Abstract
Recommender systems nowadays tend to become a necessity against information and product overloading. They aim to facilitate users browsing the World Wide Web by suggesting relevant products, websites or services according to users’ preferences. In this paper we present a hybrid framework that analyzes Web logs using social network analysis and data mining techniques, to extract useful information about users browsing patterns. Based on the identified results the framework builds a recommendation engine that is integrated in the Web browser of the user. A case study based on real data from an organization of 250 employees is finally presented using the system prototype which was constructed based on the proposed framework.
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Anastopoulos, V., Karampelas, P., Alhajj, R. (2013). Developing a Hybrid Framework for a Web-Page Recommender System. In: Özyer, T., Erdem, Z., Rokne, J., Khoury, S. (eds) Mining Social Networks and Security Informatics. Lecture Notes in Social Networks. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6359-3_9
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DOI: https://doi.org/10.1007/978-94-007-6359-3_9
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