Matching of Users and Creators in Two-Sided Markets with Departures
This addresses the challenge of maintaining platform engagement by considering both sides in recommendation algorithms, which is incremental as it extends existing user-centric models to include creator incentives.
The paper tackles the problem of content recommendation in two-sided markets by modeling user and creator departures due to insufficient engagement, showing that ignoring creator departures can lead to arbitrarily poor total engagement and proving NP-hardness for approximating maximum engagement with two-sided departures.
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.