An Integrated Model for a Price Forecasting Agent in Online Auctions

Kaur, P; Goyal, M; Lu, J

An Integrated Model for a Price Forecasting Agent in Online Auctions

Kaur, P Goyal, M

Lu, J

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Publication Type:: Journal Article
Citation:: Journal of Internet Commerce, 2012, 11 (3), pp. 208 - 225
Issue Date:: 2012-11-01

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Field	Value	Language
dc.contributor.author	Kaur, P	en_US
dc.contributor.author	Goyal, M https://orcid.org/0000-0003-2853-9393	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2012-11-01	en_US
dc.identifier.citation	Journal of Internet Commerce, 2012, 11 (3), pp. 208 - 225	en_US
dc.identifier.issn	1533-2861	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29383
dc.description.abstract	In an environment of multiple online auctions for same or similar items, the biggest challenge faced by the participants is opting for the best bidding strategies. Deciding which auction to participate in, whether to bid early or late, and how much to bid is very complicated for the bidders. This article presents the design of a data mining-based price forecasting agent (PFA) that makes these decisions on behalf of the bidders. The agent selects an auction to participate in and then predicts its end price for the late bidders based on a clustering, and a bid mapping and selection approach. The experimental results demonstrated improved end price prediction outcomes. © 2012 Copyright Taylor and Francis Group, LLC.	en_US
dc.relation.ispartof	Journal of Internet Commerce	en_US
dc.relation.isbasedon	10.1080/15332861.2012.729467	en_US
dc.title	An Integrated Model for a Price Forecasting Agent in Online Auctions	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	11	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1504 Commercial Services	en_US
utslib.for	1505 Marketing	en_US
utslib.for	2001 Communication and Media Studies	en_US
pubs.embargo.period	Not known	en_US
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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

In an environment of multiple online auctions for same or similar items, the biggest challenge faced by the participants is opting for the best bidding strategies. Deciding which auction to participate in, whether to bid early or late, and how much to bid is very complicated for the bidders. This article presents the design of a data mining-based price forecasting agent (PFA) that makes these decisions on behalf of the bidders. The agent selects an auction to participate in and then predicts its end price for the late bidders based on a clustering, and a bid mapping and selection approach. The experimental results demonstrated improved end price prediction outcomes. © 2012 Copyright Taylor and Francis Group, LLC.

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http://hdl.handle.net/10453/29383