Advertisement

Study on Personalized Recommendation Model of Internet Advertisement

  • Ning Zhou
  • Yongyue Chen
  • Huiping Zhang
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 252)

Abstract

With the rapid development of E-Commerce, the audiences put forward higher requirements on personalized Internet advertisement than before. The main function of Personalized Advertising System is to provide the most suitable advertisements for anonymous users on Web sites. The paper offers a personalized Internet advertisement recommendation model. By mining the audiences’ historical and current behavior, and the advertisers’ and publisher’s web site content, etc, the system can recommend appropriate advertisements to corresponding audiences.

Keywords

Current Behavior Usage Mining Anonymous User Recommendation Model Internet Advertisement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    IAB Internet Advertising Revenue Report 2005. http://www.iab.net/resources/ad_revenue.asp.
  2. 2.
    The Report about the competition and development of Chinese Internet Advertising in 2005. http://www.pday.com.cn/research/2006/6201_webads.htm.
  3. 3.
    P. Kazienko, “Multi-Agent System for Web Advertising”, Lecture Notes in Artificial Intelligence, 507–513 (2005).Google Scholar
  4. 4.
    Online Advertising. DoubleClick Inc. (2004).Google Scholar
  5. 5.
    P. Kazienko and M. Kiewra, “Link Recommendation Method Based on Web Content and Usage Mining”, http://www.zsi.pwr.wroc.pl/-kazienko/pub/IIS03/pkmk.pdf.
  6. 6.
    P. Kazienko and M. Kiewra, ROSA-Multi-agent System for Web Services Personalization, E. Menasalvas et al. (Eds.): AWIC (2003).Google Scholar
  7. 7.
    G Bilchev and D Marston, “Personalized advertising — exploiting the distributed user profile”, BT Technology Journal (2003)Google Scholar
  8. 8.
    A. Milani, “Minimal Knowledge Anonymous User Profiling for Personalized Services”, IEA/AIE 2005, LNAI 3533, 709–711 (2005).Google Scholar
  9. 9.
    W. Y. LIN, “Efficient Adaptive-Support Association Rule Mining for Recommender Systems”, Data Mining and Knowledge Discovery, 83–105 (2002).Google Scholar
  10. 10.
    P. Kazienko, “Multi-agent Web Recommendation Method Based on Indirect Association Rules”, 8th International Conference on Knowledge-Based Intelligent Information & Engineering Systems, KES’2004, LNAI 3214, Springer Verlag 1157–1164 (2004).Google Scholar
  11. 11.
    D. Johansen and R.V. Renesse, “WAIF:Web of Asynchronous Information Filters”, Future Directions in DC 2002, LNCS, 2584 81–86 (2003).Google Scholar
  12. 12.
    J. Chen and J. Huang, “Design and Implementation of Internet Advertising Analysis System Based on OLAP”, Application Research of Computers (2004).Google Scholar
  13. 13.
    M.J. XIA and J. Zhang, “Web Mining Application: Customized Internet Advertising”, Journal of Zhong Yuan institute of Technology (2003).Google Scholar
  14. 14.
    X.L. Fan, “Research and Achievement on Personalized E-commerce Site”, Computer Application (2002).Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2007

Authors and Affiliations

  • Ning Zhou
    • 1
  • Yongyue Chen
    • 1
  • Huiping Zhang
    • 1
  1. 1.Center for Studies of Information ResourcesWuhan UniversityWuhanChina

Personalised recommendations