An integrated model for next page access prediction
Article
Article Title | An integrated model for next page access prediction |
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ERA Journal ID | 123714 |
Article Category | Article |
Authors | Khalil, Faten (Author), Li, Jiuyong (Author) and Wang, Hua (Author) |
Journal Title | International Journal of Knowledge and Web Intelligence |
Journal Citation | 1 (1/2), pp. 48-80 |
Number of Pages | 33 |
Year | 2009 |
Place of Publication | United Kingdom |
ISSN | 1755-8255 |
1755-8263 | |
Digital Object Identifier (DOI) | https://doi.org/10.1504/IJKWI.2009.027925 |
Web Address (URL) | http://www.inderscience.com/storage/f115368121017294.pdf |
Abstract | Accurate next web page prediction benefits many applications, e-business in particular. The most widely used techniques for this purpose are Markov Model, association rules and clustering. However, each of these techniques has its own limitations, especially when it comes to accuracy and space complexity. This paper presents an improved prediction accuracy and state space complexity by using novel approaches that combine clustering, association rules and Markov Models. The three techniques are integrated together to maximise their strengths. The integration model has been shown to achieve better prediction accuracy than individual and other integrated models. |
Keywords | web page prediction; Markov Model; association rules; clustering |
ANZSRC Field of Research 2020 | 461399. Theory of computation not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Department of Mathematics and Computing |
https://research.usq.edu.au/item/9z5qy/an-integrated-model-for-next-page-access-prediction
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