Skip to main content
Springer Nature Link
Log in

Long Tail Attributes of Knowledge Worker Intranet Interactions

  • Conference paper
Machine Learning and Data Mining in Pattern Recognition (MLDM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4571))

  • 3770 Accesses

Abstract

Elucidation of human browsing behavior in electronic spaces has been attracting substantial attention in academic and commercial spheres. We present a novel formal approach to human behavior analysis in web based environments. The framework has been applied to analyzing knowledge workers’ browsing behavior on a large corporate Intranet. Analysis indicates that users form elemental and complex browsing patterns and achieve their browsing objectives via few subgoals. Knowledge workers know their targets and exhibit diminutive exploratory behavior. Significant long tail attributes have been observed in all analyzed features. A novel distribution that accurately models it has been introduced.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Schlender, B.: Peter Drucker sets us straight. Fortune (December 29, 2003), http://www.fortune.com

  2. Davenport, T.H.: Thinking for a Living - How to Get Better Performance and Results from Knowledge Workers. Harvard Business School Press, Boston (2005)

    Google Scholar 

  3. Barabasi, A.-L.: The origin of bursts and heavy tails in human dynamics. Nature 435, 207–211 (2005)

    Article  Google Scholar 

  4. Park, Y.-H., Fader, P.S.: Modeling browsing behavior at multiple websites. Marketing Science 23, 280–303 (2004)

    Article  Google Scholar 

  5. Géczy, P., Akaho, S., Izumi, N., Hasida, K.: Navigation space formalism and exploration of knowledge worker behavior. In: Kotsis, G., Taniar, D., Pardede, E., Ibrahim, I.K. (eds.) Information Integration and Web-based Applications and Services, pp. 163–172. OCG, Vienna (2006)

    Google Scholar 

  6. Moe, W.W.: Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology 13, 29–39 (2003)

    Article  Google Scholar 

  7. Benbunan-Fich, R.: Using protocol analysis to evaluate the usability of a commercial web site. Information and Management 39, 151–163 (2001)

    Article  Google Scholar 

  8. Norman, K.L., Panizzi, E.: Levels of automation and user participation in usability testing. Interacting with Computers 18, 246–264 (2006)

    Article  Google Scholar 

  9. Bucklin, R.E., Sismeiro, C.: A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research 40, 249–267 (2003)

    Article  Google Scholar 

  10. Thakor, M.V., Borsuk, W., Kalamas, M.: Hotlists and web browsing behavior–an empirical investigation. Journal of Business Research 57, 776–786 (2004)

    Article  Google Scholar 

  11. Deshpande, M., Karypis, G.: Selective markov models for predicting web page accesses. ACM Transactions on Internet Technology 4, 163–184 (2004)

    Article  Google Scholar 

  12. Wu, H., Gordon, M., DeMaagd, K., Fan, W.: Mining web navigaitons for intelligence. Decision Support Systems 41, 574–591 (2006)

    Article  Google Scholar 

  13. Zukerman, I., Albrecht, D.W.: Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction 11, 5–18 (2001)

    Article  MATH  Google Scholar 

  14. Jozefowska, J., Lawrynowicz, A., Lukaszewski, T.: Faster frequent pattern mining from the semantic web. Intelligent Information Processing and Web Mining, Advances in Soft Computing, pp. 121–130 (2006)

    Google Scholar 

  15. Géczy, P., Akaho, S., Izumi, N., Hasida, K.: Extraction and analysis of knowledge worker activities on intranet. In: Reimer, U., Karagiannis, D. (eds.) Practical Aspects of Knowledge Management, pp. 73–85. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  16. Catledge, L., Pitkow, J.: Characterizing browsing strategies in the world wide web. Computer Networks and ISDN Systems 27, 1065–1073 (1995)

    Article  Google Scholar 

  17. Vazquez, A., Oliveira, J.G., Dezso, Z., Goh, K.-I., Kondor, I., Barabasi, A.-L.: Modeling bursts and heavy tails in human dynamics. Physical Review E73(19), 36127 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba and Tokyo, Japan

    Peter Géczy, Noriaki Izumi, Shotaro Akaho & Kôiti Hasida

Authors
  1. Peter Géczy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Noriaki Izumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shotaro Akaho
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kôiti Hasida
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Petra Perner

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Géczy, P., Izumi, N., Akaho, S., Hasida, K. (2007). Long Tail Attributes of Knowledge Worker Intranet Interactions. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73499-4_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73498-7

  • Online ISBN: 978-3-540-73499-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics