Mohammed Sharaf Al Zebdi, Tereska Karran



This research explores some of the directions for improving the performance of personalised web usage mining applications. The study uses ANP (Autonomous News Personalisation) to provide personalised news to online newsreaders according to their interests. This is achieved within an intelligent web browser which monitors users' behaviour while browsing. Web usage mining techniques are applied at the site's access log files. These are first pre-processed, and then data-mined using specific algorithms to extract the interests of each user. User profiles are created and maintained to store users' interests. User interests within the profile are ranked according to their reading frequency of news items ranked according to category and location. Profiles are refined continuously and adapt to users' behaviour. Besides being adaptive and completely autonomous, the system is expected to improve on existing performance in news retrieval and to provide higher level personalisation. A system prototype has been implemented and tested using SQL Server 2005 to pre-process logs, data-mine cleaned data, and maintain user profiles. The main system tasks can be demonstrated with further work to address all the issues.


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Paper Citation

in Harvard Style

Sharaf Al Zebdi M. and Karran T. (2008). AUTONOMOUS NEWS PERSONALISATION (ANP) . In Proceedings of the Third International Conference on Software and Data Technologies - Volume 3: ICSOFT, ISBN 978-989-8111-53-1, pages 263-267. DOI: 10.5220/0001875202630267

in Bibtex Style

author={Mohammed Sharaf Al Zebdi and Tereska Karran},
booktitle={Proceedings of the Third International Conference on Software and Data Technologies - Volume 3: ICSOFT,},

in EndNote Style

JO - Proceedings of the Third International Conference on Software and Data Technologies - Volume 3: ICSOFT,
SN - 978-989-8111-53-1
AU - Sharaf Al Zebdi M.
AU - Karran T.
PY - 2008
SP - 263
EP - 267
DO - 10.5220/0001875202630267