A fog based recommendation system for promoting the performance of E-Learning environments

Recently, Recommendation Systems (RSs) have gained a great interest. E-Learning is one of the most important working fields of RS in which many challenges that hinder users in discovering the most appropriate materials can be overcome. The fog computing technique can enrich E-Learning based RS as it bridges the gap between; the cloud and end devices. In this paper, we propose Fog based Recommendation System (FBRS), which can be successfully utilized for promoting the performance of the E-Learning environment. We discuss a framework to consolidate and improve EL environment through defining three modules of FBRS: (i) Class Identification Module (CIM), (ii) Subclass Identification Module (SIM), and (iii) Matchmaking Module (MM). Moreover, the FBRS approach achieves a high response time and security to overcome both personalization and synonymy. Experimental results show that FBRS outperforms are recent techniques in terms of recommendation accuracy.