1,721,110 research outputs found
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
Case-based recommender systems for personalized finance advisory
Full text linkWealth Management is a business model operated by banks and brokers, that offers a broad range of investment services to individual clients to help them reach their investment objectives. Wealth management services include investment advisory, subscription of mandates, sales of financial products, collection of investment orders by clients. Due to the complexity of the tasks, which largely require a deep knowledge of the financial domain, a trend in the area is the exploitation of recommendation technologies to support financial advisors and to improve the effectiveness of the process. The talk presents a framework to support financial advisors in the task of providing clients with personalized investment strategies. The methodology is based on the exploitation of case-based reasoning and the introduction of a diversification technique. A prototype of the framework has been used to generate personalized portfolios, and its performance, evaluated against 1,172 real users, shows that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings
Enhanced vector space models for content-based recommender systems
No full textThe use of Vector Space Models (VSM) in the area of Infor-mation Retrieval is an established practice within the sci-entific community. The reason is twofold: first, its very clean and solid formalism allows us to represent objects in a vector space and to perform calculations on them. On the other hand, as proved by many contributions, its simplic-ity does not hurt the effectiveness of the model. Although Information Retrieval and Information Filtering undoubt-edly represent two related research areas, the use of VSM in Information Filtering is much less analzyed. The goal of this work is to investigate the impact of vector space models in the Information Filtering area. Specifically, I will introduce two approaches: the first one, based on a technique called Random Indexing, reduces the impact of two classical VSM problems, this is to say its high dimen-sionality and the inability to manage the semantics of docu-ments. The second extends the previous one by integrating a negation operator implemented in the Semantic Vectors1 open-source package. The results emerged from an exper-imental evaluation performed on a large dataset and the applicative scenarios opened by these approaches confirmed the effectiveness of the model and induced to investigate more these techniques
Exploiting Content-based Recommender Systems and Digital Libraries for Cultural Heritage Personalization
No full textA Tag Recommender System Exploiting User and Community Behavior
No full textNowadays Web sites tend to be more and more social: users can upload any kind of information on collaborative platforms and can express their opinions about the content they enjoyed through textual feedbacks or reviews. These platforms allow users to annotate resources they like through freely chosen keywords (called tags). The main advantage of these tools is that they perfectly fit user needs, since the use of tags allows organizing the information in a way that closely follows the user mental model, making retrieval of information easier. However, the heterogeneity characterizing the communities causes some problems in the activity of social tagging: someone annotates resources with very specific tags, other people with generic ones, and so on. These drawbacks reduce the exploitation of collaborative tagging systems for retrieval and filtering tasks. Therefore, systems that assist the user in the task of tagging are required. The goal of these systems, called tag recommenders, is to suggest a set of relevant keywords for the resources to be annotated. This paper presents a tag recommender system called STaR (Social Tag Recommender system). Our system is based on two assumptions: 1) the more two or more resources are similar, the more they share common tags 2) a tag recommender should be able to exploit tags the user already used in order to extract useful keywords to label new resources. We also present an experimental evaluation carried out using a large dataset gathered from Bibsonomy
A framework for Personalized Wealth Management exploiting Case-Based Recommender Systems
No full textWealth Management is a business model operated by banks and brokers, that offers a broad range of investment
services to individual clients, in order to help them reach their investment objectives. Wealth management services include
investment advisory, subscription of mandates, sales of financial products, collection of investment orders by clients. Due to the
complexity of the task, which largely requires a deep knowledge of the financial domain, a recend trend in the area is to exploit
recommendation technologies to support financial advisors and to improve the effectiveness of the process.
This paper proposes a framework to support financial advisors in the task of providing clients with personalized investment
strategies. Our methodology is based on the exploitation of case-based reasoning. A prototype version of the platform has been
adopted to generate personalized portfolios, and the performance of the framework shows that the yield obtained by recommended
portfolios overcomes that of portfolios proposed by human advisors in most experimental settings
Il progetto Mappa Italiana dell’Intolleranza
No full textIl progetto “Mappa Italiana dell’Intolleranza” si è posto come principale obiettivo quello di analizzare i contenuti prodotti sulle Reti sociali al fine di misurare il livello di intolleranza del Paese, sulla base di cinque temi: omofobia, razzismo, violenza sulle donne, antisemitismo e disabilità. Il progetto, coordinato da Vox- Osservatorio sui diritti, ha visto la sinergia tra l’Università degli Studi di Milano, l’Università La Sapienza di Roma, ed il Dipartimento di Informatica dell’Università degli Studi di Bari, che ha messo a disposizione una piattaforma di Big Data & Content Analytics per l’analisi semantica di contenuti sociali
Context-aware graph-based recommendations exploiting Personalized PageRank
Full text linkIn this article we present a context-aware recommendation method that exploits graph-based data models and Personalized PageRank to provide users with recommendations.
In particular, our approach extends the basic graph-based representation that relies on users and items nodes by introducing a third class of nodes, that is to say, context nodes, whose goal is to model the different contextual situations in which an item can be consumed. Given such a data model, we used Personalized PageRank to identify the most suitable recommendations for each user: in a nutshell, our model is based on the intuition that context nodes shall be used to influence random walks, in order to assist the algorithm in identifying the items that are relevant in a particular contextual setting.
In the experimental evaluation we investigated the effectiveness of the approach on three different datasets. The results showed that our context-aware graph-based approach overcame the baselines in most of the experimental settings and obtained the best overall results in cold-start situations, thus confirming the validity of the methodology
Personalized Finance Advisory through Case-based Recommender Systems and Diversification Strategies
Full text linkRecommendation of financial investment strategies is a complex and knowledge-intensive task. Typically, financial advisors have to discuss at length with their wealthy clients and have to sift through several investment proposals before finding one able to completely meet investors' needs and constraints. As a consequence, a recent trend in wealth management is to improve the advisory process by exploiting recommendation technologies. This paper proposes a framework for recommendation of asset allocation strategies which combines case-based reasoning with a novel diversification strategy to support financial advisors in the task of proposing diverse and personalized investment portfolios. The performance of the framework has been evaluated by means of an experimental session conducted against 1172 real users, and results show that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings while meeting the preferred risk profile. Furthermore, our diversification strategy shows promising results in terms of both diversity and average yield
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