Advertisement

Temporal Rules for Predicting User Navigation in the Mobile Web

  • Martin Halvey
  • Mark T. Keane
  • Barry Smyth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4018)

Abstract

Numerous systems attempt to predict user navigation on the Internet through the use of past behavior, preferences and environmental factors. However many of these models have shortcomings, in that they do not take into account that browsers may have several different sets of preferences. Here we investigate time as an environmental factor in predicting user navigation in the Internet. We present methods for creating temporal rules that describe user navigation patterns. We also show the advantage of using these rules to predict user navigation and also illustrate the benefits of these models over traditional methods. An analysis is carried out on a sample of usage logs for Wireless Application Protocol (WAP) browsing, and the results of this analysis verify our theory.

Keywords

Association Rule Specific Time Period Entire Time Period Navigation Pattern User Navigation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th International Conference on Very Large Databases, VLDB 1994, pp. 487–499 (1994)Google Scholar
  2. 2.
    Anderson, C.R., Domingos, P., Weld, D.S.: Adaptive Web Navigation for Wireless Devices. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence, IJCAI 2001, pp. 879 – 884 (2001)Google Scholar
  3. 3.
    Begole, J., Tang, J.C., Hill, R.: Rhythm Modeling, Visulizations and Applications. In: Proceedings of the 16th Annual ACM Symposium on User Interface Software and Technology, UIST 2003, pp. 11–20 (2003)Google Scholar
  4. 4.
    Beitzel, S.M., Jensen, E.C., Chowdhury, A., Grossman, D., Frieder, O.: Hourly analysis of a very large topically categorized web query log. In: Proceedings of the 27th Annual ACM Conference SIGIR 2004, pp. 321–328 (2004)Google Scholar
  5. 5.
    Billsus, D., Brunk, C., Evans, C., Gladish, B., Pazzani, M.J.: Adaptive Interfaces for Ubiquitous Web Access. Communications of the ACM 45(4), 34–38 (2002)Google Scholar
  6. 6.
    Buchanan, G., Farrant, S., Jones, M., Thimleby, H.W., Marsden, G., Pazzani, M.J.: Improving Mobile Internet Usability. In: Proceedings of the 10th World Wide Web Conference, WWW 2001, pp. 673–680 (2001)Google Scholar
  7. 7.
    Halvey, M., Keane, M.T., Smyth, B.: Predicting Navigation Patterns on the Mobile-Internet Using Time of the Week, WWW (Special Interest Tracks and Posters), pp. 958–959 (2005)Google Scholar
  8. 8.
    Halvey, M., Keane, M.T., Smyth, B.: Birds of a Feather Surf Together: Using Clustering Methods to Improve Navigation Prediction from Internet Log Files. In: Perner, P., Imiya, A. (eds.) MLDM 2005. LNCS (LNAI), vol. 3587, pp. 174–183. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Halvey, M., Keane, M.T., Smyth, B.: Time Based Segmentation of Log Data for User Navigation Prediction in Personalization. In: Proceedings of the 3rd International Conference on Web Intelligence, WI 2005, pp. 636–641 (2005)Google Scholar
  10. 10.
    Halvey, M., Keane, M.T., Smyth, B.: Mobile Web Surfing is the same as Web Surfing. Communications of the ACM 49(3), 76–81Google Scholar
  11. 11.
    Halvey, M., Keane, M.T., Smyth, B.: Time Based Patterns in Mobile-Internet Surfing. In: Proceedings of CHI 2006 (accepted) (in press, 2006)Google Scholar
  12. 12.
    Horvitz, E., Koch, P., Kadie, C.M., Jacobs, A.: Coordinates: Probabililistic Forecasting of Presence and Availability. In: Proceedings of the 18th Conference in Uncertainty in Artificial Intelligence, UAI 2002, pp. 224–233 (2002)Google Scholar
  13. 13.
    Lau, T., Horvitz, E.: Patterns of Search: Analyzing and Modeling web Query Refinement. In: Proceedings of the 7th International Conference on User Modeling, UM 1999, pp. 119–128 (1999)Google Scholar
  14. 14.
    Perkowitz, M., Etzioni, O.: Towards Adaptive Web Sites: Conceptual Framework and Case Study. Artificial Intelligence 118(1-2), 245–275Google Scholar
  15. 15.
    Pirolli, P.: Distributions of Surfers’ Paths through the World Wide Web: Empirical Characterizations. The Web Journal 2, 29–45 (1998)CrossRefGoogle Scholar
  16. 16.
    Ramsay, M., Nielsen, J.: Nielsen Report: WAP Usability Deja Vu: 1994 All Over Again (2000)Google Scholar
  17. 17.
    Smyth, B., Cotter, P.: The Plight of the Navigator: Solving the Navigation Problem for Wireless Portals, AH 2000, 328–227 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Martin Halvey
    • 1
  • Mark T. Keane
    • 1
  • Barry Smyth
    • 1
  1. 1.Adaptive Information Cluster, School of Computer Science and InformaticsUniversity College DublinDublin 4Ireland

Personalised recommendations