Title: Website link prediction using a Markov chain model based on multiple time periods

Authors: Shantha Jayalal, Chris Hawksley, Pearl Brereton

Addresses: Department of Industrial Management, University of Keleniya, Dalugama, Sri Lanka. ' School of Computing and Mathematics, University of Keele, Keele, Staffordshire, ST5 5BG, UK. ' School of Computing and Mathematics, University of Keele, Keele, Staffordshire, ST5 5BG, UK

Abstract: Growing size and complexity of many websites have made navigation through these sites increasingly difficult. Attempting to automatically predict the next page for a website user to visit has many potential benefits, for example in site navigation, automatic tour generation, adaptive web applications, recommendation systems, web server optimisation, web search and web pre-fetching. This paper describes an approach to link prediction using a Markov chain model based on an exponentially smoothed transition probability matrix which incorporates site usage statistics collected over multiple time periods. The improved performance of this approach compared to earlier methods is also discussed.

Keywords: websites; link prediction; Markov chains; transition probability matrix; time weighting; link history; web engineering.

DOI: 10.1504/IJWET.2007.012057

International Journal of Web Engineering and Technology, 2007 Vol.3 No.3, pp.271 - 287

Available online: 16 Jan 2007 *

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