Massive open online courses (MOOCs), which offer open access and widespread interactive participation through the internet, are quickly becoming the preferred method for online and remote learning. Several MOOC platforms offer the service of course recommendation to users, to improve the learning experience of users. Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise. To mitigate this gap, we examine an interesting problem of concept recommendation in this paper, which can be viewed as recommending knowledge to users in a fine-grained way. We put forward a novel approach, termed HinCRec-RL, for Concept Recommendation in MOOCs, which is based on Heterogeneous Information Networks and Reinforcement Learning. In particular, we propose to shape the problem of concept recommendation within a reinforcement learning framework to characterize the dynamic interaction between users and knowledge concepts in MOOCs. Furthermore, we propose to form the interactions among users, courses, videos, and concepts into a heterogeneous information network (HIN) to learn the semantic user representations better. We then employ an attentional graph neural network to represent the users in the HIN, based on meta-paths. Extensive experiments are conducted on a real-world dataset collected from a Chinese MOOC platform, XuetangX, to validate the efficacy of our proposed HinCRec-RL. Experimental results and analysis demonstrate that our proposed HinCRec-RL performs well when comparing with several state-of-the-art models.

翻译：大规模开放在线课程（MOOCs）通过互联网提供开放访问和广泛的互动参与，正迅速成为在线与远程学习的首选方式。多个MOOC平台向用户提供课程推荐服务，以改善其学习体验。尽管该服务具有实用性，但我们认为直接向用户推荐课程可能忽略其不同程度的专业知识。为弥补这一不足，本文研究了一个有趣的概念推荐问题，可视为以细粒度方式向用户推荐知识。我们提出了一种名为HinCRec-RL的新型方法，用于MOOC中的概念推荐，该方法基于异构信息网络和强化学习。具体而言，我们建议将概念推荐问题建模到强化学习框架中，以刻画MOOCs中用户与知识概念之间的动态交互。此外，我们提出将用户、课程、视频和概念之间的交互整合成异构信息网络，以更好地学习用户的语义表示。随后，我们基于元路径采用注意力图神经网络来表示HIN中的用户。我们在从中国MOOC平台学堂在线收集的真实世界数据集上进行了大量实验，以验证所提出的HinCRec-RL的有效性。实验结果与分析表明，与多个最先进模型相比，我们提出的HinCRec-RL表现出色。