It is a recommender system simulator called Agent4Rec, designed to explore the potential of large language model (LLM)-empowered generative agents in simulating human-like behavior in recommendation environments.
It is a recommender system simulator called Agent4Rec, designed to explore the potential of large language model (LLM)-empowered generative agents in simulating human-like behavior in recommendation environments. The system uses 1,000 generative agents initialized from the MovieLens-1M dataset, each embodying unique social traits and preferences. These agents interact with personalized movie recommendations in a page-by-page manner, performing actions such as watching, rating, evaluating, exiting, and interviewing. Agent4Rec aims to simulate the behavior of independent humans in recommendation systems, providing insights into how generative agents can enhance such environments.
To set up Agent4Rec, users must create a virtual environment, manually install PyTorch, and install dependencies listed in the requirements.txt file. The system has been tested on Python 3.9.12 with PyTorch 1.13.1+cu117, and using Python versions above 3.10 may cause bugs in the ‘reckit’ package. Users must also export their OpenAI API key to power the simulation, which relies on ChatGPT-3.5. A toy simulation with three agents can be run to observe agent responses to recommendations, taking approximately three minutes to complete.
Agent4Rec supports various recommendation systems and simulation configurations. For example, users can run a simulation named “MyExp” with 10 agents, each browsing up to five pages with four items per page, using the Matrix Factorization (MF) recommender. The simulation can be executed in parallel to speed up the process. Results are saved in a designated directory, with interaction histories logged for analysis. A full simulation involving 1,000 agents costs approximately $16, or $0.016 per user.
The system is part of the implementation of the paper “On Generative Agents in Recommendation,” presented at SIGIR 2024. It provides a platform for experimenting with different recommender models and configurations, offering valuable insights into the behavior of generative agents in recommendation systems.
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