  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.
