AgentSociety挑战赛：设计用于网络平台用户建模与推荐的LLM智能体
AgentSociety Challenge: Designing LLM Agents for User Modeling and Recommendation on Web Platforms
AI总结：
该挑战赛探索LLM智能体在网络平台上的用户建模与推荐，吸引295队参赛，开发阶段性能提升21.9%和20.3%，最终阶段提升9.1%和15.9%，并开源了基准环境。
摘要：
AgentSociety挑战赛是Web会议上的首个竞赛，旨在探索大语言模型（LLM）智能体在网络平台上建模用户行为和增强推荐系统的潜力。该挑战赛包含两个赛道：用户建模赛道和推荐赛道。参赛者需利用来自Yelp、Amazon和Goodreads的组合数据集，以及一个交互式环境模拟器，来开发创新的LLM智能体。挑战赛吸引了全球295支队伍，在37个官方比赛日内共收到超过1,400份提交。参赛者在开发阶段为赛道1和赛道2分别实现了21.9%和20.3%的性能提升，在最终阶段分别实现了9.1%和15.9%的提升，这代表了显著的成就。本文讨论了挑战赛的详细设计，分析了结果，并重点介绍了最成功的LLM智能体设计。为了支持进一步的研究与开发，我们已在https://tsinghua-fib-lab.github.io/AgentSocietyChallenge开源了基准环境。
英文摘要：
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 at https://tsinghua-fib-lab.github.io/AgentSocietyChallenge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
