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An uncertainty-aware computational trust model considering the co-existence of trust and distrust in social networks

机译:考虑到社交网络中的信任和不信任的共存和不信任的不确定性感知的计算信任模型

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摘要

There are few trust models capable of incorporating the co-existence of trust and distrust as distinct concepts. In this regard, most of the existing trust models implicitly use distrust parameters to refine and calculate trust values. However, recent studies have indicated that trust and distrust are two distinct but co-existing concepts. In other words, although trust and distrust are constructed based on different characteristics, they can be used together in the decision making and recommendation processes. In this paper, we present a trust-distrust model for social networks considering subjective and objective characteristics of trust and distrust simultaneously. Competence, honesty, satisfaction, similarity, motivation, availability, tendency to be trusted, the existence of long-term connection/friendship, and centrality are the trustworthiness characteristics covered by the model. Also, surprisal, dishonesty, dissatisfaction, conflict degree, account lifetime, and sudden changes in the number of friends, likes, and comments are the distrust characteristics considered by the model. The proposed model takes into account the uncertainty, sharpness, and vagueness of the beliefs by using subjective logic. The results of the conducted evaluations demonstrate that the proposed model is highly accurate in the decision-making process and has a 90% accuracy in calculating the trust and distrust. We have also compared the results with other similar approaches, by which the proposed model showed a 34% improvement. (C) 2019 Elsevier Inc. All rights reserved.
机译:很少有信任模型能够将信任和不信任的共存作为截然不同的概念。在这方面,大多数现有的信任模型都隐含地利用不信任参数来改进和计算信任值。然而,最近的研究表明,信任和不信任是两个独特但共同存在的概念。换句话说,虽然基于不同特征构建信任和不信任,但它们可以在决策和推荐过程中一起使用它们。在本文中,我们为同时信任和不信任的主观和客观特征提出了一个信任的信任模型。能力,诚实,满意度,相似性,动机,可信度,倾向于信任,长期联系/友谊的存在,以及中心的存在是模型所涵盖的可信度特征。此外,令人满意的,不诚实,不满,程度,账户终生的冲突,概率,以及朋友数量,喜欢和评论的突然变化是模型考虑的不信任特征。拟议的模型通过使用主观逻辑来考虑信仰的不确定性,清晰度和模糊性。所进行评估的结果表明,拟议的模型在决策过程中高度准确,在计算信任和不信任时具有90%的准确性。我们还将结果与其他类似的方法进行了比较,所提出的模型显示出34%的改善。 (c)2019 Elsevier Inc.保留所有权利。

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