Recommendation of Learning Resources based on Social Relations

Mohammed Tadlaoui, Karim Sehaba, Sébastien George

Abstract

Recommender systems are able to estimate the interest for a user of a given resource from some information about similar users and resources properties. In our work, we focus on the recommendations of educational resources in the field of Technology Enhanced Learning (TEL) and more specifically the recommendations which are based on social information. Based on the results of research in recommender systems and TEL, we define an approach to recommend learning resources using social information present in social networks. We have developed a formal model for the calculation of similarity between users and the generation of three types of recommendation. We also developed a platform that implements our approach.

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Paper Citation


in Harvard Style

Tadlaoui M., Sehaba K. and George S. (2015). Recommendation of Learning Resources based on Social Relations . In Proceedings of the 7th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-108-3, pages 425-432. DOI: 10.5220/0005452304250432


in Bibtex Style

@conference{csedu15,
author={Mohammed Tadlaoui and Karim Sehaba and Sébastien George},
title={Recommendation of Learning Resources based on Social Relations},
booktitle={Proceedings of the 7th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2015},
pages={425-432},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005452304250432},
isbn={978-989-758-108-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - Recommendation of Learning Resources based on Social Relations
SN - 978-989-758-108-3
AU - Tadlaoui M.
AU - Sehaba K.
AU - George S.
PY - 2015
SP - 425
EP - 432
DO - 10.5220/0005452304250432