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Playlist generation is an important task in music information retrieval. While previous work has treated a playlist collection as an undifferentiated whole, we propose to build playlist models which are tuned to specific categories or dialects of playlists. Toward this end, we develop a general class of flexible and scalable playlist models based upon hypergraph random walks. To evaluate the proposed models, we present a large corpus of categorically annotated, user-generated playlists. Experimental results indicate that category-specific models can provide substantial improvements in accuracy over global playlist models.
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Hypergraph models ofplaylist dialects
Brian McFeeCenter for Jazz Studies/LabROSAColumbia University
Gert LanckrietElectrical & Computer Engineering University of California, San Diego
Lab
Laboratory for the Recognition andOrganization of Speech and Audio
ROSA
Automatic playlist generation
Evaluating playlist algorithms
1. Observe playlists from users
...2. Compute playlist
likelihoods
3. Compare algorithmsby likelihood scores
?
>
[M. & Lanckriet, 2011]
Evaluating playlist algorithms
Key idea:
Playlist algorithm =
Probability distributionover song sequences
[M. & Lanckriet, 2011]
Modeling playlist diversity
Playlists
Modeling playlist diversity
Road trip
Party mix
MixedGenre
Hip-hop
Data collection
http://www.artofthemix.org/
Started in 1998, users upload and share playlists
[Ellis, Whitman, Berenzweig, and Lawrence, ISMIR 2002]
The data: AotM-2011
• 98K songs indexed to Million Song Dataset
• 87K playlists (1998-2011), ~210K contiguous segments
• 40 playlist categories, user meta-data available
# Playlists per category
Mixed genreTheme
Rock-popAlternating DJ
IndieSingle artist
RomanticRoad trip
DepressionPunk
Break-upNarrativeHip-hop
SleepDance-house
ElectronicRhythm & blues
CountryCover
HardcoreRockJazzFolk
AmbientBlues
100 1000 104 105
# Playlists per category
Mixed genreTheme
Rock-popAlternating DJ
IndieSingle artist
RomanticRoad trip
DepressionPunk
Break-upNarrativeHip-hop
SleepDance-house
ElectronicRhythm & blues
CountryCover
HardcoreRockJazzFolk
AmbientBlues
100 1000 104 105
• Majority of playlists are Mixed genre
• Remaining categories: contextual/mood, genre, other
Our goals
• Which categories can we model? Are some harder than others?
• Which features are useful for playlist generation?
• Do transitions matter? Are some categories less diverse?
A simple playlist model
1. Start with a set of songs
A simple playlist model
2. Select a subset (e.g., jazz songs)
A simple playlist model
3. Select a song
A simple playlist model
4. Find subsets containing the current song
A simple playlist model
4. Select a new subset
A simple playlist model
5. Select a new song
A simple playlist model
6. Repeat...
A simple playlist model
6. Repeat...
Connecting the dots...
• Random walk on a hypergraph - Vertices = songs - Edges = subsets
Connecting the dots...
• Random walk on a hypergraph - Vertices = songs - Edges = subsets
• Learning: optimize edge weights from example playlists
Connecting the dots...
• Random walk on a hypergraph - Vertices = songs - Edges = subsets
• Learning: optimize edge weights from example playlists
• Sampling is efficient, edge labels provide transparency
The hypergraph random walk model
exp. prior
playlists
transitions
edge weights
Edge construction: example
• Audio: cluster songs by timbre
Edge construction: example
• Audio: cluster songs by timbre
• Multiple clusterings (k=16, 64, 256)
Audio-1 Audio-2
Audio-3
Audio-4
Edge construction: the kitchen sink
• Audio
• MSD taste profile
• Era
• Familiarity
• Lyrics
• Social tags
• Uniform shuffle
• Conjunctions: "TAG_jazz-&-YEAR_1959"
• 6390 edges, 98K vertices (songs)
Evaluation protocol
• Repeat x10: - Split playlist collection into 75% train/25% test - Learn edge weights on training playlists - Evaluate average likelihood of test playlists
• Compare gain in likelihood over uniform shuffle baseline
Experiment 1: global vs. categorical
• Fit one model per category
• Fit one global model to all categories
• Test on each category and compare likelihoods
• Question: When does categorical training improve accuracy?
Experiment 1: global vs. categorical
ALLMixed
ThemeRock-pop
Alternating DJIndie
Single artistRomanticRoad trip
PunkDepression
Break upNarrativeHip-hop
SleepElectronic
Dance-houseR&B
CountryCover songs
HardcoreRockJazzFolk
ReggaeBlues
0% 5% 10% 1 5% 20% 25%
Log-likelihood gain over uniform shuffle
Global modelCategory-specific
Uniform
Experiment 1: global vs. categorical
ALLMixed
ThemeRock-pop
Alternating DJIndie
Single artistRomanticRoad trip
PunkDepression
Break upNarrativeHip-hop
SleepElectronic
Dance-houseR&B
CountryCover songs
HardcoreRockJazzFolk
ReggaeBlues
0% 5% 10% 1 5% 20% 25%
Log-likelihood gain over uniform shuffle
Global modelCategory-specific
Uniform • Largest gains for genre playlists• No change for "hard" categories (e.g., Mixed, Alternating DJ, Theme)
Experiment 1: learned edge weights
Audio CF Era Familiarity Lyrics Tags Uniform
ALLMixed
ThemeRock-pop
Alternating DJIndie
Single ArtistRomanticRoadTrip
PunkDepression
Break UpNarrativeHip-hop
SleepElectronic music
Dance-houseRhythm and Blues
CountryCover
HardcoreRockJazzFolk
ReggaeBlues
Experiment 2: continuity?
• Do we need to model playlist continuity?
• Simplified model: - ignore transitions - choose each edge IID
• Question: Are some categories more diverse than others?
playlists
songs
exp. prior
edge weights
Uniform
Experiment 2: continuity
ALLMixed
ThemeRock-pop
Alternating DJIndie
Single artistRomanticRoad trip
PunkDepression
Break upNarrativeHip-hop
SleepElectronic
Dance-houseR&B
CountryCover songs
HardcoreRockJazzFolk
ReggaeBlues
Log-likelihood gain over uniform shuffle
-15% -10% -5% 0% 5% 10% 15% 20%
Global modelCategory-specific
Uniform
Experiment 2: continuity
ALLMixed
ThemeRock-pop
Alternating DJIndie
Single artistRomanticRoad trip
PunkDepression
Break upNarrativeHip-hop
SleepElectronic
Dance-houseR&B
CountryCover songs
HardcoreRockJazzFolk
ReggaeBlues
Log-likelihood gain over uniform shuffle
-15% -10% -5% 0% 5% 10% 15% 20%
Global modelCategory-specific• Most categories exhibit both
continuity AND diversity• Transitions are important!
EDGE SONG
Example playlists
EDGE SONG
70s & soulAudio #14 & funkDECADE 1965 & soul
Lyn Collins - ThinkIsaac Hayes - No Name BarMichael Jackson - My Girl
Rhythm & Blues
Audio #11 & downtempoDECADE 1990 & trip-hopAudio #11 & electronica
Everything but the Girl - BlameMassive Attack - Spying GlassBjörk - Hunter
Electronic music
Conclusions
• Category-specific models outperform global playlist models.
• Continuity matters!
• Proposed model is simple, efficient, and transparent
• AotM-2011 dataset available now!http://cosmal.ucsd.edu/cal/projects/aotm2011
Obrigado!
