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

1. Story of the algorithms behind
Deezer Flow
RecSysFr, Paris, 2016 March 23th
B. Mathieu, Data Architect
T. Bouabca, Data Scientist

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. Context
/01
Story of the algorithms behind Deezer Flow

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. 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. Initial system
/02
Story of the algorithms behind Deezer Flow

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. /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. /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. Content tagging system
/03
Story of the algorithms behind Deezer Flow

11. /03 Content tagging system
Story of the algorithms behind Deezer Flow
Building a content tagging system

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. /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. Live adaptive algorithms
/04
Story of the algorithms behind Deezer Flow

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. 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. 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. Conclusion
/05
Story of the algorithms behind Deezer Flow

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. 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
Thanks for your attention
Questions?