Understanding Music Playlists

10 July 2015 ICML 2015 Workshop - Machine Learning and Music Discovery

Keunwoo ChoiGeorge FazekasMark Sandler

@c4dm @Queen Mary University of London

Content

• Music Playlist

• Finding 1

• Finding 2

• Finding 3

• Conclusion

Playlist and Recommendation

• Music recommendation == playlist generation in many cases; especially for common music listener.

• Because recommending a song doesn’t make sense.

• Because simply picking top-N songs might fail.

Music Playlist• What is playlist?

• “Sequence of music items”for ( ), by ( ), …

• Ill-posed definition, inductively defined by how people use

• Many people use it

• 1.5B playlists on Spotify

Different Assumptions• What is a good playlist?

Sequence of similar songs

Smooth transitions

Fixed start/end song

Given duration

?

Datasets

Deezer-2015

144,726 songs 50,000 playlists

during 2007-2015

EchoNest track features (high-level

features such as speechness,

dancability, …)

AoTM-2011

97,411 songs 86,310 playlists

during 1998-2011

EchoNest Timbre Features, energy/

key/loudness/mode…

+ playlist category

Datasets• Hierarchy of playlist categories

Genre

Rock Jazz Hiphop R&B Electronic Folk Rock/Pop

Mixed Genre Blues Raggae Country

Punk Hardcore Dance/House

Activity

Sleep Road trip

Emotion

Break up

Depression

Others

Indie Alternating DJ Theme Single

artist Cover Narrative

Three Findings

F1. User clusters in audio-based feature domain

Jennings, 2007

• No structural difference

• Playlist itself doesn’t represent the user that much. (Or is not easily observed.)

• Usage data may be required

• Usage hours, number of songs/artists, diversity of preferences, price tier, social activities, …

F2. Similarity vs. Diversity

“Songs in a playlist should be similar”

“Songs in a playlist shouldn’t be too similar”

Similar Familiar Unified

vs.Interesting Not boring

Diverse

• Audio feature similarity between songs

• within-playlistvs.arbitrary pairs

Mean 25% 75%

0.080 0.061 0.094

0.095 0.041 0.126

3. Different similarity given category

• Compute the similarity of songs in the playlists for each category (for each feature)

• Get rankings of categories (for each feature)

• Get average of the rankings

• Plot it (with nice colours)

3. Different similarity given category

Summary• User (behaviour) information is required to build user

model based on clustering

• Should find an appropriate range of similarity for better playlist generation

• which varies given dataset, features, and similarity measure.

• Desired song similarity may be different for each category

• Different parameters/prior should be set.

Future work , or just curious about…

• How much do people care about playlist? How much do people put efforts on it?

• Mix Tape/CD was important for us (music researchers), so as (modern) playlist for people?

• Looks like they are just containers for a set of songs rather than a sequence songs.