A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers
This addresses fairness and stability issues in recommendation systems for platforms dealing with strategic providers, presenting a novel mechanism rather than an incremental improvement.
The paper tackles the problem of ensuring fairness and stability in recommendation systems with strategic content providers, proposing the Shapley mediator which meets these requirements, runs in linear time, and is uniquely economically efficient.
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 fulfills the fairness and stability requirements, runs in linear time, and is the only economically efficient mechanism satisfying these properties.