Khalil, Faten and Li, Jiuyong and Wang, Hua (2008) Integrating recommendation models for improved web page prediction accuracy. In: ACSC 2008: 31st Australasian Computer Science Conference, 22-25 Jan 2008, Wollongong, Australia.
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[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.
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|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Item Status:||Live Archive|
|Additional Information (displayed to public):||Published version deposited in accordance with the copyright policy of the publisher. Copyright c 2008, Australian Computer Society, Inc. This pa per appeared at the Thirty-First Australasian Computer Sci- ence Conference (ACSC2008), Wollongong, Australia. Con- ferences in Research and Practice in Information Technology (CRPIT), Vol. 74. Gillian Dobbie and Bernard Mans, Ed. Reproduction for academic, not-for profit purposes permitted provided this text is included.|
|Depositing User:||Dr Hua Wang|
|Faculty / Department / School:||Historic - Faculty of Sciences - Department of Maths and Computing|
|Date Deposited:||19 Oct 2009 00:08|
|Last Modified:||28 Aug 2014 06:46|
|Uncontrolled Keywords:||web page prediction, association rules, clustering, Markov model|
|Fields of Research (FoR):||08 Information and Computing Sciences > 0804 Data Format > 080401 Coding and Information Theory|
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