.png){kind=icon}
.png){kind=icon}
Return
Personalized e-learning resource recommendation using multimodal-enhanced collaborative filtering
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2025.113605.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
E-learning
Learning resource recommendation
Multimodal recommendation
Collaborative filtering
Deep learning
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W