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

1. IIIA - CSIC
Uncovering affinity of artists
to multiple genres
from social behaviour data
Claudio Baccigalupo – October 2008

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. 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 artists
When 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. 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 share
collections 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. THE TECHNIQUE 3
Combining and normalising artist-to-artist and artist-to-genre associations
1. 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. 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. THE RESULTS 4
Artists can be compared in terms of centrality to different genres
Genre-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