In this presentation I introduce various Machine Learning methods that we utilize for music recommendations and discovery at Spotify. Specifically, I focus on Implicit Matrix Factorization for Collaborative Filtering, how to implement a small scale version using python, numpy, and scipy, as well as how to scale up to 20 Million users and 24 Million songs using Hadoop and Spark.

1. Algorithmic Music
Discovery at Spotify
Chris Johnson
@MrChrisJohnson
January 13, 2014
Monday, January 13, 14

2. Who am I??
•Chris Johnson
– Machine Learning guy from NYC
– Focused on music recommendations
– Formerly a graduate student at UT Austin
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3. What is Spotify?
•
•
On demand music streaming service
“iTunes in the cloud”
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4. Section name
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5. Data at Spotify....
• 20 Million songs
• 24 Million active users
• 6 Million paying users
• 8 Million daily active users
• 1 TB of compressed data generated from users per day
• 700 node Hadoop Cluster
• 1 Million years worth of music streamed
• 1 Billion user generated playlists
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6. Challenge: 20 Million songs... how do we
recommend music to users?
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7. Recommendation Features
• Discover (personalized recommendations)
• Radio
• Related Artists
• Now Playing
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8. 8
How can we find good
recommendations?
• Manual Curation
• Manually Tag Attributes
• Audio Content,
Metadata, Text Analysis
• Collaborative Filtering
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9. Collaborative Filtering - “The Netflix Prize”
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10. Collaborative Filtering
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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!
Image via Erik Bernhardsson
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11. Section name
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12. Difference between movie and music recs
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Scale of catalog
60,000 movies
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20,000,000 songs
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13. Difference between movie and music recs
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Repeated consumption
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14. Difference between movie and music recs
•
Music is more niche
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15. “The Netflix Problem” Vs “The Spotify Problem
•Netflix:
Users explicitly “rate” movies
•Spotify:
Feedback is implicit through streaming behavior
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16. Section name
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17. Explicit Matrix Factorization
•Users explicitly rate a subset of the movie catalog
•Goal: predict how users will rate new movies
Movies
Users
Chris
Inception
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18. Explicit Matrix Factorization
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•Approximate ratings matrix by the product of lowdimensional user and movie matrices
Minimize RMSE (root mean squared error)
•
?
1
2
?
5
•
•
•
3
?
?
?
2
5
?
3
?
?
?
1
2
5
4
= user
= user
rating for movie
latent factor vector
= item
latent factor vector
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X
Y
Inception
Chris
•
•
•
= bias for user
= bias for item
= regularization parameter

19. Implicit Matrix Factorization
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•Replace Stream counts with binary labels
– 1 = streamed, 0 = never streamed
•Minimize weighted RMSE (root mean squared error) using a
function of stream counts as weights
10001001
00100100
10100011
01000100
00100100
10001001
•
•
•
•
= 1 if user
= user
=i tem
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streamed track
latent factor vector
latent factor vector
X
else 0
Y
•
•
•
= bias for user
= bias for item
= regularization parameter

20. Alternating Least Squares
• Initialize user and item vectors to random noise
• Fix item vectors and solve for optimal user vectors
– Take the derivative of loss function with respect to user’s vector, set
–
equal to 0, and solve
Results in a system of linear equations with closed form solution!
• Fix user vectors and solve for optimal item vectors
• Repeat until convergence
code: https://github.com/MrChrisJohnson/implicitMF
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21. Alternating Least Squares
• Note that:
• Then, we can pre-compute
–
–
once per iteration
and
only contain non-zero elements for tracks that
the user streamed
Using sparse matrix operations we can then compute each user’s
vector efficiently in
time where
is the number of
tracks the user streamed
code: https://github.com/MrChrisJohnson/implicitMF
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22. Alternating Least Squares
code: https://github.com/MrChrisJohnson/implicitMF
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23. How do we use the learned vectors?
•User-Item score is the dot product
•Item-Item similarity is the cosine similarity
•Both operations have trivial complexity based on the number of
latent factors
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24. Latent Factor Vectors in 2 dimensions
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25. Section name
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26. Scaling up Implicit Matrix Factorization
with Hadoop
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27. Hadoop at Spotify 2009
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28. Hadoop at Spotify 2014
700 Nodes in our London data center
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29. Implicit Matrix Factorization with Hadoop
Map step
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Reduce step
item vectors
item%L=0
item vectors
item%L=1
user vectors
u%K=0
u%K=0
i%L=0
u%K=0
i%L=1
...
u%K=0
i % L = L-1
u%K=0
user vectors
u%K=1
u%K=1
i%L=0
u%K=1
i%L=1
...
...
u%K=1
...
...
...
...
u % K = K-1
i%L=0
...
...
u % K = K-1
i % L = L-1
user vectors
u % K = K-1
item vectors
i % L = L-1
u % K = K-1
all log entries
u%K=1
i%L=1
Figure via Erik Bernhardsson
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30. Implicit Matrix Factorization with Hadoop
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One map task
Distributed
cache:
All user vectors
where u % K = x
Distributed
cache:
All item vectors
where i % L = y
Mapper
Emit contributions
Reducer
New vector!
Map input:
tuples (u, i, count)
where
u%K=x
and
i%L=y
Figure via Erik Bernhardsson
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31. Implicit Matrix Factorization with Spark
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Spark
Vs
Hadoop
http://www.slideshare.net/Hadoop_Summit/spark-and-shark
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32. Section name
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33. Approximate Nearest Neighbors
code: https://github.com/Spotify/annoy
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34. Ensemble of Latent Factor Models
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Figure via Erik Bernhardsson
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35. AB-Testing Recommendations
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36. Open Problems
•How to go from predictive model to related artists? (learning
to rank?)
How do you learn from user feedback?
How do you deal with observation bias in the user feedback?
(active learning?)
How to factor in temporal information?
How much value in content based recommendations?
How to best evaluate model performance?
How to best train an ensemble?
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37. Section name
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Thank You!
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