Inproceedings
Book title :
International Conference on Digital Technologies and Applications (ICDTA'22)
Series :
Lecture Notes in Networks and Systems (LNNS)
Editors : Saad Motahhir; Badre Bossoufi
Address :
Cham
ISBN : 978-3-031-02447-4
Publisher :
Information
Year : 2022
Volume : 455
Pages : 361-371
Details
A Hybrid Recommender System for Pedagogical Resources
Yassamina Mediani Mohamed Gharzouli Chahrazed Mediani
The present paper proposes an e-learning system that combines popularity and collaborative filtering techniques to recommend pedagogical resources. A recommender system helps users get a correct and personalized decision by applying several recommendation methods such as content-based, collaborative filtering, and other hybrid approaches. However, predicting a relevant resource with a specific context, like pedagogical content, becomes a challenge. In our work, we propose a model to ameliorate the traditional collaborative filtering technique by (i) using the Singular Value Decomposition (SVD) to tackle the problem of scalability and data sparsity; (ii) extracting the most popular resources that the user does not interact with before to resolve the cold start problem; and (iii) combining the results of popularity and SVD factorization methods to improve the recommendation accuracy that evaluated by applying the recall, precision and f1-score of each approach. The comparison shows that the obtained results exhibit an encouraging performance of our model.
Key words :
Recommender system E-learning Collaborative filtering Popularity SVD
Ref. laboratory citation :
misc-lab-390
DOI :
10.1007/978-3-031-02447-4_38
Link :
Texte intégral
ACM :
Y. Mediani, M. Gharzouli and C. Mediani. 2022. A Hybrid Recommender System for Pedagogical Resources. In Proceedings of the International Conference on Digital Technologies and Applications (ICDTA'22), Cham. Lecture Notes in Networks and Systems (LNNS), Saad Motahhir; Badre Bossoufi, Ed., 455, Springer, 361-371. DOI: https://doi.org/10.1007/978-3-031-02447-4_38.
APA :
Mediani, Y., Gharzouli, M. & Mediani, C. (2022). A Hybrid Recommender System for Pedagogical Resources. In Proceedings of the International Conference on Digital Technologies and Applications (ICDTA'22), Cham. In Saad Motahhir; Badre Bossoufi (Ed.). Lecture Notes in Networks and Systems (LNNS), 455, Springer, 361-371. DOI: https://doi.org/10.1007/978-3-031-02447-4_38
IEEE :
Y. Mediani, M. Gharzouli and C. Mediani, "A Hybrid Recommender System for Pedagogical Resources". In Proceedings of the International Conference on Digital Technologies and Applications (ICDTA'22), Cham. Lecture Notes in Networks and Systems (LNNS), vol. 455, Springer, pp. 361-371, 2022. DOI: https://doi.org/10.1007/978-3-031-02447-4_38.
BibTeX :
@inproceedings{misc-lab-390,
author = {Mediani, Yassamina and Gharzouli, Mohamed and Mediani, Chahrazed},
title = {A Hybrid Recommender System for Pedagogical Resources},
volume = {455},
booktitle = {International Conference on Digital Technologies and Applications (ICDTA'22)},
series = {Lecture Notes in Networks and Systems (LNNS)},
location = {Cham},
isbn = {978-3-031-02447-4},
pages = {361--371},
editor = {Motahhir, Saad and Bossoufi, Badre},
publisher = {Springer},
year = {2022},
doi = {10.1007/978-3-031-02447-4\_38},
url = {https://link.springer.com/chapter/10.1007/978-3-031-02447-4\_38},
keywords = {Recommender system, E-learning, Collaborative filtering, Popularity, SVD}
}
RIS :
TY  - CONF
TI - A Hybrid Recommender System for Pedagogical Resources
AU - Y. Mediani
AU - M. Gharzouli
AU - C. Mediani
PY - 2022
VL - 455
BT - International Conference on Digital Technologies and Applications (ICDTA'22), Cham
SN - 978-3-031-02447-4
SP - 361
EP - 371
ED - S. Motahhir
ED - B. Bossoufi
PB - Springer
AB - The present paper proposes an e-learning system that combines popularity and collaborative filtering techniques to recommend pedagogical resources. A recommender system helps users get a correct and personalized decision by applying several recommendation methods such as content-based, collaborative filtering, and other hybrid approaches. However, predicting a relevant resource with a specific context, like pedagogical content, becomes a challenge. In our work, we propose a model to ameliorate the traditional collaborative filtering technique by (i) using the Singular Value Decomposition (SVD) to tackle the problem of scalability and data sparsity; (ii) extracting the most popular resources that the user does not interact with before to resolve the cold start problem; and (iii) combining the results of popularity and SVD factorization methods to improve the recommendation accuracy that evaluated by applying the recall, precision and f1-score of each approach. The comparison shows that the obtained results exhibit an encouraging performance of our model.
KW - Recommender system
KW - E-learning
KW - Collaborative filtering
KW - Popularity
KW - SVD
DO - 10.1007/978-3-031-02447-4_38
UR - https://link.springer.com/chapter/10.1007/978-3-031-02447-4_38
ID - misc-lab-390
ER -