Machine Learning & Big Data for Music Discovery at Spotify
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Spotify Machine Learning Solution for Music Discovery
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Machine Learning & Big Data for Music Discovery at Spotify
- 1. Machine Learning & Big Data for Music Discovery Galvanize NYC, Mar 9th, 2017 Vidhya Murali @vid052 Ching-Wei Chen @cweichen
- 2. 100M users in 60 markets 50M subscribers Over 30M songs, and 2B playlists $5B paid to rightsholders Spotify Music for everyone.
- 6. Discover Discover Weekly How What to recommend?
- 7. Many flavors of recommendations Radio
- 8. Many flavors of recommendations Daily MixRadio
- 9. Many flavors of recommendations This Is:Daily MixRadio
- 10. Many flavors of recommendations Recommended SongsThis Is:Daily MixRadio
- 12. ‣ Editorial ‣ Algorithmic ○Content-based ■ Metadata ■ Audio Signals ○Collaborative ■ Usage based Recommendation approaches
- 13. ‣ Editorial ‣ Algorithmic ○Content-based ■ Metadata ■ Audio Signals ○Collaborative ■ Usage based ‣ Algotorial Recommendation approaches
- 14. ‣ Editorial ‣ Algorithmic ○Content-based ■ Metadata ■ Audio Signals ○Collaborative ■ Usage based ‣ Algotorial Recommendation approaches
- 15. ‣ Find patterns from user’s past behavior to generate recommendations. ‣ Domain independent ‣ Scalable Collaborative Filtering
- 16. Compact representation for each user and item (song): f-dimensional vectors Latent Factor Models
- 17. NLP Models
- 18. Context & Co-occurrence is key!
- 19. Document : Playlist Word : Song NLP Models work great on playlists!
- 21. Music in Latent Space
- 23. Music + Math = Epic
- 24. Songs as vectors
- 25. Recommendations User Profile: ● Aggregation over user interactions on Spotify ● Clustering to capture distinct user tastes/ contexts ● Time Sensitive profiling
- 26. ‣ Scale of catalog ● 30M tracks; 2B playlists ● Training ○ 25B data points ○ 100M users ○ 60 countries represented Challenges unique to spotify
- 27. Data Pipelines
- 28. Data Pipelines Big Table Big Table GCS D A T A F L O W Pub Sub Scio
- 29. ‣ Scale of catalog ● 30M tracks; 2B playlists ● Training ○ 25B data points ○ 100M users ○ 60 countries represented ‣ Cold-Start ○ New Users ○ New Music Challenges unique to spotify
- 31. What’s in a sound?
- 32. What’s in a sound? AmplitudeTime Frequencies Loudness
- 33. What’s in a sound? Melody Beats Chords Voices Instruments Lyrics AmplitudeTime Frequencies Loudness
- 34. What’s in a sound? Melody Beats Chords Voices Instruments Lyrics AmplitudeTime Frequencies Loudness Popularity Era Region Genre Mood Purpose
- 36. Deep Learning
- 37. Deep Learning 1. No feature extraction necessary 2. LOTS of simple learning nodes in many layers 3. Propogate errors backwards to learn optimal weights 4. Needs LOTS of data
- 39. Typical Convolutional Neural Network
- 40. Deep Learning on Audio at Spotify Sander Dieleman: http://benanne.github.io/2014/08/05/spotify-cnns.html Input: Audio spectrogram Output: Latent Space Vector
- 42. Cold Start? Problem solved! * * Not completely, of course!
- 43. Recommending new music Release Radar Fresh Finds
- 44. Recommendations at Spotify Recommended SongsThis Is:Daily MixRadio Discover Weekly Release Radar
- 45. What’s next? ?
- 46. Join the band! www.spotify.com/jobs Ching-Wei (@cweichen): cw@spotify.com Vidhya (@vid052): vidhya@spotify.com