A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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Authors

Omer Ben-Porat, Moshe Tennenholtz

Abstract

We introduce a game-theoretic approach to the study of recommendation systems with strategic content providers. Such systems should be fair and stable. Showing that traditional approaches fail to satisfy these requirements, we propose the Shapley mediator. We show that the Shapley mediator satisfies the fairness and stability requirements, runs in linear time, and is the only economically efficient mechanism satisfying these properties.