Abstract
With the development of network technology and E-commerce, more and more enterprises have accepted the management pattern of E-commerce. In order to meet the personalized needs of customers in E-supermarket, customer classification based on their interests is a key technology for developing personalized E-commerce. Therefore, it is highly needed to have a personalized system for extracting customer features effectively, and analyzing customer interests. In this paper, we proposed a new method based on the modified fuzzy neural network to group the customers dynamically according to their Web access patterns. The results suggest that this clustering algorithm is effective and efficacious. Taking one with another, this new proposed approach is a practical solution to make more visitors become to customers, improve the loyalty degree of customer, and strengthen cross sale ability of websites in E-commerce.
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Tan, YA., Wang, Z., Luo, Q. (2007). Research on Customer Classification in E-Supermarket by Using Modified Fuzzy Neural Networks. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_37
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DOI: https://doi.org/10.1007/978-3-540-72393-6_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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