Integrating recommendation models for improved web page prediction accuracy
Paper
Paper/Presentation Title | Integrating recommendation models for improved web page prediction accuracy |
---|---|
Presentation Type | Paper |
Authors | Khalil, Faten (Author), Li, Jiuyong (Author) and Wang, Hua (Author) |
Editors | Dobbie, Gillian and Mans, Bernard |
Journal or Proceedings Title | Conferences in Research and Practice in Information Technology (CRPIT) |
ERA Conference ID | 42479 |
Journal Citation | 74, pp. 91-100 |
Number of Pages | 10 |
Year | 2008 |
Place of Publication | Sydney, Australia |
ISBN | 9781920682552 |
Web Address (URL) of Paper | http://delivery.acm.org/10.1145/1380000/1378296/p91-khalil.pdf?key1=1378296&key2=3150195521&coll=GUIDE&dl=GUIDE&CFID=57112003&CFTOKEN=36712265 |
Conference/Event | ACSC 2008: 31st Australasian Computer Science Conference |
Australasian Computer Science Conference | |
Event Details | Australasian Computer Science Conference ACSC Rank B B |
Event Details | ACSC 2008: 31st Australasian Computer Science Conference Event Date 22 to end of 25 Jan 2008 Event Location Wollongong, Australia |
Abstract | [Abstract]: Recent research initiatives have addressed the need for improved performance of Web page prediction accuracy that would profit many applications, e-business in particular. Different Web usage mining frameworks have been implemented for this purpose specifically Association rules, clustering, and Markov model. Each of these frameworks has its own strengths and weaknesses and it has been proved that using each of these frameworks individually does not provide a suitable solution that answers today's Web page prediction needs. This paper endeavors to provide an improved Web page prediction accuracy by using a novel approach that involves integrating clustering, association rules and Markov models according to some constraints. Experimental results prove that this integration provides better prediction accuracy than using each technique individually. |
Keywords | web page prediction, association rules, clustering, Markov model |
ANZSRC Field of Research 2020 | 461301. Coding, information theory and compression |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Department of Mathematics and Computing |
University of South Australia |
https://research.usq.edu.au/item/9z3xy/integrating-recommendation-models-for-improved-web-page-prediction-accuracy
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