推荐系统反馈环会误导用户多样性评估，长期看反而加剧热门内容集中。
The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems
- 构建包含隐式反馈与周期重训练的动态反馈模型
- 发现用户采纳率提升反致个体消费多样化，但集体需求更趋集中
- 揭示静态评估误判多样性，长期演化中个体多样性持续下降
推荐系统通过用户行为与算法推荐的协同演化形成反馈环，其系统性影响仍不明确，部分源于现有模拟研究的不现实假设。本文提出一个新模型，捕捉隐式反馈、周期性重训练、推荐概率采纳及异构推荐系统特征。在在线零售与音乐流媒体数据上应用该框架，分析反馈环的系统效应。结果表明：提高推荐采纳率可能引发个体消费逐渐多样化，但集体需求分布随模型与领域变化，常加剧流行度集中。时间序列分析进一步显示，静态评估中观察到的个体多样性增长是假象——当采纳率固定且时间推移时，所有模型下个体多样性均持续下降。研究强调需超越静态评估，设计推荐系统时必须显式考虑反馈环动态。
原文摘要 · Abstract (English)
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.
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