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dc.contributor.authorWong, K.
dc.contributor.authorFung, C.
dc.contributor.authorEren, Halit
dc.date.accessioned2017-01-30T12:29:37Z
dc.date.available2017-01-30T12:29:37Z
dc.date.created2016-09-12T08:36:39Z
dc.date.issued2009
dc.identifier.citationWong, K. and Fung, C. and Eren, H. 2009. Soft computing techniques for product filtering in E-commerce personalisation: A comparison study, pp. 402-406.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/22143
dc.identifier.doi10.1109/DEST.2009.5276689
dc.description.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. ©2009 IEEE.

dc.titleSoft computing techniques for product filtering in E-commerce personalisation: A comparison study
dc.typeConference Paper
dcterms.source.startPage402
dcterms.source.endPage406
dcterms.source.title2009 3rd IEEE International Conference on Digital Ecosystems and Technologies, DEST '09
dcterms.source.series2009 3rd IEEE International Conference on Digital Ecosystems and Technologies, DEST '09
dcterms.source.isbn9781424423460
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


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