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

TL;DR

This paper proposes the Shapley mediator, a game-theoretic mechanism for recommendation systems that ensures fairness, stability, and economic efficiency, addressing limitations of traditional methods.

Contribution

Introduction of the Shapley mediator, a novel mechanism that guarantees fairness, stability, and efficiency in recommendation systems with strategic content providers.

Findings

Shapley mediator satisfies fairness and stability.

It operates in linear time.

It is uniquely economically efficient among mechanisms meeting these criteria.

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 fulfills the fairness and stability requirements, runs in linear time, and is the only economically efficient mechanism satisfying these properties.