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Conference Paper: Decision support and data mining for direct consumer-to-consumer trading

TitleDecision support and data mining for direct consumer-to-consumer trading
Authors
KeywordsArtificial neural network
Data mining
Decision making
E-Commerce
Online trading
Used goods
Issue Date2014
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1003084
Citation
The 9th International Conference for Internet Technology and Secured Transactions (ICITST 2014), London, UK., 8-10 December 2014. In Conference Proceedings, 2014, p. 205-208 How to Cite?
AbstractThis paper describes a decision support system that integrates a hybrid neighborhood search algorithm for determining the price of sale item when it is placed for trading in the Internet. The seller would provide the condition and number of years of usage of the used item, and the intelligent system would provide real-time search on related items in the marketplace and suggest a price for trading. Data mining techniques are explored for efficient processing of a vast amount of information in the database tables. In addition, the trading system would also have the intelligence of recommending items or products to a potential buyer given the previous purchase patterns. Related items to a recently purchased item would also be suggested with an aim of providing friendly reminders and recommendations so that the user of the website would obtain a pleasant trading experience. © 2014 Infonomics Society.
Persistent Identifierhttp://hdl.handle.net/10722/214836
ISBN

 

DC FieldValueLanguage
dc.contributor.authorChan, V-
dc.contributor.authorWu, TH-
dc.contributor.authorPang, G-
dc.date.accessioned2015-08-21T11:58:05Z-
dc.date.available2015-08-21T11:58:05Z-
dc.date.issued2014-
dc.identifier.citationThe 9th International Conference for Internet Technology and Secured Transactions (ICITST 2014), London, UK., 8-10 December 2014. In Conference Proceedings, 2014, p. 205-208-
dc.identifier.isbn978-1-908320-39-
dc.identifier.urihttp://hdl.handle.net/10722/214836-
dc.description.abstractThis paper describes a decision support system that integrates a hybrid neighborhood search algorithm for determining the price of sale item when it is placed for trading in the Internet. The seller would provide the condition and number of years of usage of the used item, and the intelligent system would provide real-time search on related items in the marketplace and suggest a price for trading. Data mining techniques are explored for efficient processing of a vast amount of information in the database tables. In addition, the trading system would also have the intelligence of recommending items or products to a potential buyer given the previous purchase patterns. Related items to a recently purchased item would also be suggested with an aim of providing friendly reminders and recommendations so that the user of the website would obtain a pleasant trading experience. © 2014 Infonomics Society.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1003084-
dc.relation.ispartofInternational Conference for Internet Technology and Secured Transactions (ICITST)-
dc.rightsInternational Conference for Internet Technology and Secured Transactions (ICITST). Copyright © IEEE.-
dc.rights©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectArtificial neural network-
dc.subjectData mining-
dc.subjectDecision making-
dc.subjectE-Commerce-
dc.subjectOnline trading-
dc.subjectUsed goods-
dc.titleDecision support and data mining for direct consumer-to-consumer trading-
dc.typeConference_Paper-
dc.identifier.emailPang, G: gpang@eee.hku.hk-
dc.identifier.authorityPang, G=rp00162-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICITST.2014.7038806-
dc.identifier.scopuseid_2-s2.0-84924745316-
dc.identifier.hkuros250198-
dc.identifier.spage205-
dc.identifier.epage208-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 151008-

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