A Framework for Context-Aware Recommendation in Mobile Social Learning

Quite recently the involvement and incorporation of context-aware recommender systems in different commercial disciplines as well as the Technology Enhanced Learning (TEL) community has gained the need for extensive research. Traditional recommender systems such as collaborative filtering, content-based filtering and their hybridizations apply users and items to generate recommendations. However, the incorporation and modeling of contextual information in a recommendation prediction will improve and make recommendations more accurate and efficient for a user. With a focus of contextual information, this paper discusses and elaborates on a framework for the recommendation of learning resources to learners in a mobile social learning community. © 2013 Universal Research Publications. All rights reserved

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