A Customer-Oriented Assortment Selection in the Big Data Environment

Saberi, M; Saberi, Z; Aasadabadi, MR; Hussain, OK; Chang, E

A Customer-Oriented Assortment Selection in the Big Data Environment

Saberi, M

Saberi, Z Aasadabadi, MR Hussain, OK Chang, E

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Advances in E-Business Engineering for Ubiquitous Computing, 2020, 41, pp. 161-172
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Saberi, Z
dc.contributor.author	Aasadabadi, MR
dc.contributor.author	Hussain, OK
dc.contributor.author	Chang, E
dc.contributor.editor	Chao, KM
dc.contributor.editor	Jiang, L
dc.contributor.editor	Hussain, OK
dc.contributor.editor	Ma, SP
dc.contributor.editor	Fei, X
dc.date	2019-10-11
dc.date.accessioned	2021-04-08T05:06:22Z
dc.date.available	2021-04-08T05:06:22Z
dc.date.issued	2020-01-01
dc.identifier.citation	Advances in E-Business Engineering for Ubiquitous Computing, 2020, 41, pp. 161-172
dc.identifier.isbn	978-3-030-34985-1
dc.identifier.issn	2367-4512
dc.identifier.issn	2367-4520
dc.identifier.uri	http://hdl.handle.net/10453/147894
dc.description.abstract	© 2020, Springer Nature Switzerland AG. Customers prefer the availability of a range of products when they shop online. This enables them to identify their needs and select products that best match their desires. This is addressed through assortment planning. Some customers have strong awareness of what they want to purchase and from which provider. When considering customer taste as an abstract concept, such customers’ decisions may be influenced by the existence of the variety of products and the current variant market may affect their initial desire. Previous studies dealing with assortment planning have commonly addressed it from the retailer’s point of view. This paper will provide customers with a ranking method to find what they want. We propose that this provision benefits both the retailer and the customer. This study provides a customer-oriented assortment ranking approach. The ranking model facilitates browsing and exploring the current big market in order to help customers find their desired item considering their own taste. In this study, a scalable and customised multi-criteria decision making (MCDM) method is structured and utilised to help customers in the process of finding their most suitable assortment while shopping online. The proposed MCDM method is tailored to fit the big data environment.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Advances in E-Business Engineering for Ubiquitous Computing
dc.relation.ispartof	International Conference on e-Business Engineering
dc.relation.ispartofseries	Lecture Notes on Data Engineering and Communications Technologies
dc.relation.isbasedon	10.1007/978-3-030-34986-8_11
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Customer-Oriented Assortment Selection in the Big Data Environment
dc.type	Conference Proceeding
utslib.citation.volume	41
utslib.location.activity	Shanghai, China
utslib.for	0806 Information Systems
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/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-08T05:06:21Z
pubs.finish-date	2019-10-13
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2019-10-11
pubs.volume	41
dc.location	Switzerland

Abstract:

© 2020, Springer Nature Switzerland AG. Customers prefer the availability of a range of products when they shop online. This enables them to identify their needs and select products that best match their desires. This is addressed through assortment planning. Some customers have strong awareness of what they want to purchase and from which provider. When considering customer taste as an abstract concept, such customers’ decisions may be influenced by the existence of the variety of products and the current variant market may affect their initial desire. Previous studies dealing with assortment planning have commonly addressed it from the retailer’s point of view. This paper will provide customers with a ranking method to find what they want. We propose that this provision benefits both the retailer and the customer. This study provides a customer-oriented assortment ranking approach. The ranking model facilitates browsing and exploring the current big market in order to help customers find their desired item considering their own taste. In this study, a scalable and customised multi-criteria decision making (MCDM) method is structured and utilised to help customers in the process of finding their most suitable assortment while shopping online. The proposed MCDM method is tailored to fit the big data environment.

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http://hdl.handle.net/10453/147894