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

Wong, K.; Fung, C.; Eren, Halit

doi:10.1109/DEST.2009.5276689

Access Status

Fulltext not available

Authors

Wong, K.

Fung, C.

Eren, Halit

Date

2009

Type

Conference Paper

Metadata

Show full item record

Citation

Wong, K. and Fung, C. and Eren, H. 2009. Soft computing techniques for product filtering in E-commerce personalisation: A comparison study, pp. 402-406.

Source Title

2009 3rd IEEE International Conference on Digital Ecosystems and Technologies, DEST '09

DOI

10.1109/DEST.2009.5276689

ISBN

9781424423460

School

Department of Electrical and Computer Engineering

URI

http://hdl.handle.net/20.500.11937/22143

Collection

Curtin Research Publications

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.