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Hypergraph Models of Playlist Dialects

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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 of Playlist Dialects

  1. 1. 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
  2. 2. Automatic playlist generation
  3. 3. Evaluating playlist algorithms [M. & Lanckriet, 2011] ... 2. Compute playlist 1. Observe playlists from users likelihoods ? > 3. Compare algorithms by likelihood scores
  4. 4. Evaluating playlist algorithms [M. & Lanckriet, 2011] Key idea: Playlist algorithm = Probability distribution over song sequences
  5. 5. Modeling playlist diversity Playlists
  6. 6. Modeling playlist diversity Road trip Mixed Genre Party mix Hip-hop
  7. 7. Data collection http://www.artofthemix.org/ Started in 1998, users upload and share playlists [Ellis, Whitman, Berenzweig, and Lawrence, ISMIR 2002]
  8. 8. 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
  9. 9. # 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
  10. 10. # 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
  11. 11. 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?
  12. 12. A simple playlist model 1. Start with a set of songs
  13. 13. A simple playlist model 2. Select a subset (e.g., jazz songs)
  14. 14. A simple playlist model 3. Select a song
  15. 15. A simple playlist model 4. Find subsets containing the current song
  16. 16. A simple playlist model 4. Select a new subset
  17. 17. A simple playlist model 5. Select a new song
  18. 18. A simple playlist model 6. Repeat...
  19. 19. A simple playlist model 6. Repeat...
  20. 20. Connecting the dots...• Random walk on a hypergraph - Vertices = songs - Edges = subsets
  21. 21. Connecting the dots...• Random walk on a hypergraph - Vertices = songs - Edges = subsets• Learning: optimize edge weights from example playlists
  22. 22. 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
  23. 23. The hypergraph random walk model exp. prior edge weights transitions playlists
  24. 24. Edge construction: example• Audio: cluster songs by timbre
  25. 25. Edge construction: example• Audio: cluster songs by timbre Audio-1 Audio-2 Audio-4 Audio-3• Multiple clusterings (k=16, 64, 256)
  26. 26. 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)
  27. 27. 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
  28. 28. 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?
  29. 29. 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
  30. 30. 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
  31. 31. 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
  32. 32. 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?
  33. 33. 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
  34. 34. 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
  35. 35. 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
  36. 36. 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
  37. 37. Obrigado!

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