Online platforms such as YouTube or Instagram heavily rely on recommender systems to decide what content to show to which users. Producers often aim to produce content that is likely to be shown to users and have the users engage. 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 who are interested in maximizing user engagement. We study two variants of the content-serving rule for the platform's recommender system, and provide a structural characterization of producer behavior at equilibrium: namely, each producer chooses to focus on a single embedded feature. We further show that specialization, defined as different producers optimizing for different types of content, naturally arises 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 highlight how i) the performance and convergence of our heuristic, ii) the level of producer specialization, and iii) the producer and user utilities at equilibrium are affected by the choice of content-serving rule and provide guidance on how to set the content-serving rule to use in engagement games.

翻译：诸如YouTube或Instagram等在线平台严重依赖推荐系统来决定向用户展示何种内容。生产者通常致力于创作可能被展示给用户并能促使用户参与的内容。为此，生产者尝试使其内容与目标用户群体的偏好保持一致。在本研究中，我们探讨了以最大化用户参与度为目标的生产者的均衡行为。我们研究了平台推荐系统内容分发规则的两种变体，并对均衡状态下生产者的行为进行了结构性刻画：即每位生产者选择聚焦于一个单一的嵌入特征。我们进一步表明，专业化——定义为不同生产者针对不同类型内容进行优化——自然产生于生产者为最大化用户参与度而展开的竞争。我们提出了一种计算参与度博弈均衡的启发式方法，并通过实验进行了评估。我们重点分析了以下方面如何受到内容分发规则选择的影响：i) 启发式方法的性能与收敛性，ii) 生产者的专业化水平，以及iii) 均衡状态下生产者与用户的效用，并据此为参与度博弈中内容分发规则的设定提供了指导。