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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
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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…
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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
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Song Playsz }| {s1,1 s
1,2 14 · · · s1,n
s2,1 s
2,2 2 · · · s2,n
···
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1
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8>>>>>><
>>>>>>:
0
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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
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Call Me Maybe
Esh
Esh listened to call me maybe once…
⇡
0
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u1
u2.........
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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