An Optimized Feature Selection Method for E-Learning Recommender System Using Deep Neural Network based on Multilayer Perceptron

Abstract

An effective E-Learning system must identify learning content appropriate for the needs of the specific learner from among the many sources of learning content available. The learning content can consist of different types of media content namely Text, Video and Image and to determine a rating. Recommendation systems become increasingly used in information systems and especially in e-learning platform. These systems are used to propose and recommend content of these platforms to users according to needs of the later in order to allow them to have the maximum information for learning. In this paper, a recommendation system based on data mining and deep learning has been proposed to help the learner by recommending the appropriate learning content. To improve the recommendations a content matrix is created and based on this, the logistic regression and deep learning methods could classify recommendations. Experimental results show that the precision, recall and f measure for Top 5 & 10 Recommendations using Deep Neural Network performsby 1.439% better than Top 5 & 10 Recommendations using Logistic Regression among Chi Squared, SA, PSO and ICA methods