Abstract
E-consumers face biggest challenge of opting for the best bidding strategies for competing in an environment of multiple and simultaneous online auctions for same or similar items. It becomes very complicated for the bidders to make decisions of selecting which auction to participate in, place single or multiple bids, early or late bidding and how much to bid. In this paper, we present the design of an autonomous dynamic bidding agent (ADBA) that makes these decisions on behalf of the buyers according to their bidding behaviors. The agent develops a comprehensive method for initial price prediction and an integrated model for bid forecasting. The initial price prediction method selects an auction to participate in and then predicts its closing price (initial price). Then the bid forecasting model forecasts the bid amount by designing different bidding strategies followed by the late bidders. The experimental results demonstrated improved initial price prediction outcomes by proposing a clustering based approach. Also, the results show the proficiency of the bidding strategies amongst the late bidders with desire for bargain.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ockenfels, A., Reiley Jr., D.H., Sadrieh, A.: Online auctions. National Bureau of Economic Research Cambridge, Mass, USA (2006)
Haruvy, E.: Internet auctions. Foundations and Trends in Marketing 4(1), 1–75 (2009)
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(4), 470–482 (2005)
Jank, W., Zhang, S.: An automated and data-driven bidding strategy for online auctions. Journal of Computing 23(2), 238–253 (2011)
Anthony, P., et al.: Autonomous agents for participating in multiple online auctions. In: IJCAI Workshop on E-Business and the Intelligent Web, Seattle, USA, pp. 54–64 (2001)
Greenwald, A., Stone, P.: Autonomous bidding agents in the trading agent competition. IEEE Internet Computing 5(2), 52–60 (2001)
Bapna, R., et al.: User heterogeneity and its impact on electronic auction market design. An Empirical Exploration. Mis Quarterly 28(1), 21–43 (2004)
Shah, H., et al.: Mining eBay: Bidding strategies and shill detection. In: WEBKDD 2002-MiningWeb Data for Discovering Usage Patterns and Profiles, pp. 17–34 (2003)
Trevathan, J., Read, W.: Detecting shill bidding in online English auctions. In: Handbook of Research on Social and Organizational Liabilities in Information Security (2008)
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). IEEE (2010)
Rasmusen, E.B.: Strategic implications of uncertainty over one’s own private value in auctions. The BE Journal of Theoretical Economics 6, 7 (2006)
Ockenfels, A., Roth, A.E.: Late and multiple bidding in second price Internet auctions: Theory and evidence concerning different rules for ending an auction. Games and Economic Behavior 55(2), 297–320 (2006)
Xuefeng, L., et al.: Predicting the final prices of online auction items. Expert Systems with Applications 31(3), 542–550 (2006)
Nikolaidou, V., Mitkas, P.: A Sequence Mining Method to Predict the Bidding Strategy of Trading Agents. In: Agents and Data Mining Interaction, pp. 139–151 (2009)
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. IEEE/WIC/ACM (2007)
Heijst, D., Potharst, R., Wezel, M.: A support system for predicting ebay end prices. Econometric Institute Report (2006)
Ghani, R., Simmons, H.: Predicting the end-price of online auctions (2004)
Zhang, S., Jank, W., Shmuel, G.: Real-time forecasting of online auctions via functional k-nearest neighbors. International Journal of Forecasting 26(4), 666–683 (2010)
Cao, L., Weiss, G., Yu, P.S.: A Brief Introduction to Agent Mining. Journal of Autonomous Agents and Multi-Agent Systems 25, 419–424 (2012)
Cao, L., Gorodetsky, V., Mitkas, P.A.: Agent Mining: The Synergy of Agents and Data Mining. IEEE Intelligent Systems 24(3), 64–72 (2009)
Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Robotics and Autonomous Systems 24, 159–182 (1998)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kaur, P., Goyal, M., Lu, J. (2013). A Proficient and Dynamic Bidding Agent for Online Auctions. In: Cao, L., Zeng, Y., Symeonidis, A.L., Gorodetsky, V.I., Yu, P.S., Singh, M.P. (eds) Agents and Data Mining Interaction. ADMI 2012. Lecture Notes in Computer Science(), vol 7607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36288-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-36288-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36287-3
Online ISBN: 978-3-642-36288-0
eBook Packages: Computer ScienceComputer Science (R0)