Online platforms such as YouTube, Instagram, TikTok heavily rely on recommender systems to decide what content to show to which users. Content producers often aim to produce material that is likely to be shown to users and lead them to engage with said producer. To do so, producers try to align their content with the preferences of their targeted user base. In this work, we explore the equilibrium behavior of producers that are interested in maximizing user engagement. We study two variants of the content-serving rule that the platform's recommender system uses, and we show structural results on producers' production at equilibrium. We leverage these structural results to show that, in simple settings, we see specialization naturally arising from the competition among producers trying to maximize user engagement. We provide a heuristic for computing equilibria of our engagement game, and evaluate it experimentally. We show i) the performance and convergence of our heuristic, ii) the producer and user utilities at equilibrium, and iii) the level of producer specialization.

翻译：诸如YouTube、Instagram和TikTok等在线平台高度依赖推荐系统来决定向哪些用户展示哪些内容。内容生产者通常致力于制作有可能被展示给用户并引导用户与该生产者互动的内容。为此，生产者试图使其内容与目标用户群体的偏好保持一致。在本研究中，我们探讨了旨在最大化用户参与度的生产者的均衡行为。我们研究了平台推荐系统采用的两种内容分发规则变体，并展示了生产者均衡状态下生产内容的结构性结果。我们利用这些结构性结果表明，在简单情境下，生产者之间为最大化用户参与度而展开的竞争会自然导致专业化分工的产生。我们提供了一种用于计算参与度博弈均衡的启发式算法，并通过实验对其进行了评估。我们展示了：i）该启发式算法的性能与收敛性，ii）均衡状态下生产者与用户的效用，以及iii）生产者专业化程度。