Abstract
This paper presents a new prediction model for predicting when an online customer leaves a current page and which next Web page the customer will visit. The model can forecast the total number of visits of a given Web page by all incoming users at the same time. The prediction technique can be used as a component for many Web based applications . The prediction model regards a Web browsing session as a continuous-time Markov process where the transition probability matrix can be computed from Web log data using the Kolmogorov’s backward equations. The model is tested against real Web-log data where the scalability and accuracy of our method are analyzed.
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.
Similar content being viewed by others
References
Anderson, W.J.: Continuous Time Markov Chains: An Applications-Oriented Approach. Springer, New York (1991)
Leung, C.M., Schormans, J.A.: Measurement-based end to end latency performance prediction for SLA verification. In: 41st European Telecommunications Congress, Genoa (2002)
Pitts, J.M., Schormans, J.A.: Introduction to IP and ATM design and performance, 2nd edn. Wiley, Chichester (2000)
Yang, Q., Huang, J.Z., Ng, M.: A Data Cube Model for Prediction-based Web Prefetching. Journal of Intelligent Information Systems 20(2003), 11–30 (2002)
Yang, Q., Wang, H., Zhang, W.: Web-log Mining for Quantitative Temporal-Event Prediction. IEEE Computer Society, Computational Intelligence Bulletin 1(1) (December 2002)
Pitkow, J., Pirolli, P.: Mining Longest Repeating Subsequences to Predict World Wide Web Surfing. In: Proceedings of USENIX Symposium on Internet Technologies and Systems (1999)
Cadez, I., Heckerman, D., Meek, C., Smyth, P., White, S.: Visualization of Navigation Patterns on a Web Site Using Model-Based Clustering. In: Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, USA (2000)
Deshpande, M., Karypis, G.: Selective Markov Models for Predicting Web-Page Accesses. In: Proceedings SIAM Int. Conference on Data Mining (2001)
Taha, H.: Operations Research, 3rd edn. Collier Macmillan, N.Y. (1991)
Ross, S.M.: Stochastic Process. Wiley, Chichester (1996)
Anderson, C.R., Domingos, P., Weld, D.S.: Relational Markov Models and Their Application to Adaptive Web Navigation. In: Proceedings of SIGKDD 2002 (2002)
Jennings, A., Mckeown, J.J.: Matrix Computation. John Wiley & Sons, Chichester (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huang, Q., Yang, Q., Huang, J.Z., Ng, M.K. (2004). Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_65
Download citation
DOI: https://doi.org/10.1007/978-3-540-24775-3_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22064-0
Online ISBN: 978-3-540-24775-3
eBook Packages: Springer Book Archive