E-learning Recommendation Systems: A Literature Review

Lately, the problem of choosing and searching data is becoming more and more difficult to solve, especially with the significant increase in the mass of information that exists on the internet. Computer techniques exist to facilitate research and allow the relevant extraction of information, one of these techniques is the recommended process that guides the user during his exploration, seeking for him the information that seems relevant. Different systems are capable of providing personalized recommendations to guide the user to interesting and useful resources within a large data space. Their goal is to reduce information overload through a process of collecting, filtering, and proactively recommending information. Recommendation systems introduce the notions inherent in the recommendation, based, among other things, on information retrieval, filtering, machine learning, and collaborative approaches. It also deals with the evaluation of such systems and presents different application cases. This article aims to provide a review of recommendation systems used in an e-learning environment. In the first part, we will present the concept of recommendation systems, their operating principle, its different approaches with their advantages and disadvantages, the second one, concerned this type of system in an educational environment and especially e-learning with a precision of its different approaches used and adopted in the same field.