Poolcasting

Claudio Baccigalupo, IIIA–CSICBellaterra, November 6th, 2009

Poolcasting:

an intelligent technique to

customise music programmes

for their audience

Create something intelligent

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Nobody understands me!

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Let’s search for an actual problem

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PartyStrands

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Develop an intelligent technique

to satisfy a group of listeners

by delivering a sequence of songs

adapted for the entire audience

Scope of the research

Desired properties

Variety avoiding repetitions

Smoothness nice musical transitions

Customisation adapted for the audience

Fairness satisfactory for everyone

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

Chapter 2.Musical associations from a Web of experiences

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

Collecting listening habits

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A Web of music data

How often do and occur in the same playlists? Do they always occur in the same order? Contiguously?

Playlists

X

Y

Co-occurrence analysis

X Y

measures the association between and based on their co-occurrences in a set of playlists

Playlists

s(X, Y )X

Y

Co-occurrence analysis

s(X,Y ) ! [0, 1] X

Y

From playlists to associations

Initial data set: 993,825 playlists

After noise removal: 465,438 playlists

estimated for ~400K songs by ~50K artists

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

010

0,00

020

0,00

030

0,00

0

0 4 8 12 16 20 24 28 32 36 40

010

,000

20,000

30,000

40,000

50,000

Fig 2.2

Alphabetically ordered songs/artists [limited to 1~15]

Num

ber o

f pla

ylis

ts

Number of songs [limited to 1~40]N

umbe

r of p

layl

ists

songsartists

! !

s(X, Y )

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

2. Sammy Davis Jr.

the same result of

Lists of associated songs


1. Introduction

2. Musical associations

3. Individual listening behaviours customisation




7. Conclusions

s(X, Y )

Chapter 3.Individual listening behaviours

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)

Listening habits data

estimates the implicit preference of for a song combining the observed rating and play count

From habits to implicit preferences

Implicit user modeling

X

U

i(U, X)

i(U, X) ! [0, 1] U

X


1. Introduction


3. Individual listening behaviours




7. Conclusions

i(U, X)

s(X, Y )

Chapter 4.The poolcasting technique

Overview

C3U3U1C1 C2

U2

timeT = 1 T = 2 T = 3

H1 H2 H3 . . .

Poolcasting Poolcasting Poolcasting

Adding one song to the sequence

C3U3

timeT = 1 T = 2 T = 3

H1 H2

PoolcastingCase-Based Reasoning

A collection of Case Bases

Build one Case Base for each userC3

U3

timeT = 1 T = 2 T = 3

H1 H2

Listeninghabits

Case Bases

Individual preferences

U1

X

0.5

. . .

X

. . .

1.00.2

!0.7

U2

i(U1, X)

i(U2, X)

The Retrieve process

Extract from the Case Bases a subset of songs that:

- have not been played recently

- maximise the degree

C3U3

timeT = 1 T = 2 T = 3

H1 H2

Listeninghabits

Case Bases

Retrieve

Musical

association


variety

smoothnesss(H2, Y )

Y

The Reuse process

Rank the retrieved set accordingto the aggregated preferences ofall the members of the audience

C3U3

timeT = 1 T = 2 T = 3

H1 H2

Listeninghabits

Case Bases

Retrieve

Musical

association


Reuse

customisationfairness

H3

The Revise process

Update the implicit preferenceswith the users’ explicit feedback

C3U3

timeT = 1 T = 2 T = 3

H1 H2

Listeninghabits

Case Bases

Retrieve

Musical

association

Revise

H3 . . .


Reuse

i(U, X)

e(U, X, T )

p(U, X, T )

implicit

explicit

preference

The iterated CBR technique

C3U3

Listeninghabits

Case Bases

Retrieve

Musical

association

Revise

U1C1 C2U2

timeT = 1 T = 2 T = 3

H1 H2 H3 . . .


Reuse


MusicalassociationReuse

RetrieveRevise

Listeninghabits

Case Bases


Reuse

RetrieveRevise

Listeninghabits

Case Bases

Aggregating individual preferences

From multiple preference degrees :

to an aggregated group-preference :

p(U1, X, T ) p(U2, X, T ) p(U3, X, T )X

g(X, T ) ! ["1, 1]

p(U, X, T ) ! ["1, 1]

From multiple preference degrees :

to an aggregated group-preference :

defined as a satisfaction-weighted average

Aggregating individual preferences

p(U1, X, T ) p(U2, X, T )

g(X, T ) =!

U!UT

(1! q(U, T ! 1)) ·p(U, X, T )

#(UT )

p(U3, X, T )X

X

weight average

g(X, T ) ! ["1, 1]

p(U, X, T ) ! ["1, 1]

Avoiding misery

The satisfaction-weighted aggregation is completed with a measure intended to avoid misery:

This results is an acceptable compromise for the group

g(X, T ) =

!"""""#

"""""$

!1 if "U # UT :p(U, X, T ) < µ

%U!UT

(1! q(U, T ! 1)) ·p(U, X, T )

#(UT )otherwise.

assign the minimum degree if any user strongly dislikes X

g(X, T ) ! ["1, 1]


1. Introduction


3. Individual listening behaviours

4. The poolcasting CBR technique



7. Conclusions

Chapter 5.Group-customised Web radio

What is Poolcasting radio?

A Poolcasting radio channel

Listeners can play music

Listeners can create public channels

Participants contribute with own music

Listeners can interact

Listeners influence the music played

The Poolcasting radio architecture

Participant ParticipantPersonal LibraryMediaPlayer

I N T E R N E T

share library

list ofshared songs

ratings andplay counts

PREFERENCES

MUSIC POOL

availablesongs

Library Parser

MUSICAL ASSOCIATIONSplaylists

CURRENT LISTENERS

CHANNELS

Streaming Server

Stream Generator

list oflisteners

audio signal

OGG stream(256 Kbps)

MP3 stream(64 Kbps)

metadata

rate songs

Song Scheduler

knowledge toschedule

create channel

uploadsong

Database

Web Interface

Chapter 6.Experiments and evaluation

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

Artificially created profiles

Individualpreferences

Satisfactiondegrees

Five users with random profiles

5 10 15 20 25

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5 10 15 20 25

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1.0

A worst-case scenario


Satisfactiondegrees


Two groups with discordant tastes

5 10 15 20 25

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A worst-case scenario


Satisfactiondegrees


Two groups with discordant tastes

(non-weighted aggregation)

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A realistic scenario


Satisfactiondegrees


Five users with concordant

profiles

5 10 15 20 25

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Scalability

Satisfactiondegrees


Two and twenty users with

random profiles

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Other experiments

Size of the retrieval set(defaults to )

Misery threshold(defaults to )

Initial satisfaction(defaults to )

k = 15

µ = !0.75

! = 0.45 10 15 20 25

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Chapter 7.Conclusions

Contributions

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

Listening habits

Playlists

Poolcasting

Tasks

Group Customisation

Experience

Web

Musical Sequence

Musical Associations

Individual Preferences

Future work

1. Generalising poolcasting to other domains

2. Abstracting the iterated social choice problem

3. Uncovering associations for movies, TV shows, …

Content

Audience

Poolcasting

system

Delivers a sequence of items

to satisfy the group of people

…

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.

[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.

Best Application Paper

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.

Education

Poolcasting