Abstract
Recommender Systems (RSs) have been applied recently in Technology Enhanced Learning (TEL) to let recommending relevant learning resources to teachers or learners. In this paper, we propose a novel recommendation technique that combines a fuzzy collaborative filtering algorithm with content based one to make better recommendation, using learners’ preferences and importance of knowledge to recommend items with different context corresponding to their different interests and tastes. Empirical evaluations show that the proposed technique is feasible and effective.
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© 2012 Springer-Verlag Berlin Heidelberg
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Maâtallah, M., Seridi-Bouchelaghem, H. (2012). Multi-context Recommendation in Technology Enhanced Learning. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2012. Lecture Notes in Computer Science, vol 7315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30950-2_137
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DOI: https://doi.org/10.1007/978-3-642-30950-2_137
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30949-6
Online ISBN: 978-3-642-30950-2
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