Machine Learning

Machine learning touches every aspect of Spotify’s business. It is used to help listeners discover content via recommendations and search, generate playlists, extract audio content-rich signals for cataloging and other content-based applications, understanding voice commands, serve ads, develop business metrics and optimization algorithms, create music with AI-assisted tools, and more. Central to these endeavors is a commitment to cultivate expertise in the latest approaches as we advance the state of the art in machine learning methodology and applications. Of particular interest are approaches in reinforcement learning, approximate inference, graphical models, causal inference, deep learning, time series modeling, and meta-model learning.

Latest Machine Learning Publications

November 2022 | NeurIPS

Society of Agents: Regrets Bounds of Concurrent Thompson Sampling

Yan Chen, Perry Dong, Qinxun Bai, Maria Dimakopoulou, Wei Xu, Zhengyuan Zhou

November 2022 | NeurIPS

Temporally-Consistent Survival Analysis

Lucas Maystre, Daniel Russo

November 2022 | NeurIPS

Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders

Olivier Jeunen, Ciarán M. Gilligan-Lee, Rishabh Mehrotra, Mounia Lalmas

Other Research Areas