Overview of Recommendation Systems

In recent years, the web dominates internet users. To ensure the satisfaction of these later, a large amount of data is generated in various fields. However, the web suffers from a great deal of information overload. This issue makes the user perplexed in choosing the resource that meets his needs. Recommendation Systems (RSs) have emerged as an unavoidable solution to address information overload. This offer the user the most appropriate resource that meets his profile. The most common techniques for filtering irrelevant resources fall into three categories: Collaborative Filtering (CF), Content-Based filtering (CB), and hybrid filtering. In spite of their success, these techniques remain limited as the user requirements increase. To overcome the limitations of traditional techniques, several approaches have been developed. In the present paper, we establish the state of the art of recommendation systems, including the current work. It serves as a compass for researchers. This allows them to have a clear idea of the existent in order to easily propose an effective solution. A part of this paper is devoted to the recommendation systems carried out for the educational purpose and support smart education.