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Story of the algorithms behind Deezer Flow

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Talk by Benoit Mathieu and Thomas Bouabca during the recsysfr meetup on March 23rd 2016

Published in: Data & Analytics

Story of the algorithms behind Deezer Flow

  1. 1. Story of the algorithms behind Deezer Flow RecSysFr, Paris, 2016 March 23th B. Mathieu, Data Architect T. Bouabca, Data Scientist
  2. 2. /01 /02 /03 /04 /05 Context Initial system Content tagging system Live adaptive algorithms Conclusion Story of the algorithms behind Deezer Flow Story of the algorithms behind Deezer Flow
  3. 3. Context /01 Story of the algorithms behind Deezer Flow
  4. 4. Deezer overview /01 Context Story of the algorithms behind Deezer Flow ● Music streaming service ● 6M paying users ● 40M tracks ● 180+ countries ● Up to 200+ tracks / user / day
  5. 5. Story of the algorithms behind Deezer Flow Adapt tracklist to ● Music tastes ● Localization ● Activity ● Mood ● Time & day ● Discovery preferences Interesting debate Should we ask questions to the user or let data science do the magic? Deezer Flow: Initial pitch The magic play button Context/01
  6. 6. Initial system /02 Story of the algorithms behind Deezer Flow
  7. 7. /02 Initial system Story of the algorithms behind Deezer Flow Available data: ● User likes (artists, albums, tracks) ● User streams logs ● Album recommendation algorithm (collaborative filtering) Initial System (2014) Strategy: ● Tracklist computed offline ● Tracks from library / listening habits ● Tracks from recommended albums
  8. 8. /02 Initial system Story of the algorithms behind Deezer Flow Cold start problem: addressing new users 1. New users are asked to select some musical genres, and some artists 2. Build tracklist based on liked artists & similar artists 3. Fallback to top tracks in country
  9. 9. /02 Initial system Story of the algorithms behind Deezer Flow ● Tracklist only fits user’s tastes ● Tracklist do not fit user’s mood or user’s activity or time ... To reach this goal: ● Immediately take into account user’s last interactions ● Refresh tracklist more often ● Insights into the content of a track Need a more content-based approach First Flow limitations
  10. 10. Content tagging system /03 Story of the algorithms behind Deezer Flow
  11. 11. /03 Content tagging system Story of the algorithms behind Deezer Flow Building a content tagging system
  12. 12. /03 Story of the algorithms behind Deezer Flow ● Heterogenous sources ● Millions of songs, artists, playlists or albums to tag everyday Quality assessment: ● Monitoring every sources ● Benchmarking ● Studying new metrics How to consolidate such data? Content tagging system
  13. 13. /03 Content tagging system Story of the algorithms behind Deezer Flow Architecture overview Content data: - Tags - Popularity User data: - Taste model - Hot tracks - Behaviors Build tracklist - Data cache - User action history - Update user models - Consolidate tags data - Build indexes actions logs
  14. 14. Live adaptive algorithms /04 Story of the algorithms behind Deezer Flow
  15. 15. The live Flow (2015) ● Generated user profile ● User history analyzed offline ● Recently played tracks ● Recent actions ● Querying tracks from ElasticSearch index /04 Live adaptive algorithms Story of the algorithms behind Deezer Flow
  16. 16. Story of the algorithms behind Deezer Flow Flat tag profiles can lead to mistakes ● Tag clustering ● Querying ES with different tag queries ● Serving tracks according to cluster proportion /04 We can be more precise! Live adaptive algorithms
  17. 17. Different metrics to follow: ● Listening time ● Satisfaction ● User interaction (skipped / liked) ● Reconnection to Flow Live evaluation - AB Testing /04 Live adaptive algorithms Story of the algorithms behind Deezer Flow
  18. 18. Conclusion /05 Story of the algorithms behind Deezer Flow
  19. 19. Story of the algorithms behind Deezer Flow What‘s next ? ● Fitting to user’s mood ● Increased performance on first days Where are we now? ● Collaborative filtering combined with Content-Based approach (coming soon) ● More adaptation to the context Conclusion/05
  20. 20. We are hiring! Story of the algorithms behind Deezer Flow ● Data scientist ● Data architect ● Search scientist https://www.deezer.com/jobs Conclusion/05
  21. 21. 21 Thanks for your attention Questions?

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