An Integrated Model for a Price Forecasting Agent in Online Auctions

Publisher:
Taylor & Francis (Routledge)
Publication Type:
Journal Article
Citation:
Journal of Internet Commerce, 2012, 11 (3), pp. 208 - 225
Issue Date:
2012
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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.
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