Browsing by Author "Loia, Vincenzo"

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    Fuzzy Group Decision Making for Influence-Aware Recommendations
    (Elsevier, 2018-11-13) Capuano, Nicola; Chiclana, Francisco; Herrera-Viedma, Enrique; Fujita, Hamido; Loia, Vincenzo
    Group Recommender Systems are special kinds of Recommender Systems aimed at suggesting items to groups rather than individuals taking into account, at the same time, the preferences of all (or the majority of) members. Most existing models build recommendations for a group by aggregating the preferences for their members without taking into account social aspects like user personality and interpersonal trust, which are capable of affecting the item selection process during interactions. To consider such important factors, we propose in this paper a novel approach to group recommendations based on fuzzy influence-aware models for Group Decision Making. The proposed model calculates the influence strength between group members from the available information on their interpersonal trust and personality traits (possibly estimated from social networks). The estimated influence network is then used to complete and evolve the preferences of group members, initially calculated with standard recommendation algorithms, toward a shared set of group recommendations, simulating in this way the effects of influence on opinion change during social interactions. The proposed model has been experimented and compared with related works.
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    Fuzzy Group Decision Making with Incomplete Information Guided by Social Influence
    (IEEE Xplore, 2017-08-24) Chiclana, Francisco; Fujita, Hamido; Herrera-Viedma, Enrique; Capuano, Nicola; Loia, Vincenzo
    A promising research area in the field of Group Decision Making (GDM) is the study of interpersonal influence and its impact on the evolution of experts’ opinions. In conventional GDM models, a group of experts express their individual preferences on a finite set of alternatives, then preferences are aggregated and the best alternative, satisfying the majority of experts, is selected. Nevertheless, in real situations, experts form their opinions in a complex interpersonal environment where preferences are liable to change due to social influence. In order to take into account the effects of social influence during the GDM process, we propose a new influence-guided GDM model based on the following assumptions: experts influence each other and the more an expert trusts in another expert, the more his opinion is influenced by that expert. The effects of social influence are especially relevant to cases when, due to domain complexity, limited expertise or pressure to make a decision, an expert is unable to express preferences on some alternatives, i.e. in presence of in-complete information. The proposed model adopts fuzzy rank-ings to collect both experts’ preferences on available alternatives and trust statements on other experts. Starting from collected information, possibly incomplete, the configuration and the strengths of interpersonal influences are evaluated and repre-sented through a Social Influence Network (SIN). The SIN, in its turn, is used to estimate missing preferences and evolve them by simulating the effects of experts’ interpersonal influence before aggregating them for the selection of the best alternative. The proposed model has been experimented with synthetic data to demonstrate the influence driven evolution of opinions and its convergence properties.
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    Fuzzy Rankings for Preferences Modeling in Group Decision Making
    (Wiley, 2018-03-31) Capuano, Nicola; Chiclana, Francisco; Herrera-Viedma, Enrique; Fujita, Hamido; Loia, Vincenzo
    Although fuzzy preference relations (FPRs) are among the most commonly used preference models in group decision making (GDM), they are not free from drawbacks. First of all, especially when dealing with many alternatives, the definition of FPRs becomes complex and time consuming. Moreover, they allow to focus on only two options at a time. This facilitates the expression of preferences but let experts lose the global perception of the problem with the risk of introducing inconsistencies that impact negatively on the whole decision process. For these reasons, different preference models are often adopted in real GDM settings and, if necessary, transformation functions are applied to obtain equivalent FPRs. In this paper, we propose fuzzy rankings, a new approximate preference model that offers a higher level of user‐friendliness with respect to FPRs while trying to maintain an adequate level of expressiveness. Fuzzy rankings allow experts to focus on two alternatives at a time without losing the global picture so reducing inconsistencies. Conversion algorithms from fuzzy rankings to FPRs and backward are defined as well as similarity measures, useful when evaluating the concordance between experts’ opinion. A comparison of the proposed model with related works is reported as well as several explicative examples.
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    Guest Editorial: Intelligent Decision Making and Consensus Under Uncertainty in Inconsistent and Dynamic Environments
    (Elsevier, 2018-12-15) Herrera-Viedma, Enrique; Chiclana, Francisco; Dong, Yucheng; Loia, Vincenzo; Kou, Gang; Fujita, Hamido