Web page recommendation model for web personalization

Web usage mining has gained more popularity among researchers in discovering the users browsing behavior mining the web server log that records all the users transactions activities. In this paper, we developed a usage model for predictions based on association rule. Similarity between items contain...

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Main Authors: Ahmad, Abdul Manan, Ahmad Hijazi, Mohd. Hanafi
Format: Book Section
Language:English
Published: Springer Berlin / Heidelberg 2004
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Online Access:http://eprints.utm.my/id/eprint/9802/1/AbdulMananAhmad2004_web_page_recommendation_model_for_web_personalization.pdf
http://eprints.utm.my/id/eprint/9802/
http://dx.doi.org/10.1007/b100910
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spelling my.utm.98022010-06-02T02:03:34Z http://eprints.utm.my/id/eprint/9802/ Web page recommendation model for web personalization Ahmad, Abdul Manan Ahmad Hijazi, Mohd. Hanafi QA75 Electronic computers. Computer science Web usage mining has gained more popularity among researchers in discovering the users browsing behavior mining the web server log that records all the users transactions activities. In this paper, we developed a usage model for predictions based on association rule. Similarity between items contained in the active user profile will be calculated upon the matched rules and finally the top-N most similar items are then recommended to the user. We used the time spent on each page for weighting the pages instead of binary. Two evaluation metrics were applied to evaluate the accuracy of the recommendations, namely precision and coverage. Springer Berlin / Heidelberg 2004-10-14 Book Section PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/9802/1/AbdulMananAhmad2004_web_page_recommendation_model_for_web_personalization.pdf Ahmad, Abdul Manan and Ahmad Hijazi, Mohd. Hanafi (2004) Web page recommendation model for web personalization. In: Knowledge-Based Intelligent Information and Engineering Systems. Lecture Notes in Computer Science, 3214/2 . Springer Berlin / Heidelberg, pp. 587-593. ISBN 978-3-540-23206-3 http://dx.doi.org/10.1007/b100910 DOI : 10.1007/b100910
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ahmad, Abdul Manan
Ahmad Hijazi, Mohd. Hanafi
Web page recommendation model for web personalization
description Web usage mining has gained more popularity among researchers in discovering the users browsing behavior mining the web server log that records all the users transactions activities. In this paper, we developed a usage model for predictions based on association rule. Similarity between items contained in the active user profile will be calculated upon the matched rules and finally the top-N most similar items are then recommended to the user. We used the time spent on each page for weighting the pages instead of binary. Two evaluation metrics were applied to evaluate the accuracy of the recommendations, namely precision and coverage.
format Book Section
author Ahmad, Abdul Manan
Ahmad Hijazi, Mohd. Hanafi
author_facet Ahmad, Abdul Manan
Ahmad Hijazi, Mohd. Hanafi
author_sort Ahmad, Abdul Manan
title Web page recommendation model for web personalization
title_short Web page recommendation model for web personalization
title_full Web page recommendation model for web personalization
title_fullStr Web page recommendation model for web personalization
title_full_unstemmed Web page recommendation model for web personalization
title_sort web page recommendation model for web personalization
publisher Springer Berlin / Heidelberg
publishDate 2004
url http://eprints.utm.my/id/eprint/9802/1/AbdulMananAhmad2004_web_page_recommendation_model_for_web_personalization.pdf
http://eprints.utm.my/id/eprint/9802/
http://dx.doi.org/10.1007/b100910
_version_ 1643645257055731712
score 13.2014675