Supply-Side Equilibria in Recommender Systems

          @misc{ scivideos_22932,
            doi = {},
            url = {https://old.simons.berkeley.edu/node/22932},
            author = {},
            keywords = {},
            language = {en},
            title = {Supply-Side Equilibria in Recommender Systems},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {nov},
            note = {22932 see, \url{https://scivideos.org/simons-institute/22932}}
          }
          

Abstract

Digital recommender systems such as Spotify and Netflix affect not only consumer behavior but also producer incentives: producers seek to supply content that will be recommended by the system. But what content will be produced? To understand this, we model users and content as D-dimensional vectors, and assume the system recommends the content that has the highest dot product with each user. In contrast to traditional economic models, here the producer decision space is high-dimensional and the user base is heterogeneous. This gives rise to new qualitative phenomena at equilibrium: the formation of genres,and the possibility of positive profit at equilibrium. We characterize these phenomena in terms of the geometry of the users and the structure of producer costs. At a conceptual level, our work serves as a starting point to investigate how recommender systems shape supply-side competition between producers. Joint work with Meena Jagadeesan and Nikhil Garg