Customer online shopping behaviours analysis using Bayesian networks

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dc.contributor.author Lu, Z
dc.contributor.author Lu, J
dc.contributor.author Bai, C
dc.contributor.author Zhang, G
dc.contributor.editor Sattar, A
dc.contributor.editor Kang, BH
dc.date.accessioned 2009-11-09T02:45:17Z
dc.date.issued 2006-01
dc.identifier.citation AI 2006: Advances in artificial intelligence, 2006, pp. 1293 - 1297
dc.identifier.issn 0302-9743
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/1759
dc.description.abstract This study applies Bayesian network technique to analyse the relationships among customer online shopping behaviours and customer requirements. This study first proposes an initial behaviour-requirement relationship model as domain knowledge. Through conducting a survey customer data is collected as evidences for inference of the relationships among the factors described in the model. After creating a graphical structure, this study calculates conditional probability distribution among these factors, and then conducts inference by using the Junction-tree algorithm. A set of useful findings has been obtained for customer online shopping behaviours and their requirements with motivations. These findings have potential to help businesses adopting more suitable online system development.
dc.publisher Springer
dc.relation.isbasedon 10.1007/11941439_163
dc.title Customer online shopping behaviours analysis using Bayesian networks
dc.type Conference Proceeding
dc.parent AI 2006: Advances in artificial intelligence
dc.journal.number en_US
dc.publocation Berlin, Germany en_US
dc.publocation Miami, Florida, USA
dc.publocation Berlin, Germany
dc.publocation Berlin, Germany
dc.publocation Berlin, Germany
dc.publocation Berlin, Germany
dc.identifier.startpage 1293 en_US
dc.identifier.endpage 1297 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.conference International Conference on FRP Composites in Civil Engineering
dc.conference Australasian Joint Conference on Artificial Intelligence
dc.conference Australasian Joint Conference on Artificial Intelligence
dc.conference Australasian Joint Conference on Artificial Intelligence
dc.conference Australasian Joint Conference on Artificial Intelligence
dc.conference.location Hobart, Australia en_US
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 001038
dc.personcode 020014
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.custom Australasian Joint Conference on Artificial Intelligence en_US
dc.date.activity 20061201 en_US
dc.date.activity 2006-12-13
dc.date.activity 2006-12-01
dc.date.activity 2006-12-01
dc.date.activity 2006-12-01
dc.date.activity 2006-12-01
dc.location.activity Hobart, Australia en_US
dc.location.activity Miami, Florida, USA
dc.location.activity Hobart, Australia
dc.location.activity Hobart, Australia
dc.location.activity Hobart, Australia
dc.location.activity Hobart, Australia
dc.description.keywords e-services, bayesian network, customer behaviours, inference en_US
dc.description.keywords FRP, Bond, Strengthening, Timber, External Bonding
dc.description.keywords e-services, bayesian network, customer behaviours, inference
dc.description.keywords e-services, bayesian network, customer behaviours, inference
dc.description.keywords e-services, bayesian network, customer behaviours, inference
dc.description.keywords e-services, bayesian network, customer behaviours, inference
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems


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