Agent4Rec
Overview
An open-source recommender system simulator utilizing 1,000 LLM-empowered generative agents to emulate user interactions with personalized movie recommendations.
Agent4Rec is an innovative recommender system simulator that leverages Large Language Models (LLMs) to create 1,000 generative agents, each initialized from the MovieLens-1M dataset. These agents exhibit diverse social traits and preferences, engaging in realistic interactions with personalized movie recommendations. Actions include watching, rating, evaluating, exiting, and conducting interviews about recommended content. Designed to provide insights into human behavior within recommendation environments, Agent4Rec serves as a valuable tool for researchers and developers aiming to study and enhance recommender systems.
Autonomy level
72%
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Some of the use cases of Agent4Rec:
- Simulating user interactions to study behavior in recommendation systems.
- Testing and refining recommendation algorithms with realistic user simulations.
- Analyzing the impact of diverse user preferences on recommender performance.
- Exploring phenomena such as the filter bubble effect in recommendation environments.
- Conducting large-scale simulations without the need for real user studies.
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Popularity level: 57%
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