Recommender systems shape individual choices through feedback loops in which user behavior and algorithmic recommendations coevolve over time. The systemic effects of these loops remain poorly understood, in part due to unrealistic assumptions in existing simulation studies. We propose a feedback-loop model that captures implicit feedback, periodic retraining, probabilistic adoption of recommendations, and heterogeneous recommender systems. We apply the framework on online retail and music streaming data and analyze systemic effects of the feedback loop. We find that increasing recommender adoption may lead to a progressive diversification of individual consumption, while collective demand is redistributed in model- and domain-dependent ways, often amplifying popularity concentration. Temporal analyses further reveal that apparent increases in individual diversity observed in static evaluations are illusory: when adoption is fixed and time unfolds, individual diversity consistently decreases across all models. Our results highlight the need to move beyond static evaluations and explicitly account for feedback-loop dynamics when designing recommender systems.

翻译：推荐系统通过反馈循环塑造个体选择，其中用户行为与算法推荐随时间共同演化。这些循环的系统性效应迄今仍未得到充分理解，部分原因在于现有仿真研究中的不现实假设。我们提出了一个反馈循环模型，该模型能够捕捉隐式反馈、周期性再训练、推荐的概率性采纳以及异构推荐系统。我们将该框架应用于在线零售与音乐流媒体数据，并分析了反馈循环的系统性效应。研究发现，提高推荐采纳率可能导致个体消费逐渐多样化，而集体需求则会以模型相关和领域相关的方式重新分配，且通常会加剧流行度集中。时间序列分析进一步揭示，静态评估中观察到的个体多样性表面增长具有欺骗性：当采纳率固定且时间推移时，所有模型的个体多样性均持续下降。我们的结果强调，在设计推荐系统时，必须超越静态评估，并明确考虑反馈循环的动态特性。