Hybrid Recommendation Systems: A State of Art

Fatima Trabelsi, Amal Khtira, Bouchra El Asri

2021

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.

Paper Citation

in Harvard Style

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 - Volume 1: ENASE, ISBN 978-989-758-508-1, pages 281-288. DOI: 10.5220/0010452202810288

in Bibtex Style

@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 - Volume 1: ENASE,},
year={2021},
pages={281-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010452202810288},
isbn={978-989-758-508-1},
}

in EndNote Style

TY - CONF

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