Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Fraud-Proof Revenue Division on Subscription Platforms
Authors: Abheek Ghosh, Tzeh Yuan Neoh, Nicholas Teh, Giannis Tyrovolas
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experiments with both real-world and synthetic streaming data support SCALEDUSERPROP as a fairer alternative compared to existing rules. |
| Researcher Affiliation | Academia | 1University of Oxford, UK 2Harvard University, USA. |
| Pseudocode | No | The paper describes methods and rules textually but does not contain a dedicated 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Our code is accessible at https://github.com/nicteh/Fraud-Proof-Revenue-Division. |
| Open Datasets | Yes | We utilize data from the Music Listening Histories Dataset (Vigliensoni & Fujinaga, 2017), that contains the listening history of approximately 583, 000 users, 439, 000 artists, and a cumulative total of 27 billion listening events (i.e., user-artist interactions). |
| Dataset Splits | No | The paper describes how synthetic datasets were generated and uses a real-world dataset, but does not provide specific training/test/validation splits for either. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, used to replicate the experiment. |
| Experiment Setup | Yes | We generate synthetic problem instances involving 10, 000 users and 1, 000 artists. For each user, we ο¬rst determine the number of artists they interact with by drawing a value uniformly at random from the range [1, 100]. ... For each chosen artist, the number of times the user streams their music is sampled from a Poisson distribution with Ξ» = 1. We repeat the experiments 100 times. ... we analyze the top and bottom few users based on their pay-per-stream (PPS) relative to GLOBALPROP s PPS, as the revenue share ( ) varies. |