Semantic Collaborative Filtering for Learning Objects Recommendation

Lamia Berkani, Omar Nouali


The present paper proposes a personalized recommendation approach of learning objects (LOs) within an online Community of Practice (CoP). Three strategies of recommendation have been proposed: (1) a semantic filtering (SemF) by member’s interests; (2) a collaborative filtering (CF) based on the member’s expertise level; and (3) a semantic collaborative filtering combining in different ways the two approaches. The expertise level of a member is calculated in relation to all of his domains of expertise using the domain knowledge ontology (DKOnto). A similarity measure is proposed based on a set of rules which cover all the possible cases for the relative positions of two domains in DKOnto. In order to illustrate our work, some preliminary results of experimentation have been presented.


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Paper Citation

in Harvard Style

Berkani L. and Nouali O. (2013). Semantic Collaborative Filtering for Learning Objects Recommendation . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013) ISBN 978-989-8565-75-4, pages 52-63. DOI: 10.5220/0004550500520063

in Bibtex Style

author={Lamia Berkani and Omar Nouali},
title={Semantic Collaborative Filtering for Learning Objects Recommendation},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013)},

in EndNote Style

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013)
TI - Semantic Collaborative Filtering for Learning Objects Recommendation
SN - 978-989-8565-75-4
AU - Berkani L.
AU - Nouali O.
PY - 2013
SP - 52
EP - 63
DO - 10.5220/0004550500520063