WebPUM : a web-based recommendation system to predict user future movements.
Web usage mining has become the subject of exhaustive research, as its potential for Web-based personalized services, prediction of user near future intentions, adaptive Web sites, and customer profiling are recognized. Recently, a variety of recommendation systems to predict user future movements...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier Ltd.
2010
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/17633/ http://psasir.upm.edu.my/id/eprint/17633/1/WebPUM.pdf |
| _version_ | 1848843295317295104 |
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| author | Jalali, Mehrdad Mustapha, Norwati Sulaiman, Md. Nasir Mamat, Ali |
| author_facet | Jalali, Mehrdad Mustapha, Norwati Sulaiman, Md. Nasir Mamat, Ali |
| author_sort | Jalali, Mehrdad |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Web usage mining has become the subject of exhaustive research, as its potential for Web-based personalized services, prediction of user near future intentions, adaptive Web sites, and customer profiling are
recognized. Recently, a variety of recommendation systems to predict user future movements through Web usage mining have been proposed. However, the quality of recommendations in the current systems to predict user future requests in a particular Web site is below satisfaction. To effectively provide online prediction, we have developed a recommendation system called WebPUM, an action using Web usage mining system and propose a novel approach online prediction for classifying user navigation patterns to predict users’ future intentions. The approach is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining phase. Furthermore, longest common subsequence algorithm is used for classifying current user activities to predict user next movement. The proposed system has been tested on CTI and MSNBC datasets. The results show an improvement in the quality of recommendations. Furthermore, experiments on scalability prove that the size of dataset and the number of the users in dataset do not significantly contribute to the percentage of accuracy. |
| first_indexed | 2025-11-15T08:12:45Z |
| format | Article |
| id | upm-17633 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:12:45Z |
| publishDate | 2010 |
| publisher | Elsevier Ltd. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-176332015-10-08T06:29:33Z http://psasir.upm.edu.my/id/eprint/17633/ WebPUM : a web-based recommendation system to predict user future movements. Jalali, Mehrdad Mustapha, Norwati Sulaiman, Md. Nasir Mamat, Ali Web usage mining has become the subject of exhaustive research, as its potential for Web-based personalized services, prediction of user near future intentions, adaptive Web sites, and customer profiling are recognized. Recently, a variety of recommendation systems to predict user future movements through Web usage mining have been proposed. However, the quality of recommendations in the current systems to predict user future requests in a particular Web site is below satisfaction. To effectively provide online prediction, we have developed a recommendation system called WebPUM, an action using Web usage mining system and propose a novel approach online prediction for classifying user navigation patterns to predict users’ future intentions. The approach is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining phase. Furthermore, longest common subsequence algorithm is used for classifying current user activities to predict user next movement. The proposed system has been tested on CTI and MSNBC datasets. The results show an improvement in the quality of recommendations. Furthermore, experiments on scalability prove that the size of dataset and the number of the users in dataset do not significantly contribute to the percentage of accuracy. Elsevier Ltd. 2010 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/17633/1/WebPUM.pdf Jalali, Mehrdad and Mustapha, Norwati and Sulaiman, Md. Nasir and Mamat, Ali (2010) WebPUM : a web-based recommendation system to predict user future movements. Expert Systems with Applications, 37. pp. 6201-6212. ISSN 0957-4174 Web usage mining. Web usage mining - Computer programs. Web databases. http://dx.doi.org/10.1016/j.eswa.2010.02.105 |
| spellingShingle | Web usage mining. Web usage mining - Computer programs. Web databases. Jalali, Mehrdad Mustapha, Norwati Sulaiman, Md. Nasir Mamat, Ali WebPUM : a web-based recommendation system to predict user future movements. |
| title | WebPUM : a web-based recommendation system to predict user future movements. |
| title_full | WebPUM : a web-based recommendation system to predict user future movements. |
| title_fullStr | WebPUM : a web-based recommendation system to predict user future movements. |
| title_full_unstemmed | WebPUM : a web-based recommendation system to predict user future movements. |
| title_short | WebPUM : a web-based recommendation system to predict user future movements. |
| title_sort | webpum : a web-based recommendation system to predict user future movements. |
| topic | Web usage mining. Web usage mining - Computer programs. Web databases. |
| url | http://psasir.upm.edu.my/id/eprint/17633/ http://psasir.upm.edu.my/id/eprint/17633/ http://psasir.upm.edu.my/id/eprint/17633/1/WebPUM.pdf |