Matching of Users and Creators in Two-Sided Markets with Departures

• URL: http://arxiv.org/abs/2401.00313v3
• Date: Sat, 20 Jan 2024 01:38:45 GMT
• Title: Matching of Users and Creators in Two-Sided Markets with Departures
• Authors: Daniel Huttenlocher, Hannah Li, Liang Lyu, Asuman Ozdaglar and James Siderius
• Abstract summary: We propose a model of content recommendation that focuses on the dynamics of user-content matching. We show that a user-centric greedy algorithm that does not consider creator departures can result in arbitrarily poor total engagement. 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.
• Score: 0.6649753747542209
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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