A dynamic trust evaluation mechanism based on affective intensity computing

Abstract

A complete description of trust relationship is key to construct a high precision trust model. But most of existing models not only miss the negative information and the hesitation information of trust, but also ignore the discordance between text comments and ratings. To solve the problems, a dynamic trust evaluation model based on the affect intensity is proposed. In the model, the intensity of sentimental polarities are calculated from words in comments. The corresponding relation between evaluations of trust property and the emotional intensity vector can be also described. Time series weights to distinguish the importance of transaction at different time are determined by the inverse form of exponential distribution. So the dynamic attenuation of trust can be described. To improve the polymerization ability of trust information, the local trust, the feedback trust and the overall trust are calculated by operators of fuzzy logic. The experimental results show the proposed model can effectively describe the trust relationship between nodes to identify and eliminate various malicious attacks significantly. Copyright © 2016 John Wiley & Sons, Ltd.

Web User Trust Evaluation: A Novel Approach Using Fuzzy Petri Net and Behavior Analysis

“…Although machine learning technology has been applied in many fields, it is still in its infancy in terms of network user trust evaluation.The current shortcomings of machine learning used in trust evaluation mainly include: high requirements for data quality, strong objectivity of trust evaluation results, lack of subjective and objective combination, and prone to "overfitting" phenomenon, etc. Zhou et al [16] proposed a dynamic trust evaluation model based on affective intensity computing, which used fuzzy logic operators to calculate partial trust, feedback trust, and overall trust. The advantage of this model lies in the introduction of a feedback trust mechanism, but the disadvantage is that it does not solve the influence of weights on the evaluation results.…”