Recommendation System for Learning Resources Using Collaborative Learning of Ensemble Neural Networks

Abstract

With the increase in e-learning applications and its allied benefits, improving e-learning resources based on the requirements of the users has become a challenging undertaking due to the large number of resources. Different people prefer different learning resources especially based on their age, nation, neighborhood and requirements. There is no specific method for fitting make the recommendation system which can help users get to the best resources inside the least measure of time. However, collaborative learning has come up as an effective technique in machine learning based approaches for the design of recommendation systems. In this paper, a collaborative learning-based approach has been proposed for ensemble neural networks (ENNs) to design a recommendation system for learning resources. The proposed approach uses the resilient back propagation-based way to deal with train the ensemble neural network. It has been shown that the proposed approach outperforms previously existing techniques both in terms of error and iterations which indicates higher precision and lesser time which is a basic aspect in real time e-learning recommendation system applications.