Paper Info
Title
Exploring the combination of Dempster-Shafer theory and neural network for predicting trust and distrust
Abstract
AbstractIn 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.
Year
DOI
Venue
2016
10.1155/2016/5403105
Periodicals
Field
DocType
Volume
Social relation,Data science,Data mining,Social media,Homophily,Computer science,Artificial intelligence,Distrust,Artificial neural network,Dempster–Shafer theory,Machine learning,Disadvantage
Journal
2016
Issue
ISSN
Citations 
1
1687-5265
1
PageRank 
References 
Authors
0.34
19
3
Name
Order
Citations
PageRank
Xin Wang141.79
Ying Wang271.13
Hongbin Sun328551.80