Recommender systems have been studied for decades with numerous promising models been proposed. Among them, Collaborative Filtering (CF) models are arguably the most successful one due to its high accuracy in recommendation and elimination of privacy-concerned personal meta-data from training. This paper extends the usage of CF-based model to the task of course recommendation. We point out several challenges in applying the existing CF-models to build a course recommendation engine, including the lack of rating and meta-data, the imbalance of course registration distribution, and the demand of course dependency modeling. We then propose several ideas to address these challenges. Eventually, we combine a two-stage CF model regularized by course dependency with a graph-based recommender based on course-transition network, to achieve AUC as high as 0.97 with a real-world dataset.

翻译：推荐系统已历经数十年的研究，期间涌现出众多性能优异的模型。其中，协同过滤模型因其在推荐精度上的卓越表现以及训练过程中无需涉及用户元数据等隐私信息，堪称最成功的模型之一。本文将协同过滤模型的应用拓展至课程推荐任务。我们指出，将现有协同过滤模型应用于构建课程推荐引擎面临若干挑战，包括：评分数据与元数据的匮乏、课程选课分布的不均衡性，以及课程依赖关系建模的需求。针对上述问题，本文提出多项创新解决方案。最终，我们通过引入课程依赖性正则化的两阶段协同过滤模型，结合基于课程转换网络的图推荐算法，在真实数据集上实现了高达0.97的AUC指标。