music personalization at spotify
TRANSCRIPT
Music Personalization @
Spotify
Vidhya Murali@vid052
RecSys 2016
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
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30 Million Tracks…
What to recommend?
What to recommend?
Personalization @ Spotify
Features: Discover Discover Weekly Fresh Finds Home Radio Release Radar
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Approaches
‣Manual Curation by Experts
‣Metadata (e.g: Label Provided Data, News, Blogs)
‣Audio Signals
‣Collaborative Filtering
‣ Hybrid
Latent Factor Models“Compact” representation for each user and items(songs): f-dimensional vectors
Latent Factor Models“Compact” representation for each user and items(songs): f-dimensional vectors
Vidhya Rise
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. .mUsers
Songs
Latent Factor Models“Compact” representation for each user and items(songs): f-dimensional vectors
Vidhya Rise
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. .mUsers
SongsUser Vector
Matrix: X: (m x f)
Latent Factor Models“Compact” representation for each user and items(songs): f-dimensional vectors
Vidhya Rise
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. .mUsers
SongsUser Vector
Matrix: X: (m x f)Song Vector
Matrix: Y: (n x f)
Latent Factor Models“Compact” representation for each user and items(songs): f-dimensional vectors
(here, f = 2)
Vidhya Rise
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. .mUsers
SongsUser Vector
Matrix: X: (m x f)Song Vector
Matrix: Y: (n x f)
NLP Models on News and Blogs
NLP Models work great on Playlists!
Document : Playlist
NLP Models work great on Playlists!
Document : Playlist
Word : Song
NLP Models work great on Playlists!
[1] http://benanne.github.io/2014/08/05/spotify-cnns.html
Deep Learning on Audio
BlackBoxing Algorithms
Music in Latent Space
Vectors“COMPACT” representation for users and items musical fingerprint.
Normalized Song Vectors
Vectors“COMPACT” representation for users and items musical fingerprint.
Normalized Song Vectors
User Vector
Why Vectors?Encodes higher order dependencies
Users and Items in the same latent spaceUser - Item recommendationsItem - Item similarities
Easy to scale upComplexity is linear in order of latent factors
Recommendations
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Normalized Song Vectors
User Vector
Recommendations
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Normalized Song Vectors
User Vector
RankingSimilarity score can be used for ranking
RankingSimilarity score can be used for ranking
Balance relevance, diversity, popularity, freshness
RankingSimilarity score can be used for ranking
Balance relevance, diversity, popularity, freshness
Heuristic based
RankingSimilarity score can be used for ranking
Balance relevance, diversity, popularity, freshness
Heuristic based
MAB Interactions
Impressions Clicks Streams
Music Personalization Data Flow
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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.
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
What Next?
‣Personalization!
‣Content signals such as lyrics, audio, images
‣Expanded Catalog: Shows, Podcasts
‣New Markets
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We are hiring!
