Revenue-sharing allocation strategies for two-sided media platforms : pro-rata versus user-centric

We consider a two-sided streaming service platform which generates revenues by charging users a subscription fee for unlimited access to the content and compensates content providers (artists) through a revenue-sharing allocation rule. Platform users are heterogeneous in both their overall consumption and the distribution of their consumption over different artists. We study two primary revenue allocation rules used by market-leading music streaming platforms--- \emph{pro-rata} and \emph{user-centric}. With pro-rata, artists are paid proportionally to their share in the overall streaming volume, while with user-centric each user’s subscription fee is divided proportionally among artists based on the consumption of that user. We characterize when these two allocation rules can sustain a set of artists on the platform and compare them from both the platform and the artists’ perspectives. In particular, we show that, despite the cross-subsidization between low and high streaming volume users, the pro-rata rule can be preferred by both the platform and the artists. Further, the platform's problem of selecting an optimal portfolio of artists is NP-complete. However, by establishing connections to the Knapsack problem, we develop a Polynomial Time Approximation Scheme (PTAS) for the optimal platform's profit. In addition to determining the platform's optimal revenue allocation rule in the class of pro-rata and user-centric rules, we consider the optimal revenue allocation rule in the class of arbitrary rules. Building on duality theory, we develop a polynomial time algorithm which outputs a set of artists so that the platform's profit is within a single artist's revenue from the optimal profit