Discovering stages in web navigation for problem-oriented navigation support

  • Vera Hollink
  • Maarte van Someren
  • Bob J Wielinga
Original Paper


Users of web sites often do not know exactly which information they are looking for nor what the site has to offer. The purpose of their interaction is not only to fulfill but also to articulate their information needs. In these cases users need to pass through a series of pages before they can use the information that will eventually answer their questions. Current systems that support navigation predict which pages are interesting for the users on the basis of commonalities in the contents or the usage of the pages. They do not take into account the order in which the pages must be visited. In this paper we propose a method to automatically divide the pages of a web site on the basis of user logs into sets of pages that correspond to navigation stages. The method searches for an optimal number of stages and assigns each page to a stage. The stages can be used in combination with the pages’ topics to give better recommendations or to structure or adapt the site. The resulting navigation structures guide the users step by step through the site providing pages that do not only match the topic of the user’s search, but also the current stage of the navigation process.


Navigation support Information needs Web usage mining Navigation stages 


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  1. Alpay L.L., Toussaint P.J., Ezendam N.P.M., Rövekamp A.J.M., Graafmans W.C., Westendorp R.G.J. (2004) Easing internet access of health information for elderly users. Health Informatics J. 10(3): 185–194CrossRefGoogle Scholar
  2. Alpay, L.L., Ezendam, N.P.M., Zwetsloot-Schonk, J.H.M.: Final report of the Geriwijzer/Senior Gezond project.Technical Report, Leiden University Medical Center, Leiden, The Netherlands (2005)Google Scholar
  3. Anderson, C.R., Domingos, P., Weld, D.S.: Adaptive web navigation for wireless devices. Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pp. 879–884. Seattle, Washington, USA (2001)Google Scholar
  4. Anderson, C.R., Horvitz, E.: Web montage: a dynamic personalized start page. Proceedings of the 11th International Conference on World Wide Web, pp. 704–712. Honolulu, Hawaii, USA (2000)Google Scholar
  5. De Bra P., Calvi L. (1998) AHA! an open adaptive hypermedia architecture. New Rev Hypermedia 4, 115–139Google Scholar
  6. Brusilovsky P. (2001) Adaptive hypermedia. User Model. User-Adapt. 11(1–2): 87–110zbMATHCrossRefGoogle Scholar
  7. Brusilovsky, P., Eklund, J., Schwarz, E.: Web-based education for all: a tool for developing adaptive courseware. Proceedings of the Seventh International World Wide Web Conference, pp. 291–300. Brisbane, Australia (1998)Google Scholar
  8. Cadez I., Heckerman D., Meek C., Smyth P., White S. (2003) Model-based clustering and visualization of navigation patterns on a web site. Data Mining Knowledge Discovery 7(4): 399–424CrossRefGoogle Scholar
  9. Carroll J.D. (1972) Individual differences and multidimensional scaling. Multidimensional Scaling: Theory Appl. Behav. Scio. 1, 105–155Google Scholar
  10. Choo, C., Detlor, B., Turnbull, D.: working the web: an empirical model of web use. Proceedings of the 33rd Hawaii International Conference on System Sciences. Maui, Hawaii (2000)Google Scholar
  11. Clancey W. (1985) Heuristic classification. Artif. Intell. 27(3): 289–350CrossRefGoogle Scholar
  12. Cooley R., Mobasher B., Srivastava J. (1999) Data preparation for mining world wide web browsing patterns. J. Knowledge Inform. Syst. 1(1): 5–32Google Scholar
  13. Deshpande M., Karypis G. (2004) Selective Markov models for predicting web page accesses. ACM Trans. Internet Technol. 4(2): 163–184CrossRefGoogle Scholar
  14. Dempster A., Laird N., Rubin D. (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. 39, 1–38zbMATHMathSciNetGoogle Scholar
  15. Domshlak, C., Joachims, T.: Efficient and non-parametric reasoning over user preferences. This issue.Google Scholar
  16. Ezendam, N.P.M., Alpay, L.L., Rövekamp, A.J.M., Toussaint, PJ.: Enhancing accessibility of the content of a fall prevention website for elderly: a cross sectional study. Technical Report, Leiden University Medical Center, Leiden, The Netherlands (2005)Google Scholar
  17. Herder, E.: Sniffing around for providing navigation assistance. Proceedings of the Workshop on Adaptivity and User Modeling in Interactive Systems, pp. 20–24. Berlin, Germany (2004)Google Scholar
  18. Hollink, V., van Someren, M., ten Hagen, S.: Discovering stages in web navigation. Proceedings of the 10th International Conference on User Modeling, pp. 473–482. Edinburgh, UK (2005a)Google Scholar
  19. Hollink, V., van Someren, M., ten Hagen, S., Wielinga, B.: Recommending informative links. Proceedings of the IJCAI-05 Workshop on Intelligent Techniques for Web Personalization, pp. 65–72. Edinburgh, UK (2005b)Google Scholar
  20. Jin, X., Zhou, Y., Mobasher, B.: Task-oriented web user modeling for recommendation’. Proceedings of the 10th International Conference on User Modeling, pp. 109–118. Edinburgh, UK (2005)Google Scholar
  21. Mobasher B., Dai H., Luo T., Nakagawa M. (2002) Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining Knowledge Discovery 6, 61–82CrossRefMathSciNetGoogle Scholar
  22. Perkowitz M., Etzioni O. (2000) Towards adaptive web sites: conceptual framework and case study. Intell. 118, 245–275zbMATHCrossRefGoogle Scholar
  23. Pierrakos, D., Paliouras, G.: Exploiting probabilistic latent information for the construction of community web directories. Proceedings of the 10th International Conference on User Modeling, pp. 89–98. Edinburgh, UK. (2005)Google Scholar
  24. Pierrakos D., Paliouras G., Papatheodorou C., Spyropoulos C. D. (2003) Web usage mining as a tool for personalization: A survey. User Model. User-Adapt. 13(4): 311–372CrossRefGoogle Scholar
  25. Pirolli, P., Fu, W.-T.: SNIF-ACT: a model of information foraging on the world wide web. Ninth International Conference on User Modeling, pp 45–54. Johnstown, USA (2003)Google Scholar
  26. Pitkow, J.E., Pirolli, P.: Mining longest repeated subsequences to predict world wide web surfing. Proceedings of the Second USENIX Symposium on Internet Technologies and Systems, Boulder, USA (1999)Google Scholar
  27. Sarukkai, R.: Link prediction and path analysis using markov chains. Proceedings of the Ninth International World Wide Web Conference, pp. 377–386. Amsterdam, The Netherlands (2000)Google Scholar
  28. Schwab, I., Pohl, W.: Learning user profiles from positive examples. Proceedings of the ACAI’99 Workshop on Machine Learning in User Modeling, pp. 15–20. Chania, Greece (1999)Google Scholar
  29. Ypma A., Heskes T. (2003) Automatic categorization of web pages and user clustering with mixtures of hidden markov models. Lecture Notes in Computer Science 2703, 35–49CrossRefGoogle Scholar
  30. Zhu, T., Greiner, R., Häubl, G.: Learning a model of a web user’s interests. Proceedings of the Ninth International Conference on User Modeling, pp. 65–75. Johnstown, USA (2003)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  • Vera Hollink
    • 1
  • Maarte van Someren
    • 1
  • Bob J Wielinga
    • 1
  1. 1.Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands

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