Multi-context recommendation in technology enhanced learning

Authors:
Majda Maâtallah;Hassina Seridi-Bouchelaghem
Affiliations:
LABGED Laboratory, University of Badji Mokhtar Annaba, Algeria;LABGED Laboratory, University of Badji Mokhtar Annaba, Algeria
Venue:
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Year:
2012

Citing 2
Cited 0

An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering

User Modeling and User-Adapted Interaction
Collaborative filtering adapted to recommender systems of e-learning

Knowledge-Based Systems

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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.