Trust with Social Network Learning in E-Commerce

Trust among Internet users and thus social networks plays an important role in e-commerce and other Internet applications. However, the precise mathematical model of trust and thus applications based on trust in e-commerce system has not been satisfactorily established yet. In this paper, we present a probability theoretic framework to quantitatively measure trust as mathematical reasoning and to model the behaviors of consumers and sellers in the e-commerce system based on trust measure. We first summarize properties of trust in Internet users and their social networking. Then we construct the topology of e-commerce system and apply the statistical inference to derive more reliable trust measure. A reliable algorithm, which is robust to malicious behaviors of the sellers, is therefore developed. Via social network learning, distributed decision is proposed to maintain the accuracy of trust estimation and to better against potential malicious behaviors. Simulations demonstrate that our proposed scheme shows good accuracy in estimation of confidence level and retains robust performance facing a number of malicious users in the e-commerce system.