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Algorithmic Music
Discovery at Spotify
Chris Johnson
@MrChrisJohnson
January 13, 2014

Monday, January 13, 14
Who am I??
•Chris Johnson

– Machine Learning guy from NYC
– Focused on music recommendations
– Formerly a graduate student at UT Austin

Monday, January 13, 14
What is Spotify?

•
•

On demand music streaming service
“iTunes in the cloud”

Monday, January 13, 14

3
Section name

Monday, January 13, 14

4
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

Monday, January 13, 14

5
Challenge: 20 Million songs... how do we
recommend music to users?

Monday, January 13, 14

6
Recommendation Features
• Discover (personalized recommendations)
• Radio
• Related Artists
• Now Playing

Monday, January 13, 14

7
8

How can we find good
recommendations?
• Manual Curation

• Manually Tag Attributes

• Audio Content,
Metadata, Text Analysis

• Collaborative Filtering

Monday, January 13, 14
Collaborative Filtering - “The Netflix Prize”

Monday, January 13, 14

9
Collaborative Filtering

10

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
Monday, January 13, 14
Section name

Monday, January 13, 14

11
Difference between movie and music recs

•

Scale of catalog

60,000 movies

Monday, January 13, 14

20,000,000 songs

12
Difference between movie and music recs

•

Repeated consumption

Monday, January 13, 14

13
Difference between movie and music recs

•

Music is more niche

Monday, January 13, 14

14
“The Netflix Problem” Vs “The Spotify Problem

•Netflix:

Users explicitly “rate” movies

•Spotify:

Feedback is implicit through streaming behavior

Monday, January 13, 14

15
Section name

Monday, January 13, 14

16
Explicit Matrix Factorization

•Users explicitly rate a subset of the movie catalog
•Goal: predict how users will rate new movies
Movies

Users
Chris
Inception

Monday, January 13, 14

17
Explicit Matrix Factorization

18

•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

Monday, January 13, 14

X

Y
Inception
Chris

•
•
•

= bias for user
= bias for item
= regularization parameter
Implicit Matrix Factorization

19

•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

Monday, January 13, 14

streamed track
latent factor vector
latent factor vector

X

else 0

Y

•
•
•

= bias for user
= bias for item
= regularization parameter
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
Monday, January 13, 14

20
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
Monday, January 13, 14

21
Alternating Least Squares

code: https://github.com/MrChrisJohnson/implicitMF
Monday, January 13, 14

22
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

Monday, January 13, 14

23
Latent Factor Vectors in 2 dimensions

Monday, January 13, 14

24
Section name

Monday, January 13, 14

25
Scaling up Implicit Matrix Factorization
with Hadoop

Monday, January 13, 14

26
Hadoop at Spotify 2009

Monday, January 13, 14

27
Hadoop at Spotify 2014
700 Nodes in our London data center

Monday, January 13, 14

28
Implicit Matrix Factorization with Hadoop
Map step

29

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
Monday, January 13, 14
Implicit Matrix Factorization with Hadoop

30

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
Monday, January 13, 14
Implicit Matrix Factorization with Spark

31

Spark

Vs
Hadoop

http://www.slideshare.net/Hadoop_Summit/spark-and-shark
Monday, January 13, 14
Section name

Monday, January 13, 14

32
Approximate Nearest Neighbors

code: https://github.com/Spotify/annoy
Monday, January 13, 14

33
Ensemble of Latent Factor Models

34

Figure via Erik Bernhardsson
Monday, January 13, 14
AB-Testing Recommendations

Monday, January 13, 14

35
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?

•
•
•
•
•
•

Monday, January 13, 14

36
Section name

37

Thank You!

Monday, January 13, 14
Section name

Monday, January 13, 14

38
Section name

Monday, January 13, 14

39
Section name

Monday, January 13, 14

40
Section name

Monday, January 13, 14

41
Section name

Monday, January 13, 14

42

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Algorithmic Music Recommendations at Spotify

  • 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 Monday, January 13, 14
  • 3. What is Spotify? • • On demand music streaming service “iTunes in the cloud” Monday, January 13, 14 3
  • 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 Monday, January 13, 14 5
  • 6. Challenge: 20 Million songs... how do we recommend music to users? Monday, January 13, 14 6
  • 7. Recommendation Features • Discover (personalized recommendations) • Radio • Related Artists • Now Playing Monday, January 13, 14 7
  • 8. 8 How can we find good recommendations? • Manual Curation • Manually Tag Attributes • Audio Content, Metadata, Text Analysis • Collaborative Filtering Monday, January 13, 14
  • 9. Collaborative Filtering - “The Netflix Prize” Monday, January 13, 14 9
  • 10. Collaborative Filtering 10 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 Monday, January 13, 14
  • 12. Difference between movie and music recs • Scale of catalog 60,000 movies Monday, January 13, 14 20,000,000 songs 12
  • 13. Difference between movie and music recs • Repeated consumption Monday, January 13, 14 13
  • 14. Difference between movie and music recs • Music is more niche Monday, January 13, 14 14
  • 15. “The Netflix Problem” Vs “The Spotify Problem •Netflix: Users explicitly “rate” movies •Spotify: Feedback is implicit through streaming behavior Monday, January 13, 14 15
  • 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 Monday, January 13, 14 17
  • 18. Explicit Matrix Factorization 18 •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 Monday, January 13, 14 X Y Inception Chris • • • = bias for user = bias for item = regularization parameter
  • 19. Implicit Matrix Factorization 19 •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 Monday, January 13, 14 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 Monday, January 13, 14 20
  • 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 Monday, January 13, 14 21
  • 22. Alternating Least Squares code: https://github.com/MrChrisJohnson/implicitMF Monday, January 13, 14 22
  • 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 Monday, January 13, 14 23
  • 24. Latent Factor Vectors in 2 dimensions Monday, January 13, 14 24
  • 26. Scaling up Implicit Matrix Factorization with Hadoop Monday, January 13, 14 26
  • 27. Hadoop at Spotify 2009 Monday, January 13, 14 27
  • 28. Hadoop at Spotify 2014 700 Nodes in our London data center Monday, January 13, 14 28
  • 29. Implicit Matrix Factorization with Hadoop Map step 29 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 Monday, January 13, 14
  • 30. Implicit Matrix Factorization with Hadoop 30 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 Monday, January 13, 14
  • 31. Implicit Matrix Factorization with Spark 31 Spark Vs Hadoop http://www.slideshare.net/Hadoop_Summit/spark-and-shark Monday, January 13, 14
  • 33. Approximate Nearest Neighbors code: https://github.com/Spotify/annoy Monday, January 13, 14 33
  • 34. Ensemble of Latent Factor Models 34 Figure via Erik Bernhardsson Monday, January 13, 14
  • 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? • • • • • • Monday, January 13, 14 36