# Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
## 6 Conclusions  
This paper proposes a course recommendation model, HGE-CRec, based on heterogeneous graph and Graph Neural Network and adopts a heterogeneous graph approach to model multi-source heterogeneous information in the scenario of course recommendation for distance education. The proposed HGE-CRec model uses GCN and GAT to improve the meta-path embedding process in HG. Specifically, after using the skip-gram algorithm to generate the original meta-path embeddings, GCN is used to generate new embeddings to simulate more meta-path embeddings, effectively expanding the range of feature distribution of the meta-path embedded samples. Next, GAT is used to learn the neighborhood contribution degree, aggregate the neighbor embedding information of nodes, and obtain the node embedding vector with importance semantics. The node embedding vectors of different meta-paths are weighted nonlinearly and aggregated to form the low dimensional embedding vectors of each node, and the recommendation is implemented under the framework of matrix decomposition.  
Data sparsity is a difficult problem in large-scale online course recommendation. The model proposed in this paper uses a heterogeneous graph structure to represent online course learning and expands the relationship domain of entities (e.g., students, courses) through the meta-paths, thereby alleviating the problem of data sparsity. This work can expand recommendation methods for large-scale online courses from a local perspective to a global perspective, providing a reference for further optimization of online course platforms. On the whole, our proposed model fully considers the different contributions of different neighbors and distinguishes the weight of different neighbor embeddings in order to better model the heterogeneous graph and improve the recommendation effect. Moreover, traditional meta-path generation methods rely on researchers manually predefining the meta-path. In order to find a better meta-path, repeated experiments need to be carried out. Using graph convolution network to generate new embeddings for simulation of more meta-paths might be a good way to solve this problem, as defining only a few meta-paths can already achieve a better effect than that achieved by manually predefining the meta-path. In this way, the problem of tedious and uncertain manual adjustment of predefined meta-paths can be solved. In our future work, we intend to make our model more flexible and extensible. For example, by considering the prevalence of streaming education as a global trend [32], this model can be adjusted to recommend interdisciplinary and cross-domain courses by adding attribute variables to generate meta-paths in the model. In addition, the extension of our framework into an advanced version with further consideration of the multi-modal information of courses and the social network behavior of learners [33] could potentially enrich the knowledge concept representation.  
Conceptualization, Z.W. and Z.Z.; Software, Q.L.; Data curation, Q.L.; Writing--original draft, Z.W. and Q.L.; Writing--review and editing, Z.Z.; Funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (62277018; 62237001), the China Ministry of Education Project in the Humanities and Social Sciences (22Y)C880106), and the Major Project in the Social Sciences of South China Normal University (ZDPY2208).  
Not applicable.