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Soft computing techniques for product filtering in E-commerce personalisation: A comparison study

Wong, K.W., Fung, C.C. and Eren, H. (2009) Soft computing techniques for product filtering in E-commerce personalisation: A comparison study. In: 3rd IEEE International Conference on Digital Ecosystems and Technologies (DEST '09), 1-3 June 2009, Istanbul, Turkey pp. 402-406.

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    Link to Published Version: http://dx.doi.org/10.1109/DEST.2009.5276689
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    Abstract

    In this paper, we compare two soft computing methods used for product filtering in web personalisation for E-commerce. Due to the diversely behaving nature, and the complexity to model the customers’ behaviour using market research methodologies, it is difficult to build a universal model relating the purchasing behaviour mathematical in E-commerce. For this reason, soft computing techniques may be considered as more appropriate in such case. In this study, we have investigated and compared an artificial neural network (ANN) and a fuzzy based method on a particular simulated data set. Initial results indicated that the fuzzy method could be a better choice as there are means to improve the results and human users may understand and modify the model.

    Publication Type: Conference Paper
    Murdoch Affiliation: School of Information Technology
    Publisher: IEEE
    Copyright: (c) Copyright 2009 IEEE
    Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    URI: http://researchrepository.murdoch.edu.au/id/eprint/783
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