# MACRec: a Multi-Agent Collaboration Framework for Recommendation arXiv: https://arxiv.org/abs/2402.15235 ยท HTML: https://mlsanity.com/paper/2402.15235/ ## Key facts - Cited by 58 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 9 of its 26 references that are in the map. - First posted on arXiv 23 Feb 2024. ## Placed in - LLM agents and tool use / [Agentic recommender systems](https://mlsanity.com/agents/agentic-recommender-systems/) ## Builds on (most cited first, 9 of 9) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023) - [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023) - [Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations](https://mlsanity.com/paper/2308.16505/) (2023) - [AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems](https://mlsanity.com/paper/2310.09233/) (2023) - [Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering](https://mlsanity.com/paper/2402.11523/) (2024) - [GPT-in-the-Loop: Adaptive Decision-Making for Multiagent Systems](https://mlsanity.com/paper/2308.10435/) (2023) ## Built on by (most cited first, 30 of 58) - [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025) - [Large Language Model Agent: A Survey on Methodology, Applications and Challenges](https://mlsanity.com/paper/2503.21460/) (2025) - [Teams of LLM Agents can Exploit Zero-Day Vulnerabilities](https://mlsanity.com/paper/2406.01637/) (2024) - [A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges](https://mlsanity.com/paper/2508.05668/) (2025) - [A Survey on LLM-powered Agents for Recommender Systems](https://mlsanity.com/paper/2502.10050/) (2025) - [A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications](https://mlsanity.com/paper/2503.17003/) (2025) - [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025) - [Multi-Agent Collaboration via Cross-Team Orchestration](https://mlsanity.com/paper/2406.08979/) (2024) - [iAgent: LLM Agent as a Shield between User and Recommender Systems](https://mlsanity.com/paper/2502.14662/) (2025) - [AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems](https://mlsanity.com/paper/2505.19623/) (2025) - [MemRec: Collaborative Memory-Augmented Agentic Recommender System](https://mlsanity.com/paper/2601.08816/) (2026) - [The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems](https://mlsanity.com/paper/2507.02097/) (2025) - [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026) - [GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration](https://mlsanity.com/paper/2410.18032/) (2024) - [Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach](https://mlsanity.com/paper/2509.16736/) (2025) - [Knowledge Graph Enhanced Language Agents for Recommendation](https://mlsanity.com/paper/2410.19627/) (2024) - [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) - [Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism](https://mlsanity.com/paper/2508.15030/) (2025) - [MAS4POI: a Multi-Agents Collaboration System for Next POI Recommendation](https://mlsanity.com/paper/2409.13700/) (2024) - [Agentic Services Computing](https://mlsanity.com/paper/2509.24380/) (2025) - [Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation](https://mlsanity.com/paper/2512.01372/) (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) - [LLMs as Orchestrators: Constraint-Compliant Multi-Agent Optimization for Recommendation Systems](https://mlsanity.com/paper/2601.19121/) (2026) - [MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination](https://mlsanity.com/paper/2603.24579/) (2026) - [Agentic Recommender System with Hierarchical Belief-State Memory](https://mlsanity.com/paper/2605.14401/) (2026) - [Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems](https://mlsanity.com/paper/2607.04433/) (2026) - [LLM-Based User Simulation for Low-Knowledge Shilling Attacks on Recommender Systems](https://mlsanity.com/paper/2505.13528/) (2025) - [Simulating Human-Like Learning Dynamics with LLM-Empowered Agents](https://mlsanity.com/paper/2508.05622/) (2025) ## Abstract (arXiv) 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.