Due to strong capabilities in conducting fluent, multi-turn conversations with users, Large Language Models (LLMs) have the potential to further improve the performance of Conversational Recommender System (CRS). Unlike the aimless chit-chat that LLM excels at, CRS has a clear target. So it is imperative to control the dialogue flow in the LLM to successfully recommend appropriate items to the users. Furthermore, user feedback in CRS can assist the system in better modeling user preferences, which has been ignored by existing studies. However, simply prompting LLM to conduct conversational recommendation cannot address the above two key challenges. In this paper, we propose Multi-Agent Conversational Recommender System (MACRS) which contains two essential modules. First, we design a multi-agent act planning framework, which can control the dialogue flow based on four LLM-based agents. This cooperative multi-agent framework will generate various candidate responses based on different dialogue acts and then choose the most appropriate response as the system response, which can help MACRS plan suitable dialogue acts. Second, we propose a user feedback-aware reflection mechanism which leverages user feedback to reason errors made in previous turns to adjust the dialogue act planning, and higher-level user information from implicit semantics. We conduct extensive experiments based on user simulator to demonstrate the effectiveness of MACRS in recommendation and user preferences collection. Experimental results illustrate that MACRS demonstrates an improvement in user interaction experience compared to directly using LLMs.

翻译：由于能够与用户进行流畅的多轮对话，大型语言模型（LLM）有潜力进一步提升对话式推荐系统的性能。与LLM擅长的漫无目的的闲聊不同，对话式推荐系统具有明确的目标，因此必须控制LLM中的对话流程，以成功向用户推荐合适的物品。此外，对话式推荐系统中的用户反馈能够帮助系统更好地建模用户偏好，这一点已被现有研究忽略。然而，简单地提示LLM执行对话推荐无法解决上述两个关键挑战。本文提出基于多智能体的对话式推荐系统，该系统包含两个核心模块。首先，我们设计了一个多智能体行为规划框架，该框架基于四个LLM智能体控制对话流程。这种协作式多智能体框架能够根据不同对话行为生成多种候选回复，并选择最合适的回复作为系统输出，从而帮助MACRS规划适当的对话行为。其次，我们提出了一种用户反馈感知的反思机制，该机制利用用户反馈推理前一回合中的错误，调整对话行为规划，并从隐含语义中获取更高层次的用户信息。我们基于用户模拟器进行了大量实验，证明了MACRS在推荐和用户偏好收集方面的有效性。实验结果表明，与直接使用LLM相比，MACRS在用户交互体验上有所提升。