A Hybrid E-Learning Recommendation System Incorporating User Reviews and Ratings for Enhanced Course Selection

A recommendation system can be used in E-learning to suggest relevant and personalized resources. It uses data about learners' behaviors, such as the courses they have taken, the content they have viewed, and the assessments they have completed, to make recommendations for additional learning opportunities. In this work, we build a hybrid educational video recommender system based on learners’ reviews and ratings. We use a Latent Dirichlet Allocation (LDA) topic model on textual data extracted from educational videos to train language models as an input to a supervised Convolutional Neural Network (CNN) model. Additionally, we use a Latent Factor Model (LFM) to extract educational video features and learners' preferences from their historical data (ratings and reviews) as an output CNN model. In our proposed technique, we use hybrid user ratings and reviews to tackle sparsity and cold start problems in the recommender system. Our recommender uses reviews to predict a new sentiment matrix. For cases where reviews are absent (represented as empty cells in the sentiment matrix) or comments are ambiguous, the system utilizes normalized user ratings from the rating matrix, employing a tailored mathematical framework specifically designed for this purpose.