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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)

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.

Keywords

Online auctions Software agents Bid forecasting Bidding strategies Data mining Clustering 

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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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