Authors: Md. Shahjahan, Md. Asaduzzaman, Mineki Ohkura, Kazuyuki Murase
Addresses: Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology (KUET), Khulna-9203, Bangladesh. ' Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology (KUET), Khulna-9203, Bangladesh. ' Start Today Co. Ltd., WBG West 16F, 2-6 Nakase, Mihama-ku, Chiba 261-7116, Japan. ' Department of Human and Artificial Intelligence Systems, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan
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.
Keywords: heterogeneous data; neural networks; canonical correlation analysis; customer behaviour; e-commerce; electronic commerce; customer attitudes; web pages; websites; internet; world wide web; commodities; web searches; search histories; business performance; online traders; web design; equal length patterns; search patterns; competitive learning; clusters; electronic marketing; e-marketing; electronic retailing; e-retailing.
International Journal of Electronic Marketing and Retailing, 2011 Vol.4 No.1, pp.30 - 48
Received: 24 Mar 2009
Accepted: 12 Jan 2010
Published online: 21 Oct 2014 *