Multi-knowledge enhanced graph convolution for learning resource recommendation

In recent years, E-learning has gained immense popularity as a prominent mode of education. However, accurately recommending learning resources from a vast amount of data remains a significant challenge. This study addresses two primary challenges impacting recommendation performance. Firstly, an imbalance exists between the abundance of available learning resources and the limited interaction behavior of learners. Secondly, the existing algorithms often overlook dynamic preference information, focusing primarily on learners’ short-term, static preferences only by learning interactive behavior but disregarding the multi-correlation between learning resources and learners. To tackle these challenges, we propose MkEGC (Multi-knowledge Enhanced Graph Convolution), a novel framework for learning resource recommendation. We approach the recommendation process as a Markov decision process. Initially, we construct a dual knowledge graph convolutional network, operating in learning resource-knowledge and learner-knowledge domains. This network facilitates the extraction of vector features from learning resources, enhances the learner vector representation, and captures higher-order preferences. Subsequently, we design hierarchical and attention weighting strategies to effectively extract latent hierarchical information from the knowledge graph. Finally, we integrate the learning resource state, the learning interaction state, and the sequence state to represent a multi-dimensional learner-state within the Markov decision framework, enabling prise learning resource recommendations. To validate the effectiveness of MkEGC, we conduct extensive experiments, comparing multiple sets of metrics with six state-of-the-art recommendation algorithms, utilizing two real-world datasets.