Uncovering affinity of artists to multiple genres from social behaviour data
14
IIIA - CSIC Claudio Baccigalupo – October 2008 Uncovering affinity of artists to multiple genres from social behaviour data
description
Slides for the seminar at the Artificial Intelligence Research Institute (IIIA), Barcelona, October 2008
Transcript of Uncovering affinity of artists to multiple genres from social behaviour data
IIIA - CSIC
Claudio Baccigalupo – October 2008
Uncovering affinity of artists to multiple genres
from social behaviour data
Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
Music stores Online
In most organisational schemas, artists have a unique genre label attached1 THE ISSUE
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’?
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, …)
Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
Each artist has a degree of affinity to each genre, depending on how people use that artist2 THE IDEA
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
value Mx(gi) indicates how much the artist x has affinity to the genre gi.
appears with songs like:
…mostly with Rock/Pop artists
appears with songs like:
…mostly with R&B artists
[Mx(g1), Mx(g2), . . . ]xMx(gi) gix
Madonna: Holiday (1983) Madonna: Secret (1994)
Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
Many social Web communities provide collections of user-compiled music playlists
Madonna: MusicStrands members share playlists from the plug-in or Web site
Co-occurrences analysis of 4,000 artists and their genres in a set of playlists from the Web3
To calculate the genre-affinity degree of an artist to a genre :
• 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
THE TECHNIQUE
x gMx(g)
x
Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
1. Aggregate the number of artist-to-artist co-occurrences in the playlists:
3. Cumulate the association Ax(y) from artist x to any artist of genre g:
2. Normalise the association Ax(y) with respect to artist popularity:
4. Normalise the association Px(g) with respect to genre popularity:
Combining and normalising artist-to-artist and artist-to-genre associations3
5. Weight the association Px(g) with the association Ax(y) and normalise to [0,1]:
THE TECHNIQUE
Mx(g) =12
!"y!A
#Ax(y)#Py(g)n
+ 1
$
Px(g)
Ax(y)
Ax(y)x g
!Px(g) !Ax(y) [0, 1]
Ax(y) = ! · [d0(x, y) + d0(y, x)]+ " · [d1(x, y) + d1(y, x)]+ # · [d2(x, y) + d2(y, x)]
Px(g) =!
y!X :!(y)=g
Ax(y)
!Ax(y) =Ax(y)!Ax
|max(Ax(y)!Ax)|
!Px(g) =
"y!X :!(y)=g Ax(y)"
y!X Ax(y)
Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
Artists can be described as genre-affinity vectors4 THE RESULTS
The genre-affinity degree Mx(g) is high when artists that often co-occur with x belong to genre g and artists that rarely co-occur with x do not belong to genre g.
Mx(g) =12
!"y!A
#Ax(y)#Py(g)n
+ 1
$
xxg
Mx(g)g
Mx(g) ! [0, 1]
Rock/Pop
R&B
Country
Jazz
Rap0.5000.500
Rap
Jazz
Country
R&B
Rock/Pop
CountryR&B
Rap
From a ‘Boolean’ approach: Madonna is Rock/Pop
To a ‘Fuzzy’ approach: membership degrees Mx(g) € [0,1]
Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
Genre-centrality comparison of two artists, both originally labelled as ‘Rock/Pop’
Genre-centrality comparison of two artists, one labelled ‘Rock/Pop’, the other ‘R&B’
Artists can be compared in terms of centrality to different genres4 THE RESULTS
The genre-centrality of an artist x to a genre g is the percentage of artists whose genre affinity to g is ≤ Mx(g)
! [0, 1]R&B
Rock/Pop
Rap
CountryCountry Rap
Rock/Pop
R&B
25%
50%
75%
100%
R&B
Rock/Pop
Rap
Country
Country
Rap
Rock/Pop
R&B
25%
50%
75%
100%
x gg ! Mx(g)
Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
Hidden relationships are uncovered in the domain of artists4 THE RESULTS
Artists with the highest genre affinity to a genre g are called core
artists, and are good representatives of g
Metric distances can be used to compare artists or visualise them
using a multi-dimensionality reduction method
Tanya Tucker
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Nelly
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Anthrax
Whitney Houston
The Stone Roses
Shakira
Shirley Bassey
WU-TANG CLAN
CHICK COREA
SHAKIRA
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ANTHRAX
PATTY SMITH
BRUCE SPRINGSTEEN
WHITNEY HOUSTON
JOHNNY CASH
THE STREETS
AALIYAH
NELLY
THE STONE ROSESTHE STONE ROSES
Country
R&B
Diamond Rio Confederate Railroad
Loose Ends Gerald Levert Zhane
Too Short Westside Connection Masta Ace Inc.
Rap
g
g
First core artists of 3 different genres 2D reduction of the Euclidean distance among artists (as 28-dimensional vectors)
Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
Genre-affinity of artists to two independent genres: ‘Rap’ and ‘Country’
Genre-affinity of artists to two correlated genres: ‘Rap’ and ‘R&B’
Hidden relationships are uncovered in the domain of genres4 THE RESULTS
Two genres g and h are correlated when artists with a high genre affinity degree Mx(g) also have a high value for Mx(h)
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0.4995 0.5000 0.5005 0.50100.4990
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R&B
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g hMx(g) Mx(h)
Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
Hidden relationships are uncovered in the domain of genres4
Rg_h Country Blues Jazz Reggae R&B Rap
Country
Blues
Jazz
Reggae
R&B
Rap
1 0.2 0.1 0 0.1 -0.1
0.2 1 0.4 0.1 0 -0.2
0.1 0.4 1 0.1 0.2 -0.1
0 0.1 0.1 1 0.4 0.4
0.1 0 0.2 0.4 1 0.6
-0.1 -0.2 -0.1 0.4 0.6 1
THE RESULTS
Two genres g and h are correlated when artists with a high genre affinity degree Mx(g) also have a high value for Mx(h)
Pearson coefficient r(g,h) for 6 genre-centrality vectors
Mx(g) Mx(h)
!g,h
ρg,h
g h
Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
Tag-centrality comparison of two artists Pearson coefficient r(g,h) for 7 tags
Co-occurrences analysis of 4,000 artists and their tags in MusicStrands playlists5 OTHER DOMAINS
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)
! [0, 1]
(pop, rock, electronic, rock & pop, spoken
word, interview)
rock
heavy metal hardcore punk
electronicdance
r&b
r&b
dance
electronichardcore punk
heavy metal
rock
25%
50%
75%
100%
(rock, heavy metal, rock & pop, speed thrash metal, gold disc, spoken word, interview)
Rg_h Indie Rock Awesome Brazilian Salsa Trumpet Jazz
Indie
Rock
Awesome
Brazilian
Salsa
Trumpet
Jazz
1 0.7 0.8 -0.3 -0.3 -0.1 -0.2
0.7 1 0.9 -0.7 -0.4 -0.4 -0.5
0.8 0.9 1 -0.5 -0.3 -0.3 -0.4
-0.3 -0.7 -0.5 1 0.3 0.2 0.2
-0.3 -0.4 -0.3 0.3 1 0.2 0.2
-0.1 -0.4 -0.3 0.2 0.2 1 1
-0.2 -0.5 -0.4 0.2 0.2 1 1
g hMx(g) Mx(h)
!g,h
ρg,h
Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
A movie represented as a genre- centrality vector (8 genres shown)
Genre-affinity of movies to two exclusive genres: ‘Romance’ and ‘Sci-Fi’
Co-rating analysis of 8,865 movies and their IMDB Genres in Netflix competition dataset5 OTHER DOMAINS
Two movies are socially related when some Netflix customer watched both movies and assigned them the maximum user rating
! [0, 1]
Drama
Romance
Fantasy
Action
Thriller
Sci−Fi
Comedy
Crime
25%
50%
75%
100%
(Romance, Action, Thriller, Adventure, Sci-Fi)
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0.4998 0.4999 0.5000 0.5001 0.50020.49995
0.50000
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0.50010
0.50015
Romance
Sci−Fi
Claudio Baccigalupo – Uncovering affinity of artists to multiple genres from social behaviour data – October 2008
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:
A new genre ontology, a social-based analysis method, a public real-world dataset6 CONTRIBUTIONS
http://labs.strands.com/music/affinity
IIIA - CSIC
Claudio Baccigalupo – October 2008
Questions?
http://www.iiia.csic.es/~claudio
