Supply-Side Equilibria in Recommender Systems

Algorithmic recommender systems such as Spotify and Netflix affect not only consumer behavior but also producer incentives. Producers seek to create content that will be shown by the recommendation algorithm, which can impact both the diversity and quality of their content. In this work, we investigate the resulting supply-side equilibria in personalized content recommender systems. We model the decisions of producers as choosing multi-dimensional content vectors and users as having heterogenous preferences, which contrasts with classical low-dimensional models. Multi-dimensionality and heterogeneity creates the potential for specialization, where different producers create different types of content at equilibrium.