Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail. Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations. In this Thesis we stress the importance of the user's perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution. The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user's relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.

1. Music Recommendation and Discovery in
the Long Tail
Òscar Celma
Doctoral Thesis Defense
(Music Technology Group ~ Universitat Pompeu Fabra)

2. PhD defense // UPF // Feb 16th 2009
Music
Recommendation
(personalized)
and Discovery
(explore large music collections)
in the Long Tail
(non-obvious, novel, relevant music)

3. PhD defense // UPF // Feb 16th 2009
“The Paradox of Choice: Why More Is Less”, Barry Schwartz (2004)
The problem
Paradox of choice

4. PhD defense // UPF // Feb 16th 2009
music overload
• Today(August, 2007)
iTunes: 6M tracks

P2P: 15B tracks

53% buy music on line

• Finding unknown, relevant music is hard!
Awareness vs. access to content


5. PhD defense // UPF // Feb 16th 2009
music overload?
Digital Tracks – Sales data for 2007
●
●
Nearly 1 billion sold in 2007
●
●
1% of tracks account for 80% of sales
●
●
3.6 million tracks sold less than 100 copies, and
●
1 million tracks sold exactly 1 copy
●
•
•
•Data from Nielsen Soundscan 'State of the (US) industry' 2007 report

6. PhD defense // UPF // Feb 16th 2009
the Long Tail of popularity
• Help me find it! [Anderson, 2006]

7. PhD defense // UPF // Feb 16th 2009
research questions
• 1) How can we evaluate/compare different music
recommendation approaches?
• 2) How far into the Long Tail do music
recommenders reach?
• 3) How do users perceive novel (unknown to
them), non-obvious recommendations?

8. PhD defense // UPF // Feb 16th 2009
If you like
The Beatles
you might like ...

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• popularity bias
• low novelty
ratio

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FACTORS AFFECTING RECOMMENDATIONS:
Novelty
Relevance
Diversity
Cold start
Coverage
Explainability
Temporal effects

14. PhD defense // UPF // Feb 16th 2009
FACTORS AFFECTING RECOMMENDATIONS:
Novelty
Relevance
Diversity
Cold start
Coverage
Explainability
Temporal effects

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novelty vs. relevance

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how can we measure novelty?
• predictive accuracy vs. perceived quality
• metrics
MAE, RMSE, P/R/F-measure, ...

Test
Train
Can't measure novelty


17. PhD defense // UPF // Feb 16th 2009
how can we measure novelty?
• predictive accuracy vs. perceived quality
• metrics
MAE, RMSE, P/R/F-measure, ...

Can measure novelty


18. PhD defense // UPF // Feb 16th 2009
how can we measure relevance?
quot;The key utility measure is user happiness. It
seems reasonable to assume that relevance of
the results is the most important factor:
blindingly fast, useless answers do not make a
user happy.quot;
 quot;Introduction to Information Retrievalquot;
(Manning, Raghavan, and Schutze, 2008)

19. PhD defense // UPF // Feb 16th 2009

20. PhD defense // UPF // Feb 16th 2009
research in music recommendation
• Google Scholar
Papers that contain “music recommendation” or “music recommender”
in the title (Accessed October 1st, 2008)

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research in music recommendation
• ISMIR community

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music recommendation approaches
• Expert-based
• Collaborative filtering
• Context-based
• Content-based
• Hybrid (combination)

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music recommendation approaches
• Expert-based
AllMusicGuide

Pandora

• Collaborative filtering
• Context-based
• Content-based
• Hybrid (combination)

24. PhD defense // UPF // Feb 16th 2009
music recommendation approaches
• Expert-based
• Collaborative filtering
User-Item matrix
 [Resnick, 1994], [Shardanand, 1995], [Sarwar, 2001]
• Context-based
• Content-based

25. PhD defense // UPF // Feb 16th 2009
music recommendation approaches
• Expert-based
• Collaborative filtering
User-Item matrix
 [Resnick, 1994], [Shardanand, 1995], [Sarwar, 2001]
Similarity

 Cosine
 Adj. cosine
 Pearson
 SVD / NMF: matrix factorization
• Context-based
• Content-based

26. PhD defense // UPF // Feb 16th 2009
music recommendation approaches
• Expert-based
• Collaborative filtering
User-Item matrix
 [Resnick, 1994], [Shardanand, 1995], [Sarwar, 2001]
Similarity

 Cosine
 Adj. cosine
 Pearson
 SVD / NMF: matrix factorization
Prediction (user-based)

 Avg. weighted

27. PhD defense // UPF // Feb 16th 2009
music recommendation approaches
• Expert-based
• Collaborative filtering
• Context-based
WebMIR

thrash
[Schedl, 2008]
Content Reviews Lyrics Blogs heavy metal Tags Bios Playlists
Social
Edgy
Weird
concert
90s
Loud
rock
[Hu&Downie, 2006] [Celma et al., 2006] [Levy&Sandler, 2007] [Baccigalupo, 2008]
[Symeonidis, 2008]
• Content-based
• Hybrid (combination)

28. PhD defense // UPF // Feb 16th 2009
music recommendation approaches
• Expert-based
• Collaborative filtering
• Context-based
• Content-based
Audio features

 Bag-of-frames (MFCC) [Aucouturier, 2004], Rhythm [Gouyon,
2005], Harmony [Gomez, 2006], ...
Similarity

 KL-divergence: GMM [Aucouturier, 2002]
 EMD [Logan, 2001]
 Euclidean: PCA [Cano, 2005]
 Cosine: mean/var (feature vectors)
 Ad-hoc
• Hybrid (combination)

29. PhD defense // UPF // Feb 16th 2009
music recommendation approaches
• Expert-based
• Collaborative filtering
• Context-based
• Content-based
• Hybrid (combination)
Weighted

Cascade

Switching


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Work done

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contributions

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contributions
1) Network-based evaluation
Item Popularity + Complex networks

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contributions
1) Network-based evaluation
Item Popularity + Complex networks
2) User-based evaluation

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contributions
1) Network-based evaluation
Item Popularity + Complex networks
2) User-based evaluation
3) Systems

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contributions

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contributions

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complex network analysis :: artists
• 3 Artist similarity (directed) networks
CF*: Social-based, incl. item-based CF (Last.fm)

 “people who listen to X also listen to Y”
CB: Content-based Audio similarity

 “X and Y sound similar”
EX: Human expert-based (AllMusicGuide)

 “X similar to (or influenced by) Y”

38. PhD defense // UPF // Feb 16th 2009
complex network analysis :: artists
• 3 Artist similarity (directed) networks
CF*: Social-based, incl. item-based CF (Last.fm)

 “people who listen to X also listen to Y”
CB: Content-based Audio similarity

 “X and Y sound similar”
EX: Human expert-based (AllMusicGuide)

 “X similar to (or influenced by) Y”

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complex network analysis :: artists
• Small-world networks [Watts & Strogatz, 1998]
Network traverse in a few clicks


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complex network analysis :: artists
• Indegree – avg. neighbor indegree correlation
r = Pearson correlation
 [Newman, 2002]

41. PhD defense // UPF // Feb 16th 2009
complex network analysis :: artists
• Indegree – avg. neighbor indegree correlation

42. PhD defense // UPF // Feb 16th 2009
complex network analysis :: artists
• Indegree – avg. neighbor indegree correlation

43. PhD defense // UPF // Feb 16th 2009
complex network analysis :: artists
• Indegree – avg. neighbor indegree correlation
Kin(Bruce Springsteen)=534
=>
avg(Kin(sim(Bruce Springsteen)))=463

44. PhD defense // UPF // Feb 16th 2009
complex network analysis :: artists
• Indegree – avg. neighbor indegree correlation
Kin(Bruce Springsteen)=534
=>
avg(Kin(sim(Bruce Springsteen)))=463
Kin(Mike Shupp)=14
=>
avg(Kin(sim(Mike Shupp)))=15

45. PhD defense // UPF // Feb 16th 2009
complex network analysis :: artists
• Indegree – avg. neighbor indegree correlation
Kin(Bruce Springsteen)=534
=>
avg(Kin(sim(Bruce Springsteen)))=463
Kin(Mike Shupp)=14
=>
avg(Kin(sim(Mike Shupp)))=15
Homophily effect!

46. PhD defense // UPF // Feb 16th 2009
complex network analysis :: artists
• Indegree – avg. neighbor indegree correlation
Last.fm presents assortative mixing (homophily)

 Artists with high indegree are connected together,
and similarly for low indegree artists

47. PhD defense // UPF // Feb 16th 2009
complex network analysis :: artists
• Last.fm is a scale-free network [Barabasi, 2000]
power law exponent for the cumulative indegree

distribution [Clauset, 2007]
A few artists (hubs) control the network


48. PhD defense // UPF // Feb 16th 2009
complex network analysis :: artists
• Summary: artist similarity networks
|------------|---------|-----|-----------|
| | Last.fm | CB | Exp (AMG) |
|------------|---------|-----|-----------|
|Small World | yes | yes | yes |
| | | | |
|Ass. mixing | yes | No | No |
| | | | |
| Scale-free | yes | No | No |
|------------|---------|-----|-----------|
Last.fm artist similarity network resembles to a social

network (e.g. facebook)

49. PhD defense // UPF // Feb 16th 2009
complex network analysis :: artists
• But, still some remaining questions...
Are the hubs the most popular artists?

How can we navigate along the Long Tail, using

the artist similarity network?

50. PhD defense // UPF // Feb 16th 2009
contributions
Long Tail analysis

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the Long Tail in music
• last.fm dataset (~260K artists)

52. PhD defense // UPF // Feb 16th 2009
the Long Tail in music
• last.fm dataset (~260K artists)
the beatles (50,422,827)
radiohead (40,762,895)
red hot chili peppers (37,564,100)
muse (30,548,064)
death cab for cutie (29,335,085)
pink floyd (28,081,366)
coldplay (27,120,352)
metallica (25,749,442)

53. PhD defense // UPF // Feb 16th 2009
the Long Tail model [Kilkki, 2007]
• F(x) = Cumulative distribution up to x

54. PhD defense // UPF // Feb 16th 2009
the Long Tail model [Kilkki, 2007]
• Top-8 artists: F(8)~ 3.5% of total plays
50,422,827 the beatles
40,762,895 radiohead
37,564,100 red hot chili peppers
30,548,064 muse
29,335,085 death cab for cutie
28,081,366 pink floyd
27,120,352 coldplay
25,749,442 metallica

55. PhD defense // UPF // Feb 16th 2009
the Long Tail model [Kilkki, 2007]
• Split the curve in three parts
(82 artists) (6,573 artists) (~254K artists)

56. PhD defense // UPF // Feb 16th 2009
contributions
+
Long Tail analysis

57. PhD defense // UPF // Feb 16th 2009
artist indegree vs. artist popularity
• Are the network hubs the most popular artists?
???

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artist indegree vs. artist popularity
Last.fm: correlation between Kin and playcounts

 r = 0.621

59. PhD defense // UPF // Feb 16th 2009
artist indegree vs. artist popularity
Audio CB similarity: no correlation

 r = 0.032

60. PhD defense // UPF // Feb 16th 2009
artist indegree vs. artist popularity
Expert: correlation between Kin and playcounts

 r = 0.475

61. PhD defense // UPF // Feb 16th 2009
navigation along the Long Tail
• “From Hits to Niches”
# clicks to reach a Tail artist, starting in the Head

how many clicks?

62. PhD defense // UPF // Feb 16th 2009
navigation along the Long Tail
• “From Hits to Niches”
Audio CB similarity example (VIDEO)


63. PhD defense // UPF // Feb 16th 2009
navigation along the Long Tail
• “From Hits to Niches”
Audio CB similarity example

 Bruce Springsteen (14,433,411 plays)

64. PhD defense // UPF // Feb 16th 2009
navigation along the Long Tail
• “From Hits to Niches”
Audio CB similarity example

 Bruce Springsteen (14,433,411 plays)
 The Rolling Stones (27,720,169 plays)

65. PhD defense // UPF // Feb 16th 2009
navigation along the Long Tail
• “From Hits to Niches”
Audio CB similarity example

 Bruce Springsteen (14,433,411 plays)
 The Rolling Stones (27,720,169 plays)
 Mike Shupp (577 plays)

66. PhD defense // UPF // Feb 16th 2009
artist similarity vs. artist popularity
• navigation in the Long Tail
Similar artists, given an artist in the HEAD part:

CF CB EXP
64,74%
60,92%
54,68%
45,32%
33,26%
28,80%
(0%) 6,46% 5,82%
Head Mid Tail Head Mid Tail Head Mid Tail
Also, it can be seen as a Markovian Stochastic

process...

67. PhD defense // UPF // Feb 16th 2009
artist similarity vs. artist popularity
• navigation in the Long Tail
Markov transition matrix


68. PhD defense // UPF // Feb 16th 2009
artist similarity vs. artist popularity
• navigation in the Long Tail
Markov transition matrix


69. PhD defense // UPF // Feb 16th 2009
artist similarity vs. artist popularity
• navigation in the Long Tail
Last.fm Markov transition matrix


70. PhD defense // UPF // Feb 16th 2009
artist similarity vs. artist popularity
• navigation in the Long Tail
From Head to Tail, with P(T|H) > 0.4

Number of clicks needed

 CF : 5
 CB : 2
 EXP: 2 HEAD
#clicks?
TAIL

71. PhD defense // UPF // Feb 16th 2009
artist popularity
Summary
|-----------------------|---------|-----|-----------|
| | Last.fm | CB | Exp (AMG) |
|-----------------------|---------|-----|-----------|
| Indegree / popularity| yes | no | yes |
| | | | |
|Similarity / popularity| yes | no | no |
|-----------------------|---------|-----|-----------|

72. PhD defense // UPF // Feb 16th 2009
summary: complex networks+popularity
|-----------------------|---------|-----|-----------|
| | Last.fm | CB | Exp (AMG) |
|-----------------------|---------|-----|-----------|
| Small World | yes | yes | yes |
| | | | |
| Scale-free | yes | no | no |
| | | | |
| Ass. mixing | yes | no | no |
|-----------------------|---------|-----|-----------|
| Indegree / popularity| yes | no | yes |
| | | | |
|Similarity / popularity| yes | no | no |
|-----------------------|---------|-----|-----------|
| POPULARITY BIAS | YES | NO | FAIRLY |
|-----------------------|---------|-----|-----------|

73. PhD defense // UPF // Feb 16th 2009
contributions
1) Network-based evaluation
Item Popularity + Complex networks
2) User-based evaluation
3) Systems

74. PhD defense // UPF // Feb 16th 2009
contribution #2: User-based evaluation
• How do users perceive novel, non-obvious
recommendations?
Survey

 288 participants
Method: blind music recommendation

 no metadata (artist name, song title)
 only 30 sec. audio excerpt

75. PhD defense // UPF // Feb 16th 2009
music recommendation survey
• 3 approaches:
CF: Social-based Last.fm similar tracks

CB: Pure audio content-based similarity

HYbrid: AMG experts + audio CB to rerank songs

 (Not a combination of the two previous approaches)
• User profile:
last.fm, top-10 artists

• Procedure
Do you recognize the song?

 Yes, Only Artist, Both Artist and Song title
Do you like the song?

 Rating: [1..5]

76. PhD defense // UPF // Feb 16th 2009

77. PhD defense // UPF // Feb 16th 2009
music recommendation survey: results
• Overall results

78. PhD defense // UPF // Feb 16th 2009
music recommendation survey: results
• Overall results

79. PhD defense // UPF // Feb 16th 2009
music recommendation survey: results
• Familiar recommendations (Artist & Song)

80. PhD defense // UPF // Feb 16th 2009
music recommendation survey: results
• Ratings for novel recommendations

81. PhD defense // UPF // Feb 16th 2009
music recommendation survey: results
• Ratings for novel recommendations
one-way ANOVA within subjects (F=29.13, p<0.05)

Tukey's test (pairwise comparison)


82. PhD defense // UPF // Feb 16th 2009
music recommendation survey: results
• % of novel recommendations

83. PhD defense // UPF // Feb 16th 2009
music recommendation survey: results
• % of novel recommendations
one-way ANOVA within subjects (F=7,57, p<0.05)

Tukey's test (pairwise comparison)


84. PhD defense // UPF // Feb 16th 2009
music recommendation survey: results
• Novel recommendations
Last.fm provides less % of novel songs, but of

higher quality

85. PhD defense // UPF // Feb 16th 2009
contributions
1) Network-based evaluation
Item Popularity + Complex networks
2) User-based evaluation
3) Systems

86. PhD defense // UPF // Feb 16th 2009
Why?
besides better understanding of music recommendation...
Open questions in the State of the Art in music discovery &
recommendation:
Is it possible to create a music discovery engine exploiting the
music content in the WWW? How to build it? How can we
describe the available music content?
=> SearchSounds
Is it possible to recommend, filter and personalize music
content available on the WWW? How to describe a user
profile? What can we recommend beyond similar artists?
=> FOAFing the Music

87. PhD defense // UPF // Feb 16th 2009
contribution #3: two complete systems
• Searchsounds
Music search engine

 keyword based search
 “More like this” (audio CB)

88. PhD defense // UPF // Feb 16th 2009
contribution #3: two complete systems
• Searchsounds
Crawl MP3 blogs

> 400K songs analyzed


89. PhD defense // UPF // Feb 16th 2009
contribution #3: two complete systems
• Searchsounds
Further work: improve song descriptions using

 Auto-tagging [Lamere, 2008] [Turnbull, 2007]
audio CB similarity [Sordo et al., 2007]
tags from the text (music dictionary)
 Feedback from the users
thumbs-up/down
tag audio content

90. PhD defense // UPF // Feb 16th 2009
contribution #3: two complete systems
• FOAFing the music
Music recommendation

 constantly gathering music related info via RSS feeds
 It offers:
artist recommendation
new music releases (iTunes, Amazon, eMusic, Rhapsody, Yahoo! Shopping)
album reviews
concerts close to user's locations
related mp3 blogs and podcasts

91. PhD defense // UPF // Feb 16th 2009
contribution #3: two complete systems
• FOAFing the music
Integrates different user accounts (circa 2005!)

Semantic Web (FOAF, OWL/RDF) + Web 2.0

2nd prize Semantic Web Challenge (ISWC 2006)


92. PhD defense // UPF // Feb 16th 2009
contribution #3: two complete systems
• FOAFing the music
Further work:

 Follow Linking Open Data best practices
 Link our music recommendation ontology with
Music Ontology [Raimond et al., 2007]
 (Automatically) add external information from:
Myspace
Jamendo
Garageband
...

93. PhD defense // UPF // Feb 16th 2009
summary of contributions :: research questions
• 1) How can we evaluate/compare different music
recommendation approaches?
• 2) How far into the Long Tail do music
recommenders reach?
• 3) How do users perceive novel (unknown to
them), non-obvious recommendations?

94. PhD defense // UPF // Feb 16th 2009
summary of contributions :: research questions
• 1) How can we evaluate/compare different music
recommendation approaches?
Objective framework comparing music rec.

approaches (CF, CB, EX) using Complex Network
analysis
Highlights fundamental differences among the

approaches
• 2) How far into the Long Tail do music
recommenders reach?
• 3) How do users perceive novel (unknown to
them), non-obvious recommendations?

95. PhD defense // UPF // Feb 16th 2009
summary of contributions :: research questions
• 1) How can we evaluate/compare different music
recommendation approaches?
• 2) How far into the Long Tail do music
recommenders reach?
Combine 1) with the Long Tail model, and Markov

model theory
Highlights differences in terms of discovery and

navigation
• 3) How do users perceive novel (unknown to
them), non-obvious recommendations?

96. PhD defense // UPF // Feb 16th 2009
summary of contributions :: research questions
• 1) How can we evaluate/compare different music
recommendation approaches?
• 2) How far into the Long Tail do music
recommenders reach?
• 3) How do users perceive novel (unknown to
them), non-obvious recommendations?
Survey with 288 participants

Still room to improve novelty (3/5 or less...)

 To appreciate novelty users need to understand the
recommendations

97. PhD defense // UPF // Feb 16th 2009
summary of contributions :: research questions
• 1) How can we evaluate/compare different music
recommendation approaches?
• 2) How far into the Long Tail do music
recommenders reach?
• 3) How do users perceive novel (unknown to
them), non-obvious recommendations?
=>
Systems that perform best (CF) do not exploit the

Long Tail, and
Systems that can ease Long Tail navigation (CB) do

not perform good enough
Combine (hybrid) different approaches!


98. PhD defense // UPF // Feb 16th 2009
Systems that perform

best (CF) do not exploit
the Long Tail, and
Systems that can ease

Long Tail navigation (CB)
do not perform good
enough
Combine different

approaches!

99. PhD defense // UPF // Feb 16th 2009
summary of contributions :: systems
• Furthermore...
2 web systems that improved existing State of the

Art work in music discovery and recommendation
 Searchsounds: music search engine exploiting music
related content in the WWW
 FOAFing the Music: music recommender based on a
FOAF user profile, also offering a number of extra
features to complement the recommendations

100. PhD defense // UPF // Feb 16th 2009
further work :: limitations
• 1) How can we evaluate/compare different
recommendations approaches?
Dynamic networks
 [Leskovec, 2008]
 track item similarity over time
 track user's taste over time
 trend and hype detection

101. PhD defense // UPF // Feb 16th 2009
further work :: limitations
• 2) How far into the Long Tail do recommendation
algorithms reach?
Intercollections

how to detect bad quality music in the tail?


102. PhD defense // UPF // Feb 16th 2009
further work :: limitations
• 3) How do users perceive novel, non-obvious
recommendations?
 User understanding [Jennings, 2007]
 savant, enthusiast, casual, indifferent
Transparent, steerable recommendations
 [Lamere &
Maillet, 2008]
 Why? as important as What?

103. PhD defense // UPF // Feb 16th 2009
summary: articles
• #1) Network-based evaluation for RS
 O. Celma and P. Cano. “From hits to niches? or how
popular artists can bias music recommendation and
discovery”. ACM KDD, 2008.
 J. Park, O. Celma, M. Koppenberger, P. Cano, and J. M.
Buldu. “The social network of contemporary popular
musicians”. Journal of Bifurcation and Chaos (IJBC),
17:2281–2288, 2007.
 M. Zanin, P. Cano, J. M. Buldu, and O. Celma. “Complex
networks in recommendation systems”. WSEAS, 2008
 P. Cano, O. Celma, M. Koppenberger, and J. M. Buldu
“Topology of music recommendation networks”. Journal
Chaos (16), 2006.
• #2) User-based evaluation for RS
 O. Celma and P. Herrera. “A new approach to
evaluating novel recommendations”. ACM RecSys, 2008.

104. PhD defense // UPF // Feb 16th 2009
summary: articles
• #3) Prototypes
FOAFing the Music

 O. Celma and X. Serra. “FOAFing the music: Bridging
the semantic gap in music recommendation”. Journal of
Web Semantics, 6(4):250–256, 2008.
 O. Celma. “FOAFing the music”. 2nd Prize Semantic Web
Challenge ISWC, 2006.
 O. Celma, M. Ramirez, and P. Herrera. “FOAFing the
music: A music recommendation system based on rss
feeds and user preferences”. ISMIR, 2005.
 O. Celma, M. Ramirez, and P. Herrera. “Getting music
recommendations and filtering newsfeeds from foaf
descriptions”. Scripting for the Semantic Web, ESWC,
2005.

105. PhD defense // UPF // Feb 16th 2009
summary: articles
• #3) Prototypes
Searchsounds

 O. Celma, P. Cano, and P. Herrera. “Search sounds: An
audio crawler focused on weblogs”. ISMIR, 2006.
 V. Sandvold, T. Aussenac, O. Celma, and P. Herrera.
“Good vibrations: Music discovery through personal
musical concepts”. ISMIR, 2006.
 M. Sordo, C. Laurier, and O. Celma. “Annotating music
collections: how content-based similarity helps to
propagate labels”. ISMIR, 2007.

106. PhD defense // UPF // Feb 16th 2009
summary: articles
• Misc. (mainly MM semantics)
 R. Garcia C. Tsinaraki, O. Celma, and S. Christodoulakis.
“Multimedia Content Description using Semantic Web
Languages” book, Chapter 2. Springer–Verlag, 2008.
 O. Celma and Y. Raimond. “Zempod: A semantic web
approach to podcasting”. Journal of Web Semantics,
6(2):162–169, 2008.
 S. Boll, T. Burger, O. Celma, C. Halaschek-Wiener, E.
Mannens. “Multimedia vocabularies on the Semantic
Web”. W3C Technical report, 2007.
 O. Celma, P. Herrera, and X. Serra. “Bridging the music
semantic gap”. SAMT, 2006.
 R. Garcia and O. Celma. “Semantic integration and
retrieval of multimedia metadata”. ESWC, 2005

107. PhD defense // UPF // Feb 16th 2009
summary: articles
 R. Troncy, O. Celma, S. Little, R. Garcia and C. Tsinaraki.
“MPEG-7 based multimedia ontologies: Interoperability
support or interoperability issue?” MARESO, 2007.
 M. Sordo, O. Celma, M. Blech, and E. Guaus. “The quest
for musical genres: Do the experts and the wisdom of
crowds agree?”. ISMIR, 2008.
• Music Recommendation Tutorials -- with Paul Lamere
 ACM MM, 2008 (Vancouver, Canada)
 ISMIR, 2007 (Vienna, Austria)
 MICAI, 2007 (Aguascalientes, Mexico)

108. PhD defense // UPF // Feb 16th 2009
summary: dissemination
• PhD Webpage
http://mtg.upf.edu/~ocelma/PhD

 PDF
 Source code
Long Tail Model in R
 References
Citeulike
 Related links
delicious

109. PhD defense // UPF // Feb 16th 2009
acknowledgments
NB: The complete list of acknowledgments can be found in the document

110. Music Recommendation and Discovery in
the Long Tail
Òscar Celma
Doctoral Thesis Defense
(Music Technology Group ~ Universitat Pompeu Fabra)

111. PICA-PICA
UPF-Tanger, 3rd floor