1,721,683 research outputs found
Personalized Searching by Learning WordNet-based User Profiles
No full textThe amount of information available on the Web and in Digital Libraries is increasing over time. In this context, the role of user modeling and personalized information access is becoming crucial: Users need a personalized support in sifting through large amounts of retrieved information according to their interests. Information filtering and retrieval systems relying on this idea adapt their behavior to individual users by learning their preferences during the interaction in order to construct a profile of the user that can be later exploited in the search process. We propose a novel technique to learn user profiles which exploits word sense disambiguation based on the WordNet lexical database, in an attempt to produce semantic user profiles that might discover topics semantically closer to the user interests. Semantic profiles are used in the definition of a retrieval model that turns the traditional document-query search paradigm into a novel document-query-profile paradigm. As an example of this paradigm, we present an extension of the vector space model in which profiles are used to modify the ranking of search results obtained in response to a query, hopefully putting personally relevant items on the top of the result list. Experimental results in a movie retrieval scenario indicate that the proposed model to personalize Web search is effective
Student profiles to improve searching in e-learning systems
No full textEuropean countries have accumulated an enormous quantity of information in Digital Libraries (DLs). Offering seamless universal access to those collections will have a formidable impact on citizens' activities. Students could use information in DLs for improving their curricula, but it is difficult to find the exact chunk of material that solves a specific problem. A possible solution is to develop technologies that learn user preferences for customising information search. This paper focuses on a system based on Machine Learning techniques, the Profile Extractor, which automatically builds student models. An experimental session has been performed, evaluating the accuracy of the system
Information Visualization in the Interaction with IDL
No full textWe briefly discuss the state of the art of the research in information visualization.
Then, we describe a technique for visualizing meta-information about the content of a
networked information system in the context of a digital library, which is being developed at
the University of Bari
Personalized Wealth Management through Case-Based Recommender Systems
No full textWealth management services have become a priority for most financial services
organizations firms.
As investors are pressing wealth managers to justify
their value proposition,
turbulence in financial
markets reinforced the need
to improve the advisory
offering with more customized
and sophisticated services.
As a consequence, a recent
trend in wealth management is
to improve the advisory process by exploiting recommendation technologies.
However, widespread recommendation approaches,
such as content-based (CB) and collaborative filtering
(CF), can hardly be put into
practice in this domain.
In
fact,
in
this
domain
each
user
is
typically
modeled
through
his
risk
profile
and
other
simple
features,
while
each
financial
product
is
described
through
a
rating
provided
by
credit
rating
agencies,
an
average
yield
and
the
category
it
belongs
to.
In
this
scenario
a
pure
CB
strategy
is
likely
to
fail
since
content
information
is
too
poor
and
not
meaningful
to
feed
a
CB
recommendation
algorithm.
Furthermore,
the
over-‐specialization
problem,
typical
of
CB
recommenders,
may
collide
with
the
fact
that
turbulence
and
fluctuations
in
financial
markets
suggest
to
change
and
diversify
the
investments
over
time.
Similarly,
CF
algorithms
can
hardly
be
adopted
since
they
may
lead
to
the
well-‐known
problem
of
flocking:
given
that
user-‐based
CF
provides
recommendations
by
assuming
that
a
user
is
interested
in
the
asset
classes
other
people
similar
to
her
already
invested
in,
this
could
move
many
similar
users
to
invest
in
the
same
asset
classes
at
the
same
time,
making
the
recommendation
algorithm
victim
of
potential
trader
attacks1.
These
dynamics
suggest
to
focus
on
different
recommendation
paradigms.
Given
that
financial
advisors
have
to
analyze
and
sift
through
several
investment
portfolios2
before
providing
the
user
with
a
solution
able
to
meet
his
investment
goals,
the
insight
behind
our
recommendation
framework
is
to
exploit
case-‐based
reasoning
(CBR)
to
tailor
investment
proposals
on
the
ground
of
a
case
base
of
previously
proposed
investments.
Our
recommendation
process
is
based
on
the
typical
CBR
workflow
and
is
structured
in
three
different
steps:
1) Retrieve
and
Reuse:
retrieval
of
similar
portfolios
is
performed
by
representing
each
user
through
a
feature
vector
(as
feature
risk
profile,
inferred
through
the
standard
MiFiD questionnaire3,
investment
goals,
temporal
goals,
financial
experience,
and
financial
situation
were
chosen.
Each
feature
is
represented
on
a
five-‐point
ordinal
scale,
from
very
low
to
very
high).
Next,
cosine
similarity
is
adopted
to
retrieve
the
most
similar
users
(along
with
the
portfolios
they
agreed)
from
the
case
base.
2)
Revise:
candidate
solutions
retrieved
by
the
first
step
are
typically
too
many
to
be
consulted
by
a
human
advisor.
Thus,
the
Revise
step
further
filters
this
set
to
obtain
the
final
solutions.
To
revise
the
candidate
solutions
four
techniques
were
compared:
a
basic
(temporal)
ranking,
a
Greedy
diversification
which
implements
a
Greedy
algorithm
to
select
the
solutions
with
the
best
compromise
between
quality
and
diversity
and
FCV,
a
novel
scoring
methodology
which
computes
how
close
to
the
optimal
one
is
the
distribution
of
the
asset
classes
in
the
portfolio.
3) Review
and
Retain:
in
the
Review
step
human
advisor
and
client
can
further
discuss
and
modify
the
portfolio,
before
generating
the
final
solution
for
the
user.
If
the
yield
obtained
by
the
newly
recommended
portfolio
is
acceptable,
the
solution
is
stored
in
the
case
base
and
can
be
used
in
the
future
as
input
to
resolve
similar
cases.
The
performance
of
the
framework
has
been
evaluated
in
an
experimental
session
against
1172
real
users.
Results
show
that
the
yield
obtained
by
recommended
portfolios
overcomes
that
of
portfolios
proposed
by
human
advisors
in
many
experimental
settings.
Specifically,
experiments
showed
that
FCV
Ranking
significantly
outperforms
human
recommendations
(from
0.18
to
almost
0.30
of
average
monthly
yield).
The
experimental
results
were
further
confirmed
by
an
ex-‐post
evaluation
performed
on
real
financial
data
from
January
to
Aprile
2014.
In
this
setting,
our
FCV
strategy
outperforms
the
recommendations
provided
by
human
advisors
as
well
as
those
based
on
classical
collaborative
recommendation
algorithm.
This
confirmed
the
effectiveness
of
the
approach
and
paved
the
way
for
future
research
in
the
area
E-Commerce and Web Technologies, 11th International Conference, EC-Web 2010, Bilbao, Spain, September 2010, Proceedings
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