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A Multi-Agent Conversational Recommender System
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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.
Forward citations
Cited by 7 Pith papers
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The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
A framework for agentic recommender systems plus a pilot study showing multi-agent pipelines beat a single-shot LLM only on high-diversity user histories.
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Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent
TAIRA, a thought-pattern-augmented multi-agent recommender, outperforms prior LLM agents in simulated interactive recommendation, with the largest gains on complex user intents.
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RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systems
RecUserSim combines profile, memory, action, and refinement modules in an LLM agent to generate realistic, diverse user utterances and multi-dimensional ratings for evaluating conversational recommender systems.
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Proactive Guidance of Multi-Turn Conversation in Industrial Search
A framework combining goal-adaptive supervised fine-tuning and click-based reinforcement learning improves proactive guidance quality and speed in an industrial search assistant.
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Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems
Agentic recommender systems are organized by agent role (assisted, as-recommender, as-simulator) crossed with autonomy levels L2–L5, yielding a roadmap of architectures, evaluation limits, and open challenges.
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CESRec: Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback
CESRec improves sequential recommenders by converting simulated user feedback into edited pseudo-interaction sequences and masking outlier items, with gains reported on three benchmarks.
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On Mitigating Data Sparsity in Conversational Recommender Systems
DACRS combines LLM-based dialogue augmentation, knowledge-graph entity substitution, and an entity similarity constraint to improve conversational recommendation accuracy on ReDial and Inspired.
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