Soft computing techniques for product filtering in E-commerce personalisation: A comparison study
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 relat...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/22143 |
| _version_ | 1848750788467228672 |
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| author | Wong, K. Fung, C. Eren, Halit |
| author_facet | Wong, K. Fung, C. Eren, Halit |
| author_sort | Wong, K. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:42:24Z |
| format | Conference Paper |
| id | curtin-20.500.11937-22143 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:42:24Z |
| publishDate | 2009 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-221432018-03-29T09:06:33Z Soft computing techniques for product filtering in E-commerce personalisation: A comparison study Wong, K. Fung, C. Eren, Halit 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. 2009 Conference Paper http://hdl.handle.net/20.500.11937/22143 10.1109/DEST.2009.5276689 restricted |
| spellingShingle | Wong, K. Fung, C. Eren, Halit Soft computing techniques for product filtering in E-commerce personalisation: A comparison study |
| title | Soft computing techniques for product filtering in E-commerce personalisation: A comparison study |
| title_full | Soft computing techniques for product filtering in E-commerce personalisation: A comparison study |
| title_fullStr | Soft computing techniques for product filtering in E-commerce personalisation: A comparison study |
| title_full_unstemmed | Soft computing techniques for product filtering in E-commerce personalisation: A comparison study |
| title_short | Soft computing techniques for product filtering in E-commerce personalisation: A comparison study |
| title_sort | soft computing techniques for product filtering in e-commerce personalisation: a comparison study |
| url | http://hdl.handle.net/20.500.11937/22143 |