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Revenue-Sharing Allocation Strategies for Two-Sided Media Platforms: Pro-Rata vs. User-Centric

收入 计算机科学 背包问题 利润(经济学) 集合(抽象数据类型) 运筹学 经济 微观经济学 算法 数学 财务 程序设计语言
作者
Saeed Alaei,Ali Makhdoumi,Azarakhsh Malekian,Saša Pekeč
出处
期刊:Management Science [Institute for Operations Research and the Management Sciences]
卷期号:68 (12): 8699-8721 被引量:46
标识
DOI:10.1287/mnsc.2022.4307
摘要

We consider a two-sided streaming service platform that 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—pro-rata and user-centric. With pro-rata, artists are paid proportionally to their share of the overall streaming volume, whereas 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’s 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. Furthermore, 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 that outputs a set of artists so that the platform’s profit is within a single artist’s revenue from the optimal profit. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4307 .
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