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Hypergraph models ofplaylist dialectsBrian McFee LabCenter for Jazz Studies/LabROSAColumbia University ROSA Laboratory for the Recognition and Organization of Speech and AudioGert LanckrietElectrical & Computer EngineeringUniversity of California, San Diego

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Automatic playlist generation

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Evaluating playlist algorithms [M. & Lanckriet, 2011] ... 2. Compute playlist 1. Observe playlists from users likelihoods ? > 3. Compare algorithms by likelihood scores

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Evaluating playlist algorithms [M. & Lanckriet, 2011] Key idea: Playlist algorithm = Probability distribution over song sequences

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Modeling playlist diversity Playlists

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Modeling playlist diversity Road trip Mixed Genre Party mix Hip-hop

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Data collection http://www.artofthemix.org/ Started in 1998, users upload and share playlists [Ellis, Whitman, Berenzweig, and Lawrence, ISMIR 2002]

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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

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# Playlists per category Mixed genre Theme Rock-pop Alternating DJ Indie Single artist Romantic Road trip Depression Punk Break-up Narrative Hip-hop Sleep Dance-house ElectronicRhythm & blues Country Cover Hardcore Rock Jazz Folk Ambient Blues 100 1000 104 105

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# Playlists per category Mixed genre Theme Rock-pop Alternating DJ Indie Single artist Romantic Road trip Depression Punk Break-up Narrative Hip-hop Sleep Dance-house Electronic • Majority of playlists are Mixed genreRhythm & blues Country Cover Hardcore • Remaining categories: Rock Jazz Folk contextual/mood, genre, other Ambient Blues 100 1000 104 105

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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?

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A simple playlist model 1. Start with a set of songs

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A simple playlist model 2. Select a subset (e.g., jazz songs)

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A simple playlist model 3. Select a song

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A simple playlist model 4. Find subsets containing the current song

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A simple playlist model 4. Select a new subset

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A simple playlist model 5. Select a new song

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A simple playlist model 6. Repeat...

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A simple playlist model 6. Repeat...

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Connecting the dots...• Random walk on a hypergraph - Vertices = songs - Edges = subsets

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Connecting the dots...• Random walk on a hypergraph - Vertices = songs - Edges = subsets• Learning: optimize edge weights from example playlists

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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

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The hypergraph random walk model exp. prior edge weights transitions playlists

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Edge construction: example• Audio: cluster songs by timbre

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Edge construction: example• Audio: cluster songs by timbre Audio-1 Audio-2 Audio-4 Audio-3• Multiple clusterings (k=16, 64, 256)

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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)

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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

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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?

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Experiment 1: global vs. categorical Unifo rm ALL Mixed Global model Theme Category-specific Rock-pop Alternating DJ Indie Single artist Romantic Road trip Punk Depression Break up Narrative Hip-hop Sleep Electronic Dance-house R&B Country Cover songs Hardcore Rock Jazz Folk Reggae Blues 0% 5% 10% 1 5% 20% 25% Log-likelihood gain over uniform shuffle

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Experiment 1: global vs. categorical Unifo • Largest gains for genre playlists rm ALL Mixed • No change for "hard" categories Global model Theme Category-specific Rock-pop Alternating DJ (e.g., Mixed, Alternating DJ, Theme) Indie Single artist Romantic Road trip Punk Depression Break up Narrative Hip-hop Sleep Electronic Dance-house R&B Country Cover songs Hardcore Rock Jazz Folk Reggae Blues 0% 5% 10% 1 5% 20% 25% Log-likelihood gain over uniform shuffle

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Experiment 1: learned edge weights ALL Mixed Theme Rock-pop Alternating DJ Indie Single Artist Romantic RoadTrip Punk Depression Break Up Narrative Hip-hop Sleep Electronic music Dance-houseRhythm and Blues Country Cover Hardcore Rock Jazz Folk Reggae Blues Audio CF Era Familiarity Lyrics Tags Uniform

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Experiment 2: continuity?• Do we need to model playlist continuity? edge weights songs• Simplified model: - ignore transitions - choose each edge IID exp. prior playlists• Question: Are some categories more diverse than others?

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Experiment 2: continuity Unifo rm ALL Mixed Global model Theme Category-specific Rock-pop Alternating DJ Indie Single artist Romantic Road trip Punk Depression Break up Narrative Hip-hop Sleep Electronic Dance-house R&B Country Cover songs Hardcore Rock Jazz Folk Reggae Blues -15% -10% -5% 0% 5% 10% 15% 20% Log-likelihood gain over uniform shuffle

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Experiment 2: continuity Unifo rm ALL Mixed Global model Theme Category-specific • Most categories exhibit both Rock-pop Alternating DJ Indie continuity AND diversity Single artist Romantic • Transitions are important! Road trip Punk Depression Break up Narrative Hip-hop Sleep Electronic Dance-house R&B Country Cover songs Hardcore Rock Jazz Folk Reggae Blues -15% -10% -5% 0% 5% 10% 15% 20% Log-likelihood gain over uniform shuffle

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Example playlists Rhythm & Blues EDGE SONG 70s & soul Lyn Collins - Think Audio #14 & funk Isaac Hayes - No Name Bar DECADE 1965 & soul Michael Jackson - My Girl Electronic music EDGE SONG Audio #11 & downtempo Everything but the Girl - Blame DECADE 1990 & trip-hop Massive Attack - Spying Glass Audio #11 & electronica Björk - Hunter

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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

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Obrigado!