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IIIA - CSICUncovering affinity of artists     to multiple genres from social behaviour data                Claudio Baccigalu...
1                                         THE ISSUE        In most organisational schemas, artists have a unique genre lab...
2                                           THE IDEA Each artist has a degree of affinity to each genre, depending on how pe...
3                                        THE TECHNIQUE  Co-occurrences analysis of 4,000 artists and their genres in a set...
THE TECHNIQUE 3         Combining and normalising artist-to-artist and artist-to-genre associations1. Aggregate the number...
4                                           THE RESULTS                        Artists can be described as genre-affinity ve...
THE RESULTS             4               Artists can be compared in terms of centrality to different genresGenre-centrality ...
Uncovering affinity of artists to multiple genres from social behaviour data
Uncovering affinity of artists to multiple genres from social behaviour data
Uncovering affinity of artists to multiple genres from social behaviour data
Uncovering affinity of artists to multiple genres from social behaviour data
Uncovering affinity of artists to multiple genres from social behaviour data
Uncovering affinity of artists to multiple genres from social behaviour data
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

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

  1. 1. IIIA - CSICUncovering affinity of artists to multiple genres from social behaviour data Claudio Baccigalupo – October 2008
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. 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
  6. 6. 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
  7. 7. 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
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