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.

agent4rec

An open-source recommender system simulator utilizing 1,000 LLM-empowered generative agents to emulate user interactions with personalized movie recommendations.

AI agents tagged with "LLM generative agents"

Agent4Rec