Evolutionary Prediction of Online Keywords Bidding

  • Liwen Hou
  • Liping Wang
  • Jinggang Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5183)


Online keywords bidding as a new business model for search engine market facilitates the prosperity of internet economy as well as attracts myriad small business to directly target their customers. In order to effectively manage each advertising campaign the manager needs to figure out smart strategy to bid. This paper successively developed two models (static and dynamic) with four crucial variants at keyword bidding (bid, rank, click through and impression) based on the bayesian network to assist the prediction of bidding. Herein dynamic model, evolved from the static model, takes the influence of click through on the last period into account and extends the strategy space. Empirical study by aid of data from the largest online travel agency in China is carried out to test both models and the results indicate that they are effective while the dynamic model is more attractive in terms of prediction accuracy. Finally further research directions along this paper is shown.


Dynamic Bayesian Network Keyword Bidding Click Through 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Animesh, A., Ramachandran, V., Viswanathan, S.: Online Advertisers Bidding Strategies for Search, Experience, and Credence Goods: An Empirical Investigation. Working paper (2005)Google Scholar
  2. 2.
    Animesh, A., Ramachandran, V., Viswanathan, S.: Quality Uncertainty and Adverse Selection In Sponsored Search Markets, working paper (2005)Google Scholar
  3. 3.
    Edelman, B., Ostrovsky, M.: Strategic Bidder Behavior in Sponsored Search Auctions. Int. J. Internet Marketing and Advertising 2, 92–108 (2005)CrossRefGoogle Scholar
  4. 4.
    Kitts, B., Leblanc, B.: Optimal Bidding on Keyword Auctions. Electronic Markets, special issue: innovative auction markets 14, 186–201 (2004)Google Scholar
  5. 5.
    Feng, J., Bhargava, H.K., Pennock, D.M.: Implementing Sponsored Search in Web Search Engines: Computational Evaluation of Alternative Mechanisms. working paper (2005)Google Scholar
  6. 6.
    Park, S., Durfee, E.H., Birmingham, W.P.: Use of Markov Chains to Design an Agent Bidding Strategy for Continuous Double Auctions. Journal of Artificial Intelligence Research 22, 175–214 (2004)zbMATHMathSciNetGoogle Scholar
  7. 7.
    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 XLII, 470–482 (2005)CrossRefGoogle Scholar
  8. 8.
    Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  9. 9.
    Murphy, K.: Bayes Net Toolbox for Matlab (Accessed November 15, 2007) (2007),
  10. 10.
    Kitts, B., Leblanc, B.: Optimal Bidding on Keyword Auctions. Electronic Markets 14, 186–201 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Liwen Hou
    • 1
  • Liping Wang
    • 1
  • Jinggang Yang
    • 1
  1. 1.Department of Management Information SystemShanghai Jiaotong UniversityShanghaiChina

Personalised recommendations