Here are the slides from my talk at RecSys 2016.

1. Music Personalization
@
Spotify
Vidhya Murali
@vid052
RecSys 2016

2. Spotify’s Big Data
‣ Started in 2006, now available in 58 countries
‣ 100+ million active users, 35+ million paid subscribers
‣ 30+ million songs in our catalog, ~20K added every
day
‣ 2+ billion playlists
‣ 1 TB of log data every day
‣ Hadoop cluster with ~2500 nodes

3. 3
30 Million Tracks…

4. What to recommend?

5. What to recommend?

6. Personalization @ Spotify
Features:
Discover
Discover Weekly
Fresh Finds
Home
Radio
Release Radar
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7. Approaches
‣Manual Curation by Experts
‣Metadata (e.g: Label Provided Data, News, Blogs)
‣Audio Signals
‣Collaborative Filtering
‣ Hybrid

8. Latent Factor Models
“Compact” representation for each user and items(songs): f-dimensional
vectors

9. Latent Factor Models
“Compact” representation for each user and items(songs): f-dimensional
vectors
Vidhya
Rise
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mUsers
Songs

10. Latent Factor Models
“Compact” representation for each user and items(songs): f-dimensional
vectors
Vidhya
Rise
.. . . . .
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mUsers
Songs
User Vector
Matrix: X: (m x f)

11. Latent Factor Models
“Compact” representation for each user and items(songs): f-dimensional
vectors
Vidhya
Rise
.. . . . .
.. . . . .
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.. .
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...
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mUsers
Songs
User Vector
Matrix: X: (m x f)
Song Vector
Matrix: Y: (n x f)

12. Latent Factor Models
“Compact” representation for each user and items(songs): f-dimensional
vectors
(here, f = 2)
Vidhya
Rise
.. . . . .
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.. . . . .
.. . . . .
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...
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mUsers
Songs
User Vector
Matrix: X: (m x f)
Song Vector
Matrix: Y: (n x f)

13. NLP Models on News and Blogs

14. NLP Models work great on Playlists!

15. Document : Playlist
NLP Models work great on Playlists!

16. Document : Playlist
Word : Song
NLP Models work great on Playlists!

17. [1] http://benanne.github.io/2014/08/05/spotify-cnns.html
Deep Learning on Audio

18. BlackBoxing Algorithms

19. Music in Latent Space

20. Vectors
“COMPACT” representation for users and items musical fingerprint.
Normalized Song Vectors

21. Vectors
“COMPACT” representation for users and items musical fingerprint.
Normalized Song Vectors
User Vector

22. Why Vectors?
Encodes higher order dependencies
Users and Items in the same latent space
User - Item recommendations
Item - Item similarities
Easy to scale up
Complexity is linear in order of latent factors

23. Recommendations
15
Normalized Song Vectors
User Vector

24. Recommendations
15
Normalized Song Vectors
User Vector

25. Ranking
Similarity score can be used for ranking

26. Ranking
Similarity score can be used for ranking
Balance relevance, diversity, popularity, freshness

27. Ranking
Similarity score can be used for ranking
Balance relevance, diversity, popularity, freshness
Heuristic based

28. Ranking
Similarity score can be used for ranking
Balance relevance, diversity, popularity, freshness
Heuristic based
MAB
Interactions
Impressions
Clicks
Streams

29. Music Personalization Data Flow

30. 18

31. Challenges Unique to Spotify
Scale of catalog
Music is “niche”
Music consumption has heavy correlation to users’ context
Repeated consumption of music is NOT so uncommon.

32. Challenge Accepted!
Cold start problem for both users and new music/upcoming artists:
Content Based Signals
Real Time Recommendations
Measuring Quality:
Implicit: A/B Test Metrics
Explicit: Feedback from social forums
Scam Attacks:
Rule based model to detect scammers
Humans choices are not always predictable:
Faith in humanity

33. What Next?
‣Personalization!
‣Content signals such as lyrics, audio, images
‣Expanded Catalog: Shows, Podcasts
‣New Markets
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34. We are hiring!

35. Thank You!
You can reach me @
Email: vidhya@spotify.com
Twitter: @vid052
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