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Agent4Rec

Agent4Rec: A recommender system simulator with 1,000 LLM-empowered generative agents for exploring recommendation environments.

Introduction

On Generative Agents in Recommendation

Agent4Rec is a recommender system simulator featuring 1,000 LLM-empowered generative agents. Initialized from the MovieLens-1M dataset, these agents embody diverse social traits and preferences.

Key features:

  • LLM-Empowered Agents: Simulates human-like behavior in recommendation environments using generative agents.
  • Diverse Agent Personalities: Agents are initialized with varied social traits and preferences from the MovieLens-1M dataset.
  • Interactive Simulation: Agents interact with personalized movie recommendations, performing actions like watching, rating, and evaluating.
  • Customizable Recommender Systems: Supports various recommendation algorithms, including Random, Pop, MF, MultVAE, and LightGCN.
  • Parallel Execution: Offers parallel execution mode to speed up simulations.
  • Detailed Logging: Records interaction history for each agent, enabling in-depth analysis.

Use Cases:

  • Evaluating the effectiveness of different recommendation algorithms.
  • Studying the impact of user behavior on recommendation systems.
  • Exploring the potential of LLM-empowered agents in simulating real-world interactions.

Information

  • Publisher
    Jeremy Xiao
  • Websitegithub.com
  • Published date2025/03/08

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