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.
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CITATION STYLE
Huang, Q., Yang, Q., Huang, J. Z., & Ng, M. K. (2004). Mining of web-page visiting patterns with continuous-time markov models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3056, pp. 549–558). Springer Verlag. https://doi.org/10.1007/978-3-540-24775-3_65
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