Data Mining pp 317-334 | Cite as

Behaviorally Founded Recommendation Algorithm for Browsing Assistance Systems

  • Peter Géczy
  • Noriaki Izumi
  • Shotaro Akaho
  • Kôiti Hasida
Part of the Annals of Information Systems book series (AOIS, volume 8)


We present a novel recommendation algorithm for browsing assistance systems. The algorithm efficiently utilizes a priori knowledge of human interactions in electronic environments. The human interactions are segmented according to the temporal dynamics. Larger behavioral segments – sessions – are divided into smaller segments – subsequences. The observations indicate that users’ attention is primarily focused on the starting and the ending points of subsequences. The presented algorithm offers recommendations at these essential navigation points. The recommendation set comprises of suitably selected desirable targets of the observed subsequences and the consecutive initial navigation points. The algorithm has been evaluated on a real-world data of a large-scale organizational intranet portal. The portal has extensive number of resources, significant traffic, and large knowledge worker user base. The experimental results indicate satisfactory performance.


Recommender System Assistance System Knowledge Worker Recommendation Algorithm Attractor Mapping 


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The authors would like to thank Tsukuba Advanced Computing Center (TACC) for providing raw web log data.


  1. 1.
    M. Alvesson. Knowledge Work and Knowledge-Intensive Firms. Oxford University Press, Oxford, 2004.Google Scholar
  2. 2.
    T.H. Davenport. Thinking for a Living – How to Get Better Performance and Results from Knowledge Workers. Harvard Business School Press, Boston, MA, 2005.Google Scholar
  3. 3.
    D. Sullivan. Proven Portals: Best Practices for Planning, Designing, and Developing Enterprise Portal. Addison-Wesley, Boston, MA, 2004.Google Scholar
  4. 4.
    H. Collins. Enterprise Knowledge Portals. Amacom, New York, NY, 2003.Google Scholar
  5. 5.
    P. Géczy, S. Akaho, N. Izumi, and K. Hasida. Knowledge worker intranet behaviour and usability. International Journal of Business Intelligence and Data Mining, 2:447–470, 2007.CrossRefGoogle Scholar
  6. 6.
    G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17:734–749, 2005.CrossRefGoogle Scholar
  7. 7.
    S. Perugini, M.A. Gonçalves, and E.A. Fox. Recommender systems research: A connection-centric survey. Journal of Intelligent Information Systems, 23(2):107–143, 2004.CrossRefGoogle Scholar
  8. 8.
    R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331–370, 2002.CrossRefGoogle Scholar
  9. 9.
    O. Nasraoui, C. Cardona, and C. Rojas. Using retrieval measures to assess similarity in mining dynamic web clickstreams. In Proceedings of KDD, pp. 439–448, Chicago, IL, USA, 2005.Google Scholar
  10. 10.
    K. Ali and S.P. Kechpel. Golden path analyzer: Using divide-and-conquer to cluster web clickstreams. In Proceedings of KDD, pp. 349–358, Washington, DC, USA, 2003.Google Scholar
  11. 11.
    N. Kammenhuber, J. Luxenburger, A. Feldmann, and G. Weikum. Web search clickstreams. In Proceedings of The 6th ACM SIGCOMM on Internet Measurement, pp. 245–250, Rio de Janeriro, Brazil, 2006.Google Scholar
  12. 12.
    E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In Proceedings of The 29th SIGIR, pp. 19–26, Seattle, WA, USA, 2006.Google Scholar
  13. 13.
    R.J.K. Jacob and K.S. Karn. Eye tracking in human-computer interaction and usability research: Ready to deliver the promises. In J. Hyona, R. Radach, and H. Deubel, editors, The Mind’s Eye: Cognitive and Applied Aspects of Eye Movement Research, pp. 573–605, Elsevier Science, Amsterdam, 2003.Google Scholar
  14. 14.
    L.A. Granka, T. Joachims, and G. Gay. Eye-tracking analysis of user behavior in www search. In Proceedings of The 27th SIGIR, pp. 478–479, Sheffield, United Kingdom, 2004.Google Scholar
  15. 15.
    Y-H. Park and P.S. Fader. Modeling browsing behavior at multiple websites. Marketing Science, 23:280–303, 2004.CrossRefGoogle Scholar
  16. 16.
    R.E. Bucklin and C. Sismeiro. A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research, 40:249–267, 2003.CrossRefGoogle Scholar
  17. 17.
    M. Ahuya, B. Gupta, and P. Raman. An empirical investigation of online consumer purchasing behavior. Communications of the ACM, 46:145–151, 2003.CrossRefGoogle Scholar
  18. 18.
    W.W. Moe. Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13:29–39, 2003.Google Scholar
  19. 19.
    M. Deshpande and G. Karypis. Selective markov models for predicting web page accesses. ACM Transactions on Internet Technology, 4:163–184, 2004.CrossRefGoogle Scholar
  20. 20.
    H. Wu, M. Gordon, K. DeMaagd, and W. Fan. Mining web navigaitons for intelligence. Decision Support Systems, 41:574–591, 2006.CrossRefGoogle Scholar
  21. 21.
    J.D. Martín-Guerrero, P.J.G. Lisboa, E. Soria-Olivas, A. Palomares, and E. Balaguer. An approach based on the adaptive resonance theory for analysing the viability of recommender systems in a citizen web portal. Expert Systems with Applications, 33(3):743–753, 2007.CrossRefGoogle Scholar
  22. 22.
    I. Zukerman and D.W. Albrecht. Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction, 11:5–18, 2001.CrossRefGoogle Scholar
  23. 23.
    L.M. de Campos, J.M. Fernández-Luna, and J.F. Huete. A collaborative recommender system based on probabilistic inference from fuzzy observations. Fuzzy Sets and Systems, 159(12):1554–1576, 2008.CrossRefGoogle Scholar
  24. 24.
    M. Zanker and S. Gordea. Recommendation-based browsing assistance for corporate knowledge portals. In Proceedings of the 2006 ACM Symposium on Applied Computing, pp. 1116–1117, New York, NY, USA, 2006. ACM.Google Scholar
  25. 25.
    J. Jozefowska, A. Lawrynowicz, and T. Lukaszewski. Faster frequent pattern mining from the semantic web. Intelligent Information Processing and Web Mining, Advances in Soft Computing, pp. 121–130, 2006.Google Scholar
  26. 26.
    M. Shyu, C. Haruechaiyasak, and S. Chen. Mining user access patterns with traversal constraint for predicting web page requests. Knowledge and Information Systems, 10(4):515–528, 2006.CrossRefGoogle Scholar
  27. 27.
    P. Symeonidis, A. Nanopoulos, A.N. Papadopoulos, and Y. Manolopoulos. Collaborative recommender systems: Combining effectiveness and efficiency. Expert Systems with Applications, 34(4):2995–3013, 2008.CrossRefGoogle Scholar
  28. 28.
    Z. Dezso, E. Almaas, A. Lukacs, B. Racz, I. Szakadat, and A.-L. Barabasi. Dynamics of information access on the web. Physical Review, E73:066132(6), 2006.Google Scholar
  29. 29.
    A.-L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 435:207–211, 2005.CrossRefGoogle Scholar
  30. 30.
    L. Catledge and J. Pitkow. Characterizing browsing strategies in the world wide web. Computer Networks and ISDN Systems, 27:1065–1073, 1995.CrossRefGoogle Scholar
  31. 31.
    M.V. Thakor, W. Borsuk, and M. Kalamas. Hotlists and web browsing behavior – An empirical investigation. Journal of Business Research, 57:776–786, 2004.CrossRefGoogle Scholar
  32. 32.
    P. Géczy, S. Akaho, N. Izumi, and K. Hasida. Long tail attributes of knowledge worker intranet interactions. (P. Perner, Ed.), Machine Learning and Data Mining in Pattern Recognition, pp. 419–433, Springer-Verlag, Heidelberg, 2007.Google Scholar
  33. 33.
    A.B. Downey. Lognormal and pareto distributions in the internet. Computer Communications, 28:790–801, 2005.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

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)TokyoJapan

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