Journal of King Saud University: Computer and Information Sciences (Sep 2022)
An Analysis of learners’ affective and cognitive traits in Context-Aware Recommender Systems (CARS) using feature interactions and Factorization Machines (FMs)
Abstract
Massive Open Online Courses (MOOCs) have witnessed a fast emergence in recent years thanks to their open and massive nature. A huge number of learners with different profiles, knowledge and learning objectives follow the same online courses with different personalized experiences. With this rapid development, course recommendation based on traits and characteristics becomes a necessity during the learning process to determine potentially suitable learning objects while analyzing user’s behaviours. Context-Aware Recommender Systems (CARS) have been shown to be effective in recommending items according to user’s interests and affect. In this paper, we propose a novel approach for Context-Aware Recommender Systems based on Factorization Machines (FMs). We propose a new algorithm called Unsupervised Graph Predictor Factorization Machines (UGPFMs) that models feature interactions during the recommendation process. UGPFMs apply Convolutional Neural Networks (CNNs) in the graph predictor to model interactions between context features in the rating matrice, then it employs the Factorization Machine (FM) to select the suitable items for the recommendation. UGPFM is a generic model that extends FM to improve the accuracy of recommendation, it outperforms state-of-the-art feature interactions-based FM techniques using MSE, RMSE, MAE and R-Squared metrics. We applied UGPFMs on MOOC while considering the sentiment, the cognition and the confusion as contexts for the recommendation. We compared different context combinations’ impact on the recommendation accuracy, then we computed each context-to-context feature interactions to better understand each context efficiency on e-learning recommendation.
