Pemodelan rekomendasi halaman web berasaskan teknik perlombongan data

The continuous growth of the information on the Internet makes it necessary for users to be provided with a convenient and yet accurate tools to capture the information needed. Web usage mining has gained more popularity among researchers in discovering the users browsing behavior by mining the web...

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Main Author:	Ahmad Hijazi, Mohd. Hanafi
Format:	Thesis
Language:	English
Published:	2005
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/4291/1/MohdHanafiAhmadHijaziMFSKSM2005.pdf
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id	my-utm-ep.4291
record_format	uketd_dc
spelling	my-utm-ep.42912018-01-16T08:15:05Z Pemodelan rekomendasi halaman web berasaskan teknik perlombongan data 2005-03 Ahmad Hijazi, Mohd. Hanafi QA75 Electronic computers. Computer science The continuous growth of the information on the Internet makes it necessary for users to be provided with a convenient and yet accurate tools to capture the information needed. Web usage mining has gained more popularity among researchers in discovering the users browsing behavior by mining the web server log that records all the usersâ€™ transactions activities. By applying it into the recommendation engine, Web personalization can be executed based on the discovered userâ€™s behavior. Nevertheless, the efficiency of the generated recommendations is still an issue for researchers. This thesis is focusing on the development of a usage model for predictions based on association rule and similarity measures, named ARsim. Additional parameter was used to measure the similarities between URLs, which is the time user spent on a particular page. To generate the final recommendation, similarity between URLs contained in the active user profile was calculated upon the matched Web usage profiles and finally the top- N most similar URLs are then recommended to the user. Three evaluation metrics, which is commonly used by other researchers for evaluation of Web page recommendation model, was applied to evaluate the efficacy of ARsim, namely precision, coverage and F1. Comparison to two other different techniques, traditional association rule and eVZpro found that the integration of rules and similarity measures allow only the most appropriate URLs to be recommended and thus increase the efficiency of the Web page recommendation engine. 2005-03 Thesis http://eprints.utm.my/id/eprint/4291/ http://eprints.utm.my/id/eprint/4291/1/MohdHanafiAhmadHijaziMFSKSM2005.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Ahmad Hijazi, Mohd. Hanafi Pemodelan rekomendasi halaman web berasaskan teknik perlombongan data
description	The continuous growth of the information on the Internet makes it necessary for users to be provided with a convenient and yet accurate tools to capture the information needed. Web usage mining has gained more popularity among researchers in discovering the users browsing behavior by mining the web server log that records all the usersâ€™ transactions activities. By applying it into the recommendation engine, Web personalization can be executed based on the discovered userâ€™s behavior. Nevertheless, the efficiency of the generated recommendations is still an issue for researchers. This thesis is focusing on the development of a usage model for predictions based on association rule and similarity measures, named ARsim. Additional parameter was used to measure the similarities between URLs, which is the time user spent on a particular page. To generate the final recommendation, similarity between URLs contained in the active user profile was calculated upon the matched Web usage profiles and finally the top- N most similar URLs are then recommended to the user. Three evaluation metrics, which is commonly used by other researchers for evaluation of Web page recommendation model, was applied to evaluate the efficacy of ARsim, namely precision, coverage and F1. Comparison to two other different techniques, traditional association rule and eVZpro found that the integration of rules and similarity measures allow only the most appropriate URLs to be recommended and thus increase the efficiency of the Web page recommendation engine.
format	Thesis
qualification_level	Master's degree
author	Ahmad Hijazi, Mohd. Hanafi
author_facet	Ahmad Hijazi, Mohd. Hanafi
author_sort	Ahmad Hijazi, Mohd. Hanafi
title	Pemodelan rekomendasi halaman web berasaskan teknik perlombongan data
title_short	Pemodelan rekomendasi halaman web berasaskan teknik perlombongan data
title_full	Pemodelan rekomendasi halaman web berasaskan teknik perlombongan data
title_fullStr	Pemodelan rekomendasi halaman web berasaskan teknik perlombongan data
title_full_unstemmed	Pemodelan rekomendasi halaman web berasaskan teknik perlombongan data
title_sort	pemodelan rekomendasi halaman web berasaskan teknik perlombongan data
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2005
url	http://eprints.utm.my/id/eprint/4291/1/MohdHanafiAhmadHijaziMFSKSM2005.pdf
_version_	1747814508408078336

Pemodelan rekomendasi halaman web berasaskan teknik perlombongan data

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