Recent scholarly work has extensively examined the phenomenon of algorithmic collusion driven by AI-enabled pricing algorithms. However, online platforms commonly deploy recommender systems that influence how consumers discover and purchase products, thereby shaping the reward structures faced by pricing algorithms and ultimately affecting competition dynamics and equilibrium outcomes. To address this gap in the literature and elucidate the role of recommender systems, we propose a novel repeated game framework that integrates several key components. We first develop a structural search model to characterize consumers' decision-making processes in response to varying recommendation sets. This model incorporates both observable and unobservable heterogeneity in utility and search cost functions, and is estimated using real-world data. Building on the resulting consumer model, we formulate personalized recommendation algorithms designed to maximize either platform revenue or consumer utility. We further introduce pricing algorithms for sellers and integrate all these elements to facilitate comprehensive numerical experiments. Our experimental findings reveal that a revenue-maximizing recommender system intensifies algorithmic collusion, whereas a utility-maximizing recommender system encourages more competitive pricing behavior among sellers. Intriguingly, and contrary to conventional insights from the industrial organization and choice modeling literature, increasing the size of recommendation sets under a utility-maximizing regime does not consistently enhance consumer utility. Moreover, the degree of horizontal differentiation moderates this phenomenon in unexpected ways. The "more is less" effect does not arise at low levels of differentiation, but becomes increasingly pronounced as horizontal differentiation increases.

翻译：近期学术研究广泛探讨了由AI定价算法驱动的算法合谋现象。然而，在线平台普遍部署的推荐系统会影响消费者发现和购买产品的方式，从而改变定价算法面临的收益结构，并最终影响竞争动态与均衡结果。为填补这一文献空白并阐明推荐系统的作用，我们提出了一个整合多个关键模块的新型重复博弈框架。我们首先构建了一个结构化搜索模型，用以刻画消费者面对不同推荐集时的决策过程。该模型在效用函数与搜索成本函数中同时纳入了可观测与不可观测的异质性，并基于真实世界数据进行了估计。基于所得消费者模型，我们进一步设计了以平台收益最大化或消费者效用最大化为目标的个性化推荐算法。我们同时引入了卖方的定价算法，并将所有模块整合以进行全面的数值实验。实验结果表明：收益最大化的推荐系统会加剧算法合谋，而效用最大化的推荐系统则会促使卖方采取更具竞争性的定价行为。有趣的是，与产业组织理论和选择模型文献的传统认知相反，在效用最大化机制下增加推荐集规模并不总能提升消费者效用。此外，横向差异化程度会以出人意料的方式调节这一现象：“多反为少”效应在低差异化水平下并不出现，但随着横向差异化的增强而变得日益显著。