1. Music
Recommendation
Evan Casey and Erin Coughlan

2. Problem
● Million Song Dataset Challenge (Kaggle)
○ 110k Users, 1m+ unique songs
● Music Recommendation
○ Recommend songs for each user based on a larger
training set of user listening histories
● Winner - 0.17910 (17.9%)
● Benchmark - 0.02079 (2.1%)

3. Data
● Million Song Dataset
● Two subsets of 1000
users (random and
most active)
● Echonest API to get
metadata

4. Echonest Data
Metadata we
obtained:
●
●
●
●
●
Tempo
Danceability
Energy
Speech
Acousticness
Unavailable
metadata:
●
●
●
●
●
Genre
Artist popularity
Song popularity
Location
Year released

5. Previous Approaches
Dynamic K-Means:
● Kim et. al (6th Int’l Conference on ML)
● Li et. al (University of Michigan)
Item and user-based collaborative-filtering:
● Niu et. al (Stanford)
● Lu et. al (Stanford)

6. K-Means

7. K-Means for our Problem
Step 1:
K-Means from all
songs listened to by all
users

8. K-Means for our Problem
Step 2:
K-Means from user
listening history

9. K-Means for our Problem
Step 3:
Predict based on
location of user
centroids

10. Mean Average Precision
Predicted:
Actual:

11. Mean Average Precision

12. What are the Results?
All Metadata
0.00200326282427
Weighted Centroids
0.00375567272976
Multiple Centroids (2)
0.00364834470835
Modified Metadata
0.00994279218087
All Improvements
0.01008282844
More Data
0.00266295400221

13. Number of Clusters?

14. Collaborative Filtering
Shawn
1
4
1
3
4
8
9
Billy
8
8
Paul
2
3
4
2
4

15. Collaborative Filtering

16. User-based Collaborative Filtering
Step 1:
Obtain user history
profile

17. User-based Collaborative Filtering
Step 2:
Calculate similarity
between users to find
their nearest neighbors

18. User-based Collaborative Filtering
Step 3:
Compute weighted
average of the ratings
by the neighbors and
find the items with the
highest average

19. Implementation Details
Used Amazon EMR
with MRJob to
parallelize the
algorithm across
multiple machines
MRJob

20. What are the Results?
User Collaborative Filtering (1k Users)
0.008223545412
User Collaborative Filtering (10k Users)
0.012654713312
User Collaborative Filtering (110k Users)
0.112794360446

21. Compiled Results
Benchmark (1k Users)
0.0104030562401
K-means
0.01008282844
Benchmark (110k Users)
0.02079
User Collaborative Filtering
0.112794360446

22. Improvements?
● Ensemble techniques
● More metadata from echonest (genre, artist
popularity, etc.)
● MapReduce for k-means

23. Questions?

24. References
http://cs229.stanford.edu/proj2012/NiuYinZhang-MillionSongDatasetChallenge.
pdf
http://cs229.stanford.edu/proj2012/LuXiongLiuMusicRecommenderSystemUtilizingUsers%E2%80%
99ListeningHistoryandSocialNetworkInformation.pdf
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4457263&tag=1
http://www-personal.umich.edu/~yjli/content/projectreport.pdf

25. Github Repo
https://github.
com/erinkidd01/CS158FinalProject.git