New Trends in Web User Behaviour Analysis

  • Pablo E. Román
  • Juan D. Velásquez
  • Vasile Palade
  • Lakhmi C. Jain
Part of the Studies in Computational Intelligence book series (SCI, volume 452)

Abstract

The analysis of human behaviour has been conducted within diverse disciplines, such as psychology, sociology, economics, linguistics, marketing and computer science. Hence, a broad theoretical framework is available, with a high potential for application into other areas, in particular to the analysis of web user browsing behaviour. The above mentioned disciplines use surveys and experimental sampling for testing and calibrating their theoretical models. With respect to web user browsing behaviour, the major source of data is the web logs, which store every visitor’s action on a web site. Such files could contain millions of registers, depending on the web site traffic, and represents a major data source about human behaviour. This chapter surveys the new trends in analysing web user behaviour and revises some novel approaches, such as those based on the neurophysiological theory of decision making, for describing what web users are looking for in a web site.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abraham, A., Ramos, V.: Web usage mining using artificial ant colony clustering and genetic programming. In: Procs. of the 2003 IEEE Congress on Evolutionary Computation (CEC 2003), pp. 1384–1391 (2003)Google Scholar
  2. 2.
    Anderson, C.: Wired Magazine, Editorial (June 2008)Google Scholar
  3. 3.
    Blum, A., Chan, T.-H.H., Rwebangira, M.R.: A random-surfer web-graph model. In: Proceedings of the Eigth Workshop on Algorithm Engineering and Experiments and the Third Workshop on Analytic Algorithmics and Combinatorics. Society for Industrial and Applied Mathematics, pp. 238–246 (2006)Google Scholar
  4. 4.
    Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Reviews of Modern Physics 81(2), 591+ (2009)CrossRefGoogle Scholar
  5. 5.
    Grannis, K., Davis, E.: Online sales to climb despite struggling economy. According to Shop.Org/Forrester Research Study (2008)Google Scholar
  6. 6.
    Jin, X., Zhou, Y., Mobasher, B.: Web usage mining based on probabilistic latent semantic analysis. In: KDD 2004: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 197–205. ACM, New York (2004)CrossRefGoogle Scholar
  7. 7.
    Kausshik, A.: Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity. Sybex (2009)Google Scholar
  8. 8.
    Kosala, R., Blockeel, H.: Web mining research: A survey. SIGKDD Explorations: Newsletters of the Special Interest Group (SIG) on Knowledge Discovery and Data Mining 1(2), 1–15 (2000)Google Scholar
  9. 9.
    Laming, D.R.J.: Information theory of choice reaction time. Wiley (1968)Google Scholar
  10. 10.
    Lohr, S.: A 1 million dollars research bargain for netflix, and maybe a model for others. New York Times (2009)Google Scholar
  11. 11.
    Masseglia, F., Poncelet, P., Teisseire, M., Marascu, A.: Web usage mining: extracting unexpected periods from web logs. Data Min. Knowl. Discov. 16(1), 39–65 (2008)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ratcliff, R.: A theory of memory retrieval. Psychological Review (83), 59–108 (1978)Google Scholar
  13. 13.
    Romn, P.E., Dell, R.F., Velsquez, J.D., Loyola, P.: Identifying user sessions from web server logs with integer programming. Intelligent Data Analysis (to appear, 2012)Google Scholar
  14. 14.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Computer Networks and ISDN Systems, pp. 107–117 (1998)Google Scholar
  15. 15.
    Schall, J.D.: Neural basis of deciding, choosing and acting. National Review of Neuroscience 2(1), 33–42 (2001)CrossRefGoogle Scholar
  16. 16.
    Schneider-Mizell, C.M., Sander, L.M.: A generalized voter model on complex networks. Technical Report arXiv:0804.1269, Department of Physics, University of Michigan, 15 pages, 3 figures (April 2008)Google Scholar
  17. 17.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)CrossRefGoogle Scholar
  18. 18.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations 2(1), 12–23 (2000)CrossRefGoogle Scholar
  19. 19.
    Stone, M.: Models for choice reaction time. Psychometrika (25), 251–260 (1960)Google Scholar
  20. 20.
    Tao, Y.-H., Hong, T.-P., Lin, W.-Y., Chiu, W.-Y.: A practical extension of web usage mining with intentional browsing data toward usage. Expert Syst. Appl. 36(2), 3937–3945 (2009)CrossRefGoogle Scholar
  21. 21.
    Tomlin, J.A.: A new paradigm for ranking pages on the world wide web. In: WWW 2003, Budapest, Hungary, May 20-24 (2003); In: Computer Networks and ISDN Systems, pp. 107–117 (1998)Google Scholar
  22. 22.
    Ullrich, C., Borau, K., Luo, H., Tan, X., Shen, L., Shen, R.: Why web 2.0 is good for learning and for research: principles and prototypes. In: WWW 2008: Proceeding of the 17th International Conference on World Wide Web, pp. 705–714. ACM, New York (2008)CrossRefGoogle Scholar
  23. 23.
    Usher, M., McClelland, J.: The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review 2(1), 550–592 (2001)CrossRefGoogle Scholar
  24. 24.
    Velasquez, J.D., Palade, V.: Building a knowledge base for implementing a web?based computerized recommendation system. International Journal of Artificial Intelligence Tools 16(5), 793–828 (2007)CrossRefGoogle Scholar
  25. 25.
    Velasquez, J.D., Palade, V.: A knowledge base for the maintenance of knowledge extracted from web data. Knowledge?Based Systems Journal 20(3), 238–248 (2007)CrossRefGoogle Scholar
  26. 26.
    Velásquez, J.D., Palade, V.: Adaptive web sites: A knowledge extraction from web data approach. IOS Press, Amsterdam (2008)Google Scholar
  27. 27.
    Wolpert, D.H.: Stacked generalization. Neural Networks 5, 241–259 (1992)CrossRefGoogle Scholar
  28. 28.
    Zhou, Y., Leung, H., Winoto, P.: Mnav: A markov model-based web site navigability measure. IEEE Trans. Softw. Eng. 33(12), 869–890 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pablo E. Román
    • 1
  • Juan D. Velásquez
    • 1
  • Vasile Palade
    • 2
  • Lakhmi C. Jain
    • 3
  1. 1.Web Intelligence Consortium, Chile Research Centre, Department of Industrial Engineering School of Engineering and ScienceUniversity of ChileSantiagoChile
  2. 2.Department of Computer ScienceUniversity of OxfordOxfordUK
  3. 3.KES Centre, School of Electrical and Information EngineeringUniversity of South AustraliaAdelaideAustralia

Personalised recommendations