采用局部子图嵌入的MOOCs知识概念推荐模型

doi:10.3778/j.issn.1673-9418.2209056

摘要/Abstract

摘要： 大规模开放在线课程（MOOCs）在减少用户学习盲区和改善用户体验方面已经有大量的研究，尤其是基于图神经网络的个性化课程资源推荐，但现有工作主要集中在固定或同质图上，容易受到数据稀疏问题的影响且难以扩展。在局部子图上使用图卷积，并结合扩展的矩阵分解（MF）模型来解决这一问题。首先，将异构图分解为多个基于元路径的子图，结合随机游走采样方法实现在采样节点富有影响力邻域的同时捕获实体之间复杂的语义关系，并在局部邻域上进行图卷积平滑各节点表示，实现高可扩展性；然后，使用注意力机制适应性地融合不同子图的上下文信息，更全面地构建用户偏好；最后，通过扩展矩阵分解优化模型参数，获得推荐列表。为了验证提出模型的性能，在公开的MOOCs数据集上进行对比实验，相较于最优基线，性能提升了2%，内存计算需求降低了近500%，缓解数据稀疏问题的同时仍具有较强的可扩展性。

关键词: 大规模开放在线课程（MOOCs）, 图神经网络, 个性化课程推荐, 图卷积, 基于元路径的子图, 扩展矩阵分解

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.

Key words: massive online open courses (MOOCs), graph neural networks, personalized course recommendation, graph convolution, meta-path-based subgraphs, extended matrix factorization

居程程, 祝义. 采用局部子图嵌入的MOOCs知识概念推荐模型[J]. 计算机科学与探索, 2024, 18(1): 189-204.

JU Chengcheng, ZHU Yi. Knowledge Concept Recommendation Model for MOOCs with Local Subgraph Embedding[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(1): 189-204.

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