Authors:
Fatima Zohra 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.