Agent4Rec

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.