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Multi-objective Optimization for Adaptive Web Site Generation

  • Prateek Jain
  • Pabitra Mitra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

Designing web sites is a complex problem. Adaptive sites are those which improve themselves by learning from user access patterns. In this paper we have considered a problem of index page synthesis for an adaptive website and framed it in a new type of Multi-Objective Optimization problem. We give a solution to index page synthesis which uses a popular clustering algorithm DBSCAN alongwith NSGA-II–an evolutionary algorithm–to find out best index pages for a website. Our experiments shows that very good candidate index pages can be generated automatically, and that our technique outperforms various existing methods such as PageGather, K-Means and Hierarchical Agglomerative Clustering.

References

  1. 1.
    Perkowitz, M., Etzioni, O.: Towards adaptive web sites: Conceptual framework and case study. Computer Networks 31, 1245–1258 (1999)CrossRefGoogle Scholar
  2. 2.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, pp. 226–231 (1996)Google Scholar
  3. 3.
    Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Guervós, J.J.M., Schwefel, H.P.: PPSN 2000. LNCS, vol. 1917. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Joachims, T., Freitag, D., Mitchell, T.M.: Web watcher: A tour guide for the world wide web. IJCAI (1), 770–777 (1997)Google Scholar
  5. 5.
    Fink, J., Koenemann, J., Noller, S., Schwab, I.: Putting personalization into practice. Commun. ACM 45, 41–42 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Prateek Jain
    • 1
  • Pabitra Mitra
    • 2
  1. 1.IBM India Research LabNew DelhiIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of TechnologyKanpurIndia

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