Recommendation systems in the e-learning domain: A survey

E-learning recommender system are exponentially raised in the e-learning domain, due to the massive learning materials and resources in the e-learning frameworks, many researchers have conducted in the recommender system filed, and they proposed interesting approaches to address and solve problems related to the recommendation filed. In this article, we investigate different current State-of-the-Art approaches in the recommendation system of e-learning domain such as, traditional recommendation techniques including (CF, CBF, and Hybrid). Ontology based, knowledge based, context-aware, attribute-aware, sentiment based, big data based, fog based, cloud based, machine learning based, social based, web mining based, data mining based, self-organization based, and deep learning based to inspire other researcher interested in the recommender system of e-learning domain to have better understanding of various recommendation techniques.