Recommendation @Deezer
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RecSysFr #3Recommendation @Deezer
RecSysFr, Paris, 2016 June 22th
B. Mathieu, Head of Data Science
Deezer
/01
RecSysFr #3
Deezer overview
RecSysFr #3
420 employees in 20 cities 5M albums 40M tracks 100M playlists
16M MAU 6M subscribers
~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
RecSysFr #3
Recommendation opportunities
/02
RecSysFr #3
Interactive recommendation
Understand user feedbacks
Interactive Radios
Algorithms and Evaluation
/03
RecSysFr #3
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
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
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
RecSysFr #3
Offline testing / benchmarking
Offline Testing
User Study
AB Testing
Candidates Best Offline Candidates
Best User Studies Candidates
Final choice
2013 - Shany, Gunawardana
Thanks for your attention
Enjoy RecSysFr #3 @Deezer !
http://www.deezer.com/jobs
https://www.deezer.com/company/jobshttps://www.deezer.com/company/jobs