Abstract
The large number of Web pages on many Web sites has raised navigational problems. Markov chains have recently been used to model user navigational behavior on the World Wide Web (WWW). In this paper, we propose a method for constructing a Markov model of a Web site based on past visitor behavior. We use the Markov model to make link predictions that assist new users to navigate the Web site. An algorithm for transition probability matrix compression has been used to cluster Web pages with similar transition behaviors and compress the transition matrix to an optimal size for efficient probability calculation in link prediction. A maximal forward path method is used to further improve the efficiency of link prediction. Link prediction has been implemented in an online system called ONE (Online Navigation Explorer) to assist users’ navigation in the adaptive Web site.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Albrecht, D., Zukerman, I., and Nicholson, A.: Pre-sending Documents on the WWW: A Comparative Study. IJCAI99 (1999)
Brusilovsky, P.: Adaptive hypermedia. User Modeling and User Adapted Interaction 11 (1/2). (2001) 87–110
Chen, M. S., Park, J. S., Yu, P. S.: Data mining for path traversal in a web environment. In Proc. of the 16th Intl. Conference on Distributed Computing Systems, Hong Kong. (1996)
Cooley, R., Mobasher, B., and Srivastava, J. Data Preparation for Mining World Wide Web Browsing Patterns. Journal of Knowledge and Information Systems, Vol. 1, No. 1. (1999)
Hallam-Baker, P. M. and Behlendorf, B.: Extended Log File Format. W3C Working Draft WD-logfile-960323. http://www.w3.org/TR/WD-logfile. (1996)
Hong, J.: Graph Construction and Analysis as a Paradigm for Plan Recognition. Proc. of AAAI-2000: Seventeenth National Conference on Artificial Intelligence, (2000) 774–779
Kalpazidou, S.L. Cycle Representations of Markov Processes, Springer-Verlag, NY. (1995)
Kijima, M.: Markov Processes for Stochastic Modeling. Chapman&Hall, London. (1997)
Mobasher, B., Cooley, R., Srivastava, J.: Automatic Personalization Through Web Usage Mining. TR99-010, Dept. of Computer Science, Depaul University. (1999)
Nielsen, J.: Designing Web Usability, New Riders Publishing, USA. (2000)
Perkowitz, M., Etzioni, O.: Adaptive web sites: an AI challenge. IJCAI97 (1997)
Perkowitz, M., Etzioni, O.: Towards adaptive Web sites: conceptual framework and case study. WWW8. (1999)
Pirolli, P., Pitkow, J. E.: Distributions of Surfers’ Paths Through the World Wide Web: Empirical Characterization. World Wide Web 1: 1–17. (1999)
Sarukkai, R.R.: Link prediction and path analysis using Markov chains. WWW9, (2000)
Spears, W. M.: A compression algorithm for probability transition matrices. In SIAM Matrix Analysis and Applications, Volume 20, #1. (1998) 60–77
Spiliopoulou, M.: Web usage mining for site evaluation: Making a site better fit its users. Comm. ACM Personalization Technologies with Data Mining, 43(8). (2000) 127–134
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, J., Hong, J., Hughes, J.G. (2002). Using Markov Chains for Link Prediction in Adaptive Web Sites. In: Bustard, D., Liu, W., Sterritt, R. (eds) Soft-Ware 2002: Computing in an Imperfect World. Soft-Ware 2002. Lecture Notes in Computer Science, vol 2311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46019-5_5
Download citation
DOI: https://doi.org/10.1007/3-540-46019-5_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43481-8
Online ISBN: 978-3-540-46019-0
eBook Packages: Springer Book Archive