Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64338
Title: Using similarity measure to enhance the robustness of web access prediction model
Authors: Niu, B
Shiu, SCK 
Issue Date: 2005
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), v. 3683, p. 107-111 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Prefetching web content by predicting users’ web requests can reduce the response time of the web server and optimize the network traffic. The Markov model that is based on the conditional probability has been studied by many researchers for web access path prediction. The prediction accuracy rate can reach up to 60 to 70 percent high. However a drawback of this type of model is that as the length of the access path grows the chance of successful path matching will decrease and the model will become inapplicable. In order to preserving the applicability as well as improving the accuracy rate, we extend the model by introducing a similarity measure among access paths. Therefore, the matching process becomes less rigid and the model will be more applicable and robust to the change of the path length.
Description: 9th International Conference Knowledge-Based Intelligent Information and Engineering Systems (KES 2005), Melbourne, Australia, September 14-16, 2005
URI: http://hdl.handle.net/10397/64338
ISBN: 978-3-540-28896-1 (print)
978-3-540-31990-0 (online)
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/11553939_16
Appears in Collections:Conference Paper

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