DAWN – A System for Context-Based Link Recommendation in Web Navigation

  • Sebastian Stober
  • Andreas Nürnberger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


In this paper, we present the system “DAWN” (direction anticipation in web navigation) that learns navigational patterns to help users navigating through the world wide web. We motivate the purpose of such a system and the approach taken and point out relations to other approaches. Further, we briefly outline the architecture of the system and focus on the prediction model and the algorithm for link recommendation. Evaluation on real-world data gave promising results.


Link Prediction Model Induction Navigational Pattern Navigational Path Candidate Page 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lieberman, H.: Letizia: An Agent That Assists Web Browsing. In: IJCAI (1995)Google Scholar
  2. 2.
    Joachims, T., Freitag, D., Mitchell, T.: WebWatcher: A Tour Guide for the World Wide Web. In: IJCAI (1997)Google Scholar
  3. 3.
    Chen, L., Sycara, K.: WebMate: A Personal Agent for Browsing and Searching. In: Proc. 2nd Intl. Conf. on Auton. Agents and Multi Agent Sys. AGENTS (1998)Google Scholar
  4. 4.
    Jaczynski, M., Trousse, B.: Broadway, A Case-Based Browsing Advisor for the Web. In: Nikolaou, C., Stephanidis, C. (eds.) ECDL 1998. LNCS, vol. 1513, Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Mladenic, D.: Machine learning used by Personal WebWatcher. In: Proc. of ACAI 1999 Workshop on Machine Learning and Intelligent Agents (1999)Google Scholar
  6. 6.
    Sarukkai, R.: Link Prediction and Path Analysis using Markov Chains. Computer Networks 33, 337–386 (2000)CrossRefGoogle Scholar
  7. 7.
    Anderson, C., Domingos, P., Weld, D.: Relational Markov Models and their Application to adaptive Web Navigation. In: Proc. 8th ACM SIGKDD Intl. Conf. on Knowl. Discovery and Data Mining (2004)Google Scholar
  8. 8.
    Zhu, J., Hong, J., Hughes, J.: Using Markov Chains for Link Prediction in Adaptive Web Sites. In: SoftWare 2002: Comp. in an Imperfect World: 1st Intl. Conf. (2002)Google Scholar
  9. 9.
    Cadez, I., Heckerman, D., Meek, C., Smyth, D., White, S.: Model-Based Clustering and Visualization of Navigation Patterns on a Web Site. Data Min. Knowl. Discov. 7, 399–424 (2003)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Borges, J., Levene, M.: Generating Dynamic Higher-Order Markov Models in Web Usage Mining. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Bade, K., De Luca, E., Nürnberger, A., Stober, S.: CARSA - An Architecture for the Development of Context Adaptive Retrieval Systems. In: Adaptive Multimedia Retrieval: User, Context, and Feedback: 3rd Intl. Workshop (2005)Google Scholar
  12. 12.
    Stober, S.: Kontextbasierte Web-Navigationsunterstützung mit Markov-Modellen. Diplomarbeit, Otto-von-Guericke-University Magdeburg (December 2005)Google Scholar
  13. 13.
    Schechter, S., Krishnan, M., Smith, M.: Using path profiles to predict http requests. In: Proc. of the 7th intl. conf. on World Wide Web (1998)Google Scholar
  14. 14.
    Cooley, R., Mobasher, B., Srivastava, J.: Data Preparation for Mining World Wide Web Browsing Patterns. Knowl. and Information Sys. 1(1), 5–32 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sebastian Stober
    • 1
  • Andreas Nürnberger
    • 1
  1. 1.Institute for Knowledge and Language Engineering, School of Computer ScienceOtto-von-Guericke-University MagdeburgMagdeburgGermany

Personalised recommendations