Deriving the Personalized Individual Semantics of Linguistic Information from Flexible Linguistic Preference Relations

Author(s):  
Le Jiang ◽  
Hongbin Liu ◽  
Yue Ma ◽  
Yongfeng Li
2016 ◽  
Vol 32 (1) ◽  
pp. 31-45
Author(s):  
Phạm Hồng Phong ◽  
Bùi Công Cường

In \cite{Cuong14, Phong14}, we first introduced the notion of intuitionistic linguistic labels. In this paper, we develop two symbolic computational models for intuitionistic linguistic labels (intuitionistic linguistic information). Various operators are proposed, their properties are also examined. Then, an application in group decision making using intuitionistic linguistic preference relations is discussed.


Author(s):  
LUIS MARTÍNEZT ◽  
LUIS G. PÉREZ ◽  
MANUEL BARRANCO ◽  
MACARENA ESPINILLA

In the e-commerce arena new methods and tools have been recently developed to improve and customize the e-commerce web sites, according to users' necessities and preferences, that are usually vague and uncertain. The most successful tool in this field has been the Recommender Systems. Their aim is to assist e-shops customers to find out the most suitable products by using recommendations. Sometimes, these systems face situations where there is a lack of information or the information is vague or imprecise that yield unsuccessful results. Although several solutions have been proposed, they still present some limitations. In this paper, we present a Knowledge-Based Recommender System that manages and models the uncertainty related to users' preferences by using linguistic information. This system will overcome the problem of lack of information by computing recommendations through completing incomplete linguistic preference relations provided by the users.


2020 ◽  
Vol 39 (3) ◽  
pp. 4041-4058
Author(s):  
Fang Liu ◽  
Xu Tan ◽  
Hui Yang ◽  
Hui Zhao

Intuitionistic fuzzy preference relations (IFPRs) have the natural ability to reflect the positive, the negative and the non-determinative judgements of decision makers. A decision making model is proposed by considering the inherent property of IFPRs in this study, where the main novelty comes with the introduction of the concept of additive approximate consistency. First, the consistency definitions of IFPRs are reviewed and the underlying ideas are analyzed. Second, by considering the allocation of the non-determinacy degree of decision makers’ opinions, the novel concept of approximate consistency for IFPRs is proposed. Then the additive approximate consistency of IFPRs is defined and the properties are studied. Third, the priorities of alternatives are derived from IFPRs with additive approximate consistency by considering the effects of the permutations of alternatives and the allocation of the non-determinacy degree. The rankings of alternatives based on real, interval and intuitionistic fuzzy weights are investigated, respectively. Finally, some comparisons are reported by carrying out numerical examples to show the novelty and advantage of the proposed model. It is found that the proposed model can offer various decision schemes due to the allocation of the non-determinacy degree of IFPRs.


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