论推荐系统中的生成式智能体
On Generative Agents in Recommendation
AI总结：
针对推荐系统离线与在线性能脱节问题，提出基于大语言模型的生成式智能体用户模拟器Agent4Rec，通过画像、记忆和动作模块模拟用户行为，实验验证其模拟能力并探索过滤气泡等因果现象。
摘要：
推荐系统是当今信息传播的基石，然而离线指标与在线性能之间的脱节极大地阻碍了其发展。针对这一挑战，我们设想了一个推荐模拟器，利用大型语言模型（LLMs）在人类水平智能方面取得的最新突破。我们提出了Agent4Rec，一个推荐系统中的用户模拟器，利用LLM赋能的生成式智能体，这些智能体配备了专门为推荐系统定制的用户画像、记忆和动作模块。特别地，这些智能体的画像模块使用真实世界数据集（如MovieLens、Steam、Amazon-Book）进行初始化，捕捉用户的独特品味和社会特征；记忆模块记录事实性和情感性记忆，并与情感驱动的反思机制相结合；动作模块支持多种行为，涵盖品味驱动和情感驱动的动作。每个智能体以逐页方式与个性化推荐模型交互，依赖于预先实现的基于协同过滤的推荐算法。我们深入探讨了Agent4Rec的能力和局限性，旨在探索一个基本的研究问题：“LLM赋能的生成式智能体在多大程度上能够忠实地模拟真实、自主人类在推荐系统中的行为？”对Agent4Rec的广泛多方面评估突显了智能体与用户个性化偏好之间的一致性和偏差。除了单纯的性能比较，我们还进行了富有洞察力的实验，如模拟过滤气泡效应和发现推荐任务中的潜在因果关系。我们的代码可在https://github.com/LehengTHU/Agent4Rec获取。
英文摘要：
Recommender systems are the cornerstone of today's information dissemination, yet a disconnect between offline metrics and online performance greatly hinders their development. Addressing this challenge, we envision a recommendation simulator, capitalizing on recent breakthroughs in human-level intelligence exhibited by Large Language Models (LLMs). We propose Agent4Rec, a user simulator in recommendation, leveraging LLM-empowered generative agents equipped with user profile, memory, and actions modules specifically tailored for the recommender system. In particular, these agents' profile modules are initialized using real-world datasets (e.g. MovieLens, Steam, Amazon-Book), capturing users' unique tastes and social traits; memory modules log both factual and emotional memories and are integrated with an emotion-driven reflection mechanism; action modules support a wide variety of behaviors, spanning both taste-driven and emotion-driven actions. Each agent interacts with personalized recommender models in a page-by-page manner, relying on a pre-implemented collaborative filtering-based recommendation algorithm. We delve into both the capabilities and limitations of Agent4Rec, aiming to explore an essential research question: ``To what extent can LLM-empowered generative agents faithfully simulate the behavior of real, autonomous humans in recommender systems?'' Extensive and multi-faceted evaluations of Agent4Rec highlight both the alignment and deviation between agents and user-personalized preferences. Beyond mere performance comparison, we explore insightful experiments, such as emulating the filter bubble effect and discovering the underlying causal relationships in recommendation tasks. Our codes are available at https://github.com/LehengTHU/Agent4Rec.
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。
