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Authors: Fatima Trabelsi ; Amal Khtira and Bouchra El Asri

Affiliation: IMS Team, ADMIR Laboratory, Rabat IT Center, ENSIAS, Mohammed V University, Rabat, Morocco

Keyword(s): Recommendation Systems, Hybrid Filtering, Collaborative Filtering, Content-based Filtering, Recommendation Problems, State of Art.

Abstract: Recommendation systems have become more important and popular in many application areas such as music, movies, e-commerce, advertisement and social networks. Recommendation systems use either collaborative filtering, content-based filtering or hybrid filtering in order to propose items to users, and each type has its weaknesses and strengths. In this paper, we present the results of a literature review that focuses specifically on hybrid recommendation systems. The objective of this review is to identify the problems that hybrid filtering tends to solve and the different techniques used to this end.

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Paper citation in several formats:
Trabelsi, F.; Khtira, A. and El Asri, B. (2021). Hybrid Recommendation Systems: A State of Art. In Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE, ISBN 978-989-758-508-1; ISSN 2184-4895, pages 281-288. DOI: 10.5220/0010452202810288

@conference{enase21,
author={Fatima Trabelsi. and Amal Khtira. and Bouchra {El Asri}.},
title={Hybrid Recommendation Systems: A State of Art},
booktitle={Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE,},
year={2021},
pages={281-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010452202810288},
isbn={978-989-758-508-1},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE,
TI - Hybrid Recommendation Systems: A State of Art
SN - 978-989-758-508-1
IS - 2184-4895
AU - Trabelsi, F.
AU - Khtira, A.
AU - El Asri, B.
PY - 2021
SP - 281
EP - 288
DO - 10.5220/0010452202810288

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