Reputation system of E-commerce based on artificial neural network
With the fast development, E-commerce is more and more popular in our daily life. To be successful in the E-commerce system, it is essential to keep a good reputation, which can help to get more customers. In this paper, we employ the Back error propagation neural network to balance weight between difference service components. Taobao as the most famous online mall is selected as the data resource. 1000 data sets as the training examples are obtained from Taobao. We get the gain value of each component. The training time for the 1000 data sets is 5.732 second and the overall accuracy is 96.8%.
Neural Network; Reputation; E-commerce
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