The AgentSociety Challenge is the first competition in the Web Conference that aims to explore the potential of Large Language Model (LLM) agents in modeling user behavior and enhancing recommender systems on web platforms. The Challenge consists of two tracks: the User Modeling Track and the Recommendation Track. Participants are tasked to utilize a combined dataset from Yelp, Amazon, and Goodreads, along with an interactive environment simulator, to develop innovative LLM agents. The Challenge has attracted 295 teams across the globe and received over 1,400 submissions in total over the course of 37 official competition days. The participants have achieved 21.9% and 20.3% performance improvement for Track 1 and Track 2 in the Development Phase, and 9.1% and 15.9% in the Final Phase, representing a significant accomplishment. This paper discusses the detailed designs of the Challenge, analyzes the outcomes, and highlights the most successful LLM agent designs. To support further research and development, we have open-sourced the benchmark environment atthis https URL.
View on arXiv@article{yan2025_2502.18754,
title={ AgentSociety Challenge: Designing LLM Agents for User Modeling and Recommendation on Web Platforms },
author={ Yuwei Yan and Yu Shang and Qingbin Zeng and Yu Li and Keyu Zhao and Zhiheng Zheng and Xuefei Ning and Tianji Wu and Shengen Yan and Yu Wang and Fengli Xu and Yong Li },
journal={arXiv preprint arXiv:2502.18754},
year={ 2025 }
}