A Proficient and Dynamic Bidding Agent for Online Auctions

  • Preetinder Kaur
  • Madhu Goyal
  • Jie Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7607)


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.


Online auctions Software agents Bid forecasting Bidding strategies Data mining Clustering 


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  1. 1.
    Ockenfels, A., Reiley Jr., D.H., Sadrieh, A.: Online auctions. National Bureau of Economic Research Cambridge, Mass, USA (2006)Google Scholar
  2. 2.
    Haruvy, E.: Internet auctions. Foundations and Trends in Marketing 4(1), 1–75 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Jank, W., Zhang, S.: An automated and data-driven bidding strategy for online auctions. Journal of Computing 23(2), 238–253 (2011)MathSciNetzbMATHGoogle Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Greenwald, A., Stone, P.: Autonomous bidding agents in the trading agent competition. IEEE Internet Computing 5(2), 52–60 (2001)CrossRefGoogle Scholar
  7. 7.
    Bapna, R., et al.: User heterogeneity and its impact on electronic auction market design. An Empirical Exploration. Mis Quarterly 28(1), 21–43 (2004)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Trevathan, J., Read, W.: Detecting shill bidding in online English auctions. In: Handbook of Research on Social and Organizational Liabilities in Information Security (2008)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Rasmusen, E.B.: Strategic implications of uncertainty over one’s own private value in auctions. The BE Journal of Theoretical Economics 6, 7 (2006)MathSciNetGoogle Scholar
  12. 12.
    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)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Xuefeng, L., et al.: Predicting the final prices of online auction items. Expert Systems with Applications 31(3), 542–550 (2006)CrossRefGoogle Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    Heijst, D., Potharst, R., Wezel, M.: A support system for predicting ebay end prices. Econometric Institute Report (2006)Google Scholar
  17. 17.
    Ghani, R., Simmons, H.: Predicting the end-price of online auctions (2004)Google Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    Cao, L., Gorodetsky, V., Mitkas, P.A.: Agent Mining: The Synergy of Agents and Data Mining. IEEE Intelligent Systems 24(3), 64–72 (2009)CrossRefGoogle Scholar
  21. 21.
    Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Robotics and Autonomous Systems 24, 159–182 (1998)CrossRefGoogle Scholar
  22. 22.
    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)Google Scholar
  23. 23.
    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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Preetinder Kaur
    • 1
  • Madhu Goyal
    • 1
  • Jie Lu
    • 1
  1. 1.DeSI lab, Centre for Quantum Computation and Intelligent Systems, School of SoftwareUniversity of TechnologyAustralia

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