Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
M. Alvesson. Knowledge Work and Knowledge-Intensive Firms. Oxford University Press, Oxford, 2004.
T.H. Davenport. Thinking for a Living – How to Get Better Performance and Results from Knowledge Workers. Harvard Business School Press, Boston, MA, 2005.
D. Sullivan. Proven Portals: Best Practices for Planning, Designing, and Developing Enterprise Portal. Addison-Wesley, Boston, MA, 2004.
H. Collins. Enterprise Knowledge Portals. Amacom, New York, NY, 2003.
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.
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.
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.
R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331–370, 2002.
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.
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.
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.
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.
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.
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.
Y-H. Park and P.S. Fader. Modeling browsing behavior at multiple websites. Marketing Science, 23:280–303, 2004.
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.
M. Ahuya, B. Gupta, and P. Raman. An empirical investigation of online consumer purchasing behavior. Communications of the ACM, 46:145–151, 2003.
W.W. Moe. Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13:29–39, 2003.
M. Deshpande and G. Karypis. Selective markov models for predicting web page accesses. ACM Transactions on Internet Technology, 4:163–184, 2004.
H. Wu, M. Gordon, K. DeMaagd, and W. Fan. Mining web navigaitons for intelligence. Decision Support Systems, 41:574–591, 2006.
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.
I. Zukerman and D.W. Albrecht. Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction, 11:5–18, 2001.
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.
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.
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.
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.
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.
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.
A.-L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 435:207–211, 2005.
L. Catledge and J. Pitkow. Characterizing browsing strategies in the world wide web. Computer Networks and ISDN Systems, 27:1065–1073, 1995.
M.V. Thakor, W. Borsuk, and M. Kalamas. Hotlists and web browsing behavior – An empirical investigation. Journal of Business Research, 57:776–786, 2004.
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.
A.B. Downey. Lognormal and pareto distributions in the internet. Computer Communications, 28:790–801, 2005.
Acknowledgments
The authors would like to thank Tsukuba Advanced Computing Center (TACC) for providing raw web log data.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Géczy, P., Izumi, N., Akaho, S., Hasida, K. (2010). Behaviorally Founded Recommendation Algorithm for Browsing Assistance Systems. In: Stahlbock, R., Crone, S., Lessmann, S. (eds) Data Mining. Annals of Information Systems, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1280-0_14
Download citation
DOI: https://doi.org/10.1007/978-1-4419-1280-0_14
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-1279-4
Online ISBN: 978-1-4419-1280-0
eBook Packages: Computer ScienceComputer Science (R0)