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An Approach to Early Recognition of Web User Tasks by the Surfing Behavior

  • Anne Gutschmidt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6725)

Abstract

A study was conducted to investigate Web users’ information seeking behavior on online newspapers, distinguishing between the task categories fact finding, information gathering and browsing. Over a period of four weeks, the surfing behavior of 41 users was recorded who additionally kept a diary to document their activities. It was scrutinized whether the surfing behavior shows significant differences depending on the kind of task already at the beginning of an activity, which is a prerequisite for timely reaction to current user needs. According to the results, behavioral aspects, such as the number of pages viewed, scroll and mouse movement behavior etc. produce significant differences already during the first 60 seconds of a task. Nevertheless, classification tests show that these behavioral attributes do not yet lead to a prediction accuracy sufficient for a sound real-time task recognition.

Keywords

Task Category Early Recognition Time Slice User Behavior Information Gathering 
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.

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References

  1. 1.
    Mobasher, B.: Data mining for web personalization. The Adaptive Web, 90–135 (2007)Google Scholar
  2. 2.
    Das, A., Datar, M., Garg, A.: Google news personalization: Scalable online collaborative filtering. In: Proceedings of the 16th International Conference on World Wide Web, pp. 271–280. ACM Press, New York (2007)CrossRefGoogle Scholar
  3. 3.
    Brusilovsky, P., Millán, E.: User models for adaptive hypermedia and adaptive educational systems. The Adaptive Web, 3–53 (2007)Google Scholar
  4. 4.
    Marchionini, G.: Information seeking in electronic environments. Cambridge University Press, Cambridge (1997)Google Scholar
  5. 5.
    Kellar, M., Watters, C., Shepherd, M.: A field study characterizing web-based information-seeking tasks. Journal of the American Society for Information Science and Technology 58(7), 999–1018 (2007)CrossRefGoogle Scholar
  6. 6.
    Beane, T., Ennis, D.: Market segmentation: a review. European Journal of Marketing 21(5), 20–42 (1987)CrossRefGoogle Scholar
  7. 7.
    Belk, R.: An exploratory assessment of situational effects in buyer behavior. Journal of Marketing Research 11(2), 156–163 (1974)CrossRefGoogle Scholar
  8. 8.
    Hall, J., Lockshin, L.: Using means-end chains for analysing occasions-not buyers. Australasian Marketing Journal 8(1), 45–54 (2000)CrossRefGoogle Scholar
  9. 9.
    Paterno, F.: Model-based design and evaluation of interactive applications (1999)Google Scholar
  10. 10.
    Marchionini, G.: Information-seeking strategies of novices using a full-text electronic encyclopedia (1989)Google Scholar
  11. 11.
    Catledge, L.D., Pitkow, J.E.: Characterizing browsing strategies in the world-wide web. Computer Networks and ISDN systems 27(6), 1065–1073 (1995)CrossRefGoogle Scholar
  12. 12.
    Cove, J.F., Walsh, B.C.: Online text retrieval via browsing. Information Processing & Management 24(1), 31–37 (1988)CrossRefGoogle Scholar
  13. 13.
    Rozanski, H.D., Bollman, G., Lipman, M.: Seize the occasion - usage-based segmentation for internet marketers. Technical report, Booz Allen & Hamilton (2001)Google Scholar
  14. 14.
    Morrison, J.B., Pirolli, P., Card, S.K.: A taxonomic analysis of what world wide web activities significantly impact people’s decisions and actions. In: CHI 2001 Extended Abstracts on Human Factors in Computing Systems, pp. 163–164. ACM, New York (2001)Google Scholar
  15. 15.
    Sellen, A.J., Murphy, R., Shaw, K.L.: How knowledge workers use the web. In: CHI 2002: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 227–234. ACM, New York (2002)Google Scholar
  16. 16.
    Kellar, M., Watters, C.: Using web browser interactions to predict task. In: WWW 2006: Proceedings of the 15th International Conference on World Wide Web, pp. 843–844. ACM Press, New York (2006)Google Scholar
  17. 17.
    Gutschmidt, A.: The prediction of web user tasks by analyzing client logs. IADIS International Journal on WWW/Internet 6 (2009)Google Scholar
  18. 18.
    Atterer, R., Wnuk, M., Schmidt, A.: Knowing the user’s every move: user activity tracking for website usability evaluation and implicit interaction. In: Proceedings of the 15th International Conference on World Wide Web, WWW 2006, pp. 203–212. ACM, New York (2006)Google Scholar
  19. 19.
    Claypool, M., Le, P., Wased, M., Brown, D.: Implicit interest indicators. In: Proceedings of the 6th International Conference on Intelligent User Interfaces, IUI 2001, pp. 33–40. ACM, New York (2001)Google Scholar
  20. 20.
    Cox, A., Silva, M.: The role of mouse movements in interactive search. In: Proceedings of the 28th Annual CogSci. Conference, pp. 26–29 (2006)Google Scholar
  21. 21.
    Chen, M.C., Anderson, J.R., Sohn, M.H.: What can a mouse cursor tell us more? correlation of eye/mouse movements on web browsing. In: Conference on Human Factors in Computing Systems, CHI 2001 Extended Abstracts on Human Factors in Computing Systems, pp. 281–282 (2001)Google Scholar
  22. 22.
    Mueller, F., Lockert, A.: Cheese: Tracking mouse movement activity on websites, a tool for user modeling. In: Conference on Human Factors in Computing Systems, CHI 2001 Extended Abstracts on Human Factors in Computing Systems, pp. 289–290 (2001)Google Scholar
  23. 23.
    Goecks, J., Shavlik, J.: Learning users’ interests by unobtrusively observing their normal behavior. In: Proceedings of the 5th International Conference on Intelligent User Interfaces, pp. 129–132. ACM, New York (2000)Google Scholar
  24. 24.
    Rodden, K., Fu, X., Aula, A., Spiro, I.: Eye-mouse coordination patterns on web search results pages. In: CHI 2008 Extended Abstracts on Human Factors in Computing Systems, pp. 2997–3002. ACM, New York (2008)Google Scholar
  25. 25.
    Arroyo, E., Selker, T., Wei, W.: Usability tool for analysis of web designs using mouse tracks. In: CHI 2006 Extended Abstracts on Human Factors in Computing Systems, pp. 484–489. ACM, New York (2006)Google Scholar
  26. 26.
    MacKenzie, I., Kauppinen, T., Silfverberg, M.: Accuracy measures for evaluating computer pointing devices. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 9–16. ACM, New York (2001)Google Scholar
  27. 27.
    Rasmussen, S.: News as a service: Adoption of web 2.0 by online newspapers. In: Management of the Interconnected World: ItAIS: the Italian Association for Information Systems, pp. 11–19 (2010)Google Scholar
  28. 28.
    Dupont, W.: Statistical modeling for biomedical researchers: a simple introduction to the analysis of complex data. Cambridge University Press, Cambridge (2002)zbMATHGoogle Scholar
  29. 29.
    Black, K.: Business statistics: Contemporary decision making. Wiley, Chichester (2009)Google Scholar
  30. 30.
    Cohen, J.: Statistical power analysis. Current Directions in Psychological Science 1(3), 98–101 (1992)CrossRefGoogle Scholar
  31. 31.
    Nakagawa, S.: A farewell to bonferroni: the problems of low statistical power and publication bias. Behavioral Ecology 15(6), 1044–1045 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anne Gutschmidt
    • 1
  1. 1.Department of Economic and Organizational PsychologyUniversity of RostockRostockGermany

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