Abstract
Markov models have been widely utilized for modeling user web navigation behavior. In this paper, we propose a novel adaptive weighting hybrid-order Markov model – HFTMM for Web pre-fetching based on optimizing HTMM (hybrid-order tree-like Markov model). The model can minimize the number of nodes in HTMM and improve the prediction accuracy, which are two significant sources of overhead for web pre-fetching. The experimental results show that HFTMM excels HTMM in better predicting performance with fewer nodes.
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© 2004 Springer-Verlag Berlin Heidelberg
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He, S., Qin, Z., Chen, Y. (2004). Web Pre-fetching Using Adaptive Weight Hybrid-Order Markov Model. In: Zhou, X., Su, S., Papazoglou, M.P., Orlowska, M.E., Jeffery, K. (eds) Web Information Systems – WISE 2004. WISE 2004. Lecture Notes in Computer Science, vol 3306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30480-7_32
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DOI: https://doi.org/10.1007/978-3-540-30480-7_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23894-2
Online ISBN: 978-3-540-30480-7
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