1. Groove Radio: A Bayesian Hierarchical Model for Personalized Playlist Generation
Shay Ben-Elazar, Gal Lavee, Noam Koenigstein,
Oren Barkan, Hilik Berezin, Ulrich Paquet, Tal Zaccai
ACM Conference on Web Search and Data Mining (WSDM'17), Cambridge UK, February 2017.
Presented by: Noam Koenigstein

2. Groove Radio

5. Confidential Microsoft Corporation
The Task
Goal: Given a seed artist, generate a track playlist
Millions of users, tens of millions of tracks
Support different type of similarities
Personalization
Real world online execution
Confidential Microsoft Corporation

6. How can we choose the next track?
Goal: Given a seed artist, generate a tracks playlist.
context
Seed artist ….
Track 1
Track 2
Track i-1
Track i
Track i+1
label
𝑟 𝑖 ∈ 0,1
model 𝑃 𝑟 𝑖 | 𝐱 𝑖
𝐱 𝑖 = 𝑥 𝑖,1 , 𝑥 𝑖,2 ,…, 𝑥 𝑖,𝑑

7. Creating Playlists – A Classification Problem
Let 𝐱 𝑖 = 𝑥 𝑖,1 , 𝑥 𝑖,2 ,…, 𝑥 𝑖,𝑑 denote a feature vector encoding the proposition of appending a particular track 𝑖 to a playlist.
Feature are defined relative to a “context” which includes the seed artist and previously chosen tracks.
The label 𝑟 𝑖 ∈ 0,1 indicates the success/ failure of the proposition encoded by the feature vector.
We build a generative model to predict the success of a proposition.

8. Types of Similarity - Usage

9. Types of Similarity - Audio
Audio Features:
Spectral distribution with GMMs:
Defining acoustic similarity:

10. Types of similarity – Meta-data

11. Types of similarity – Meta-data
Warm
Provocative

12. Types of Similarity - Popularity
Number of users who consumed a track by 𝑎 1
Total users in the dataset

13. The classification problem
context
Seed artist ….
Track 1
Track 2
Track i-1
Track i
Track i+1
label
𝑟 𝑖 ∈ 0,1
model 𝑃 𝑟 𝑖 | 𝐱 𝑖
𝐱 𝑖 = 𝑥 𝑖,1 , 𝑥 𝑖,2 ,…, 𝑥 𝑖,𝑑

14. The classification problem
context
Previous tracks in Playlist:
Seed artist:
Candidate Track:
Candidate artist to seed artist similarity
Candidate artist to previous artist similarity
Candidate track to previous track similarity

15. A Naïve Solution
Simple logistic regression model: 𝑃 𝑟 𝑖 =1 𝐱 𝑖 =𝜎 𝐰 T 𝐱 𝑖 where 𝜎 𝑧 = 1 1+ exp −𝑧
We can create a playlist by choosing the candidate track with the largest 𝑃 𝑟 𝑖 =1 𝐱 𝑖 .
Each weight 𝑤 𝑗 indicates the relative importance of the feature 𝑥 𝑖,𝑗 in determining the success of the candidate track 𝑖.

16. Different models for different artists

17. Different models for different artists

18. Different models for different users

19. Our Approach
We want to construct a model with the following properties:
Affords music domain heterogeneity
Affords user personalization
Deals gracefully with “coldness”
We achieve this by using the following:
Leveraging the well-understood hierarchical taxonomy of the music domain
A generative Bayesian approach with informative priors
Variational Bayes inference to model uncertainty

20. The Music Domain Taxonomy

21. The Music Domain Taxonomy

22. Hierarchical Model Naïve model:
Pr 𝑟 𝑖 =1 𝑐𝑜𝑛𝑡𝑒𝑥𝑡 𝐱 𝑖 )=𝜎 𝐱 𝑖 T 𝐰 Pr 𝐰| 𝜏 w =𝑁 𝐰;𝟎, 1 𝜏 w 𝐈
Genre model:
Pr 𝑟 𝑖 =1 𝑐𝑜𝑛𝑡𝑒𝑥𝑡 𝐱 𝑖 )=𝜎 𝐱 𝑖 T 𝐰 𝑔 𝑖 (𝑔) Pr 𝐰 𝑔 𝑖 (𝑔) 𝐰, 𝜏 g =𝑁 𝐰 𝑔 𝑖 (𝑔) ;𝐰, 1 𝜏 g 𝐈
Sub-genre model:
Artist model:
Pr 𝑟 𝑖 =1 𝑐𝑜𝑛𝑡𝑒𝑥𝑡 𝐱 𝑖 )=𝜎 𝐱 𝑖 T 𝐰 𝑠 𝑖 (𝑠) Pr 𝐰 𝑠 𝑖 (𝑠) 𝐰 𝑔 𝑖 (𝑔) , 𝜏 s =𝑁 𝐰 𝑠 𝑖 (𝑠) ; 𝐰 𝑔 𝑖 (𝑔) , 1 𝜏 s 𝐈
Pr 𝑟 𝑖 =1 𝑐𝑜𝑛𝑡𝑒𝑥𝑡 𝐱 𝑖 )=𝜎 𝐱 𝑖 T 𝐰 𝑎 𝑖 (𝑎) Pr 𝐰 𝑎 𝑖 (𝑎) 𝐰 𝑠 𝑖 (𝑠) , 𝜏 a =𝑁 𝐰 𝑎 𝑖 (𝑎) ; 𝐰 𝑠 𝑖 (𝑠) , 1 𝜏 a 𝐈

23. Hierarchical Model Cont.
Fully hierarchical model:
Pr 𝑟 𝑖 =1 𝑐𝑜𝑛𝑡𝑒𝑥𝑡 𝐱 𝑖 )=𝜎 𝐱 𝑖 T 𝐰 𝑎 𝑖 (𝑎) Pr 𝐰| 𝜏 w =𝑁 𝐰;𝟎, 1 𝜏 w 𝐈
Pr 𝐰 𝑔 𝑖 (𝑔) 𝐰, 𝜏 g =𝑁 𝐰 𝑔 𝑖 (𝑔) ;𝐰, 1 𝜏 g 𝐈
Pr 𝐰 𝑠 𝑖 (𝑠) 𝐰 𝑔 𝑖 (𝑔) , 𝜏 s =𝑁 𝐰 𝑠 𝑖 (𝑠) ; 𝐰 𝑔 𝑖 (𝑔) , 1 𝜏 s 𝐈
Pr 𝐰 𝑎 𝑖 (𝑎) 𝐰 𝑠 𝑖 (𝑠) , 𝜏 a =𝑁 𝐰 𝑎 𝑖 (𝑎) ; 𝐰 𝑠 𝑖 (𝑠) , 1 𝜏 a 𝐈

24. Personalized Model Per user parameters: 𝒘 𝑢𝑎 = 𝐰 𝑎 + 𝐰 𝑢
Pr 𝑟 𝑖 =1 𝑐𝑜𝑛𝑡𝑒𝑥𝑡 𝐱 𝑖 )=𝜎 𝐱 𝑖 T 𝐰 𝑎 𝑖 (𝑎) + 𝐰 𝑢 𝑖 (𝑢)
Pr 𝐰 𝑢 𝑖 (𝑢) | 𝜏 u =𝑁 𝐰 𝑢 𝑖 (𝑢) ;𝟎, 1 𝜏 a 𝐈

25. Graphical Model x 𝒊 𝐰 𝜏 𝑢 𝜏 𝑎 𝜏 𝑠 𝜏 𝑔 𝜏 𝑤 𝛼,𝛽 𝐰 𝑔 𝑖 (𝑔) 𝐰 𝑠 𝑖 (𝑠)
𝐰 𝑔 𝑖 (𝑔)
#𝐺𝑒𝑛𝑟𝑒𝑠
𝐰 𝑠 𝑖 (𝑠)
#𝑆𝑢𝑏𝑔𝑒𝑛𝑟𝑒𝑠
𝐰 𝑢 𝑖 (𝑢)
#𝑈𝑠𝑒𝑟𝑠
#𝐴𝑟𝑡𝑖𝑠𝑡𝑠
𝐰 𝑎 𝑖 (𝑎)
Label
𝑟 𝑖
#𝐷𝑎𝑡𝑎
x 𝒊

26. The Joint Probability

27. Expectation Propagation (EP)
Inference Approaches 𝜽 𝜽*
MAP (maximum a posteriori)
Mean field / Variational Bayes (VB)
Expectation Propagation (EP)
Laplace
Markov chain
Monte Carlo (MCMC)

28. Learning Artists

29. Learning Users

30. Learning Sub-Genres

31. Learning Genres

32. The Global Prior

33. The Precision Parameters

34. Practical Considerations
We wish to ensure different playlists even for similar activations.
We pre-compute a candidate list of 𝑀=1000 tracks for each seed artist.
Discrete multinomial transition probabilities using the softmax function:
Parameter 𝑠 tunes the desired degree of divrersity.
𝑝 𝑚 = 𝑒 𝑠⋅ 𝑟 𝑚 𝑖=1 𝑀 𝑒 𝑠⋅ 𝑟 𝑖

35. Datasets
Groove Music- a proprietary dataset from Groove music service. Positive labels are assigned to ‘true’ transitions in a user’s listening history when both tracks were played till completion. Negative labels indicate transitions where the second track was skipped in mid-play.
30Music- a publicly available dataset of user playlists. Positive labels are assigned to tracks appearing in a playlist. Negatively labeled examples were obtained by uniformly sampling from tracks that did not appear.

36. Dataset Statistics

37. Groove Music Dataset
Label
𝑟 𝑖
x 𝒊 𝐰

38. Groove Music Dataset
Label
𝑟 𝑖
x 𝒊 𝐰
𝐰 𝑔 𝑖 (𝑔)

39. Groove Music Dataset
Label
𝑟 𝑖
x 𝒊 𝐰
𝐰 𝑔 𝑖 (𝑔)
𝐰 𝑠 𝑖 (𝑠)

40. Groove Music Dataset
Label
𝑟 𝑖
x 𝒊 𝐰
𝐰 𝑔 𝑖 (𝑔)
𝐰 𝑠 𝑖 (𝑠)
𝐰 𝑎 𝑖 (𝑎)

41. Groove Music Dataset Label 𝑟 𝑖 x 𝒊 𝐰 𝑎 𝑖 (𝑎) 𝐰 𝑠 𝑖 (𝑠) 𝐰 𝑔 𝑖 (𝑔) 𝐰
𝐰 𝑎 𝑖 (𝑎)
𝐰 𝑠 𝑖 (𝑠)
𝐰 𝑔 𝑖 (𝑔) 𝐰
𝐰 𝑢 𝑖 (𝑢)
𝜏 𝑢
𝜏 𝑎
𝜏 𝑠
𝜏 𝑔
𝜏 𝑤
𝛼,𝛽

42. Groove Music Dataset Label 𝑟 𝑖 x 𝒊 𝐰 𝑎 𝑖 (𝑎) 𝐰 𝑠 𝑖 (𝑠) 𝐰 𝑔 𝑖 (𝑔) 𝐰
𝐰 𝑎 𝑖 (𝑎)
𝐰 𝑠 𝑖 (𝑠)
𝐰 𝑔 𝑖 (𝑔) 𝐰
𝐰 𝑢 𝑖 (𝑢)
𝜏 𝑢
𝜏 𝑎
𝜏 𝑠
𝜏 𝑔
𝜏 𝑤
𝛼,𝛽

43. 30Music Dataset Label 𝑟 𝑖 x 𝒊 𝐰 𝑎 𝑖 (𝑎) 𝐰 𝑠 𝑖 (𝑠) 𝐰 𝑔 𝑖 (𝑔) 𝐰
𝐰 𝑎 𝑖 (𝑎)
𝐰 𝑠 𝑖 (𝑠)
𝐰 𝑔 𝑖 (𝑔) 𝐰
𝐰 𝑢 𝑖 (𝑢)
𝜏 𝑢
𝜏 𝑎
𝜏 𝑠
𝜏 𝑔
𝜏 𝑤
𝛼,𝛽

44. Feature Contribution

46. Conclusions We described a real world playlist generation algorithm
Account for the heterogeneity across artists and genres
Support personalization
Graceful handling of “coldness”
A Bayesian model that utilizes the domain’s taxonomy
Efficient variational Bayes inference

47. Thank You!