Content Provider Dynamics and Coordination in Recommendation Ecosystems

Abstract

Recommendation Systems like YouTube are vibrant ecosystems with two types of users: Content consumers (those who watch videos) and content providers (those who create videos). While the computational task of recommending relevant content is largely solved, designing a system that guarantees high social welfare for \textit{all} stakeholders is still in its infancy. In this work, we investigate the dynamics of content creation using a game-theoretic lens. Employing a stylized model that was recently suggested by other works, we show that the dynamics will always converge to a pure Nash Equilibrium (PNE), but the convergence rate can be exponential. We complement the analysis by proposing an efficient PNE computation algorithm via a combinatorial optimization problem that is of independent interest.

Cite

Text

Ben-Porat et al. "Content Provider Dynamics and Coordination in Recommendation Ecosystems." Neural Information Processing Systems, 2020.

Markdown

[Ben-Porat et al. "Content Provider Dynamics and Coordination in Recommendation Ecosystems." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/benporat2020neurips-content/)

BibTeX

@inproceedings{benporat2020neurips-content,
  title     = {{Content Provider Dynamics and Coordination in Recommendation Ecosystems}},
  author    = {Ben-Porat, Omer and Rosenberg, Itay and Tennenholtz, Moshe},
  booktitle = {Neural Information Processing Systems},
  year      = {2020},
  url       = {https://mlanthology.org/neurips/2020/benporat2020neurips-content/}
}