Inferring the Causal Impact of New Track Releases on Music Recommendation Platforms through Counterfactual Predictions


With over 20,000 tracks being released each day, recommendation systems that power music streaming services should not only be responsive to such large volumes of content, but also be adept at understanding the impact of such new releases on, both, users’ listening behavior and popularity of artists. Inferring the causal impact of new track releases is critical to fully characterizing the interplay between artists and listeners, as well as among the artists. In this study, we infer and quantify causality using a diffusion-regression state-space model that constructs counterfactual outcomes using a set of synthetic controls, which predict potential outcomes in absence of the intervention. Based on large scale experiments spanning over 21 million users and 1 billion streams on a real world streaming platform, our findings suggest that releasing a new track has a positive impact on the popularity of other tracks by the same artist. Interestingly, other related and competing artists also benefit from a new track release, which hints at the presence of a positive platform-effect wherein some artists gain significantly from activities of other artists.


December 2020 | NeuRIPS

Model Selection for Production System via Automated Online Experiments

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