{"id":32419,"date":"2018-11-13T15:07:57","date_gmt":"2018-11-13T20:07:57","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/spotify\/"},"modified":"2018-11-13T16:24:13","modified_gmt":"2018-11-13T21:24:13","slug":"thank-you-for-the-music-spotify-and-algorithmic-music-curation","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/thank-you-for-the-music-spotify-and-algorithmic-music-curation\/","title":{"rendered":"\u201cThank You for the Music\u201d: Spotify and algorithmic music curation"},"content":{"rendered":"
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\u201cAlgorithms alone can\u2019t do that emotional task. You need a human touch.\u201d<\/em> – Jimmy Iovine, Apple Music<\/h2>\n

\u201cSpotify understands users better than anyone else… We\u2019re the biggest in terms of streaming data to bring the personalization necessary to make this feature work.\u201d<\/em> – Edward Newett, Spotify<\/h2>\n<\/blockquote>\n

The Curation Problem<\/strong><\/p>\n

Music streaming services promise users the world, with millions of songs available at any moment. But while early-adopter music enthusiasts might do their own playlisting, casual listeners need more guidance. Curation is the most important way streaming services can differentiate themselves, helping users discover music and keeping them engaged.<\/p>\n

Both humans and machines can support music discovery. According to Apple\u2019s Jimmy Iovine, \u201cAlgorithms alone can\u2019t do that emotional task. You need a human touch.\u201d[1] Other content industries like film and journalism rely heavily on human curation. But Spotify has built a sustained competitive advantage in music through its algorithms, providing users with exactly the right music at the right moment. As more and more users join the service, Spotify can better map music tastes across the globe, providing richer personalization at scale versus competitors who rely on human editors. Since its founding, Spotify has remained steadfast in its commitment to technological solutions to the curation problem.<\/p>\n

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Spotify’s website describes Discover Weekly as “the playlist made just for you, every Monday.”<\/figcaption><\/figure>\n

Innovation at Spotify<\/strong><\/p>\n

Spotify\u2019s primary path to innovation has been through acquisition. The Echo Nest\u2019s unique set of data tags for over 30 million songs provided Spotify the requisite fuel for algorithmically-driven playlists when it was acquired in 2014. Since then, Spotify has continued to acqui-hire advances in machine learning, music cataloging, search, and personalization, through companies including Seed Scientific (2015), Niland (2017), and Sonalytic (2017). Rather than silo these teams, Spotify has placed their employees in charge of some of their largest curation projects. [2]<\/p>\n

Discover Weekly, Spotify\u2019s first major personalization product, launched in 2015, powered by the technology of The Echo Nest. The algorithm finds other users with similar taste profiles who have built playlists featuring music in the user\u2019s taste, pulling songs from those playlists the user has not listened to. It then filters those songs by the user\u2019s areas of affinity and exploration. [3] The final result is far more interesting than basic collaborative filtering, delighting users and keeping them engaged with Spotify. This creates a virtuous cycle as higher user engagement yields more data points that can further improve Spotify\u2019s unsupervised machine learning models.<\/p>\n

Spotify has since developed additional discovery and recommendation products including Daily Mix and Release Radar. These playlists drive the majority of listening, with only 36% of Spotify listening hours coming from user-generated playlists. [4]<\/p>\n