A Recommendation System Involving a Hybrid Approach of Student Review and Rating for an Educational Video

Abstract

Video recommendation systems in e-learning platforms are a specific type of recommendation system that uses algorithms to suggest educational videos to students based on their interests and preferences. Student’s written feedback or reviews can provide more details about the educational video, including its strengths and weaknesses. In this paper, we build an education video recommender system based on learners’ reviews. we use LDA topic model on textual data extracted from educational videos to train language models as an input to supervised CNN model. Additionally, we used latent factor model to extract the educational videos' features and learner preferences from learners’ 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 problem in the recommender system. Our recommender uses user review to suggest new recommended videos, but in case there is no review (empty cell in matrix factorization) or unclear comment then we will take user rating on that educational video. We worked on real-world big and diverse learning courses and video content datasets from Coursera. Results show that new prediction ratings from learners' reviews can be used to make good new recommendations about videos that have not been previously seen and reduce cold start and sparsity problem effects.