42 research outputs found
Decision Analysis Linguistic Framework
Everyday human beings are faced with situations they should choose among different alternatives by means of reasoning and mental processes when solving a problem. Many of these decision problems are under uncertain environments including vague, imprecise and subjective information that is usually modeled by linguistic information due to the use of natural language and its relation to mental reasoning processes of the experts when expressing their judgments. In a decision process multiple criteria can be evaluated which involving multiple experts with different degrees of knowledge. Such process can be modeled by using Multi-granular Linguistic Information (MGLI) and Computing with Words (CW) processes to solve the related decision problems. Different methodologies and approaches have been proposed to accomplish this process in an accurate and interpretable way. In this paper we propose a useful Decision Analysis Framework to manage this kind of problems by using the Extended Linguistic Hierarchy (ELH), 2-tuples linguistic representation model and its computational method. The developed Framework has many advantages when dealing with a complex problem in a simple way and its capability of having easy and useful reasonably results.Sociedad Argentina de Informática e Investigación Operativ
A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling
This paper presents a survey of some fuzzy linguistic information access systems. The review shows
information retrieval systems, filtering systems, recommender systems, and web quality evaluation tools,
which are based on tools of fuzzy linguistic modelling. The fuzzy linguistic modelling allows us to
represent and manage the subjectivity, vagueness and imprecision that is intrinsic and characteristic of the
processes of information searching, and, in such a way, the developed systems allow users the access to
quality information in a flexible and user-adapted way.European Union (EU)
TIN2007-61079
PET2007-0460Ministry of Public Works
90/07Excellence Andalusian Project
TIC529
Decision Analysis Linguistic Framework
Everyday human beings are faced with situations they should choose among different alternatives by means of reasoning and mental processes when solving a problem. Many of these decision problems are under uncertain environments including vague, imprecise and subjective information that is usually modeled by linguistic information due to the use of natural language and its relation to mental reasoning processes of the experts when expressing their judgments. In a decision process multiple criteria can be evaluated which involving multiple experts with different degrees of knowledge. Such process can be modeled by using Multi-granular Linguistic Information (MGLI) and Computing with Words (CW) processes to solve the related decision problems. Different methodologies and approaches have been proposed to accomplish this process in an accurate and interpretable way. In this paper we propose a useful Decision Analysis Framework to manage this kind of problems by using the Extended Linguistic Hierarchy (ELH), 2-tuples linguistic representation model and its computational method. The developed Framework has many advantages when dealing with a complex problem in a simple way and its capability of having easy and useful reasonably results.Sociedad Argentina de Informática e Investigación Operativ
A group decision making support system for the Web: how to work in environments with a high number of participants and alternatives
One of the main challenges that the appearance of Web 2.0 and the overall
spreading of the Internet have generated is how to tackle with the high
number of users and information available. This problem is also inherited by
the group decision making problems that can be carried out over the Web.
In this article, to solve this issue, a group decision making support system
that allows the use of a high number of participants and alternatives is presented.
This method allows any number of participants to join the decision
making process at any time. Furthermore, they let them provide information
only about a certain subset of alternatives. The high participation rate can
provide enough information for the decision process to be carried out even
if the participants do not provide information about all the high number of
available alternatives.This paper has been developed with the financing of FEDER funds in the
project TIN2016-75850-R
Group Decision Making: Consensus Approaches Based on Soft Consensus Measures
A group decision making situation involves multiple decision makers communicating with others to reach a decision. In such a situation, the most important issue is to obtain a decision that is best acceptable by the decision makers, and, therefore, consensus has attained a great attention and it is a major goal of group decision making situations. To measure the closeness among the opinions given by the decision makers, different approaches have been proposed. At the beginning, consensus was meant to be a unanimous and full agreement. However, because this situation is often not reachable in practice, the use of a softer consensus, which assesses the level of agreement in a more flexible way and reflects the large spectrum of possible partial agreements, is a more reasonable approach. Soft consensus approaches better reflects a real human perception of the essence of consensus and, therefore, they have been widely used. The purpose of this contribution is to review the different consensus approaches based on soft consensus measures that have been proposed
Dynamic adaptation of user profiles in recommender systems
In a period of time in which the content available through the Internet
increases exponentially and is more easily accessible every day, techniques
for aiding the selection and extraction of important and personalised
information are of vital importance. Recommender Systems (RS) appear as
a tool to help the user in a decision making process by evaluating a set of
objects or alternatives and aiding the user at choosing which one/s of them
suits better his/her interests or preferences. Those preferences need to be
accurate enough to produce adequate recommendations and should be
updated if the user changes his/her likes or if they are incorrect or
incomplete. In this work an adequate model for managing user preferences
in a multi-attribute (numerical and categorical) environment is presented to
aid at providing recommendations in those kinds of contexts. The
evaluation process of the recommender system designed is supported by a
new aggregation operator (Unbalanced LOWA) that enables the
combination of the information that defines an alternative into a single
value, which then is used to rank the whole set of alternatives. After the
recommendation has been made, learning processes have been designed to
evaluate the user interaction with the system to find out, in a dynamic and
unsupervised way, if the user profile in which the recommendation process
relies on needs to be updated with new preferences. The work detailed in
this document also includes extensive evaluation and testing of all the
elements that take part in the recommendation and learning processes
An effective recommender system by unifying user and item trust information for B2B applications
© 2015 Elsevier Inc. Although Collaborative Filtering (CF)-based recommender systems have received great success in a variety of applications, they still under-perform and are unable to provide accurate recommendations when users and items have few ratings, resulting in reduced coverage. To overcome these limitations, we propose an effective hybrid user-item trust-based (HUIT) recommendation approach in this paper that fuses the users' and items' implicit trust information. We have also considered and computed user and item global reputations into this approach. This approach allows the recommender system to make an increased number of accurate predictions, especially in circumstances where users and items have few ratings. Experiments on four real-world datasets, particularly a business-to-business (B2B) case study, show that the proposed HUIT recommendation approach significantly outperforms state-of-the-art recommendation algorithms in terms of recommendation accuracy and coverage, as well as significantly alleviating data sparsity, cold-start user and cold-start item problems
Fashion-Specific Attributes Interpretation via Dual Gaussian Visual-Semantic Embedding
Several techniques to map various types of components, such as words,
attributes, and images, into the embedded space have been studied. Most of them
estimate the embedded representation of target entity as a point in the
projective space. Some models, such as Word2Gauss, assume a probability
distribution behind the embedded representation, which enables the spread or
variance of the meaning of embedded target components to be captured and
considered in more detail. We examine the method of estimating embedded
representations as probability distributions for the interpretation of
fashion-specific abstract and difficult-to-understand terms. Terms, such as
"casual," "adult-casual,'' "beauty-casual," and "formal," are extremely
subjective and abstract and are difficult for both experts and non-experts to
understand, which discourages users from trying new fashion. We propose an
end-to-end model called dual Gaussian visual-semantic embedding, which maps
images and attributes in the same projective space and enables the
interpretation of the meaning of these terms by its broad applications. We
demonstrate the effectiveness of the proposed method through multifaceted
experiments involving image and attribute mapping, image retrieval and
re-ordering techniques, and a detailed theoretical/analytical discussion of the
distance measure included in the loss function
Knowledge aggregation in people recommender systems : matching skills to tasks
People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecà rrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessà ria. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implÃcita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explÃcita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecà rrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les à rees d’interès per a caracteritzar les persones es dedueixen dels seus currÃculums i s’expressen en termes d’incertesa evitant que els interessos es demanin explÃcitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implÃcita i atributs indicats explÃcitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les à rees d'aplicació futures i noves perspectives.Postprint (published version