Uncovering affinity of artists to multiple genres from social behaviour data
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Uncovering affinity of artists to multiple genres from social behaviour data

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Slides for the seminar at the Artificial Intelligence Research Institute (IIIA), Barcelona, October 2008

Slides for the seminar at the Artificial Intelligence Research Institute (IIIA), Barcelona, October 2008

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Uncovering affinity of artists to multiple genres from social behaviour data Uncovering affinity of artists to multiple genres from social behaviour data Presentation Transcript

  • IIIA - CSICUncovering affinity of artists to multiple genres from social behaviour data Claudio Baccigalupo – October 2008
  • 1 THE ISSUE In most organisational schemas, artists have a unique genre label attached Music stores Online Browse by Genre: Alternative Rock (Britpop, Hardcore & Punk, Indie…) Blues (Regional, Blues Rock, Modern, Traditional, …) Christian & Gospel (CCM, Praise, Christian Rock…) Country (Classic, Alt-Country, Roadhouse, Bluegrass…) Dance & DJ (Techno-House, Dance-Pop, Trance, …) Folk (Contemporary, Traditional, British, Folk-Rock, …) Such a Boolean approach cannot address questions such as:• Which Country artist is the ‘most Country’?• Which artist has the ‘most genre affinity’ with Madonna?• Which genres are ‘socially related’? Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
  • 2 THE IDEA Each artist has a degree of affinity to each genre, depending on how people use that artist Madonna: Holiday (1983) Madonna: Secret (1994) appears with appears with songs like: songs like: …mostly with Rock/Pop artists …mostly with R&B artistsWhen two artists/genres occur together and closely (in magazines, radios, Web sites, playlists, …), they share some cultural affinity. Our goal is to model relationships from artists to genres as Fuzzy Sets, describing each artist x as a vector [Mx (g1 ), Mx (g2 ), . .…, where each [Mx(g1), Mx(g2), . ]value Mx (gi ) indicates how much the artist x has affinity to the genre gi. Mx(gi) gi Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
  • 3 THE TECHNIQUE Co-occurrences analysis of 4,000 artists and their genres in a set of playlists from the Web Many social Web communities provide Madonna: MusicStrands members sharecollections of user-compiled music playlists playlists from the plug-in or Web site To calculate the genre-affinity degree Mx (g) of an artist x to a genre g :• Retrieve 1,030,068 playlists compiled by members of MusicStrands• Measure the normalised association from x to any other artist, based on how many times they occur in playlists and how closely• Aggregate and normalise these associations by genre• Combine artist-to-artist and artist-to-genre associations Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
  • THE TECHNIQUE 3 Combining and normalising artist-to-artist and artist-to-genre associations1. Aggregate the number of artist-to- 2. Normalise the association Ax (y) with Ax(y) artist co-occurrences in the playlists: respect to artist popularity: Ax (y) = α · [d0 (x, y) + d0 (y, x)] Ax (y) − Ax + β · [d1 (x, y) + d1 (y, x)] Ax (y) = |max(Ax (y) − Ax )| + δ · [d2 (x, y) + d2 (y, x)]3. Cumulate the association Ax (y) from Ax(y) 4. Normalise the association Px (g) with Px(g) g artist x to any artist of genre g: respect to genre popularity: Px (g) = Ax (y) y∈X :γ(y)=g Ax (y) Px (g) = y∈X :γ(y)=g y∈X Ax (y) 5. Weight the association Px(g) with the association Ax (y) and normalise to [0, 1] Px (g) Ax(y) [0,1]: 1 y∈A Ax (y)Py (g) Mx (g) = +1 2 n Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
  • 4 THE RESULTS Artists can be described as genre-affinity vectors From a ‘Boolean’ approach: To a ‘Fuzzy’ approach: Madonna is Rock/Pop membership degrees Mx (g) ∈ [0,1] Mx(g) € [0, 1] Rock/Pop R&B Rock/Pop R&B R&B Country Country Country Rap Jazz Jazz Rap Rap 0.500 1 y∈A Ax (y)Py (g) Mx (g) = +1 2 n The genre-affinity degree Mx (g) is high when artists that often co-occur with x Mx(g)belong to genre g and artists that rarely co-occur with x do not belong to genre g. g Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
  • THE RESULTS 4 Artists can be compared in terms of centrality to different genresGenre-centrality comparison of two artists, Genre-centrality comparison of two artists, both originally labelled as ‘Rock/Pop’ one labelled ‘Rock/Pop’, the other ‘R&B’ R&B R&B 100% R&B 100% ∈ [0, 1] 75% 75% R&B 50% 50% 25% 25% Rock/Pop Rock/Pop Rock/Pop Rock/Pop Country Country Country Rap Country Rap Rap Rap The genre-centrality of an artist x to a genre g is the percentage of Mx (g) artists whose genre affinity to g is ≤ Mx(g) Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
  • 4 THE RESULTS Hidden relationships are uncovered in the domain of artists First core artists of 3 different genres 2D reduction of the Euclidean distance among artists (as 28-dimensional vectors) Country ● AALIYAH ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● NELLY ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ●● ● ● ● ●● ●● ● ●● ●● ● ● ● ● ●● ●● ●● ●● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● WU-TANG CLAN ● SHAKIRA ● ● ● ●● ● ●● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ●● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ●● ● ●● ● ● ●●● ● ● ● ●● ●● ● ● ●● ● ● ●● ●● ●● ●● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ●● ● ●● ●●● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●● ●●● ●● ● ● ● ●● ● ● ●●●● ●● ● ● ● ● ● ●● ● ● ●● ● ● Tanya Tucker Diamond Rio Confederate Railroad ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ●● ●● ● 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● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ●● ●● ● ●●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●●●●● ●●●● ● ● ●● ● Madonna ● ● ● ● ●● ● ● ●● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●●●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●● ● ●●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●●●● ● ●●●●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● Whitney Houston ● ● ●● ● ● ●● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ●●●● ● ● ● ● ●● ● ● ● ● ●● ● ● ●●● ●● ● ●●● ● ● ● ● ● ● ●●●● ● ● ● ● ● ●●●● ● ● ● ●●● ● ● ● ●● ● ● ●● ●● ● ●● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●●●● ● ● ● ●● ● ● ●● ● ●● ● ●● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ●● ●● ● ● ● ● ● ●● ● ● ●● ● ● ●●●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● 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●● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ●●●●●●● ● ● ●●●●● ●●●●●● ●●●● ● Metallica ● ●● ● ●● ●● ● ● Anthrax ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ●●●● ● ● ● ●● ●● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ● ● ● ●●● ●●●●●● ● ●●● ●●● ●● ● ●●●● ●● ●● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ●●● ● ●● ●● ●● ●● ●●● ●●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●● ●●●● ●●● ● ● ●●● ●● ●● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ●● ●● ● ● ●●●● ● ●● ●● ● ● ●●●●●● ● ● ● ● ●● ● ● ●● ●●● ● ●● ● ● ● ●● ● ●●●● ●● ● ● ● ● ● ●● ●●● ●● ●● ● ● ● ● ● ●●●●● ● ● ●● ● ● ●●● ●●● ●●●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● The Jacksons ● ● ● ●● ● ● ● ● ●● ●●● ● ●●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ●● ●●●●●● ●●● ●● ● 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●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● THE STREETS ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ●● ●● WHITNEY HOUSTON ●● ● ● ● ●● ● ● ● ● ●● ● ● ● R&B ●●● ●● ● ● ● ● ●●●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ●●● ● ● ● ● ● ● ● ●●● ●● ● ●● ●● ● ● ●● ●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ●● ● ● ●●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ●● ● ● ●● ● ● ●● ●● ● ●● ●● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ●● ● ● ● ●● ●● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ●●● ● ●● ● ●● ●● ●● ● ● ●● ●●● ●●●●● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ●● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ●●● ●● ● ● ● ● ● ●● ●●● ● 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● ●● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ●● ●● ●●● ● ● BRUCE SPRINGSTEEN ● ● ● ● ● ●● ●●●● ● ● ● ● ● ●● ●●●● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ●●●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● THE STONE ROSES ● ●●● ●● ● ●● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ●●● ●● ●● ●● ●● ●●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ●●● ● ● ●●● ● ● ● ● ● ● ● ●●● ●● ● ●● ● ● ● ● ●● ●● ● ●● ● ● ● Loose Ends Gerald Levert Zhane ● ● ●●● ● ●● ● ● ● ● ● ● ● ●● ● ●●●● ● ● ● ● ● ●● ● ● ● ● ●●●●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ●● ● ●●● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ●● ● ●● ● ● ●● ●● ● ● ● ● ● ● ●●● ● ● ●●● ● ●● ● ● ●●● ● ● ● ●●● ● ● ●●●● ● ● ●● ● ●●●● ● ● ●●● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● JOHNNY CASH ROSES ● ● ● ● ● ●● ●● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●●● ● ● ●●● ● ● ● ● ●●●●● ● ● ● THE STONE ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ●● ● ●● ● ● CHICK COREA ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ●● ●● ●●● ●●● ● ●● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ●● ●● ● ● ●●●● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ●● ● ●●● ●●● ●● ● ●● ●● ●● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ●● ●● ● ●● ● ● ●● ● ● ● ● ●●● ● ● ●● Rap ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ●● ●● ● ●● ● ● ●● ● ● ● ● ●● ● ● ●●● ● ● ● ●● ● ● ● ●●● ● ● ●● ● ●● ● ● ● ● ● ● ●●● ●●● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ●● ● ●●●● ● ●●● ●●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ●●● ● ● ● ●● ● ● ● ● ● ●●●● ●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ●●● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● SHIRLEY BASSEY ● ●● ●● ● ●● ● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● PATTY SMITH ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●●● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● Too Short Westside Connection Masta Ace Inc. Artists with the highest genre Metric distances can be used toaffinity to a genre g are called core compare artists or visualise them artists, and are good using a multi-dimensionality representatives of g g reduction method Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
  • THE RESULTS 4 Hidden relationships are uncovered in the domain of genres Genre-affinity of artists to two independent Genre-affinity of artists to two correlated genres: ‘Rap’ and ‘Country’ genres: ‘Rap’ and ‘R&B’ Country R&B 0.5015 0.5020 0.5015 0.5010 0.5010 ● ● ● ● ● ● ● ● ● 0.5005 ● ●● ● ● ● ●Country ● ● 0.5005 ● ● ● ●●● ● R&B ● ● ●● ● ● ●● ●● ● ● ● ● ●● ● ● ●● ● ● ● ●● ●● ● ● ● ●●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ●● ● ● ●● ●● ● ● ●●● ●● ● ● ●● ●●● ●● ●● ● ● ● ●● ● ●● ●●● ● ● ● ● ● ●●● ●●● ●●● ● ● ● ● ●● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ●● ●● ● ● ●● ● ● ● ● ● ● ●● 0.5000 ● ● ● ● ●● ● ● ●● ● ● ● ●●● ● ● ●● ● ●● ● ● ● ●● ●● ● ● ● ● ● 0.5000 0.4995 ● ● ● ●● ●● ● ●● ● ●●●● ●● ● ●● ● ● ●● ●●●● ●● ●●●●● ● ●●●● ●●● ●●●● ● ●● ●● ●● ● ● ●● ● ● ● ● ●●●●●● ● ● ● ● ● ●●●●●●●●●●● ●●●● ● ●● ● ●● ● ●●●● ● ●● ●● ● ●● ●●●●● ●● ●● ● ● ● ●● ●● ● ●●● ●● ● ●● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ●● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● Rap 0.4990 Rap 0.4995 0.5000 0.5005 0.5010 0.4995 0.5000 0.5005 0.5010 Rap RapTwo genres g and h are correlated when artists with a high genre affinity degree Mx (g) also have a high value for Mx (h) Mx(g) Mx(h) Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
  • 4 THE RESULTS Hidden relationships are uncovered in the domain of genres ρg,h Pearson coefficient r(g,h) for 6 genre-centrality vectors ρg,h Rg_h Country Blues Jazz Reggae R&B Rap Country 1 0.2 0.1 0 0.1 -0.1 Blues 0.2 1 0.4 0.1 0 -0.2 Jazz 0.1 0.4 1 0.1 0.2 -0.1 Reggae 0 0.1 0.1 1 0.4 0.4 R&B 0.1 0 0.2 0.4 1 0.6 Rap -0.1 -0.2 -0.1 0.4 0.6 1Two genres g and h are correlated when artists with a high genre affinity degree Mx (g) also have a high value for Mx (h) Mx(g) Mx(h) Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
  • OTHER DOMAINS 5 Co-occurrences analysis of 4,000 artists and their tags in MusicStrands playlistsTag-centrality comparison of two artists ρg,h Pearson coefficient r(g,h) for 7 tags 100% ρg,h Rg_h Indie Rock Awesome Brazilian Salsa Trumpet Jazz ∈ [0, 1] 75% ck Indie 1 0.7 0.8 -0.3 -0.3 -0.1 -0.2 50% ro ck ro 25% Rock 0.7 1 0.9 -0.7 -0.4 -0.4 -0.5 hardheavy me core pun tal heavy Awesome 0.8 0.9 1 -0.5 -0.3 -0.3 -0.4 e metal da lectr k nc on hard e ic core ele pun ctr k on ic Brazilian -0.3 -0.7 -0.5 1 0.3 0.2 0.2 r&b da nc e Salsa -0.3 -0.4 -0.3 0.3 1 0.2 0.2 r&b Trumpet -0.1 -0.4 -0.3 0.2 0.2 1 1 (pop, rock, electronic, (rock, heavy metal, rock & rock & pop, spoken pop, speed thrash metal, gold Jazz -0.2 -0.5 -0.4 0.2 0.2 1 1 word, interview) disc, spoken word, interview)Two tags g and h are correlated when artists with a high genre affinity degree Mx (g) also have a high value for Mx (h) Mx(g) Mx(h) Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
  • OTHER DOMAINS 5 Co-rating analysis of 8,865 movies and their IMDB Genres in Netflix competition datasetA movie represented as a genre- centrality Genre-affinity of movies to two exclusive vector (8 genres shown) genres: ‘Romance’ and ‘Sci-Fi’ 100% ∈ [0, 1] Crime 75% 0.50015 50% a m a Dr edy 0.50010 25% Co m * * * ● ● * * * 0.50005 ● Sci−Fi ● ● Ro ma ● ● ● ● ● * nc ● ● ● * ****** ●● ** e Sci− ● ● ● ● Fi ● ● ●● ● ● ● ● ● * ● ● *** * ● ● ● ●●● ● ● ● ● ● ● ●● ● ●● ● ●● ● ** * *** 0.50000 ●● ● ● ● ● ●●●●● ●●●●●●●●● ● ● ● ●●● ●● ● ● ●● ● ● ● ●●●● ●●● ● ●● ● ●●●● ●●●● ●●●●●●● * ** ** ●●●●●●●●●● ● ● ● ●● ● ● ● ●●●●●●●●●●●●●●● ● ● ● ● ●●● ● ● ● * ● ● ● ● ** ● ● ● ●●●● ● ●●● ●●● ●●● ●● ● ●●●● ● ● ● ● ● ● ● ●● ●● ●●● ● ●●● ●●●● ●●●●●●● ● ● ●● ●●●● ●●●●●●● ● ● ●● ●● ● ● ●● ●● ● ●● ● ●● ● ● ● ●● ●●● ●●●●●●●●●●● ● ● ● ● ●●● ●●●●●●●●●● ● ● ●● ● ● ●●●●●●●●● ●● ● ●●●●●●●● ●● ● ● ●●● ●●● ** ●● ●●● ●●●●●●●●●●●● ●● ● ●● ●● ● ●●● ● ● ●●●●●●●● ● Th ●●● ● ●● ●●●●●●●●● ● ●● ● ●● ●●●●●●●●●●●● ● ● ●●● ●●●●●●●●●●●●●● ●● ●● ●● ●●● ● ●●● ● ● ● ●●● ●● ●●● ●●●● ● ● ●● ● ● ●● ●●●●●●●●●●● ● ● ●● ● ● ● ● * * * ● ●●●●●●●●●●●●●●●●●●● ● ● ● ●●● ● ●●●●● ● ● ●●●● ● ● ●●● ●● ● ● ● ● ●●●●●●●●●● ●●●● ●●● ●● ● ●● ● ● ● ● ●●● ●●● ●●●●●● ●● ● ●● ●●● ●●● ●●●● ● ●●● ● ● ●● ● ●●●●●●●●● ● ● rill ● ●●● ●●● ● ●●●●● ●● ● ● ● ●●●● ●● ● ●●●●●●● ●●●●● ● ● ●● ● ●● ●● ● ●●●●●●●● ● ● ● ● ●●●● ● ●●●● ● ● ● ●● Fa ●●●●● ● ●●●●●●●●●●●● ●● ● ●● ●● ● ● ● ●●● ● ● ● ●● ● ●● ● ● ● ●●● ●●●● ●●●●●●● ● ●● ● ● ●● ● ●●● ● ● er ● ● ● ●● ●●●●●●●●●●● ●●● ● ● ● ●●●●● ●● ● ● ● ●● ●● ● ● ● ●●●●● ● ●●● ● ● ● ●● ●●●●●●● ● ●● ● ● ● ● ●●●●●● ●●● ● ● ● ● ●●● ●●● ● ●● ● ● ● 0.49995 n ●● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ●● ●●●●● ● ● ● ● tas ● ● ● ●●●● ● ●●●● ●● ● ● ●● Action ● ● ●● ●● ● ●●●●● ●●●● ● ●● ● ●● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●●● ● ● y ● ●●● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● 0.4998 0.4999 0.5000 0.5001 0.5002 (Romance, Action, Thriller, Adventure, Sci-Fi) Romance Two movies are socially related when some Netflix customer watched both movies and assigned them the maximum user rating Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
  • 6 CONTRIBUTIONS A new genre ontology, a social-based analysis method, a public real-world dataset Baccigalupo C., Donaldson J., Plaza E.“Uncovering Affinity of Artists to Multiple Genres From Social Behaviour Data” International Symposium on Music Information Retrieval (ISMIR), 2008.• To uncover richer relationships between artists and genres, exploiting real user behaviour data from social Web sites• To provide an ontology to describe these relationships (genre affinity degree, genre centrality, core/bridge artists, correlated genres)• To propose a content-agnostic technique, applicable to any domain where objects have categories and co-occurrences• To make public both the analysed dataset (artists, genres, and tags from MusicStrands) and the code to reproduce the analysis at: http://labs.strands.com/music/affinity Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
  • IIIA - CSIC Questions?http://www.iiia.csic.es/~claudio Claudio Baccigalupo – October 2008