, we propose the Multi-Agent Collaborative Filtering (MACF) framework for agentic recommendations, drawing an analogy between traditional collaborative filtering algorithms and LLM-based multi-agent collaboration. Specifically, given a target user and query, we instantiate similar users and relevant items as LLM agents with unique profiles. Each agent is able to call retrieval tools, suggest candidate items, and interact with other agents. Different from the static preference aggregation in traditional collaborative filtering, MACF employs a central orchestrator agent to adaptively manage the collaboration between user and item agents via dynamic agent recruitment and personalized collaboration instruction. Experimental results on datasets from three different domains show the advantages of our MACF framework compared to strong agentic recommendation baselines.

翻译：智能体化推荐将推荐系统构建为大型语言模型（LLM）智能体，使其能够在网络应用中规划、推理、使用工具并与具有不同偏好的用户进行交互。然而，现有的大多数智能体推荐系统主要关注通用的单智能体规划-执行流程或多智能体任务分解流水线。由于缺乏面向推荐任务的设计，这些系统往往未能充分利用用户-物品交互历史中的协同信号，导致推荐结果不尽如人意。为解决这一问题，我们提出面向智能体化推荐的多智能体协同过滤（MACF）框架，通过类比传统协同过滤算法与基于LLM的多智能体协作机制来实现推荐。具体而言，给定目标用户及查询，我们将相似用户和相关物品实例化为具有独特配置文件的LLM智能体。每个智能体能够调用检索工具、推荐候选物品并与其他智能体进行交互。与传统协同过滤中静态的偏好聚合方式不同，MACF采用中央编排智能体，通过动态智能体招募和个性化协作指令，自适应地管理用户智能体与物品智能体之间的协作关系。在三个不同领域数据集上的实验结果表明，相较于现有强基准智能体推荐方法，我们的MACF框架具有显著优势。