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.

翻译：推荐系统持续与用户交互，形成既塑造个体行为又影响集体市场动态的反馈回路。本文提出一种仿真框架，用于在线零售环境中对这些回路进行建模，其中推荐系统基于不断演变的用户-物品交互数据进行周期性重训练。利用亚马逊电子商务数据集，我们分析了不同推荐算法如何随时间影响多样性、购买集中度和用户同质化。结果表明存在系统性权衡：虽然反馈回路增加了个体多样性，但同时降低了集体多样性，并将需求集中在少数热门物品上。此外，对于某些推荐系统，反馈回路会随时间推移加剧用户同质化，使用户购买画像日趋相似。这些发现强调了在推荐系统设计中平衡个性化与长期多样性的必要性。