Web pre-fetching using adaptive weight hybrid-order markov model

Shengping He; Zheng Qin; Yan Chen

doi:10.1007/978-3-540-30480-7_32

Web pre-fetching using adaptive weight hybrid-order markov model

Shengping He
, Zheng Qin
, Yan Chen

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Xiaofang Zhou, Maria E. Orlowska, Stanley Su, Mike P. Papazoglou, Keith G. Jeffery
Publisher	Springer Verlag
Pages	313-318
Number of pages	6
ISBN (Electronic)	3540238948, 9783540238942
DOIs	https://doi.org/10.1007/978-3-540-30480-7_32
State	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3306
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-30480-7_32

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He, S., Qin, Z., & Chen, Y. (2004). Web pre-fetching using adaptive weight hybrid-order markov model. In X. Zhou, M. E. Orlowska, S. Su, M. P. Papazoglou, & K. G. Jeffery (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 313-318). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3306). Springer Verlag. https://doi.org/10.1007/978-3-540-30480-7_32

He, Shengping ; Qin, Zheng ; Chen, Yan. / Web pre-fetching using adaptive weight hybrid-order markov model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Xiaofang Zhou ; Maria E. Orlowska ; Stanley Su ; Mike P. Papazoglou ; Keith G. Jeffery. Springer Verlag, 2004. pp. 313-318 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Web pre-fetching using adaptive weight hybrid-order markov model",

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.",

author = "Shengping He and Zheng Qin and Yan Chen",

year = "2004",

doi = "10.1007/978-3-540-30480-7\_32",

language = "英语",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "313--318",

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booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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He, S, Qin, Z & Chen, Y 2004, Web pre-fetching using adaptive weight hybrid-order markov model. in X Zhou, ME Orlowska, S Su, MP Papazoglou & KG Jeffery (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3306, Springer Verlag, pp. 313-318. https://doi.org/10.1007/978-3-540-30480-7_32

Web pre-fetching using adaptive weight hybrid-order markov model. / He, Shengping; Qin, Zheng; Chen, Yan.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Xiaofang Zhou; Maria E. Orlowska; Stanley Su; Mike P. Papazoglou; Keith G. Jeffery. Springer Verlag, 2004. p. 313-318 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3306).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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T1 - Web pre-fetching using adaptive weight hybrid-order markov model

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AU - Qin, Zheng

AU - Chen, Yan

PY - 2004

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N2 - 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.

AB - 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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PB - Springer Verlag

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He S, Qin Z, Chen Y. Web pre-fetching using adaptive weight hybrid-order markov model. In Zhou X, Orlowska ME, Su S, Papazoglou MP, Jeffery KG, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 313-318. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-30480-7_32

Web pre-fetching using adaptive weight hybrid-order markov model

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