TY - JOUR
T1 - A final price prediction model for english auctions
T2 - A neuro-fuzzy approach
AU - Lin, Chin Shien
AU - Chou, Shihyu
AU - Weng, Shih Min
AU - Hsieh, Yu Chen
N1 - Funding Information:
This work has been funded by the Google Research Awards Program. We are grateful to M. Bianchini, E. Di Iorio and G. Monfardini for their important advises and we thank M. Diligenti for his support.
PY - 2013/2
Y1 - 2013/2
N2 - Online auction has now been a popular mechanism in setting prices for internet users. However, auction price prediction, involving the modeling of uncertainty regarding the bidding process, is a challenging task primarily due to the variety of factors changing in auction settings. Even if all the factors were accounted for, there still exist uncertainties in human behavior when bidding in auctions. In this paper, three models, regression, neural networks and neuro-fuzzy, are constructed to predict the final prices of English auctions, using real-world online auction data collected from Yahoo-Kimo Auction. The empirical results show that the neuro fuzzy system can catch the complicated relationship among the variables accurately much better than the others, which is of great help for the buyers to avoid overpricing and for the sellers to facilitate the auction. Besides, the knowledge base obtained from neuro fuzzy provides the elaborative relationship among the variables, which can be further tested for theory building.
AB - Online auction has now been a popular mechanism in setting prices for internet users. However, auction price prediction, involving the modeling of uncertainty regarding the bidding process, is a challenging task primarily due to the variety of factors changing in auction settings. Even if all the factors were accounted for, there still exist uncertainties in human behavior when bidding in auctions. In this paper, three models, regression, neural networks and neuro-fuzzy, are constructed to predict the final prices of English auctions, using real-world online auction data collected from Yahoo-Kimo Auction. The empirical results show that the neuro fuzzy system can catch the complicated relationship among the variables accurately much better than the others, which is of great help for the buyers to avoid overpricing and for the sellers to facilitate the auction. Besides, the knowledge base obtained from neuro fuzzy provides the elaborative relationship among the variables, which can be further tested for theory building.
KW - Neueo network
KW - Neuro-fuzzy System
KW - Online auction
KW - Regression
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U2 - 10.1007/s11135-011-9533-y
DO - 10.1007/s11135-011-9533-y
M3 - Article
AN - SCOPUS:84872668505
SN - 0033-5177
VL - 47
SP - 599
EP - 613
JO - Quality and Quantity
JF - Quality and Quantity
IS - 2
ER -