Examinando por Autor "Palomares, Iván"
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Ítem A Consensus-Driven Group Recommender System(Wiley Periodicals, Inc, 2015) Castro, Jorge; Quesada, Francisco J.; Palomares, Iván; Martínez, LuisRecommender systems aim at filtering large amounts of information for users, providing them with those pieces of information which better meet their preferences or needs. Such systems have been traditionally used in diverse areas, such as e-commerce or tourism. Within this context, group recommender systems address the problem of generating recommendations for groups of users who might have different interests. Although different aggregation processes have been extensively utilized in real-life applications to generate group recommendations, such processes do not guarantee that the list of products recommended to the group reflect a high agreement level among its members' individual preferences. Given the need for considering the added value of obtaining group recommendations under a high agreement level, this paper presents a novel group recommender system methodology that attempts to reach a high level of consensus among individual recommendations of group members. To do this, and inspired by existing group decision-making approaches in the literature, a consensus reaching process is carried out to bring such individual recommendations closer to each other before delivering the group recommendations.Ítem Managing experts behavior in large-scale consensus reaching processes with uninorm aggregation operators(Elsevier, 2015-10) Quesada, Francisco J.; Palomares, Iván; Martínez, LuisIn many real-life large scale group decision making problems, it can be necessary and convenient a consensus reaching process, which is an iterative procedure aimed at seeking a high degree of agreement amongst experts’ preferences before making a group decision. Although a wide variety of models and approaches have been proposed and developed to support consensus reaching processes, in large groups there are some important aspects that still require further study, such as the treatment of experts’ behaviors that could hamper reaching the wanted agreement. More specifically, it would be necessary an approach to deal with experts properly, based on the overall behavior they present during the discussion process, as well as reinforcing repeated patterns of cooperative (or uncooperative) behavior adopted by experts. This paper presents an expert weighting methodology for consensus reaching processes in large-scale group decision making, that incorporates the use of uninorm aggregation operators. Such operators, which are characterized by their property of full reinforcement, are used in the proposed methodology to allow the experts’ weighting based on their overall behavior during the consensus process and the behavior evolution across the time. This proposal is integrated in a consensus model for large-scale group decision making problems under uncertainty, and it is put in practice to show an illustrative example of its effectiveness and improvements with respect to other approaches.Ítem Using Computing with Words for Managing Non-cooperative Behaviors in Large Scale Group Decision Making(Springer International, 2015) Quesada, Francisco José; Palomares, Iván; Martínez, LuisNormally, in group decision making problems, groups are composed by individuals or experts with different goals and points of view. For these reasons, they may adopt distinct behaviors in order to achieve their own aims. Nonetheless, in such problems in general, specially those demanding a certain degree of consensus, each expert should comply with a collaboration contract in order to find a common solution for the decision problem. When decision groups are small, all experts usually attempt to fulfill the collaboration contract. However, nowadays technologies such as social media allow to make consensus-driven decisions with larger groups, in which many experts are involved, hence the possibility that some of them try to break the collaboration contract might be greater. In order to prevent the group solution from being biased by these experts, it is necessary to detect and manage their non-cooperative behaviors in this kind of problems. Recent proposals in the literature suggest managing non-cooperative behavior by reducing the importance of expert opinions. These proposals present drawbacks such as, the inability of an expert to recover his/her importance if behavior improves; and the lack of expert’s behavior measures across the time. This chapter introduces a methodology based on fuzzy sets and computing with words, with the aim of identifying and managing those experts whose behavior does not contribute to reach an agreement in consensus reaching processes. Such a methodology is characterized by allowing the importance recovery of experts and taking into account the evolution of their behavior across the time.