The knowledge concept recommendation in Massive Open Online Courses (MOOCs) is a significant issue that has garnered widespread attention. Existing methods primarily rely on the explicit relations between users and knowledge concepts on the MOOC platforms for recommendation. However, there are numerous implicit relations (e.g., shared interests or same knowledge levels between users) generated within the users' learning activities on the MOOC platforms. Existing methods fail to consider these implicit relations, and these relations themselves are difficult to learn and represent, causing poor performance in knowledge concept recommendation and an inability to meet users' personalized needs. To address this issue, we propose a novel framework based on contrastive learning, which can represent and balance the explicit and implicit relations for knowledge concept recommendation in MOOCs (CL-KCRec). Specifically, we first construct a MOOCs heterogeneous information network (HIN) by modeling the data from the MOOC platforms. Then, we utilize a relation-updated graph convolutional network and stacked multi-channel graph neural network to represent the explicit and implicit relations in the HIN, respectively. Considering that the quantity of explicit relations is relatively fewer compared to implicit relations in MOOCs, we propose a contrastive learning with prototypical graph to enhance the representations of both relations to capture their fruitful inherent relational knowledge, which can guide the propagation of students' preferences within the HIN. Based on these enhanced representations, to ensure the balanced contribution of both towards the final recommendation, we propose a dual-head attention mechanism for balanced fusion. Experimental results demonstrate that CL-KCRec outperforms several state-of-the-art baselines on real-world datasets in terms of HR, NDCG and MRR.

翻译：大规模开放在线课程（MOOC）中的知识概念推荐是一个引发广泛关注的重要问题。现有方法主要依赖MOOC平台上用户与知识概念之间的显式关系进行推荐。然而，用户在MOOC平台的学习活动中会产生大量隐式关系（例如用户间的共同兴趣或相似知识水平）。现有方法未能考虑这些隐式关系，且这些关系本身难以学习与表征，导致知识概念推荐性能欠佳，难以满足用户的个性化需求。为解决该问题，我们提出一种基于对比学习的新型框架（CL-KRec），该框架能够表征并平衡显式与隐式关系，用于MOOC中的知识概念推荐。具体而言，我们首先通过建模MOOC平台数据构建异构信息网络（HIN）。随后，利用关系更新图卷积网络和堆叠多通道图神经网络分别表征HIN中的显式与隐式关系。考虑到MOOC中显式关系数量相对隐式关系较少，我们提出一种基于原型图的对比学习方法，增强两种关系的表征能力以捕获其丰富的内在关系知识，从而指导HIN中用户偏好的传播。基于这些增强表征，为确保两者对最终推荐的均衡贡献，我们提出一种双头注意力机制进行平衡融合。实验结果表明，在真实数据集上，CL-KRec在HR、NDCG和MRR指标上均优于多个最先进基线方法。