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.

基于对比学习的 MOOC 知识概念推荐框架 (CL-KCRec) 能有效地表示和平衡 MOOC 中显式和隐式关系，提高知识概念推荐的性能，并满足用户的个性化需求。

在 MOOC 中建模平衡的显性和隐性关系，利用对比学习进行知识概念推荐

Modeling Balanced Explicit and Implicit Relations with Contrastive  Learning for Knowledge Concept Recommendation in MOOCs

Massive open online courses are becoming a modish way for education, which
provides a large-scale and open-access learning opportunity for students to
grasp the knowledge. To attract students' interest, the recommendation system
is applied by MOOCs providers to recommend courses to students. However, as a
course usually consists of a number of video lectures, with each one covering
some specific knowledge concepts, directly recommending courses overlook
students'interest to some specific knowledge concepts. To fill this gap, in
this paper, we study the problem of knowledge concept recommendation. We
propose an end-to-end graph neural network-based approach
calledAttentionalHeterogeneous Graph Convolutional Deep Knowledge
Recommender(ACKRec) for knowledge concept recommendation in MOOCs. Like other
recommendation problems, it suffers from sparsity issues. To address this
issue, we leverage both content information and context information to learn
the representation of entities via graph convolution network. In addition to
students and knowledge concepts, we consider other types of entities (e.g.,
courses, videos, teachers) and construct a heterogeneous information network to
capture the corresponding fruitful semantic relationships among different types
of entities and incorporate them into the representation learning process.
Specifically, we use meta-path on the HIN to guide the propagation of students'
preferences. With the help of these meta-paths, the students' preference
distribution with respect to a candidate knowledge concept can be captured.
Furthermore, we propose an attention mechanism to adaptively fuse the context
information from different meta-paths, in order to capture the different
interests of different students. The promising experiment results show that the
proposedACKRecis able to effectively recommend knowledge concepts to students
pursuing online learning in MOOCs.

本篇研究提出了一种基于 Attentional Heterogeneous Graph Convolutional Deep Knowledge Recommender (ACKRec) 的方法来解决 MOOC 中的知识概念推荐问题，并使用元路径指导学生偏好的传播，并且使用注意力机制来自适应性地融合不同学生的上下文信息，以有效地推荐 MOOC 在线学习中的知识概念。