Recommender system has deeply revolutionized people's daily life and
production, bringing a large amount of business value. In the recommendation
domain, simulation and real data-based studies are two typical research
paradigms, with each having different advantages. Previously, real data-based
studies occupy more important positions, since accurately simulating the user
preference is quite difficult. Recently, large language models (LLM) have shown
great potential to achieve human-like intelligence, which provides new
opportunities to overcome the shortcomings of simulation-based studies and thus
highlight their advantages, such as much more application scenarios and cheaper
data acquisition strategies. To shed lights on this direction, in this paper,
we introduce an LLM-based recommender simulator called RecAgent. Our simulator
is composed of two modules: (1) the user module and (2) the recommender module.
The user module can browse the recommendation website, communicate with other
users and broadcast messages on the social media. The recommender module is
designed to provide search or recommendation lists to the users, and one can
design different models to implement the recommender. All the users take
actions based on LLMs, and can freely evolve like in the real world. We present
several case studies to demonstrate that the users in our simulator can indeed
behave in a reasonable manner as expected. Our project has been released at
this https URL

本文介绍了一种基于大型语言模型的推荐系统模拟器 RecAgent，旨在克服仿真研究的缺陷并突出优势，通过真实用户行为来验证其有效性。