1. Knowledge-based
Music Recommendation
Models, Algorithm and Exploratory Search
Michel BUFFA Reviewer
Mounia LALMAS Reviewer
Gaël RICHARD Examiner
Tommaso DI NOIA Examiner
Pietro MICHIARDI Examiner
Benoit HUET Thesis Director
Raphäel TRONCY Thesis Co-Director
ThesisCommittee
PhD Candidate
Pasquale Lisena
11 October 2019
2. 1. Music
in particular Classical Music
2. Knowledge
Graphs as part of Semantic Web
technologies
3. ML
techniques
applied to Music KG
in particular for recommendation
What’s my
thesis about
5. 5
M. Lasar (2011). Digging into Pandora’s Music Genome with musicologist Nolan Gasser.
https://arstechnica.com/tech-policy/2011/01/digging-into-pandoras-music-genome-with-musicologist-nolan-gasser/
When it comes to classical
music, on the other hand, it's
much more about the
composition itself, because
even though the interpretation
can vary in various subtle ways.
CLASSICALPOPULAR VS
For pop music the experience of
the music is really defined by
the recording.
6. 6
CLASSICALPOPULAR VS
Track-based Work-based
70 years of history
Thousand years
from Gregorian chant to a work written last
Tuesday
Songs Multi-movement works
Major, minor
Polyphonic, homophonic,
monophonic
7. 7
M. Schedl (2015) Towards Personalizing Classical Music Recommendations. 2015 IEEE International Conference
on Data Mining Workshop (ICDMW), Atlantic City, NJ, USA, pp. 1366-1367. https://doi.org/10.1109/ICDMW.2015.8
“Fans of classical music
are underrepresented
on social media and
music streaming
platforms.”
● Less data
● Less detailed metadata
● Less involved in research
Music recommendation
research
Classical music
recommendation
research
8. 8
Data Metadata
Data which describes other data
composer
composition date
genre
performer
key
derivation type
1801
9. 9
Title, opus, movement
Who is the composer?
Who is the performer?
online music approach
Track as “atomic unit”
10. 10
music archives approach
Work as “aggregation unit”
● genre
● date
● author
● title(s)
● …
● publication
● performance
● recordings
● books
● ...
11. 11
Which model best represents these
rich data for final users and music
scholars?
What strategies to adopt for building
a music Knowledge Graph?
How to make these data accessible
to researchers and developers?
How can graph-based algorithms
support music recommender systems?
What information can be extracted
from editorial playlists?
Is Graph representation also suitable
for music content?
Research Questions
RQ1
RQ2
RQ3
RQ4
RQ5
RQ6
12. Roadmap
A. Music Model & Vocabularies
B. Data Conversion
C. Web APIs for KG
Building a
Music graph
Exploiting the
Music knowledge
PART I PART II
12
A. Embeddings and Similarity
B. Playlists and Weights
C. Learning MIDI Embeddings
13. 13
Improve music description to foster
music exchange and reuse
Travel to the heart of the musical
archives in France’s greatest
institutions
Connect sources, multiply usage,
enrich user experience
15. 15
What is a Knowledge Graph?
source: https://hackernoon.com/wtf-is-a-knowledge-graph-a16603a1a25f
It is a specific kind of knowledge base
which is:
● a directed graph
connections between nodes are first-class citizens
● semantic
the meaning of the connections are part of the data itself
● smart
allows graph-computing techniques and algorithms
● alive
easy to extend, access, reuse
Semantic Web technologies realize graphs in
which nodes and properties linking them are
identified by URIs.
18. 18
- One of the first example of describing music
using Semantic Web
- Extend FRBR, Timeline Ontology, Event
Ontology
- Uses vocabularies for Keys, Musical
Instrument (by MusicBrainz), Genres (DBpedia)
Y. Raimond, S. Abdallah, M. Sandler, and F. Giasson (2007). The Music Ontology.
In 15th International Conference on Music Information Retrieval (ISMIR). 417–422
State of the art
The Music Ontology
Building a Music graph Music Model & Vocabularies I.A
19. 19
The DOREMUS Model
- Relies on Linked Data and Semantic
Web principles
○ everything is a URI
○ RDF model
- Music specific extension of FRBRoo
- Event-based pattern: the knowledge is
represented in modules (triangles) which
describe events that give birth to
work/expression
FRBR
museum
information
bibliographic
records
P. Choffé and F. Leresche (2016). DOREMUS: connecting sources, enriching catalogues
and user experience. In 24th IFLA World Library and Information Congress.
Building a Music graph Music Model & Vocabularies I.A
20. 20
F14
Work
F22
Expression
M2
Opus
Statement
F28
Expression
Creation
R3 is
realized in
E7
Activity
5
1
“Sonate pour violoncelle et piano no 1”@fr
“Sonates" , "Sonata in F"
Ludwig van
Beethoven
Ludwig von Beethoven
composer
compositeur@fr
compositore@it
R17 created
R19createda
realizationof
U17 has opus
statement
U12 has
genre
P102 has title
U31 had
function of
type
P14 carried
out by
P9
consists of
P4 has time
span1796
Sonata
sonata@it , sonate@fr ,
klaviersonate@de
M42 Performed
Expression
Creation
M43
Performed
Expression
Berlin
P4 has time
span
1796
P7 took
place at
F24 Publication
Expression
F30
Publication
Event
P4 has time
span
1797
P7 took
place at
Vienna
U4 had princeps
publication
U54 is performed
expression of
P165
incorporates
1770
1827
P98
born
P100
died
U11 has key
F Major
F Dur@de , Fa majeur@fr,
Fa maggiore@it , Fa mayor@es
M6
Casting
M23
Casting
Detail
U13
has
casting
1
U30
quantity
U2
foresees
mop
Piano
Pianoforte@it
Fortepian@pl
M23
Casting
Detail
1
U30
quantity
U2
foresees
mop
Cello
Violoncello@it
Violoncelle@fr
F15
Complex
Work
F19
Publication
Work
M44
Performed
Work
U5 had
premiere
U38 has
descriptive
expression
R10 has member
22. GENRES
Diabolo
IAML
Itema3
Redomi
RAMEAU
Medium of performance
MIMO
Itema3
IAML
Diabolo
RAMEAU
Redomi
Musical keys
Modes
Catalogues
Derivation types
Functions
more available at
http://data.doremus.org/vocabularies
23 families of vocabularies · 11,000+ concepts · 610 links between terms
INTERLINKED
INTERLINKED
P. Lisena et al. (2018). Controlled Vocabularies for Music Metadata.
In 19th International Conference on Music Information Retrieval (ISMIR). Paris, France.
Controlled Vocabularies
Building a Music graph Music Model & Vocabularies I.A
23. These and additional
competency questions have
been collected by experts from
our partner institutions and used
as requirements and validation
for the model.
https://github.com/DOREMUS-ANR/knowledge-base/tree/master/
query-examples
23
P. Lisena et al. (2017) Modeling the Complexity of Music Metadata in
Semantic Graphs for Exploration and Discovery. In (ISMIR’17) 4th
International Workshop on Digital Libraries for Musicology (DLfM’17),
Shanghai, China.
Building a Music graph Music Model & Vocabularies
Which works have been
composed
by Mozart when he was
<10?
How many works have
been composed and
performed for the 1st
time in the same city?
Which composers had the
chance to direct their own
work in a performance
during the last decade?
I.A
24. 24
Which chamber music works have been
composed in the 19th century by
Scandinavian composers?
Edvard Grieg
1843 - 1907
Work
Genre
>1800
AND
<=1900
CHAMBER
MUSIC
Composition date
?composed
by
nationality
part of
SCANDINAVIA
Building a Music graph Music Model & Vocabularies I.A
26. 26
Music archives have
very detailed knowledge
PROBLEMS
● Multiple formats
○ sometimes complex parsing is required
● No possible interoperability
● Need for discovering overlapping
knowledge
● Information codified as free text
○ different practices in codifying the same
information (“Op. 27 n. 2” - “Op. 27 no 2”)
○ wrong fields, typos, wrong punctuation
● Not always publicly accessible
pic: wikimedia commons
Building a Music graph Data Conversion I.B
Ryszard Kruk S. andl McDaniel B. (2009). Goals of Semantic Digital Libraries.
27. Source datasets
27
Works
62 550 | XML
Scores
9 154 | XML
Concerts
340 609 | XML
Discs
9 500 | XML
Works
6 846 | UNIMARC
Scores
30 319 | UNIMARC
Concerts
5 164 | XML
Discs
8 602 | XML
Works
135 940 | INTERMARC
Scores
89 184 | INTERMARC
(3 different XML sources)
Building a Music graph Data Conversion I.B
28. 28
001 FRBNF139081882FR
100 $313891295$w.0..b.....$aBeethoven$mLudwig van$d1770-1827
144 $w....b.fre.$aSonates$bPiano$pOp. 27, no 2$tDo dièse mineur
001 FRBNF139081882FR
100 $313891295$w.0..b.....$aBeethoven$mLudwig van$d1770-1827
144 $w....b.fre.$aSonates$bPiano$pOp. 27, no 2$tDo dièse mineur
LANG TITLE MOP OPUS KEY
MARC FILE
Building a Music graph Data Conversion I.B
30. marc2rdf
MARC PARSER
FREE TEXT
INTERPRETER
STRING 2
VOCABULARY
MARC
files
vocabularies
1st performance in Moscow, December 29, 1956,
by Mstislav Rostropovich on cello and A. Dedukhin on piano
“ ”
mapping
rules
Building a Music graph Data Conversion I.B
RDF
graph
What strategies to adopt for building a music Knowledge Graph?RQ2
32. 32
What is in the Knowledge Graph?
89.872
persons
(composers,
performers, …)
18.075
corporate bodies
(orchestras, chorus,
publishers, …)
357.451
musical
works
16k components
4k derived works
193.412
concerts and
studio recordings
469.131
performed work
3.833
foreseen
concerts
31.296
publications
48.006
scores
Building a Music graph Data Conversion I.B
33. 33
pic: https://www.flickr.com/photos/franganillo/2643351571
I.C Web APIs for KG
Building a
Music graph
Pëtr Il'ič Čajkovskij
Pyotr Ilyich Tchaikovsky
Пётр Ильич Чайковский
GALLERY OF COMPOSERS
Antonio Vivaldi
Ludwig van Beethoven Johann Sebastian Bach
Jean Sébastien Bach [FR]
34. 34
SELECT * WHERE {
?composer a foaf:Person ;
foaf:name ?name ;
foaf:depiction ?img .
}
34
Pëtr Il'ič Čajkovskij
Pyotr Ilyich Tchaikovsky
Пётр Ильич Чайковский
GALLERY OF COMPOSERS
Antonio Vivaldi
Ludwig van Beethoven Johann Sebastian Bach
Jean Sébastien Bach [FR]
Building a Music graph Web APIs for KG I.C
35. 35
-- W3C specification
SPARQL result
JSON format
"bindings": [{
"composer": { "type": "uri",
"value": "http://data.doremus.org/artist/0b9d963c-bfd7-337d-b6c3-c874f5e62125"
},
"name": { "type": "literal",
"value": "Petr Ilʹič Čajkovskij"
},
"img": { "type": "uri",
"value": "http://.../Pyotr_Ilyich_Tchaikovsky.jpg"
}
}, {
"composer": { "type": "uri",
"value": "http://data.doremus.org/artist/0b9d963c-bfd7-337d-b6c3-c874f5e62125"
},
"name": { "type": "literal",
"value": "Piotr Ilitch Tchaikovski"
},
"img": { "type": "uri",
"value": "http://.../Pyotr_Ilyich_Tchaikovsky.jpg"
}
}, {
"composer": { "type": "uri",
"value": "http://data.doremus.org/artist/b34f92ab-ad86-361b-a8b8-5c3a4db784d0"
},
"name": { "type": "literal",
"value": "Antonio Vivaldi"
},
"img": { "type": "uri",
"value": "http://.../Antonio_Vivaldi.jpg"
}
}, ...
SAME
DIFFERENT
SAME
DIFFERENT
How to make these data
accessible to researchers and
developers?
RQ3
36. 36
[{
"id": "http://data.doremus.org/artist/0b9d963c...",
"name": [
"Petr Ilʹič Čajkovskij"
"Piotr Ilitch Tchaikovski"
],
"image": "http://.../Pyotr_Ilyich_Tchaikovsky.jpg"
},
{
"id": "http://data.doremus.org/artist/b34f92ab...",
"name": "Antonio Vivaldi",
"image": "http://.../Antonio_Vivaldi.jpg"
}]
2
names
1
picture
Building a Music graph Web APIs for KG I.C
37. 37
skip irrelevant
metadata
reducing and
parsing
merging “rows”
mapping to
different structures
Building a Music graph Web APIs for KG I.C
Booth et al. (2019) Toward Easier RDF. In W3C Workshop on
Web Standardization for Graph Data.
38. 38
SPARQL Transformer
/D2KLab/sparql-transformer
● JS and Python library
● A JSON-based syntax
○ template + query
● Integration in grlc.io for
web api development
{
"proto": {
"id" : "?composer",
"name": "$foaf:name$required",
"image": "$foaf:depiction$required"
},
"$where": [
"?composer a ecrm:E21_Person"
],
"$limit": 100
}
Building a Music graph Web APIs for KG I.C
Lisena P. et al. (2019). Easy Web API Development
with SPARQL Transformer. In ISWC’19.
39. 39
SPARQL Transformer
Building a Music graph Web APIs for KG I.C
QUERIES*
n. objects
(original)
n. objects
(transformed)
1.Born_in_Berlin 1132 573
2.German_musicians 290 257
3.Musicians_born_in_Berlin 172 109
4.Soccer_players 78 70
5.Games 1020 981
Evaluation #1: Queries’ results
* from https://wiki.dbpedia.org/onlineaccess
Evaluation #2: User Survey
55 subjects
Used in
Overhead < 0.1 seconds
41. 41
discover new
music
improve their streaming
music experience
background for
their activities
FINAL USERS MUSIC EXPERTS
playlist producing
help for concert
programming
automatic radio
broadcasting
How can graph-based algorithms support
music recommender systems?
RQ4
43. 43
S. Oramas, V. C. Ostuni, T. Di Noia, X. Serra, and E. Di Sciascio. Sound and music recommendation with knowledge graphs.
ACM Trans. Intell. Syst. Technol. 8, 2, Article 21 (October 2016), 21 pages.
http://dx.doi.org/10.1145/2926718
The vector representation of the
item i is computed on his
neighborhood of length l.
More two items share
entities/property at a certain
distance, more those items can be
considered similar.
State of the art
Item neighborhood mapping
44. 44
https://musiclynx.github.io/#/artist/ad79836d-9849-44df-8789-180bbc823f3c/Antonio%2520Vivaldi
Alo Allik, Florian Thalmann, and Mark Sandler. 2018. MusicLynx: Exploring Music Through Artist Similarity Graphs.
In The Web Conference 2018. Demo Track, pp 167-170.
https://doi.org/10.1145/3184558.3186970
● Access to different knowledge sources
● Maximum Degree Weighted (MDW): links to very
large categories (i.e. Living People) are discouraged
with respect to more significant ones.
State of the art
MusicLynx
46. 46
Word Embeddings (e.g. word2vec)
corpus of document -> vectors that represent the semantic
distribution of words in the text
Graph Embeddings (e.g. node2vec)
set of random walks -> vectors that represent the semantic distribution
of entity in the graph
Exploiting the Music knowledge Embeddings and Similarity II.A
Main idea: nodes that occurs in similar contexts
(neighborhood of nodes in a graph) are more similar, and
will be closer in the vector space.
Aditya Grover and Jure Leskovec. node2vec: Scalable Feature Learning for Networks.
In 22nd ACM SIGKDD , 2016.
46
47. Some problems:
● Our dataset was constantly growing
● The amount of nodes is huge
● Different purposes for recommendation:
○ radio broadcasting
○ concert programming
○ final users
47
computational-wise and time-wise
expensive
(multiple run of node2vec
on huge amount of data)
Exploiting the Music knowledge Embeddings and Similarity II.A
48. 48
Compute embeddings at
simple features level
period of time
musical key
medium of performance
genre
...
Exploiting the Music knowledge Embeddings and Similarity II.A
Solution
54. 54
VECTOR SPACE OF MoPs
ethnic
chordophones
ethnic flutes
percussions
brass
orchestra
woodwinds
orchestra
strings
rare strings
Exploiting the Music knowledge Embeddings and Similarity II.A
55. 55
VECTOR SPACE OF GENRES
Exploiting the Music knowledge Embeddings and Similarity II.A
56. 56
Combine embeddings at
complex features level
artists
works
playlists
Exploiting the Music knowledge Embeddings and Similarity II.A
58. 58
MOP
embeddings:
MOPGENRE KEY
Artist’s features:
BIRTH
DATE
DEATH
DATE
CASTING
WORKS
GENRE
WORKS
KEYS
WORKS
PLAYED
MOP
-0.02 0.01 0.01 0.00 -0.01 -0.02 0.01 0.00 -0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.00 0.00 0.00 0.07 -0.03 0.07 -0.02 -0.01 0.19 0.02 0.69 -0.19 -0.14 0.08 0.03 0.03 0.00 0.08 null null null null null -0.06 0.07 0.02 -0.03 0.00
Artist vector
BIRTH
PLACE
DEATH
PLACE
FUNCTION
FUNCTGeoNamesGeoNamesTime
DIMENSIONALITY REDUCTION (PCA)
Time
AVG AVG AVG AVG AVG
Exploiting the Music knowledge Embeddings and Similarity II.A
Some data are unknown or not applicable
null null null null null
59. 59P. Lisena, R. Troncy (2017). Combining music specific embeddings for computing artist similarity.
In 18th International Society for Music Information Retrieval Conference (ISMIR), Late-breaking & demo track.
percentage of missing
dimensions in artist 2
with respect to artist 1
Exploiting the Music knowledge Embeddings and Similarity II.A
Example: Artists
60. 60
Do all the properties have the same
importance?
Exploiting the Music knowledge Embeddings and Similarity II.A
62. 62
● Use playlists to give a weight to the influence of each dimension.
● No GOLD STANDARD available, creation of a “silver” one
Radio France Playlists
(50)
Spotify Playlists
(65)
ITEMA3 Concerts (624)
Philharmonie Concerts (186)
● Radio France Web Radio (7 channels)
● Realised by experts
● Classical section of Spotify app
● Realised by Spotify staff
● Real concerts that took place in Paris
(studio + concert hall)
Exploiting the Music knowledge Playlists and Weights II.B
63. 63
variance within < variance between
HOMOGENEOUS
good for recommendation
variance within > variance between
INHOMOGENEOUS
bad for recommendation
Exploiting the Music knowledge Playlists and Weights II.B
F test statistic = variance between / variance within Weights
64. evaluation
64
Exploiting the Music knowledge Playlists and Weights II.B
● 7 music experts
from partner institutions
● given the seed:
○ put bad items
in the trash
○ sort according
to preference
65. evaluation
65
Exploiting the Music knowledge Playlists and Weights II.B
The study of variance help us to identify which dimensions
should be promoted for better recommendations.
66. 66
Exploiting the Music knowledge Playlists and Weights II.B
Under experimentation in
live.philharmoniedeparis.fr
67. The role of titles:
Title2Rec
Exploiting the
Music knowledge
“Relax Driving”
Johannes Brahms
Symphony n.3
“Beach Party”
Luis Fonsi
Despacito
68. 68
Title2Rec: training
Exploiting the Music knowledge Playlists and Weights II.B
Content
(id of tracks)
Playlists
SEQUENTIAL
EMBEDDINGS
(word2vec)
CLUSTERS
of playlists
Titles
DOCUMENTS
fastText
MODEL
69. 69
yy :) christmas litmas guardians christmas christmas holiday christmas christmas the good stuff. xmas himym christmas pop xmas
country happy holidays holidays christmas christmas hits 25 just cause stay christmas tis the season 🎄 christmas 🎄 christmas
oldbutgold christmas christmas vibes christmas strong christmas winter wonderland christmas time december 15 xmas christmas
christmas pop flight christmas deep christmas vibes christmas oldies work in progress christmas christmas playlist christmas music
josh 🎄 christmas blah christmas & chill depression secret christmas christmas & chill christmas love :) christmas elite :) christmas
special songs christmas christmas christmas jams jessica its lit classy pump up graduation at the moment .... christmas christmas
christmas music good old days christmas mix christmas music 80s rock christmas 2015 xmas christmas christmas christmas
christmas vibes 2017 songs christmas vibes!! christmas music holidays christmas 2016! christmas christmas club music summer
2015 christmasssss christmassss christmas christmas christmas christmas!! christmas christmas feels christmas christmas(::
christmas playlist great christmas playlist christmas & chill christmas christmas trap blast from the past christmas 2016 classics grad
christmas christmas christmas christmas yessss christmas christmas rihanna christmas christmas songs christmas 2016!!!!! good
vibes christmas christmas songs christmas christmas christmas favorites christmas christmas 2016 🎄 christmas last christmas
christmas all my friends christmas christmas !! chirstmas the weeknd christmas 2015 christmas christmas lyrical party music wake up
happy vibes 🎄 christmas calm country winter christmas christmas christmas pop christmas af ❄ christmas feel good :)) christmas
christmas af christmas jams moana christmas merry christmas! christmas playlist christmas christmas silly love songs christmas </3
school 🎄 christmas christmas music christmas christmas music 🎄 christmas x-mas christmas bops christmas beachin' dance jamz
christmas new wave its christmas christmas 🎄 christmas indie 2 christmas 1980 christmas jams christmas 2015 sunrise christmas
christmas playlist christmas jams christmas white ella chirstmas sleep :))))) christmas random christmas dance christmas christmas
december; christmas christmas favs christmas old christmas songs ~holidaze~ christmas christmas music xmas christmas holidays
december christmas christmas christmas baby wedding music tis the season christmas relax holidays!! 🎅 🏼 christmas christmas
christmas december '15 christmas!! christmas new songs christmas christmas
Exploiting the Music knowledge Playlists and Weights II.B
Title2Rec: training
70. 70
Title2Rec: predicting
Exploiting the Music knowledge Playlists and Weights II.B
Given a new title:
● found the most similar titles
among the known ones
● propose the most popular
tracks among those titles
Evaluated on Spotify’s
Million Playlists Datasets
in the context of the
RecSys Challenge 2018
in the challenge:
#37 over 112
#13 over 31
72. 72
MIDI2vec
Apply graph technologies to MIDI
● Transform MIDI flow in a graph
● Apply node2vec for learning graph
embeddings
Exploiting the Music knowledge Learning MIDI Embeddings II.C
MIDI
Group
of
Notes
Pitch
Duration
Program
Time
Signature
Tempo
Velocity
+
+
+
+
+
73. 73
Experiment: genre and metadata prediction
Dataset 1: SLAC
250 MIDI, balanced on 5/10 genres
Accuracies on cross-fold validation:
Dataset 2: MuseData
438 MIDI, unbalanced, linked to DOREMUS
Accuracies on cross-fold validation:
Exploiting the Music knowledge Learning MIDI Embeddings II.C
Baseline: McKay et al (2010)
75. 75
Which model best represents these rich data for final
users and music scholars?
DOREMUS model and Vocabularies
What strategies to adopt for building a music
Knowledge Graph?
marc2rdf and other converters
result: the DOREMUS Knowledge Graph
How to make these data accessible to researchers
and developers?
SPARQL Transformer reshapes and
merges the results for easy use
RQ1
RQ2
RQ3
Main contributions
76. 76
How can graph-based algorithms support music
recommender systems?
Embedding approach with generation
and recombination of partial vectors
Which information is possible to extract from
editorial playlists?
A study of editorial playlists, for
weighting a recommender system
Title2Rec: recommend music by the
title of the playlist
Graph representation is suitable also for music
content?
MIDI2vec: learning MIDI graph
embeddings
RQ4
RQ5
RQ6
Main contributions
77. 77
Future Work (1/2)
Short Term
● Studies on simplifications of the ontology (schema.org)
● Domain-based NLP for text-field information extraction
Long Term
● Strategies for modeling librarian information
representing meta-information on a 2nd level (RDF*)
Modeling and accessing a KG
78. 78
Future Work (2/2)
Short term
● Split the dataset in historical period
more precise training, faster performances
● Title2Rec + similarity-based recommender system
application for editors
● Experiment MIDI embeddings on larger dataset
Long term
● Gold standard dataset of classical music playlists
● Combining our strategy with more traditional ones (CF)
● MIDI ontology: extend and use in MIDI2vec
Knowledge-aware Recommender system
79. Publications
Conference Poster&Demo Journal Tutorial Workshop
EKAW'16
ISWC'16
EKAW'16 2016
ISWC'17 X2
ISMIR'17
K-CAP'17 DLfM'17 2017
ISWC'18 X2
ISMIR'18
ISMIR'18 BIBLIOTHEK -
Forschung
und Praxis
ESWC'18 RecSys'18
TheWebConf'18
2018
ISWC'19 2019
PC Member
ISWC’18 P&D, SAAM’18, DLfM’18, ISWC’19 P&D, K-CAP’19
as sub-reviewer: KAARS’18, TheWebConf’19
Student
Supervision
2 Master Thesis supervisions
10 Semester Projects supervisions
Lecturer for WebInt and Aalto BootCamp
Talks
Des Catalogues au Web des Données
- BnF, Paris
Classical Music and Knowledge Graphs
- Semantic Web course, PoliTo
- WAI meeting, VU Amsterdam
- Research seminar, Deezer, Paris
80. 80
References (1/2)
● M. Schedl (2015) Towards Personalizing Classical Music Recommendations. 2015 IEEE International Conference
on Data Mining Workshop (ICDMW), Atlantic City, NJ, USA, pp. 1366-1367.
● Y. Raimond, S. Abdallah, M. Sandler, and F. Giasson (2007). The Music Ontology. In 15th International
Conference on Music Information Retrieval (ISMIR). 417–422
● P. Choffé and F. Leresche (2016). DOREMUS: connecting sources, enriching catalogues and user experience.
In 24th IFLA World Library and Information Congress.
● P. Lisena et al. (2018). Controlled Vocabularies for Music Metadata. In 19th International Conference on Music
Information Retrieval (ISMIR). Paris, France.
● P. Lisena et al. (2017) Modeling the Complexity of Music Metadata in Semantic Graphs for Exploration and
Discovery. In 4th International Workshop on Digital Libraries for Musicology (DLfM’17), Shanghai, China.
● Booth et al. (2019) Toward Easier RDF. In W3C Workshop on Web Standardization for Graph Data.
● Lisena P. et al. (2019). Easy Web API Development with SPARQL Transformer. In ISWC’19.
● S. Oramas, V. C. Ostuni, T. Di Noia, X. Serra, and E. Di Sciascio. Sound and music recommendation with
knowledge graphs. ACM Trans. Intell. Syst. Technol. 8, 2, Article 21 (October 2016), 21 pages.
81. 81
References (2/2)
● Alo Allik, Florian Thalmann, and Mark Sandler (2018). MusicLynx: Exploring Music Through Artist Similarity
Graphs. In The Web Conference 2018. Demo Track, pp 167-170.
● Aditya Grover and Jure Leskovec. (2016) node2vec: Scalable Feature Learning for Networks. In 22nd ACM
SIGKDD.
● McKay, C., Burgoyne, J., Hockman, J., B. L. Smith, J.,Vigliensoni, G., and Fujinaga, I. (2010). Evaluating the
Genre Classification Performance of Lyrical Features Relative to Audio, Symbolic and Cultural
Features. In ISMIR 2011, Utrecht, The Netherlands
● Meroño-Peñuela, A., Hoekstra, R., Gangemi, A., Bloem, P., de Valk, R., Stringer, B., Janssen, B., de Boer, V.,Allik,
A., Schlobach, S., et al. (2017). The MIDI Linked Data Cloud. In ISWC 2017, Vienna, Austria.
● Huang, A. and Wu, R. (2016). Deep Learning for Music. Computing Research Repository (CoRR),
https://arxiv.org/abs/1606.04930 .
● Peter Knees and Markus Schedl (2013). A survey of music similarity and recommendation from music
context data. ACM Trans. Multimedia Comput. Commun. Appl. 10, 1, Article 2 (December 2013), 21 pages.
● Palumbo, Rizzo, Troncy. (2017) entity2rec: Learning user-item relatedness from knowledge graphs for
top-N item recommendation. In RECSYS 2017, Como, Italy.