Machine Learning for Personalized Music Recommendations at Scale

Surfacing relevant content from among millions of candidates to users in real time is a challenging task addressed by recommender systems. Modern platforms have customers not only on the demand side (e.g. users), but also on the supply side (e.g. retailer, artists), and face an interesting problem of optimizing their models not only for user satisfaction but also for supplier preferences, and visibility. We discuss a number of ML problems which need to be addressed when developing a recommendation framework powering multistakeholder marketplaces

• Aspects of recommendations across multiple features & products
• Multi-objective ML methods for multi-stakeholder recommendations
• User & content understanding for better decisioning
• Insights from large scale experimentation and deployment of music recommender systems

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Rishabh Mehrotra – Senior Research Scientist | Spotify

Rishabh Mehrotra is a Senior Research Scientist at Spotify in London. He obtained his PhD in Machine Learning from University College London where he was partially supported by a Google Research Award. His PhD research focused on inference of search tasks from user interaction logs. His current research focuses on machine learning for marketplaces, bandit based recommendations, and multi-objective modeling of recommenders. Some of his recent work has been published at conferences including KDD, WWW, SIGIR, NAACL, RecSys and WSDM. He has co-taught a number of tutorials at leading conferences including KDD, RecSys, WWW & CIKM, and taught courses at summer schools.