A fog based recommendation system for promoting the performance of E-Learning environments. (October 2020) - Ibrahim, Taghreed S.; Saleh, Ahmed I.; Elgaml, Nehad; Abdelsalam, Mohamed M.
Highlights: FBRS (Fog based recommendation System) is proposed to enhance and improve the EL environment by defining three modules (CIM, SIM and MM). FBRS can employ Fog computing, which enables E-Learning communities to expand their services according to their demand. FBRS applied a fog computing approach to achieve a high response time (low latency) and security, which are the critical issues for building a good RS. FBRS can outperform the limitations of other RSs such as personalization issues by applying multiple techniques (association rules, Fog computing, ontology and fuzzy logic) to achieve accuracy and decrease error. The analysis of the resulting recommendation system shows that the proposed methodology not only improves the accuracy but also reduces the error and it has a higher response than the other RSs. Abstract: 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. Graphical abstract: Image, graphical abstract