LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks. Recognizing the current gap in leveraging agent capabilities for multi-agent collaboration in recommendation systems, we introduce MACRec, a novel framework designed to enhance recommendation systems through multi-agent collaboration. Unlike existing work on using agents for user/item simulation, we aim to deploy multi-agents to tackle recommendation tasks directly. In our framework, recommendation tasks are addressed through the collaborative efforts of various specialized agents, including Manager, User/Item Analyst, Reflector, Searcher, and Task Interpreter, with different working flows. Furthermore, we provide application examples of how developers can easily use MACRec on various recommendation tasks, including rating prediction, sequential recommendation, conversational recommendation, and explanation generation of recommendation results. The framework and demonstration video are publicly available at https://github.com/wzf2000/MACRec.

翻译：基于大语言模型的智能体因其决策能力和处理复杂任务的能力而受到广泛关注。鉴于当前推荐系统中利用智能体能力进行多智能体协同的研究尚存不足，我们提出了MACRec，这是一个旨在通过多智能体协同增强推荐系统的新型框架。与现有利用智能体进行用户/物品模拟的研究不同，我们的目标是部署多智能体直接处理推荐任务。在该框架中，推荐任务通过不同专业智能体的协同工作完成，这些智能体包括管理者、用户/物品分析师、反思器、搜索器和任务解释器，并采用不同的工作流程。此外，我们提供了应用示例，展示开发者如何轻松地将MACRec应用于各类推荐任务，包括评分预测、序列推荐、对话式推荐以及推荐结果的解释生成。该框架及演示视频已在 https://github.com/wzf2000/MACRec 公开。