Knowledge Concept Recommendation Model for MOOCs with Local Subgraph Embedding

Abstract

Massive online open courses (MOOCs) have been extensively researched in reducing user learning blindness and improving user experience, especially personalized course resource recommendation based on graph neural networks. However, these efforts focus primarily on fixed or homogeneous graphs, vulnerable to data sparsity problems, and difficult to scale. This paper  uses graph convolution on local subgraphs combined with an extended matrix factorization (MF) model to overcome this limitation. Firstly, the proposed method decomposes the heterogeneous graph into multiple meta-path-based subgraphs and combines random wandering sampling methods to capture complex semantic relationships between entities while sampling nodes’ influential neighborhoods, and performs graph convolution on local neighborhoods to smooth the representation of each node and achieve high scalability. Next, the attention mechanism adaptively fuses the contextual information of different subgraphs for a more comprehensive construction of user preferences. Finally, the model parameters are optimized by expanding MF to obtain recommendation list. To validate the performance of the proposed model, comparative experiments are conducted on publicly available MOOCs datasets, with a 2% performance improvement and a nearly 500% reduction in memory computation requirements compared with the optimal baseline, providing strong scalability while alleviating the data sparsity problem.

Keywords

• massive online open courses (moocs); graph neural networks; personalized course recommendation; graph convolution; meta-path-based subgraphs; extended matrix factorization