Personalized e-learning resource recommendation using multimodal-enhanced collaborative filtering

Personalized learning resource recommendation is a prominent research area in the field of e-learning, allowing learners to find appropriate resources that align with their specific learning needs. The continuous development and optimization of online learning platforms have resulted in an increasing amount of e-learning resources and learner data. This poses challenges to the existing e-learning resource recommendation approaches, most of which rely on conventional collaborative filtering (CF) exclusively. Their efficiency is constrained owing to the utilization of a sole modality or a limited subset of modalities for the recommendation. To address these challenges, this study proposes a multimodal-enhanced CF approach in e-learning. Our approach uses various modalities for modeling, including learners’ learning records, human–computer interaction patterns, and information related to the resources. It integrates techniques such as matrix factorization for the joint learner–resource pattern modeling, clustering for grouping similar learners, and the long short-term memory network for capturing the temporal dynamics of learning activities. Comprehensive experiments are conducted to evaluate the efficiency of the proposed approach, and to determine its optimal setup for a deep understanding of the contributions of each component.