Advertisement

Web Behaviormetric User Profiling Concept

  • Peter Géczy
  • Noriaki Izumi
  • Shotaro Akaho
  • Kôiti Hasida
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5183)

Abstract

We present a concept for building behaviorally centered user profiles. The concept utilizes behavioral analytics of user interactions in web environments. User interactions are temporally segmented into elemental browsing units. The browsing segments permit identification of the essential navigational points as well as higher order abstractions. The profiles incorporate relevant metrics from three major domains: temporal, navigational, and abstractions. Temporal metrics focus on aspects of durations and delays between portions of human interactions. The navigational metrics target the initial, terminal, and single user actions. The abstraction metrics encompass elemental patterns of human browsing behavior and their interconnections. The profiling concept utilizes relatively simple analytic and statistical apparatus. It facilitates computational efficiency and scalability to large user domains.

Keywords

User Interaction Page View Profile Concept Navigation Point Abstraction Characteristic 
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.
    Baraglia, R., Silvestri, F.: Dynamic personalization of web sites without user intervention. Communications of the ACM 50, 63–67 (2007)CrossRefGoogle Scholar
  2. 2.
    Mobasher, B.: Data mining for web personalization. In: Brusilovski, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, pp. 90–135. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Moe, W.W.: Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology 13, 29–39 (2003)CrossRefGoogle Scholar
  4. 4.
    Nasraoui, O., Soliman, M., Saka, E., Badia, A., Germain, R.: A web usage mining framework for mining evolving user profiles in dynamic web sites. IEEE Transactions on Knowledge and Data Engineering 20(2), 202–215 (2008)CrossRefGoogle Scholar
  5. 5.
    Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: Brusilovski, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, pp. 54–89. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Gasparetti, F., Micarelli, A.: Exploiting web browsing histories to identify user needs. In: Proceedings of the 12th International Conference on Intelligent User Interfaces, New York, NY, USA, pp. 325–328 (2007)Google Scholar
  7. 7.
    Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems 23, 103–145 (2005)CrossRefGoogle Scholar
  8. 8.
    Anand, S.S., Kearney, P., Shapcott, M.: Generating semantically enriched user profiles for web personalization. ACM Transactions on Internet Technology 7(4), 22 (2007)CrossRefGoogle Scholar
  9. 9.
    Barabasi, A.-L.: The origin of bursts and heavy tails in human dynamics. Nature 435, 207–211 (2005)CrossRefGoogle Scholar
  10. 10.
    Géczy, P., Akaho, S., Izumi, N., Hasida, K.: Knowledge worker intranet behaviour and usability. Int. J. Business Intelligence and Data Mining 2, 447–470 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Peter Géczy
    • 1
  • Noriaki Izumi
    • 1
  • Shotaro Akaho
    • 1
  • Kôiti Hasida
    • 1
  1. 1.National Institute of Advanced Industrial Science and Technology (AIST)Tokyo and TsukubaJapan

Personalised recommendations