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

Omer Ben-Porat; Moshe Tennenholtz

NeurIPS 2018poster93 citations

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

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.

BibTeX

@inproceedings{NEURIPS2018_a9a1d531,
 author = {Ben-Porat, Omer and Tennenholtz, Moshe},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers},
 url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/a9a1d5317a33ae8cef33961c34144f84-Paper.pdf},
 volume = {31},
 year = {2018}
}