Supplier competition on subscription-based platforms in the presence of recommender systems

Abstract

Subscription-based platforms offer consumers access to a large selection of content at a fixed subscription fee. Recommender systems (RS) can help consumers by reducing the size of this choice set by predicting consumers' preferences. However, because the prediction is based on limited information on the consumers and sometimes even on the content, the recommendations are susceptible to biases, a phenomenon widely evidenced in the computer science literature. Intuitively, if these biases systematically favour certain suppliers over others, this could impact competition between suppliers. To study this intuition, we introduce a simple framework of a platform that sells to consumers with quasi-linear utility functions via a recommender system. We find that RS biases lead to more concentrated markets and increased entry barriers even when the platform is not self-preferencing their own products, and users are rational. Limited-attention users can reduce the market concentrating impact of RS biases and harm top-selling products, but the platform can counteract this effect by a choice architecture that gives more prominence to popular items. Self-preferencing does not further increase concentration but it ensures that the winners are the products preferred by the platform. Although encouraging more exploration can reduce these market consolidating effects, we show that they also reduce recommendation relevance in the short-run.

Keywords: recommender systems, market concentration, recommendation system biases, subscription-based platforms