Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust

Computational intelligence and neuroscience

Ying Wang¹  Hongbin Sun³  Xin Wang⁴ 

[1] College of Computer Science and Technology, Jilin University, Changchun 130012, China, jlu.edu.cn;Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China, gliet.edu.cn;Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Changchun 130012, China, moe.edu.cn;School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, China, ccit.edu.cn

DOI  :  10.1155/2016/5403105

学科分类：生物科学（综合）

来源: Hindawi Publishing Corporation

【 摘 要 】

In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework.

【 授权许可】

CC BY