Revenue-Sharing Allocation Strategies for Two-Sided Media Platforms: Pro-Rata vs. User-Centric
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".