The rapid development of Massive Open Online Courses (MOOCs) platforms has created an urgent need for an efficient personalized course recommender system that can assist learners of all backgrounds and levels of knowledge in selecting appropriate courses. Currently, most existing methods utilize a sequential recommendation paradigm that captures the user’s learning interests from their learning history, typically through recurrent or graph neural networks. However, fewer studies have explored how to incorporate principles of human learning at both the course and category levels to enhance course recommendations. In this article, we aim at addressing this gap by introducing a novel model, named Prerequisite-Enhanced Catory-Aware Graph Neural Network (PCGNN), for course recommendation. Specifically, we first construct a course prerequisite graph that reflects the human learning principles and further pre-train the course prerequisite relationships as the base embeddings for courses and categories. Then, to capture the user’s complex learning patterns, we build an item graph and a category graph from the user’s historical learning records, respectively: (1) the item graph reflects the course-level local learning transition patterns and (2) the category graph provides insight into the user’s long-term learning interest. Correspondingly, we propose a user interest encoder that employs a gated graph neural network to learn the course-level user interest embedding and design a category transition pattern encoder that utilizes GRU to yield the category-level user interest embedding. Finally, the two fine-grained user interest embeddings are fused to achieve precise course prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of PCGNN compared with other state-of-the-art methods.

大规模开放在线课程（MOOC）平台的快速发展迫切需要一种高效的个性化课程推荐系统，可以帮助各种背景和知识水平的学习者选择合适的课程。目前，大多数现有方法利用顺序推荐范式，通常通过循环或图神经网络从用户的学习历史中捕获用户的学习兴趣。然而，很少有研究探索如何在课程和类别层面纳入人类学习原理以增强课程推荐。在本文中，我们旨在通过引入一种新颖的模型来解决这一差距，该模型名为先决条件增强型猫感知图神经网络（PCGNN），用于课程推荐。具体来说，我们首先构建一个反映人类学习原理的课程先决条件图，并进一步预训练课程先决条件关系作为课程和类别的基础嵌入。然后，为了捕获用户复杂的学习模式，我们分别根据用户的历史学习记录构建项目图和类别图：（1）项目图反映课程级别的本地学习转换模式和（2）类别图提供对用户长期学习兴趣的洞察。相应地，我们提出了一种用户兴趣编码器，它采用门控图神经网络来学习课程级用户兴趣嵌入，并设计一个利用 GRU 生成类别级用户兴趣嵌入的类别转换模式编码器。最后，融合两个细粒度的用户兴趣嵌入以实现精确的课程预测。对两个真实世界数据集的大量实验证明了 PCGNN 与其他最先进方法相比的有效性。