Agent4Rec

Description

Agent4Rec is a cutting-edge recommender system simulator that leverages the power of Large Language Models (LLMs) to create 1,000 generative agents. These agents, initialized from the MovieLens-1M dataset, simulate realistic user interactions with movie recommendations, providing unprecedented insights into human behavior in recommendation environments.

Key Features

• 1,000 LLM-Empowered Agents: Each agent embodies unique social traits and preferences, mimicking real-world diversity.
• Realistic Interactions: Agents engage with personalized movie recommendations in a page-by-page manner.
• Diverse Actions: Simulated users can watch, rate, evaluate, exit, and even conduct interviews about recommended content.
• Flexible Configuration: Supports various recommender systems and simulation settings.

Use Cases

• Research Tool: Ideal for studying user behavior in recommendation systems.
• Algorithm Testing: Test and refine recommendation algorithms with realistic user simulations.
• User Experience Optimization: Gain insights to improve recommendation interfaces and strategies.
• Scalable Testing: Simulate large-scale user interactions without the need for real user studies.

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