A Reputation Bootstrapping Model for E-Commerce Based on Fuzzy DEMATEL Method and Neural Network

Abstract

Reputation evaluation plays an important role in determining the credibility of online entities. Especially in e-commerce systems, consumers usually give priority to this indicator when choosing vendors. Reputation bootstrapping, which can determine the default reputation value of new entities, is still a challenging problem. Most current bootstrapping methods fail to consider the complexity, the ambiguity of trust, or the reputation and internal correlation characteristics of influential factors in the prediction process. Therefore, in this paper, a novel reputation bootstrapping model that combines the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method with neural network prediction is established. First, we adopt a fuzzy multi-criteria decision-making model, named fuzzy DEMATEL method to discuss the intrinsic causal relationships between influential factors and identify the critical success factors (CSFs) for reputation estimation. Then, we adopt back propagation (BP) neural network to generalize the correlations between the CSFs and the initial reputation value. Finally, a case study is constructed to verify the proposed model. The experimental results indicate that the proposed model has better accuracy and efficiency compared with other reputation bootstrapping methods.