Slides for my Ph.D. defense at the Universitat Autònoma de Barcelona, November 2009

1. Poolcasting:
an intelligent technique to
customise music programmes
for their audience
Claudio Baccigalupo, IIIA–CSIC
Bellaterra, November 6th, 2009

2. Create something intelligent
flickr.com/photos/jessicafm/451780564

3. Nobody understands me!
flickr.com/photos/jessicafm/874220566

4. Let’s search for an actual problem
flickr.com/photos/jessicafm/873365059

5. PartyStrands
flickr.com/photos/jaejongkwak/389531562/

6. Scope of the research
Develop an intelligent technique
to satisfy a group of listeners
by delivering a sequence of songs
adapted for the entire audience

7. Desired properties
Variety avoiding repetitions
Smoothness nice musical transitions
Customisation adapted for the audience
Fairness satisfactory for everyone

8. Structure of the thesis
1. Introduction
2. Musical associations smoothness
3. Individual listening behaviours customisation
4. The poolcasting CBR technique fairness
5. Poolcasting Web radio
6. Experiments and evaluation
7. Conclusions

9. Chapter 2.
Musical associations from
a Web of experiences

10. State of the art
Methods to uncover associated songs:
experts-based not scalable
content-based ignore cultural liaisons
social-based observing how people use
music in their activities

11. Collecting listening habits
flickr.com/photos/itzafineday/302929685

12. A Web of music data

13. Playlists
Co-occurrence
analysis
X
Y
How often do X and Y occur in the same playlists? Do
they always occur in the same order? Contiguously?

14. Playlists
Co-occurrence
analysis
X
s(X, Y )
Y
s(X, Y ) ∈ [0, 1] measures the association between X
and Y based on their co-occurrences in a set of playlists

15. From playlists to associations
Initial data set: 993,825 playlists
Fig 2.2
50,000
300,000
songs
40,000
! !
artists
Number of playlists
Number of playlists
200,000
30,000
20,000
100,000
10,000
0
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 4 8 12 16 20 24 28 32 36 40
Alphabetically ordered songs/artists [limited to 1~15] Number of songs [limited to 1~40]
After noise removal: 465,438 playlists
s(X, Y ) estimated for ~400K songs by ~50K artists

16. Lists of associated songs
Top associated songs with ‘New York, New York’:
1. ‘The Waters of March’ (Susannah McCorkle)
2. ‘Stardust’ (Glenn Miller)
Top associated artists with Frank Sinatra:
1. Dean Martin the same result of
2. Sammy Davis Jr.

17. Structure of the thesis
1. Introduction
2. Musical associations s(X, Y )
3. Individual listening behaviours customisation
4. The poolcasting CBR technique fairness
5. Poolcasting Web radio
6. Experiments and evaluation
7. Conclusions

18. Chapter 3.
Individual listening
behaviours

19. State of the art
Methods to compile user models:
explicit asking for a direct feedback
implicit observing behavioural patterns
(listening, purchasing, sharing,
forwarding, rating a song)

20. Listening habits data

21. From habits to implicit preferences
Implicit user
modeling
U
i(U, X)
X
i(U, X) ∈ [0, 1] estimates the implicit preference of U
for
a song X combining the observed rating and play count

22. Structure of the thesis
1. Introduction
2. Musical associations s(X, Y )
3. Individual listening behaviours i(U, X)
4. The poolcasting CBR technique fairness
5. Poolcasting Web radio
6. Experiments and evaluation
7. Conclusions

23. Chapter 4.
The poolcasting
technique

24. Overview
U1 U2 U3
C1 C2 C3
Poolcasting Poolcasting Poolcasting
H1 H2 H3 ...
T =1 T =2 T =3 time

25. Adding one song to the sequence
U3
C3
Poolcasting
Case-Based
Reasoning
H1 H2
T =1 T =2 T =3 time

26. A collection of Case Bases
Build one Case Base for each user U3
C3
X i(U1 , X) Listening
habits
0.5
−0.7 Case Bases
U1 ...
X i(U2 , X) Individual
preferences
1.0
0.2
U2 ...
H1 H2
T =1 T =2 T =3 time

27. The Retrieve process
Extract from the Case Bases a U3
C3
subset of songs Y that:
Listening
habits
- have not been played recently Case Bases
variety Retrieve
- maximise the degree s(H2 , Y ) Individual
preferences
smoothness
Musical
association
H1 H2
T =1 T =2 T =3 time

28. The Reuse process
Rank the retrieved set according U3
C3
to the aggregated preferences of
Listening
all the members of the audience habits
Case Bases
customisation Retrieve
fairness Individual
preferences
Musical
association
Reuse
H1 H2 H3
T =1 T =2 T =3 time

29. The Revise process
Update the implicit preferences U3
C3
with the users’ explicit feedback
Listening
habits
implicit Case Bases
i(U, X) Revise
Retrieve
preference
p(U, X, T ) Individual
preferences
explicit
e(U, X, T )
Musical
association
Reuse
H1 H2 H3 ...
T =1 T =2 T =3 time

30. The iterated CBR technique
U1 U2 U3
C1 C2 C3
Listening Listening Listening
habits habits habits
Case Bases Case Bases Case Bases
Revise Revise Revise
Retrieve Retrieve Retrieve
Individual Individual Individual
preferences preferences preferences
Musical Musical
association association
Reuse Reuse Reuse
H1 H2 H3 ...
T =1 T =2 T =3 time

31. Aggregating individual preferences
From multiple preference degrees p(U, X, T ) ∈ [−1, 1] :
X p(U1 , X, T ) p(U2 , X, T ) p(U3 , X, T )
to an aggregated group-preference g(X, T ) ∈ [−1, 1] :

32. Aggregating individual preferences
From multiple preference degrees p(U, X, T ) ∈ [−1, 1] :
X p(U1 , X, T ) p(U2 , X, T ) p(U3 , X, T )
to an aggregated group-preference g(X, T ) ∈ [−1, 1] :
p(U, X, T )
X g(X, T ) = (1 − q(U, T − 1)) ·
#(UT )
U ∈UT
weight average
defined as a satisfaction-weighted average

33. Avoiding misery
The satisfaction-weighted aggregation g(X, T ) ∈ [−1, 1]
is completed with a measure intended to avoid misery:
assign the minimum degree if any user strongly dislikes X

−1
 if ∃U ∈ UT :



 p(U, X, T ) < µ
g(X, T ) =




 p(U, X, T )
 (1 − q(U, T − 1)) · otherwise.
U ∈UT
#(UT )
This results is an acceptable compromise for the group

34. Structure of the thesis
1. Introduction
2. Musical associations
3. Individual listening behaviours
4. The poolcasting CBR technique
5. Poolcasting Web radio
6. Experiments and evaluation
7. Conclusions

35. Chapter 5.
Group-customised
Web radio

36. What is Poolcasting radio?

37. A Poolcasting radio channel

38. Listeners can play music

39. Listeners can create public channels

40. Participants contribute with own music

41. Listeners can interact

42. Listeners influence the music played

43. The Poolcasting radio architecture
playlists Database
MUSIC POOL MUSICAL ASSOCIATIONS CURRENT LISTENERS
metadata
PREFERENCES CHANNELS
list of
list of available listeners
shared songs songs
knowledge to Stream Generator
ratings and schedule
play counts
audio signal
Library Parser Song Scheduler Streaming Server
upload
song OGG stream
(256 Kbps)
share library rate songs create channel
MP3 stream
Web Interface (64 Kbps)
I N T E R N E T
Media
Personal Library Participant Player Participant

44. Chapter 6.
Experiments and
evaluation

45. Subjective evaluation
Poolcasting Web radio as a test platform for one year
10 users sharing 24,763 identified songs
4,828 preferences inferred from personal libraries
Positive feedback for the overall experience
Variety requirement was too weak
Smootness requirement was too strong

46. Artificially created profiles
1.0
1.0
0.8
0.8
0.6
0.6
Five users with
0.4
0.4
random profiles 0.2
0.2
0.0
0.0
5 10 15 20 25 5 10 15 20 25
Individual Satisfaction
preferences degrees

47. A worst-case scenario
1.0
1.0
0.8
0.8
0.6
0.6
Five users with
0.4
0.4
random profiles 0.2
0.2
0.0
0.0
5 10 15 20 25 5 10 15 20 25
Individual Satisfaction
preferences degrees
1.0
1.0
0.8
0.8
Two groups with
0.6
0.6
discordant tastes
0.4
0.4
0.2
0.2
0.0
0.0
5 10 15 20 25 5 10 15 20 25

48. A worst-case scenario
1.0
1.0
0.8
0.8
0.6
0.6
Five users with
0.4
0.4
random profiles 0.2
0.2
0.0
0.0
5 10 15 20 25 5 10 15 20 25
Individual Satisfaction
preferences degrees
1.0
1.0
Two groups with
0.8
0.8
discordant tastes
0.6
0.6
(non-weighted
0.4
0.4
aggregation)
0.2
0.2
0.0
0.0
5 10 15 20 25 5 10 15 20 25

49. A realistic scenario
1.0
1.0
0.8
0.8
0.6
0.6
Five users with
0.4
0.4
random profiles 0.2
0.2
0.0
0.0
5 10 15 20 25 5 10 15 20 25
Individual Satisfaction
preferences degrees
1.0
1.0
0.8
0.8
Five users with
0.6
0.6
concordant
0.4
profiles 0.4
0.2
0.2
0.0
0.0
5 10 15 20 25 0.0 0.5 1.0 1.5 2.0

50. Scalability
1.0
1.0
0.8
0.8
0.6
0.6
Five users with
0.4
0.4
random profiles 0.2
0.2
0.0
0.0
5 10 15 20 25 5 10 15 20 25
Satisfaction
degrees
1.0
1.0
0.8
0.8
Two and twenty
0.6
0.6
users with
0.4
random profiles 0.4
0.2
0.2
0.0
0.0
5 10 15 20 25 5 10 15 20 25

51. Other experiments
1.0
1.0
0.8
0.8
Size of the retrieval set
0.6
0.6
(defaults to k = 15 )
0.4
0.4
0.2
0.2
0.0
0.0
5 10 15 20 25 0 10 20 30 40
1.0
1.0
0.8
0.8
Misery threshold
0.6
0.6
(defaults to µ = −0.75 )
0.4
0.4
0.2
0.2
0.0
0.0
5 10 15 20 25 5 10 15 20 25
1.0
1.0
0.8
0.8
Initial satisfaction
0.6
0.6
(defaults to ι = 0.4 )
0.4
0.4
0.2
0.2
0.0
5 10 15 20 25 0.0 5 10 15 20 25

52. Chapter 7.
Conclusions

53. Contributions
Musical Tasks
Playlists
Associations
Musical
Experience Poolcasting
Sequence
Web
Group
Individual Customisation
Listening habits
Preferences
1. Reinterpretation of Case-Based Reasoning
2. Mining the Web for valuable experiential data
3. Iterated social choice and preference aggregation
4. A social Web radio application

54. Future work
Content Delivers a sequence of items
…
Poolcasting
system to satisfy the group of people
Audience
1. Generalising poolcasting to other domains
2. Abstracting the iterated social choice problem
3. Uncovering associations for movies, TV shows, …

55. Publications
[ECCBR ’06] Baccigalupo and Plaza. Case-based sequential ordering of songs for
playlist recommendation. In Proceedings of the 8th European Conference on Case-
Based Reasoning, volume 4106 of Lecture Notes in Computer Science, pages 286–
300, Springer 2006.
[ICCBR ’07] Baccigalupo and Plaza. A case-based song scheduler for group
customised radio. In Proceedings of the 7th International Conference on Case-
Based Reasoning, volume 4626 of Lecture Notes in Computer Science, pages 433–
448, Springer 2007. Best Application Paper
[ECML ‘07] Baccigalupo and Plaza. Mining music social networks for automating
social music services. In Workshop Notes of the ECML/PKDD 2007 Workshop on
Web Mining 2.0, pages 123–134, 2007.

56. Publications
[AXMEDIS ‘07] Baccigalupo and Plaza. Poolcasting: a social Web radio architecture
for group customisation. In Proceedings of the 3rd International Conference on
Automated Production of Cross Media Content for Multi-Channel Distribution,
pages 115–112, IEEE Computer Society 2007.
[ICMC ‘07] Baccigalupo and Plaza. Sharing and combining listening experience: a
social approach to Web radio. In Proceedings of the 2007 International Computer
Music Conference, pages 228–231, 2007.
[ISMIR ‘08] Baccigalupo, Plaza, and Donaldson. Uncovering affinity of artists to
multiple genres from social behaviour data. In Proceedings of the 8th International
Conference of Music Information Retrieval (ISMIR), pages 275–280, 2008.
[ICCBR ‘09] Plaza and Baccigalupo. Principle and praxis in the experience Web: a
case study in social music. In Proceedings of the ICCBR 2009 Workshops, pages 55–
63, University of Washington Tacoma, 2009.