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Music Recommendation at SpotifyHOW SPOTIFY RECOMMENDS MUSIC
Frederik Prüferxx.xx.2016
• Introduction
• Explicit / Implicit Feedback
• Content-Based Filtering
• Spotify Radio
• Collaborative Filtering
• Discover Weekly Playlist
• Conclusion
2
Table of Contents
Formerly
3
4
Nowadays
Spotify
• > 100 Mio monthly active users
• > 30 Mio songs
• > 2 billion Spotify playlists
5
Spotify
„ We now have more technology than ever before to ensure that if you’re […]
doing something that only 20 people in the world will dig, we can now find those
20 people and connect the dots between the artist and listeners “ - Matthew Ogle
6
Sonnad, Nikhil. “The magic that makes Spotify’s Discover Weekly Playlist so damn good”. URL: http://qz.com/571007. [Online; last accessed June 2016]
Explicit Feedback Implicit Feedback
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• Introduction• Explicit/Implicit Feedback• Content-Based Filtering• Collaborative Filtering• Conclusion
Explicit Feedback Implicit Feedback
• relies on the explicit input by
users
Actual Rating
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Explicit Feedback Implicit Feedback
• relies on the explicit input by
users
Actual Rating
7
• extracted out of the users
behavior
Predicted Rating
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Track: May 16
Artist: Lagawagon
Album: Let‘s Talk About Feelings
Release: 1998
Content-Based Filtering
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3. Content-Based Filtering
Basic Idea:
Compare sets of features which represent the items in a meaningful way
Usage:
Spotify Radio
• Introduction• Explicit/Implicit Feedback• Content-Based Filtering• Collaborative Filtering• Conclusion
10
- Artist
- Album Cover
- Lyrical Meaning
- Audio Signals
- …
But which features to compare?
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Figure 1. Audio Features for a Spotify-Track
The semantic gap in music
• the characteristics that affect user preference aren’t equal to the corresponding
audio signal.
• some properties are impossible to obtain from audio signals
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Predictable Recommendations
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Spotify Radio
Create a Radio Channel based on:
• Song
• Album
• Artist
• Playlist
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Spotify Radio
• User plays radio -> load 250 nearest neighbors and shuffle
• Thumbs up -> Load more tracks from the thumbed-up song
• Thumbs down -> remove that song / re-weight tracks
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Nearest Neighbors
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May 16
Spotify Radio
• User plays radio -> load 250 nearest neighbors and shuffle
• Thumbs up -> Load more tracks from the thumbed-up song
• Thumbs down -> remove that song / re-weight tracks
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Collaborative Filtering
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Basic Idea:
What do other people (with a similar music taste) listen to?
Usage:
Discovery Weekly Playlist
• Introduction• Explicit/Implicit Feedback• Content-Based Filtering• Collaborative Filtering• Conclusion
20
B:A:
A very big matrix…
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30mio items
100mio users
Nearest Neighbors
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Person A
Implicit Matrix Factorization
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Users
Songs
min𝑥,𝑦
𝑢,𝑖
𝑐𝑢𝑖 𝑝𝑢𝑖 − 𝑥𝑢𝑇 ∗ 𝑦𝑖
2 + λ(
𝑢
𝑥𝑢2 +
𝑖
𝑦𝑖2)
• 𝑝𝑢𝑖 =1, 𝑢𝑠𝑒𝑟 𝑢 ℎ𝑎𝑠 𝑙𝑖𝑠𝑡𝑒𝑛𝑒𝑑 𝑡𝑜 𝑠𝑜𝑛𝑔 𝑖
0, 𝑢𝑠𝑒𝑟 𝑢 ℎ𝑎𝑠𝑛′𝑡 𝑙𝑖𝑠𝑡𝑒𝑛𝑒𝑑 𝑡𝑜 𝑠𝑜𝑛𝑔 𝑖
• Goal : 𝑥𝑢*𝑦𝑖 = 𝑝𝑢𝑖
• 𝑐𝑢𝑖 : describes the confidence, that user u likessong i
• λ : regularization penalty to avoid overfitting
≈ X Yቐ
ቐ
f
e
Alternating Least Squares
1. Initialize user & item vectors
2. Fix item vectors and solve for optimal user vectors
- Take the derivative of loss function with respect to user‘s vector,
set equl to 0 and solve
3. Fix user vectors and solve for optimal item vectors
4. Repeat until convergence
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B:A:
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The Harry-Potter Effect:
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The Cold-Start Problem:The Harry-Potter Effect:
Discover Weekly Playlist
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Discover Weekly Playlist
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Spotifyfinds songs
that fit your
profile
DiscoverWeekly Playlist
- ….- ….- ….- ….- ….- ….- ….
Spotifyidentifies
similarsongs
Develops„Taste
Profile“
You listen toand save songs
Other users createbillions of Playlists
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Figure 2. The Spotify Blob
Conclusion
Music Profile Content
Other Songs Content
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Content-Based Filtering
• Introduction• Explicit/Implicit Feedback• Content-Based Filtering• Collaborative Filtering• Conclusion
Conclusion
Music Profile Content Music Profile Other users
Other Songs Content Songs they listened to
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Content-Based Filtering Collaborative Filtering
Conclusion
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References
• Balabanović, Marko, and Yoav Shoham. "Fab: content-based, collaborative recommendation." Communications of the ACM 40.3 (1997): 66-72.
• Bernhardsson, Erik. “Recommendations at Spotify v4” [Presentation Slides], URL: http://de.slideshare.net/MrChrisJohnson/algorithmic-music-recommendations-at-spotify, [Online; last accessed June 2016]
• Bernhardsson, Erik. “Nearest neighbor methods and vector models – part 1“. URL: https://erikbern.com/2015/09/24/nearest-neighbor-methods-vector-models-part-1/. [Online; last accessed June 2016]
• Dieleman, Sander. “Recommendig music on Spotify with deep learning”. URL: http://benanne.github.io/2014/08/05/spotify-cnns.html August 05, 2014 . [Online; last accessed June 2016].
• Hu, Yifan, Yehuda Koren, and Chris Volinsky. "Collaborative filtering for implicit feedback datasets." Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. Ieee, 2008.
• Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 8 (2009): 30-37.
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References
• Pazzani, Michael J., and Daniel Billsus. "Content-based recommendation systems." The adaptive web. Springer Berlin Heidelberg, 2007. 325-341.
• Spotify Developers. Audio Features [Table File]. URL: https://developer.spotify.com/web-api/console/get-audio-features-track/#complete. [Online; last accessed June 2016]
• Spotify Webpage. N.p. URL: https://www.spotify.com/de/. [Online; last accessed June 2016]
• Statista. N.p. URL: http://www.statista.com/statistics/244995/number-of-paying-spotify-subscribers/. [Online; last accessed June 2016]
• Steck, Harald, van Zwol, Roelof, Johnson, Chris. “Interactive Recommender Systems” [Presentation Slides], URL: http://de.slideshare.net/MrChrisJohnson/interactive-recommender-systems-with-netflix-and-Spotify, [Online; last accessed June 2016]
• Sonnad, Nikhil. The Spotify Blob [Image File]. URL: http://qz.com/571007. [Online; last accessed June 2016]
• Van den Oord, Aaron, Sander Dieleman, and Benjamin Schrauwen. "Deep content-based music recommendation." Advances in Neural Information Processing Systems. 2013.
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