The recent boom in online courses has necessitated personalized online course
recommendation. Modelling the learning sequences of users is key for course
recommendation because the sequences contain the dynamic learning interests of the
users. However, current course recommendation methods ignore heterogeneous course
information and collective sequential dependency between courses when modelling the
learning sequences. We thus propose a novel online course recommendation method …