Story of the algorithms behind Deezer Flow

RecSysFr, Paris, 2016 March 23th

B. Mathieu, Data ArchitectT. Bouabca, Data Scientist

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Context

Initial system

Content tagging system

Live adaptive algorithms

Conclusion

Story of the algorithms behindDeezer Flow

Story of the algorithms behind Deezer Flow

Context

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Story of the algorithms behind Deezer Flow

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

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 pitchThe magic play button

Context/01

Initial system

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Story of the algorithms behind Deezer Flow

/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

/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

/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

Content tagging system

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Story of the algorithms behind Deezer Flow

/03 Content tagging system

Story of the algorithms behind Deezer Flow

Building a content tagging system

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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

/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

Live adaptive algorithms

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Story of the algorithms behind Deezer Flow

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

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

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We can be more precise!

Live adaptive algorithms

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

Conclusion

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Story of the algorithms behind Deezer Flow

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

We are hiring!

Story of the algorithms behind Deezer Flow

● Data scientist

● Data architect

● Search scientist

https://www.deezer.com/jobs

Conclusion/05

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Thanks for your attention

Questions?