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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R.: Recommender Systems in Technology Enhanced Learning. In: The1st RSs Handbook. Springer, Berlin (2010)
Garcia, E., Romero, C., Ventura, S., Castro, C.D.: An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Modeling and User-Adapted Interaction 19(1-2) (2009)
Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative filtering adapted to recommender systems of e-learning. Journal of KBS, Knowledge-Based Systems 22(4) (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)