=Paper=
{{Paper
|id=Vol-1388/umap2015_posters_demos_preface.pdf
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1388/demo_preface.pdf
|volume=Vol-1388
}}
==None==
https://ceur-ws.org/Vol-1388/demo_preface.pdf
Proceedings
of
the
Posters
and
Demos
at
the
23rd
Conference
on
User
Modelling,
Adaptation
and
Personalisation
(UMAP
2015)
29th
June
-‐
3rd
July,
2015,
Dublin,
Ireland
Alexandra
I.
Cristea*
and
Nava
Tintarev**
*University
of
Warwick,
UK
a.i.cristea@warwick.ac.uk
**
University
of
Aberdeen,
Scotland
n.tintarev@abdn.ac.uk
Preface
The
23rd
International
Conference
on
User
Modelling,
Adaptation
and
Personalization
(UMAP
2015)
was
held
in
Dublin,
Ireland,
between
29th
of
June
and
the
3rd
of
July,
2015.
Poster
and
demonstration
papers
contain
original
and
unpublished
accounts
of
innovative
research
ideas,
preliminary
results,
industry
showcases,
and
system
prototypes,
addressing
both
the
theory
and
practice
of
User
Modelling,
Adaptation
and
Personalization.
A
total
of
21
submissions
were
received.
Similarly
to
the
main
tracks
of
the
conference,
each
of
them
was
reviewed
by
at
least
three
members
of
the
Program
Committee.
Submissions
have
been
assessed
based
on
their
originality
and
novelty,
potential
contribution
to
the
research
field,
potential
impact
in
particular
use
cases,
and
the
usefulness
of
presented
experiences,
as
well
as
their
overall
readability.
For
these
proceedings,
11
papers
(6
posters,
5
demos)
were
accepted
based
on
this
process,
for
presentation
at
the
UMAP
2015
conference,
on
the
1st
of
July
2015.
Posters:
• Anthony
Cruickshank,
Subramanian
Ramamoorthy
and
Richard
Shillcock.
Predicting
actions
using
a
probabilistic
model
of
human
decision
behaviours
Abstract:
Computer
interfaces
provide
many
different
optimal
methods
for
completing
tasks.
However,
though
users
have
a
large
degree
of
freedom,
typically
they
will
settle
on
a
smaller
set
of
preferred
solutions.
Designing
an
interface
agent
to
provide
assistance
in
this
environment
thus
requires
not
only
knowledge
of
the
objectively
optimal
solutions,
but
also
recognition
that
users
act
from
habit
and
that
adaptation
to
an
individual's
subjectively
optimal
solutions
is
required.
We
present
a
dynamic
Bayesian
network
model
for
predicting
a
user's
actions
by
inferring
whether
a
decision
is
being
made
by
deliberation
or
through
habit.
The
model
adapts
to
individuals
in
a
principled
manner
by
incorporating
observed
actions
using
Bayesian
probabilistic
techniques.
We
demonstrate
the
model's
effectiveness
using
specific
implementations
of
deliberation
and
habitual
decision
making,
that
are
simple
enough
to
transparently
expose
the
mechanisms
of
our
estimation
procedure.
We
show
that
this
implementation
achieves
>90%
prediction
accuracy
in
a
task
with
a
large
number
of
optimal
solutions
and
a
high
degree
of
freedom
in
selecting
actions.
• Dario
De
Nart,
Dante
Degl'Innocenti
and
Carlo
Tasso.
Introducing
Distiller:
a
Lightweight
Framework
for
Knowledge
Extraction
and
Filtering
Abstract:
Semantic
content
analysis
is
an
activity
that
can
greatly
support
a
broad
range
of
user
modelling
applications.
Several
automatic
tools
are
available,
however
such
systems
usually
provide
little
tuning
possibilities
and
do
not
support
integration
with
different
systems.
Personalization
applications,
on
the
other
hand,
are
becoming
increasingly
multi-‐lingual
and
cross-‐domain.
In
this
paper
we
present
a
novel
framework
for
Knowledge
Extraction,
whose
main
goal
is
to
support
the
development
of
new
strategies
and
technologies
and
to
ease
the
integration
of
the
existing
ones.
• Aidan
Jones,
Susan
Bull
and
Ginevra
Castellano.
Teacher
Scaffolding
of
a
Student's
Self-‐
regulated
Learning
using
an
Open
Learner
Model
Abstract:
This
paper
describes
a
study
of
a
teacher's
scaffolding
to
support
reflection
and
self-‐
regulated
learning
(SRL)
with
an
open
learner
model
(OLM)
in
a
geography
based
task
on
a
touch
screen.
The
study
was
carried
out
in
6
one-‐on-‐one
sessions
with
students
between
the
ages
of
10
and
11.
We
present
examples
of
teachers
scaffolding
students'
SRL
behaviours
using
the
OLM,
demonstrating
how
an
OLM
can
be
used
to
prompt
the
learner
to
monitor
their
developing
skills,
set
goals,
and
use
appropriate
tools.
• Fahim
A.
Salim,
Killian
Levacher,
Owen
Conlan
and
Nick
Campbell.
Examining
Multimodal
Characteristics
of
Video
to
Understand
User
Engagement
Abstract:
Video
content
is
being
produced
in
ever
increasing
quantities
and
offers
a
potentially
highly
diverse
source
for
personalizable
content.
A
key
characteristic
of
quality
video
content
is
the
engaging
experience
it
offers
for
end
users.
This
paper
explores
how
different
characteristics
of
a
video,
e.g.
face
detection,
paralinguistic
features
in
the
audio
track,
extracted
from
different
modalities
in
the
video
can
impact
how
users
rate
and
thereby
engage
with
the
video.
These
characteristics
can
further
be
used
to
help
segment
videos
in
a
personalized
and
contextually
aware
manner.
Initial
experimental
results
from
the
study
presented
in
this
paper
provide
encouraging
results.
• Cameron
Summers
and
Phillip
Popp.
Large
Scale
Discovery
of
Seasonal
Music
From
User
Data
Abstract:
The
consumption
history
of
online
media
content
such
as
music
and
video
offers
a
rich
source
of
data
from
which
to
mine
information.
Trends
in
this
data
are
of
particular
interest
because
they
reflect
user
preferences
as
well
as
associated
cultural
contexts
that
can
be
exploited
in
systems
such
as
recommendation
or
search.
This
paper
classifies
songs
as
seasonal
using
a
large,
real-‐world
dataset
of
user
listening
data.
Results
show
strong
performance
of
classification
of
Christmas
music
with
Gaussian
Mixture
Models.
• Robert
Moro
and
Maria
Bielikova.
Utilizing
Gaze
Data
in
Learning:
From
Reading
Patterns
Detection
to
Personalization
Abstract:
Although
a
lot
of
attention
has
been
dedicated
towards
improvement
of
the
modeling
of
learners’
knowledge
within
learning
systems,
recommendation,
or
personalization,
there
is
less
attention
on
improvement
of
the
learning
content
itself
and
providing
support
to
learning
content
creators.
In
addition,
the
complexity
of
learning
systems
requires
utilization
of
novel
sources
of
implicit
feedback,
such
as
gaze
data
in
order
to
model
learners’
interactions
in
their
entirety.
In
this
poster
paper,
we
present
a
framework
for
collection
of
gaze
data
and
its
utilization
in
the
learning
systems
environment.
We
focus
on
the
analysis
of
reading
patterns
for
the
detection
of
problematic
parts
of
text
and
present
results
of
a
preliminary
evaluation
in
a
web-‐based
learning
system
ALEF.
Demos:
• Cornelius
A.
Ludmann,
Marco
Grawunder
and
H.-‐Jürgen
Appelrath.
OdysseusRecSys:
Collaborative
Filtering
based
on
a
Data
Stream
Management
System
Abstract:
The
development
of
algorithms
for
online
Collaborative
Filtering
(CF)
in
the
past
few
years
enables
to
add
new
rating
data
to
existing
models.
The
Recommender
System
(RecSys)
task
changes
from
calculating
recommendations
from
a
static
and
finite
dataset
to
continuously
processing
rating
data.
Instead
of
using
stream
processing
frameworks
to
implement
CF
algorithms,
we
present
a
prototype
that
extends
the
open
source
Data
Stream
Management
System
(DSMS)
Odysseus
in
a
generic
and
domain-‐independent
way.
The
user
can
build
a
custom
RecSys
that
benefits
from
existing
DSMS
features
by
defining
a
continuous
query
with
a
declarative
query
language.
• Panagiotis
Germanakos,
Marios
Belk,
Argyris
Constantinides
and
George
Samaras.
The
PersonaWeb
System:
Personalizing
E-‐Commerce
Environments
based
on
Human
Factors
Abstract:
This
demonstration
paper
presents
the
PersonaWeb
system,
an
adaptive
interactive
system
that
personalizes
the
visual
and
interaction
design
aspects
of
E-‐
Commerce
product
views
based
on
individual
differences
in
cognitive
processing.
The
PersonaWeb
system
consists
of
three
main
components:
i)
the
user
modeling
component
in
which
explicit
and
implicit
user
data
collection
methods
are
performed
for
eliciting
the
users’
cognitive
processing
factors;
ii)
the
content
management
component
for
creating
and
managing
structured
Web
content;
and
iii)
the
adaptive
user
interface
that
is
responsible
for
performing
rule-‐based
mechanisms
for
deciding
and
communicating
a
personalized
visual
and
interaction
design
according
to
the
users’
cognitive
characteristics.
• Guibing
Guo,
Jie
Zhang,
Zhu
Sun
and
Neil
Yorke-‐Smith.
LibRec:
A
Java
Library
for
Recommender
Systems
Abstract:
The
large
array
of
recommendation
algorithms
proposed
over
the
years
brings
a
challenge
in
reproducing
and
comparing
their
performance.
This
paper
introduces
an
open-‐
source
Java
library
that
implements
a
suite
of
state-‐of-‐the-‐art
algorithms
as
well
as
a
series
of
evaluation
metrics.
We
empirically
find
that
LibRec
performs
faster
than
other
such
libraries,
while
achieving
competitive
evaluative
performance.
• Alejandro
Montes
García,
Natalia
Stash
and
Paul
De
Bra.
Adaptive
applications
to
assist
students
with
autism
in
succeeding
in
higher
education
Abstract:
This
demo
shows
adaptation
of
presentation
and
information
for
students
entering
a
university,
especially
for
students
with
autism.
These
students
not
only
have
specific
information
need,
they
are
also
more
concerned
about
their
privacy.
We
use
WiBAF
(Within
Browser
Adaptation
Framework)
for
user
modeling
and
adaptation
to
give
users
control
over
the
sharing
of
their
data.
• Khalil
Muhammad,
Aonghus
Lawlor,
Rachael
Rafter
and
Barry
Smyth.
Generating
Personalised
and
Opinionated
Review
Summaries
Abstract:
This
paper
describes
a
novel
approach
for
summarising
user-‐generated
reviews
for
the
purpose
of
explaining
recommendations.
We
demonstrate
our
approach
using
TripAdvisor
reviews.
We
thank
all
authors
for
submitting
and
presenting
their
works,
and
members
of
the
Program
Committee
for
providing
their
valuable
time
and
expertise
for
reviewing
and
selecting
the
papers.
All
their
efforts
made
UMAP
2015
poster
and
demo
results
possible.
Alexandra
I.
Cristea
Nava
Tintarev
PROGRAM
COMMITTEE
Kenro
Aihara
National
Institute
of
Informatics
Omar
Alonso
Microsoft
Liliana
Ardissono
University
of
Torino
Hideki
Asoh
AIST
Mathias
Bauer
mineway
GmbH
Maria
Bielikova
Slovak
University
of
Technology
in
Bratislava
Pradipta
Biswas
Wolfson
College,
Cambridge
University
Robin
Burke
DePaul
University
Iván
Cantador
Universidad
Autónoma
de
Madrid
Federica
Cena
Department
of
Computer
Science,
University
of
Torino
Min
Chi
North
Carolina
State
University
David
Chin
University
of
Hawaii
Mihaela
Cocea
School
of
Computing,
University
of
Portsmouth
Paolo
Cremonesi
Politecnico
di
Milano
Sidney
D'Mello
University
of
Notre
Dame
Paul
De
Bra
TU/e
Marco
De
Gemmis
Dipartimento
di
Informatica
-‐
University
of
Bari
Ernesto
Diaz-‐Aviles
IBM
Reseach
Vania
Dimitrova
School
of
Computing,
University
of
Leeds
Peter
Dolog
Department
of
Computer
Science,
Aalborg
University
Benedict
Du
Boulay
Informatics
Department,
University
of
Sussex
Casey
Dugan
IBM
T.J.
Watson
Research
Fabio
Gasparetti
Artificial
Intelligence
Laboratory
-‐
ROMA
TRE
University
Mouzhi
Ge
Universitaet
der
Bundeswehr
Munich
Cristina
Gena
Department
of
Computer
Science,
University
of
Torino
Werner
Geyer
IBM
T.J.
Watson
Research
Bradley
Goodman
The
MITRE
Corporation
Eelco
Herder
L3S
Research
Center
Dietmar
Jannach
TU
Dortmund
Robert
Jäschke
L3S
Research
Center
W.
Lewis
Johnson
Alelo
Inc.
Judy
Kay
University
of
Sydney
Styliani
Kleanthous
University
of
Cyprus
Bart
Knijnenburg
University
of
California,
Irvine
Tsvi
Kuflik
The
University
of
Haifa
Pasquale
Lops
University
of
Bari
Bernd
Ludwig
Chair
for
Information
Science
Gordon
McCalla
University
of
Saskatchewan
Tanja
Mitrovic
Intelligent
Computer
Tutoring
Group,
University
of
Canterbury,
Christchurch
Riichiro
Mizoguchi
Japan
Advanced
Institute
of
Science
and
Technology
Elena
Not
FBK-‐irst
Aditya
Pal
IBM
Georgios
Paliouras
Institute
of
Informatics
&
Telecommunications,
NCSR
"Demokritos"
Luiz
Augusto
Pizzato
Octosocial
Labs
Katharina
Reinecke
University
of
Zurich
Lior
Rokach
BGU
Alan
Said
Recorded
Future
Olga
C.
Santos
aDeNu
Research
Group
(UNED)
Giovanni
Semeraro
Dipartimento
di
Informatica
-‐
University
of
Bari
Aldo
Moro
Bracha
Shapira
Ben-‐Gurion
University
Barry
Smyth
University
College
Dublin
Myra
Spiliopoulou
U.
Magdeburg
Ben
Steichen
University
of
British
Columbia
Dhavalkumar
Thakker
University
of
Leeds
Marko
Tkalcic
Johannes
Kepler
University
Department
of
Computational
Perception
Christoph
Trattner
KMI,
TU-‐Graz
Jian
Wang
LinkedIn
Corporation
Stephan
Weibelzahl
Private
University
of
Applied
Sciences
Göttingen
Markus
Zanker
Alpen-‐Adria-‐Universitaet
Klagenfurt
Jie
Zhang
Nanyang
Technological
University
Yong
Zheng
DePaul
University