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IEEE-THEMES: Analysis and Exploitation of Musician Social Networks for Recommendation and Discovery

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IEEE-THEMES: Analysis and Exploitation of Musician Social Networks for Recommendation and Discovery

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Slides from IEEE-THEMES 2010 colocated with ICASSP. Paper to appear in August issue of Select Topics in Signal Processing. Abstract: This paper presents an extensive analysis of a sample of a social network of musicians. The network sample is first analyzed using standard complex network techniques to verify that it has similar properties to other web-derived complex networks. Content-based pairwise dissimilarity values between the musical data associated with the network sample are computed, and the relationship between those content-based distances and distances from network theory explored. Following this exploration, hybrid graphs and distance measures are constructed, and used to examine the community structure of the artist network. Finally, results of these investigations are presented and considered in the light of recommendation and discovery applications with these hybrid measures as their basis.

Slides from IEEE-THEMES 2010 colocated with ICASSP. Paper to appear in August issue of Select Topics in Signal Processing. Abstract: This paper presents an extensive analysis of a sample of a social network of musicians. The network sample is first analyzed using standard complex network techniques to verify that it has similar properties to other web-derived complex networks. Content-based pairwise dissimilarity values between the musical data associated with the network sample are computed, and the relationship between those content-based distances and distances from network theory explored. Following this exploration, hybrid graphs and distance measures are constructed, and used to examine the community structure of the artist network. Finally, results of these investigations are presented and considered in the light of recommendation and discovery applications with these hybrid measures as their basis.

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IEEE-THEMES: Analysis and Exploitation of Musician Social Networks for Recommendation and Discovery

  1. 1. Analysis and Exploitation of Musician Social Networks for Recommendation and Discovery Ben Fields Kurt b.fields@gold.ac.uk Jacobson Christophe Mark Michael Rhodes Sandler Casey
  2. 2. overview – motivation – dataset – experiments – social radio 2 Fields et. al - Analysis and Exploitation of Musician Social Networks
  3. 3. motivation 3 Fields et. al - Analysis and Exploitation of Musician Social Networks
  4. 4. motivation Novelty Curves 4 Fields et. al - Analysis and Exploitation of Musician Social Networks
  5. 5. motivation The Web 5 Fields et. al - Analysis and Exploitation of Musician Social Networks
  6. 6. So much music, so little time. 6 Fields et. al - Analysis and Exploitation of Musician Social Networks
  7. 7. So much music, so little of it good. 7 Fields et. al - Analysis and Exploitation of Musician Social Networks
  8. 8. How do we discover good music? 8 Fields et. al - Analysis and Exploitation of Musician Social Networks
  9. 9. listening 9 Fields et. al - Analysis and Exploitation of Musician Social Networks
  10. 10. social listening 10 Fields et. al - Analysis and Exploitation of Musician Social Networks
  11. 11. social listening 11 Fields et. al - Analysis and Exploitation of Musician Social Networks
  12. 12. social listening 12 Fields et. al - Analysis and Exploitation of Musician Social Networks
  13. 13. social listening 13 Fields et. al - Analysis and Exploitation of Musician Social Networks
  14. 14. social listening 14 Fields et. al - Analysis and Exploitation of Musician Social Networks
  15. 15. dataset 15 Fields et. al - Analysis and Exploitation of Musician Social Networks
  16. 16. dataset Sampling Myspace Randomly Selected Artist 16 Fields et. al - Analysis and Exploitation of Musician Social Networks
  17. 17. dataset Sampling Myspace Selected Artist's top friend Selected Selected Artist's top Artist's top friend friend Randomly Selected Artist Selected Selected Artist's top Artist's top friend friend 17 Fields et. al - Analysis and Exploitation of Musician Social Networks
  18. 18. dataset Sampling Myspace Artist's Artist's Artist's top friend top friend top friend Artist's Artist's top friend top friend Artist's Selected top friend Artist's Artist's top friend top friend Selected Selected Artist's Artist's Artist's top friend Artist's top friend top friend top friend Randomly Artist's Selected top friend Artist Artist's top friend Selected Selected Artist's Artist's Artist's top friend top friend top friend Artist's top friend Artist's Artist's top friend Artist's top friend Artist's top friend top friend Artist's top friend 18 Fields et. al - Analysis and Exploitation of Musician Social Networks
  19. 19. dataset Sampling Myspace – scale-free (mostly) – 15,478 nodes (artist pages) – 120,487 directed edges – 91,326 undirected edges – avg. degree – 15.5 as a directed graph – 11.8 when undirected 19 Fields et. al - Analysis and Exploitation of Musician Social Networks
  20. 20. dataset Cumulative Degree Distribution 20 Fields et. al - Analysis and Exploitation of Musician Social Networks
  21. 21. dataset Cumulative Degree Distribution 21 Fields et. al - Analysis and Exploitation of Musician Social Networks
  22. 22. experiments 22 Fields et. al - Analysis and Exploitation of Musician Social Networks
  23. 23. experiments Geodesic v. Acoustic Distance –pair nodes by geodesic distance –looking for correlation with pairwise EMD –result is inconclusive 23 Fields et. al - Analysis and Exploitation of Musician Social Networks
  24. 24. experiments Geodesic v. Acoustic Distance 24 Fields et. al - Analysis and Exploitation of Musician Social Networks
  25. 25. experiments Max Flow v. Acoustic Distance – pairs of artist nodes grouped based on Maximum Flow – a randomized network was created as well to compare the relationship – results point toward a mostly orthogonal relationship – examining the mutual information shows that most information not common across spaces 25 Fields et. al - Analysis and Exploitation of Musician Social Networks
  26. 26. experiments Max Flow v. Acoustic Distance 26 Fields et. al - Analysis and Exploitation of Musician Social Networks
  27. 27. experiments Max Flow v. EMD 27 Fields et. al - Analysis and Exploitation of Musician Social Networks
  28. 28. experiments Max Flow v. marsyas distance 28 Fields et. al - Analysis and Exploitation of Musician Social Networks
  29. 29. experiments Low Entropy Communities –looking at whether communities are more homogenous if edges are weighted with sonic similarity –uses genre entropy 29 Fields et. al - Analysis and Exploitation of Musician Social Networks
  30. 30. experiments Low Entropy Communities algorithm c SC Srand Q none 1 1.16 - - gm 42 0.81 1.13 0.61 gm+a 33 0.90 1.13 0.64 wt 195 0.80 1.08 0.61 wt+a 271 0.70 1.06 0.62 Table 1. Results of the community detection algorithms where c is the number of communities detected, SC is the average genre entropy for all communities, Srand is the average genre entropy for a random partition of the network 30 Fields et. al - Analysis and Exploitation of Musician Social of communities, and Q is the modu- into an equal number Networks
  31. 31. experiments Low Entropy Communities algorithm c SC Srand Q none 1 1.16 - - gm 42 0.81 1.13 0.61 gm+a 33 0.90 1.13 0.64 wt 195 0.80 1.08 0.61 wt+a 271 0.70 1.06 0.62 Table 1. Results of the community detection algorithms where c is the number of communities detected, SC is the average genre entropy for all communities, Srand is the average genre entropy for a random partition of the network 31 Fields et. al - Analysis and Exploitation of Musician Social of communities, and Q is the modu- into an equal number Networks
  32. 32. social radio 32 Fields et. al - Analysis and Exploitation of Musician Social Networks
  33. 33. social radio Weighted Max Flow Playlists –max flow presents an interesting opportunity to create playlists using least resistant paths –preliminary testing shows promise –needs more exhaustive testing 33 Fields et. al - Analysis and Exploitation of Musician Social Networks
  34. 34. social radio Playlist Generator 34 Fields et. al - Analysis and Exploitation of Musician Social Networks
  35. 35. social radio Playlist Generator 34 Fields et. al - Analysis and Exploitation of Musician Social Networks
  36. 36. social radio The Social Radio – produce playlists via weighted distance paths – next destination song is determined via a vote across all listeners – candidate songs selected from disparate communities 35 Fields et. al - Analysis and Exploitation of Musician Social Networks
  37. 37. social radio The Social Radio 36 Fields et. al - Analysis and Exploitation of Musician Social Networks
  38. 38. resources – http://mypyspace.sourceforge.net/ – http://dbtune.org/myspace/ – http://omras2.doc.gold.ac.uk/software/fftExtract/ – slides: http://slideshare.com/BenFields – contact: b.fields@gold.ac.uk http://blog.benfields.net twitter: @alsothings 37 Fields et. al - Analysis and Exploitation of Musician Social Networks
  39. 39. resources – http://mypyspace.sourceforge.net/ – http://dbtune.org/myspace/ – http://omras2.doc.gold.ac.uk/software/fftExtract/ – slides: http://slideshare.com/BenFields – contact: b.fields@gold.ac.uk http://blog.benfields.net twitter: @alsothings Questions? 37 Fields et. al - Analysis and Exploitation of Musician Social Networks

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