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.

我们提出了一种包含多个模块的多智能体对话推荐系统 (MACRS)，该系统可以通过控制对话流动以及利用用户反馈来改进推荐性能，并展示了在推荐和用户偏好收集方面相较于直接使用大型语言模型 (LLMs) 的用户交互体验的改进。

多智能体对话推荐系统

A Multi-Agent Conversational Recommender System

Conversational machine comprehension requires deep understanding of the
dialogue flow, and the prior work proposed FlowQA to implicitly model the
context representations in reasoning for better understanding. This paper
proposes to explicitly model the information gain through dialogue reasoning in
order to allow the model to focus on more informative cues. The proposed model
achieves state-of-the-art performance in a conversational QA dataset QuAC and
sequential instruction understanding dataset SCONE, which shows the
effectiveness of the proposed mechanism and demonstrates its capability of
generalization to different QA models and tasks.

本文提出了一种显式建模对话推理信息增益的方法来更好地理解对话流程和理解其背后的上下文，其在 QuAC 和 SCONE 数据集上取得最先进性能，展示了该方法的有效性和其推广到不同 QA 模型和任务的能力。