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。针对当前在推荐系统中利用智能体实现多智能体协作方面的研究空白，我们提出了MACRec——一种通过多智能体协作增强推荐系统的新型框架。与现有利用智能体进行用户/物品模拟的工作不同，我们旨在直接部署多智能体来执行推荐任务。在该框架中，推荐任务通过包括管理者、用户/物品分析师、反思者、搜索者和任务解释器在内的多种专业智能体，以不同的工作流程协同完成。此外，我们还提供了开发者如何将MACRec轻松应用于各种推荐任务的示例，包括评分预测、序列推荐、对话式推荐以及推荐结果解释生成。该框架及演示视频已在https://github.com/wzf2000/MACRec 公开提供。