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Learning Fast: Home Experimentation and Open Research at Spotify | Ching-Wei Chen

435 views · Published 21 December 2018 · 28:27 · Indexed 28 September 2026

Channel: WeAreDevelopers · 2018 · Science & Technology

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Spotify is the world's leading music streaming service, with over 191 million users in 78 markets. But no two users are exactly alike, so in order to serve each listener the content they will uniquely enjoy, Spotify must personalize every aspect of the user experience. From exclusive playlists like Discover Weekly and Your Daily Mix, to personalized Concert recommendations, Spotify utilizes Machine Learning technologies to learn from each user's unique listening habits and tastes, and surface the most relevant content for them. An important aspect of building Machine Learning-based systems is experimenting quickly and effectively, as well as fostering a culture and community around Machine Learning research, both inside and outside of the company. The first part of this talk will give an overview of Home screen personalization at Spotify: how the team uses Machine Learning to show users the most relevant content on the default screen of the Spotify app, and more importantly, how they experiment rapidly using infrastructure and tools for offline and online evaluation of Machine Learning models. The second part of this talk will introduce the Recommended Tracks feature for playlist creation on Spotify, and showcase the results of the RecSys Challenge 2018, which was an open data science challenge launched to promote the study of automatic playlist continuation in the greater research community.

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