Content Provider Dynamics and Coordination in Recommendation Ecosystems
Omer Ben-Porat, Itay Rosenberg, Moshe Tennenholtz
Abstract
Recommendation Systems like YouTube are vibrant ecosystems with two types of users: Content consumers (those who watch videos) and content providers (those who create videos). While the computational task of recommending relevant content is largely solved, designing a system that guarantees high social welfare for all stakeholders is still in its infancy. In this work, we investigate the dynamics of content creation using a game-theoretic lens. Employing a stylized model that was recently suggested by other works, we show that the dynamics will always converge to a pure Nash Equilibrium (PNE), but the convergence rate can be exponential. We complement the analysis by proposing an efficient PNE computation algorithm via a combinatorial optimization problem that is of independent interest.
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Cited by top-tier papers10
- Supply-Side Equilibria in Recommender SystemsMeena Jagadeesan, Nikhil Garg, Jacob SteinhardtNeurIPS 2023 · 53 citations
- Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang et al.ICML 2024 · 31 citations
- Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online PlatformsNicole Immorlica, Meena Jagadeesan, Brendan LucierWWW 2024 · 26 citations
- Performative Recommendation: Diversifying Content via Strategic IncentivesItay Eilat, Nir RosenfeldICML 2023 · 19 citations
- Learning with Exposure Constraints in Recommendation SystemsOmer Ben-Porat, Rotem TorkanWWW 2023 · 16 citations
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- FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided PlatformsGourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi et al.WWW 2020 · 268 citations
- Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching ApproachMartin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky et al.ICML 2020 · 70 citations
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