The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners and courses. While these enriched representations allow more accurate and personalized recommendations, explainability remains a significant challenge which is especially problematic for certain domains with significant impact such as education and online learning. Recently, a novel class of recommender systems that uses reinforcement learning and graph reasoning over KGs has been proposed to generate explainable recommendations in the form of paths over a KG. Despite their accuracy and interpretability on e-commerce datasets, these approaches have scarcely been applied to the educational domain and their use in practice has not been studied. In this work, we propose an explainable recommendation system for MOOCs that uses graph reasoning. To validate the practical implications of our approach, we conducted a user study examining user perceptions of our new explainable recommendations. We demonstrate the generalizability of our approach by conducting experiments on two educational datasets: COCO and Xuetang.

翻译：大规模开放在线课程（MOOCs）的日益普及催生了个性化课程推荐系统的需求。这类系统常将神经网络与知识图谱（KGs）相结合，以获取学习者和课程更丰富的表征。虽然这些增强的表征能够实现更精准和个性化的推荐，但可解释性仍是一个重大挑战，这在对教育及在线学习等具有深远影响的领域尤为棘手。近期，一类新型推荐系统被提出，它利用强化学习和基于知识图谱的图推理技术，以知识图谱中的路径形式生成可解释推荐。尽管这类方法在电子商务数据集中展现出高准确率和可解释性，但它们极少被应用于教育领域，且其实践中的应用效果尚未得到研究。本文提出了一种基于图推理的面向慕课的可解释推荐系统。为验证本方法的实际应用意义，我们开展了一项用户研究，考察用户对新型可解释推荐的感知。通过在COCO和学堂在线两个教育数据集上的实验，我们证明了本方法的泛化能力。