  The knowledge concept recommendation in Massive Open Online Courses (MOOCs)
is a significant issue that has garnered widespread attention. Existing methods
primarily rely on the explicit relations between users and knowledge concepts
on the MOOC platforms for recommendation. However, there are numerous implicit
relations (e.g., shared interests or same knowledge levels between users)
generated within the users' learning activities on the MOOC platforms. Existing
methods fail to consider these implicit relations, and these relations
themselves are difficult to learn and represent, causing poor performance in
knowledge concept recommendation and an inability to meet users' personalized
needs. To address this issue, we propose a novel framework based on contrastive
learning, which can represent and balance the explicit and implicit relations
for knowledge concept recommendation in MOOCs (CL-KCRec). Specifically, we
first construct a MOOCs heterogeneous information network (HIN) by modeling the
data from the MOOC platforms. Then, we utilize a relation-updated graph
convolutional network and stacked multi-channel graph neural network to
represent the explicit and implicit relations in the HIN, respectively.
Considering that the quantity of explicit relations is relatively fewer
compared to implicit relations in MOOCs, we propose a contrastive learning with
prototypical graph to enhance the representations of both relations to capture
their fruitful inherent relational knowledge, which can guide the propagation
of students' preferences within the HIN. Based on these enhanced
representations, to ensure the balanced contribution of both towards the final
recommendation, we propose a dual-head attention mechanism for balanced fusion.
Experimental results demonstrate that CL-KCRec outperforms several
state-of-the-art baselines on real-world datasets in terms of HR, NDCG and MRR.
