Mining web navigation profiles for recommendation systems
This study explores web usage mining, for which many data mining techniques such as clustering, classification and pattern discovery have been applied to web server logs. The output is a set of discovered patterns which form the main input to the recommendation systems which in return predict the ne...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asian Network for Scientific Information
2010
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| Online Access: | http://psasir.upm.edu.my/id/eprint/15643/ http://psasir.upm.edu.my/id/eprint/15643/1/Mining%20web%20navigation%20profiles%20for%20recommendation%20systems.pdf |
| _version_ | 1848842734811480064 |
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| author | AlMurtadha, Yahya M. Sulaiman, Md. Nasir Mustapha, Norwati Udzir, Nur Izura |
| author_facet | AlMurtadha, Yahya M. Sulaiman, Md. Nasir Mustapha, Norwati Udzir, Nur Izura |
| author_sort | AlMurtadha, Yahya M. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This study explores web usage mining, for which many data mining techniques such as clustering, classification and pattern discovery have been applied to web server logs. The output is a set of discovered patterns which form the main input to the recommendation systems which in return predict the next web navigations. Most of the recommendation systems are user-centered which make a prediction list to the users based on their long term navigation history, user’s databases or full user’s profiles. Companies wish to attract anonymous users, directed them at the early stages of their visits and get them involved with their websites. Learning and mining the web navigation profiles followed by enhanced classification to the similar activities of previous users will provide an appropriate model to recommend to the current anonymous active user with short term navigation. Using CTI dataset, the experimental results show better prediction accuracy than the previous works. An adaptive profiling to save time is a key factor for future works. |
| first_indexed | 2025-11-15T08:03:51Z |
| format | Article |
| id | upm-15643 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:03:51Z |
| publishDate | 2010 |
| publisher | Asian Network for Scientific Information |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-156432015-11-02T07:09:24Z http://psasir.upm.edu.my/id/eprint/15643/ Mining web navigation profiles for recommendation systems AlMurtadha, Yahya M. Sulaiman, Md. Nasir Mustapha, Norwati Udzir, Nur Izura This study explores web usage mining, for which many data mining techniques such as clustering, classification and pattern discovery have been applied to web server logs. The output is a set of discovered patterns which form the main input to the recommendation systems which in return predict the next web navigations. Most of the recommendation systems are user-centered which make a prediction list to the users based on their long term navigation history, user’s databases or full user’s profiles. Companies wish to attract anonymous users, directed them at the early stages of their visits and get them involved with their websites. Learning and mining the web navigation profiles followed by enhanced classification to the similar activities of previous users will provide an appropriate model to recommend to the current anonymous active user with short term navigation. Using CTI dataset, the experimental results show better prediction accuracy than the previous works. An adaptive profiling to save time is a key factor for future works. Asian Network for Scientific Information 2010 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/15643/1/Mining%20web%20navigation%20profiles%20for%20recommendation%20systems.pdf AlMurtadha, Yahya M. and Sulaiman, Md. Nasir and Mustapha, Norwati and Udzir, Nur Izura (2010) Mining web navigation profiles for recommendation systems. Information Technology Journal, 9 (4). pp. 790-796. ISSN 1812-5638; ESSN: 1812-5646 http://scialert.net/abstract/?doi=itj.2010.790.796 10.3923/itj.2010.790.796 |
| spellingShingle | AlMurtadha, Yahya M. Sulaiman, Md. Nasir Mustapha, Norwati Udzir, Nur Izura Mining web navigation profiles for recommendation systems |
| title | Mining web navigation profiles for recommendation systems |
| title_full | Mining web navigation profiles for recommendation systems |
| title_fullStr | Mining web navigation profiles for recommendation systems |
| title_full_unstemmed | Mining web navigation profiles for recommendation systems |
| title_short | Mining web navigation profiles for recommendation systems |
| title_sort | mining web navigation profiles for recommendation systems |
| url | http://psasir.upm.edu.my/id/eprint/15643/ http://psasir.upm.edu.my/id/eprint/15643/ http://psasir.upm.edu.my/id/eprint/15643/ http://psasir.upm.edu.my/id/eprint/15643/1/Mining%20web%20navigation%20profiles%20for%20recommendation%20systems.pdf |