A Dynamic Web Recommender System Using Hard and Fuzzy K-Modes Clustering

Abstract : This paper describes the design and implementation of a new dynamic Web Recommender System using Hard and Fuzzy K-modes clustering. The system provides recommendations based on user preferences that change in real time taking also into account previous searching and behavior. The recommendation engine is enhanced by the utilization of static preferences which are declared by the user when registering into the system. The proposed system has been validated on a movie dataset and the results indicate successful performance as the system delivers recommended items that are closely related to user interests and preferences.
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Communication dans un congrès
Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.40-51, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_5〉
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Panayiotis Christodoulou, Marios Lestas, Andreas Andreou. A Dynamic Web Recommender System Using Hard and Fuzzy K-Modes Clustering. Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.40-51, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_5〉. 〈hal-01459651〉

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