E-commerce comparison-shopping model of neural network based on ant colony optimization

Abstract

A new model of comparison-shopping and key contents is discussed in the paper to solve the problem of the user bias' filtering and learning. On the basis of traditional comparison shopping method, trains the BP neural networks by ant colony optimization algorithm to obtain the users' preference information. It also adopts the growth-oriented method of network structure to decrease the learning error. And the sequence of search results is reorganized based on the information, to provide users with the personalized shopping guide service to meet their needs. Besides, the application of Web 2.0 can be optimized by using the knowledge of preference to build a better comparison-shopping e-commerce website. © 2010 Springer-Verlag Berlin Heidelberg.