music personalization : real time platforms

Music Personalization: Realtime Platforms

♫ + ML + You = ❤

CrunchConf, Budapest, October 30, 2015

Esh KumarMachine Learning & Data Products @ Spotify NYC @eshvk

Who am I?

• UT Austin Machine Learning • Building Large Scale Recommendation Systems @ Mozilla, StumbleUpon & Spotify

75 M+ Active Users

58 Markets

1 TB of Logs/Day

1200+ Node Hadoop Cluster

Products

•Discover … to find new albums •Discover Weekly … A weekly Playlist •Editorial Playlist Recommendations •Radio

Music Personalization

•Understanding People ➡ User Experience, Cultural Variations

•Understanding Content ➡ Genres, Cultural knowledge

•Models ➡ Collaborative Filtering, Content Based

ML

Content

User

Music Personalization

•Understanding People ➡ User Experience, Cultural Variations

•Understanding Content ➡ Genres, Cultural knowledge

•Models ➡ Collaborative Filtering, Content Based

• News, Blogs, NLP

Music Personalization

•Understanding People ➡ User Experience, Cultural Variations

•Understanding Content ➡ Genres, Cultural knowledge

•Models ➡ Collaborative Filtering, Content Based

• News, Blogs, NLP

• Manually tag attributes

• Curation

Music Personalization

•Understanding People ➡ User Experience, Cultural Variations

•Understanding Content ➡ Genres, Cultural knowledge

•Models ➡ Collaborative Filtering, Content Based

• News, Blogs, NLP

• Manually tag attributes

• Curation

• CF

30 Million Songs…

What To Play?

75 Million Users … 1 Person Every 3 Secs…

Recommendation Systems

• Predict user response to options. • Rich field: Matrix completion, ranking, text models, latent factor models.

• Several conferences annually. RecSys, NIPS, ICML etc • Industry researchers include NFLX, GOOG, MS and more…

Collaborative Filtering

Hey,I like tracks P, Q, R, S!

Well,I like tracks Q, R, S, T!

Then you should check out track P!

Nice! Btw try track T!

Model you based on songs you played…

Predict your future based on similar users…

Millions of users and billions of streams… …. so there is someone like you out there

Collaborative Filtering

The Netflix Prize.

A million dollars for beating NFLX’s best algorithms by ~ 10%.

Similarity

Our problem is to figure out how similar two items are.

Mathematically, this means modeling a function Similarity(x,y) for all users and items, if possible.

How do we do this? Matrix Completion. A matrix expresses a system. We model the data in the form of a matrix. For example, play counts for all songs and all users could be:

Users

8>>>>>><

>>>>>>:

0

BBBBBB@

Song Playsz }| {s1,1 s

1,2 14 · · · s1,n

s2,1 s

2,2 2 · · · s2,n

···

sm,1 sm,2 1 · · · sm,n

1

CCCCCCAUsers

8>>>>>><

>>>>>>:

0

BBBBBB@

Song Playsz }| {s1,1 s

1,2 14 · · · s1,n

s2,1 s

2,2 2 · · · s2,n

···

sm,1 sm,2 1 · · · sm,n

1

CCCCCCA

Call Me Maybe

Esh

Esh listened to call me maybe once…

⇡

0

BBBBBBBBB@

u1

u2.........

um

1

CCCCCCCCCA

�t1 t2 · · · · · · · · · tn

�⇡

0

BBBBBBBBB@

u1

u2.........

um

1

CCCCCCCCCA

�t1 t2 · · · · · · · · · tn

�

Matrix Completion is well studied …Start with random vectors around the origin. Run alternating least squares or gradient descent or stochastic gradient descent… All this is Hadoopable™.

Users

8>>>>>><

>>>>>>:

0

BBBBBB@

Song Playsz }| {s1,1 s

1,2 14 · · · s1,n

s2,1 s

2,2 2 · · · s2,n

···

sm,1 sm,2 1 · · · sm,n

1

CCCCCCAUsers

8>>>>>><

>>>>>>:

0

BBBBBB@

Song Playsz }| {s1,1 s

1,2 14 · · · s1,n

s2,1 s

2,2 2 · · · s2,n

···

sm,1 sm,2 1 · · · sm,n

1

CCCCCCA

Call Me Maybe

Esh

Esh listened to call me maybe once…

⇡

0

BBBBBBBBB@

u1

u2.........

um

1

CCCCCCCCCA

�t1 t2 · · · · · · · · · tn

�⇡

0

BBBBBBBBB@

u1

u2.........

um

1

CCCCCCCCCA

�t1 t2 · · · · · · · · · tn

�

30 Million Songs…

What To Play?

75 Million People … 1 Person Every 3 Secs…

1.5 Billion Playlists

Language Models

• Language models work well too. For example, a playlist could be considered as a document and you could learn the latent vectors for tracks (words).

• Then represent a User as a linear combination of their Tracks.

word2vec

Words with similar contexts have similar meaning

word2vec

word2vec

Target Word

Context Word

word2vec

Target Words and Corresponding Contexts

shining bright trees dark green

stars 61 50 10 30 1

sun 71 60 5 2 0

cucumber 2 1 15 3 40

word2vec

Playlists CPU VectorsRead Get Vectors & Update

Vectors are awesome!

•Unique fingerprint for every users, tracks, albums, artists & even playlists in the same space.

•Similarity is easily computable. Euclidean Distance or Cosine Similarity.

Approximate Nearest Neighbors

•Fast approximate nearest neighbor search.

• Locality Sensitive Hashing

• https://github.com/spotify/annoy

Vectors are great for Infrastructure too…

•Machine Learning can be decomposed & abstracted away.

•A Lambda Architecture involving Machine Learning becomes eas(ier).

•Platforms for Personalization become possible….

The Record Store… The List Maker …

How do you scale this?

Tools of the trade

• Build models in Python. (NumPy, SciPy )

• Jobs in Scalding + Luigi ( https://github.com/spotify/luigi )

• Storm for real time.

• In house RPC for serving requests.

Storm 101

• Realtime Stream Processing.

• Like Hadoop but easier.

• Fault tolerant.

• Java, Clojure (yay!) and more!

Storm @ Spotify

• Major users are Ads & Personalization!

• Every team manages its own cluster. For personalization, we have a 12 node cluster.

• Relatively a new tech, compared to Hadoop™.

So why Storm?

• Hadoop is slowwww. Daily User Vector jobs takes ~ 16 hours to run. Small Data FTW!

• New Users are important; they need a friend!

• What moment are you in? Gym, Running etc?.

Getting Data Across The Globe

HDFS

Kafka

Pipeline …

UserListens

Playlists

Realtime Listens Spout

HDFS

Kafka

Pipeline …

UserListens

Playlists

Realtime Listens Spout

User Vector Generation Job

Latent Vector Models

Track, Artist, Album Vectors

HDFS

Kafka

Pipeline …

UserListens

Playlists

Realtime Listens Spout

User Vector Generation Job

Latent Vector Models

Track, Artist, Album Vectors

Compressed Listening History

BoltsCassandra

Cassandra

HDFS

Kafka

Pipeline + Platform

UserListens

Playlists

Realtime Listens Spout

User Vector Generation Job

Latent Vector Models

Track, Artist, Album Vectors

Compressed Listening History

BoltsCassandra

Cassandra

Backend Systems

•Top Albums •Top Tracks •Top Playlists

Discover New User

• Going from two weeks of no recommendations to recommendations as soon as a user plays a track.

• Successful A/B test • First team to build a production ready

personalization feature using Storm.

Lessons Learnt …

• Boring technology works well. Complicated Storm Topology = Bad. (Dan Mckinley)

• Storm is nice. Would have preferred reusing batch Scalding Code. Maybe Spark Streaming?

• Grow your API from one use case to another. Don’t solve for everything at one time.

Join the band!

• Machine Learning, Data & Backend Gigs.

• Now touring in New York, Boston & Stockholm!

• https://www.spotify.com/jobs/

Thanks !Esh Kumar @eshvk