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

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

2020-02-17 | |  49 |   39 |   0

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.

上一篇:Learning Others’ Intentional Models in Multi-Agent Settings Using Interactive POMDPs

下一篇:A Smoother Way to Train Structured Prediction Models

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...