Deezer is a music streaming service with 16 million monthly active users and 6 million subscribers. It has over 40 million tracks, 5 million albums, and 100 million playlists stored across 500 servers with a total of 4.5 petabytes of audio files. Deezer's data science team is working on improving interactive recommendations by better understanding user feedback from interactive radios and testing recommendation algorithms through offline evaluations before conducting more costly A/B tests.

1. RecSysFr #3
Recommendation @Deezer
RecSysFr, Paris, 2016 June 22th
B. Mathieu, Head of Data Science

2. Deezer
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3. Deezer overview
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● 420 employees in 20 cities
● 5M albums
● 40M tracks
● 100M playlists
● 16M MAU
● 6M subscribers

4. ● ~500 servers
● 4.5 PB storage for audio files
● 1.5 TB of logs / day
● ~1B requests / day
● ~30k new albums each week
● Hadoop cluster with 1.5PB storage,
4TB RAM, 1000+ vcores
Some technical numbers
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5. Recommendation opportunities
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8. ● Interactive recommendation
● Understand user feedbacks
Interactive Radios

9. Algorithms and Evaluation
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10. RecSysFr #3
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

11. RecSysFr #3
● % users listening more than 10mn
● % users who reconnect more than 3
days last week
● % users who do a like / dislike
=> take care of statistical confidence !
A/B Tests evaluation metrics

12. ● A/B tests are costly, long
● Want to test more cases
Offline testing:
● setup benchmarking methodology
● Freeze data and evaluate algos with user future actions
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Offline testing / benchmarking

13. Offline
Testing
User
Study
AB
Testing
Candidates Best Offline
Candidates
Best User Studies
Candidates
Final choice
2013 - Shany, Gunawardana

14. Thanks for your attention
Enjoy RecSysFr #3 @Deezer !
http://www.deezer.com/jobs