SentiWordNet Ontology and Deep Neural Network Based Collaborative Filtering Technique for Course Recommendation in an E-Learning Platform

Abstract

The expansion of the population that wants to learn online is growing due to several e-learning platforms, which help innovate and suggest courses to learners. Several techniques are devised for determining optimal courses for the learner. In recent days, researchers began to utilize recommendation systems in e-learning. This paper devises a novel technique for course recommendation to students in an e-learning platform, which helps learners select the best course. Here, the Butterfly Weed Optimization (BWO) is newly devised by combining Invasive Weed Optimization (IWO) and Butterfly Optimization Algorithm (BOA). At first, the process is performed by inputting the data to the Course subscription matrix for constructing the matrix based on learner interest and courses. Here, course grouping is performed using Interval type-2 Fuzzy Local Enhancement Based Rough K-means Clustering. Furthermore, the course is matched with input data based on entropy and angular distance. Finally, the sentiment classification is performed using the Ontology-based approach SentiWordNet and Deep Neural Network (DNN). Here, the DNN is trained with the proposed BWO algorithm, and thus the course recommendation is attained by offering a suitable course recommendation to learners.