标题： 用于研究推荐系统中反馈回路系统效应的仿真框架 显示英文标题

标题： A Simulation Framework for Studying Systemic Effects of Feedback Loops in Recommender Systems

摘要： 推荐系统持续与用户互动，形成反馈循环，这些循环塑造了个体行为和集体市场动态。 本文引入了一个仿真框架，以在在线零售环境中对这些循环进行建模，其中推荐系统会根据不断变化的用户-物品交互定期重新训练。 使用亚马逊电子商务数据集，我们分析了不同推荐算法随时间对多样性、购买集中度和用户同质化的影响。 结果揭示了一种系统性权衡：虽然反馈循环增加了个体多样性，但同时减少了集体多样性，并将需求集中在少数热门商品上。 此外，对于某些推荐系统，反馈循环随着时间的推移增加了用户同质化，使用户的购买档案越来越相似。 这些发现强调了需要在个性化与长期多样性之间取得平衡的推荐设计。 显示英文摘要

摘要： Recommender systems continuously interact with users, creating feedback loops that shape both individual behavior and collective market dynamics. This paper introduces a simulation framework to model these loops in online retail environments, where recommenders are periodically retrained on evolving user-item interactions. Using the Amazon e-Commerce dataset, we analyze how different recommendation algorithms influence diversity, purchase concentration, and user homogenization over time. Results reveal a systematic trade-off: while the feedback loop increases individual diversity, it simultaneously reduces collective diversity and concentrates demand on a few popular items. Moreover, for some recommender systems, the feedback loop increases user homogenization over time, making user purchase profiles increasingly similar. These findings underscore the need for recommender designs that balance personalization with long-term diversity.