Extraction of customer attitude using neural network and canonical correlation analysis Online publication date: Tue, 21-Oct-2014
by Md. Shahjahan, Md. Asaduzzaman, Mineki Ohkura, Kazuyuki Murase
International Journal of Electronic Marketing and Retailing (IJEMR), Vol. 4, No. 1, 2011
Abstract: This paper describes a method that extracts customer attitude toward their choices of web pages and commodities from their web search histories using neural network (NN) and canonical correlation analysis (CCA). Customers visit websites and leave behind valuable information about their behaviour. Customer behaviour analysis aims to ultimately improve business performance through an understanding of past and present search histories of customers so as to determine and identify attitude of customers. Online traders often want the interesting websites which are attractive to their customers. In order to do that, firstly, we generate the equal length patterns from the heterogeneous search patterns in order to facilitate the further analysis. Secondly, we apply an unsupervised competitive NN learning to cluster the customers. Thirdly, we apply a statistics CCA in order to extract the attitude and/or interest of the customers towards the web pages and commodities.
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