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

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

overview – motivation – dataset – experiments – social radio 2 Fields et. al - Analysis and Exploitation of Musician Social Networks

motivation 3 Fields et. al - Analysis and Exploitation of Musician Social Networks

motivation Novelty Curves 4 Fields et. al - Analysis and Exploitation of Musician Social Networks

motivation The Web 5 Fields et. al - Analysis and Exploitation of Musician Social Networks

So much music, so little time. 6 Fields et. al - Analysis and Exploitation of Musician Social Networks

So much music, so little of it good. 7 Fields et. al - Analysis and Exploitation of Musician Social Networks

How do we discover good music? 8 Fields et. al - Analysis and Exploitation of Musician Social Networks

listening 9 Fields et. al - Analysis and Exploitation of Musician Social Networks

social listening 10 Fields et. al - Analysis and Exploitation of Musician Social Networks

dataset 15 Fields et. al - Analysis and Exploitation of Musician Social Networks

dataset Sampling Myspace Randomly Selected Artist 16 Fields et. al - Analysis and Exploitation of Musician Social Networks

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

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

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

dataset Cumulative Degree Distribution 20 Fields et. al - Analysis and Exploitation of Musician Social Networks

dataset Cumulative Degree Distribution 21 Fields et. al - Analysis and Exploitation of Musician Social Networks

experiments 22 Fields et. al - Analysis and Exploitation of Musician Social Networks

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

experiments Geodesic v. Acoustic Distance 24 Fields et. al - Analysis and Exploitation of Musician Social Networks

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

experiments Max Flow v. Acoustic Distance 26 Fields et. al - Analysis and Exploitation of Musician Social Networks

experiments Max Flow v. EMD 27 Fields et. al - Analysis and Exploitation of Musician Social Networks

experiments Max Flow v. marsyas distance 28 Fields et. al - Analysis and Exploitation of Musician Social Networks

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

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

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

social radio 32 Fields et. al - Analysis and Exploitation of Musician Social Networks

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

social radio Playlist Generator 34 Fields et. al - Analysis and Exploitation of Musician Social Networks

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

social radio The Social Radio 36 Fields et. al - Analysis and Exploitation of Musician Social Networks

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

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

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

by Ben Fields on Mar 15, 2010

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