Many online platforms of today, including social media sites, are two-sided markets bridging content creators and users. Most of the existing literature on platform recommendation algorithms largely focuses on user preferences and decisions, and does not simultaneously address creator incentives. We propose a model of content recommendation that explicitly focuses on the dynamics of user-content matching, with the novel property that both users and creators may leave the platform permanently if they do not experience sufficient engagement. In our model, each player decides to participate at each time step based on utilities derived from the current match: users based on alignment of the recommended content with their preferences, and creators based on their audience size. We show that a user-centric greedy algorithm that does not consider creator departures can result in arbitrarily poor total engagement, relative to an algorithm that maximizes total engagement while accounting for two-sided departures. Moreover, in stark contrast to the case where only users or only creators leave the platform, we prove that with two-sided departures, approximating maximum total engagement within any constant factor is NP-hard. We present two practical algorithms, one with performance guarantees under mild assumptions on user preferences, and another that tends to outperform algorithms that ignore two-sided departures in practice.

翻译：当今许多在线平台，包括社交媒体网站，都是连接内容创作者与用户的双边市场。现有关于平台推荐算法的大量文献主要关注用户偏好与决策，并未同时解决创作者激励问题。我们提出了一种内容推荐模型，明确聚焦于用户-内容匹配的动态特性，其新颖之处在于：若用户与创作者未获得足够参与度，双方都可能永久离开平台。在该模型中，每个参与者根据当前匹配产生的效用，在每个时间步决定是否参与：用户基于推荐内容与其偏好的契合度，创作者则基于其受众规模。我们证明：相较于考虑双边离开并最大化总参与度的算法，忽略创作者离开的以用户为中心的贪心算法可能导致任意低的总参与度。此外，与仅用户或仅创作者离开平台的情况形成鲜明对比的是，我们证明了在双边离开情形下，在任意常数因子内近似最大总参与度是NP难的。我们提出了两种实用算法：一种在用户偏好的温和假设下具有性能保证，另一种在实际中往往优于忽略双边离开的算法。