SimUSER：利用大语言模型模拟用户行为以评估推荐系统
SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation
AI总结：
针对推荐系统离线评估与在线行为脱节及真实用户数据受限问题，提出SimUSER智能体框架，通过生成自洽用户画像并结合记忆等模块模拟用户交互，其模拟效果优于现有工作，还可支持相关因素分析及系统参数优化以提升真实用户参与度。
摘要：
推荐系统在众多现实应用中占据核心地位，但由于离线指标与在线行为之间存在差距，评估其性能仍是一项重大挑战。鉴于真实用户数据的稀缺性和局限性（如隐私问题），我们引入SimUSER，一个可作为可信且具成本效益的人类代理的智能体框架。SimUSER首先从历史数据中识别出自洽的用户画像，通过独特的背景和个性丰富用户档案。然后，评估的核心是配备画像、记忆、感知和大脑模块的用户，他们会与推荐系统进行交互。在微观和宏观层面上，SimUSER与真实人类的一致性均优于先前工作。此外，我们开展了富有洞察力的实验，以探究缩略图对点击率的影响、曝光效应以及评论对用户参与度的作用。最后，我们基于离线A/B测试结果优化了推荐系统参数，提升了真实场景中的用户参与度。
英文摘要：
Recommender systems play a central role in numerous real-life applications, yet evaluating their performance remains a significant challenge due to the gap between offline metrics and online behaviors. Given the scarcity and limits (e.g., privacy issues) of real user data, we introduce SimUSER, an agent framework that serves as believable and cost-effective human proxies. SimUSER first identifies self-consistent personas from historical data, enriching user profiles with unique backgrounds and personalities. Then, central to this evaluation are users equipped with persona, memory, perception, and brain modules, engaging in interactions with the recommender system. SimUSER exhibits closer alignment with genuine humans than prior work, both at micro and macro levels. Additionally, we conduct insightful experiments to explore the effects of thumbnails on click rates, the exposure effect, and the impact of reviews on user engagement. Finally, we refine recommender system parameters based on offline A/B test results, resulting in improved user engagement in the real world.
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。
