Abstract
Multiple online auctions need complex bidding decisions for selecting which auction to participate in, whether to place single or multiple bids, do early or late bidding and how much to bid. This paper designs a novel fuzzy dynamic bidding agent (FDBA) which uses a comprehensive method for initial price estimation and price forecasting. First, FDBA selects an auction to participate in and calculates its initial price based on clustering and bid selection approach. Then the price of the auction is forecasted based on the estimated initial price, attitude of the bidders to win the auction and the competition assessment for the late bidders using fuzzy reasoning technique. The experiments demonstrated improved price forecasting outcomes using the proposed approach.
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References
Ockenfels, A., Reiley, D.H., Sadrieh, J.A.: Online auctions. National Bureau of Economic Research Cambridge, Mass., USA (2006)
Park, Y.H., Bradlow, E.T.: An integrated model for bidding behavior in Internet auctions: Whether, who, when, and how much. Journal of Marketing Research 42, 470–482 (2005)
Jank, W., Zhang, S.: An automated and data-driven bidding strategy for online auctions. Journal of Computing 23, 238–253 (2011)
Greenwald, A., Stone, P.: Autonomous bidding agents in the trading agent competition. IEEE Internet Computing 5, 52–60 (2001)
Kehagias, D.D., Mitkas, P.A.: Efficient E-Commerce Agent Design Based on Clustering eBay Data. In: International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, pp. 495–498. IEEE Computer Society, Washington (2007)
Bapna, R., et al.: User heterogeneity and its impact on electronic auction market design. An Empirical Exploration. Mis Quarterly 28, 21–43 (2004)
Shah, H.S., Joshi, N.R., Sureka, A., Wurman, P.R.: Mining eBay: Bidding Strategies and Shill Detection. In: Zaïane, O.R., Srivastava, J., Spiliopoulou, M., Masand, B. (eds.) WebKDD 2003. LNCS (LNAI), vol. 2703, pp. 17–34. Springer, Heidelberg (2003)
Du, L., Chen, Q., Bian, N.: An Empirical Analysis of Bidding Behavior in Simultaneous Ascending-Bid Auctions. In: International Conference on E-Business and E-Government (ICEE), pp. 249–251. IEEE (2010)
Kaur, P., Goyal, M., Lu, J.: Data mining driven agents for predicting online auction’s end price. In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 141–147. IEEE, Paris (2011)
Goyal, M., Kaushik, S., Kaur, P.: Automated Fuzzy Bidding Strategy Using Agent’s Attitude and Market Competition. In: Bai, Q., Fukuta, N. (eds.) Advances in Practical Multi-Agent Systems. SCI, vol. 325, pp. 167–180. Springer, Heidelberg (2010)
Kaur, P., Goyal, M., Lu, J.: Pricing Analysis in Online Auctions Using Clustering and Regression Tree Approach. In: Cao, L., Bazzan, A.L.C., Symeonidis, A.L., Gorodetsky, V.I., Weiss, G., Yu, P.S. (eds.) ADMI 2011. LNCS, vol. 7103, pp. 248–257. Springer, Heidelberg (2012)
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Kaur, P., Goyal, M., Lu, J. (2012). Price Forecasting Using Dynamic Assessment of Market Conditions and Agent’s Bidding Behavior. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_13
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DOI: https://doi.org/10.1007/978-3-642-34475-6_13
Publisher Name: Springer, Berlin, Heidelberg
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