# A Multi-Agent Conversational Recommender System

arXiv: https://arxiv.org/abs/2402.01135 · HTML: https://mlsanity.com/paper/2402.01135/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use.
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Placed in

- LLM agents and tool use / [Agentic recommender systems](https://mlsanity.com/agents/agentic-recommender-systems/)

## Builds on (most cited first, 23 of 23)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://mlsanity.com/paper/2303.14524/) (2023)
- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion](https://mlsanity.com/paper/2007.04032/) (2020)
- [Agents: An Open-source Framework for Autonomous Language Agents](https://mlsanity.com/paper/2309.07870/) (2023)
- [Towards Topic-Guided Conversational Recommender System](https://mlsanity.com/paper/2010.04125/) (2020)
- [Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System](https://mlsanity.com/paper/2010.15363/) (2020)
- [Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning](https://mlsanity.com/paper/2206.09363/) (2022)
- [AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning](https://mlsanity.com/paper/2401.05268/) (2024)
- [Interactive Path Reasoning on Graph for Conversational Recommendation](https://mlsanity.com/paper/2007.00194/) (2020)
- [Leveraging Large Language Models in Conversational Recommender Systems](https://mlsanity.com/paper/2305.07961/) (2023)
- [Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations](https://mlsanity.com/paper/2311.05584/) (2023)
- [The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation](https://mlsanity.com/paper/2008.09273/) (2020)
- [Learning to Ask Appropriate Questions in Conversational Recommendation](https://mlsanity.com/paper/2105.04774/) (2021)
- [A Large Language Model Enhanced Conversational Recommender System](https://mlsanity.com/paper/2308.06212/) (2023)
- [Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation](https://mlsanity.com/paper/2212.11868/) (2022)
- [BARCOR: Towards A Unified Framework for Conversational Recommendation Systems](https://mlsanity.com/paper/2203.14257/) (2022)
- [Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue](https://mlsanity.com/paper/2310.14626/) (2023)

## Built on by (most cited first, 30 of 43)

- [A Survey on LLM-powered Agents for Recommender Systems](https://mlsanity.com/paper/2502.10050/) (2025)
- [Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks](https://mlsanity.com/paper/2409.19338/) (2024)
- [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025)
- [Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models](https://mlsanity.com/paper/2503.16734/) (2025)
- [Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent](https://mlsanity.com/paper/2506.23485/) (2025)
- [REAPER: Reasoning based Retrieval Planning for Complex RAG Systems](https://mlsanity.com/paper/2407.18553/) (2024)
- [An Electoral Approach to Diversify LLM-based Multi-Agent Collective Decision-Making](https://mlsanity.com/paper/2410.15168/) (2024)
- [The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems](https://mlsanity.com/paper/2507.02097/) (2025)
- [RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systems](https://mlsanity.com/paper/2507.22897/) (2025)
- [A Survey of Scaling in Large Language Model Reasoning](https://mlsanity.com/paper/2504.02181/) (2025)
- [Multi-Agent Collaborative Filtering: Orchestrating Users and Items for Agentic Recommendations](https://mlsanity.com/paper/2511.18413/) (2025)
- [Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition](https://mlsanity.com/paper/2504.20094/) (2025)
- [MR.Rec: Synergizing Memory and Reasoning for Personalized Recommendation Assistant with LLMs](https://mlsanity.com/paper/2510.14629/) (2025)
- [Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations](https://mlsanity.com/paper/2409.14399/) (2024)
- [Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism](https://mlsanity.com/paper/2508.15030/) (2025)
- [Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignment](https://mlsanity.com/paper/2510.14008/) (2025)
- [Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action](https://mlsanity.com/paper/2504.08754/) (2025)
- [MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions](https://mlsanity.com/paper/2509.04183/) (2025)
- [ScienceDB AI: An LLM-Driven Agentic Recommender System for Large-Scale Scientific Data Sharing Services](https://mlsanity.com/paper/2601.01118/) (2026)
- [Multi-Agent Video Recommenders: Evolution, Patterns, and Open Challenges](https://mlsanity.com/paper/2604.02211/) (2026)
- [Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentation](https://mlsanity.com/paper/2508.05657/) (2025)
- [Reasoning over User Preferences: Knowledge Graph-Augmented LLMs for Explainable Conversational Recommendations](https://mlsanity.com/paper/2411.14459/) (2024)
- [Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent](https://mlsanity.com/paper/2506.14302/) (2025)
- [Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems](https://mlsanity.com/paper/2607.04433/) (2026)
- [Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation](https://mlsanity.com/paper/2507.07307/) (2025)
- [AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System](https://mlsanity.com/paper/2508.13423/) (2025)
- [From Atom to Community: Structured and Evolving Agent Memory for User Behavior Modeling](https://mlsanity.com/paper/2601.16872/) (2026)
- [ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation](https://mlsanity.com/paper/2602.23716/) (2026)
- [RefuteBench 2.0 -- Agentic Benchmark for Dynamic Evaluation of LLM Responses to Refutation Instruction](https://mlsanity.com/paper/2502.18308/) (2025)
- [Adaptive Multi-Agent Response Refinement in Conversational Systems](https://mlsanity.com/paper/2511.08319/) (2025)

## Abstract (arXiv)

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.