# Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
## 1 Introduction  
In recent ten years the growth rate of distance education has been impressive, enabling students from all over the world to study courses at low cost. Many distance learning platforms, such as Coursera and edX, provide a large number of online courses and attract millions of registered users. The goal of users (students) is to master knowledge through course learning. The ongoing surge in the number of courses requires effective recommendation methods to help learners find suitable courses [1]. The recommendation accuracy is very important in regard to improving the service quality of online course platforms [2, 3]. The purpose of course learning is to master new knowledge or improve the level of mastery of previously learned knowledge. Course learning is different from reading or browsing news, newsletters, and documents. The learning process is continuous (usually a semester), and the interactive objects (e.g., teacher, course video, subject) are diverse. For example, a higher education course on the XuetangX platform (xuetangx.com), e.g., Introduction to Machine Learning, lasts for 12 weeks and has 6-7 sections per week, while Introduction to Psychology is a 12-week course with 5-7 sections per week. Another example can be found in higher education courses on the edX (edx.org) platform, such as Big Data and Education, each of which are 8 weeks long and require 6-12 h per week. The Architectural Imagination course takes 10 weeks and 3-5 h per week. However, massive online course resources place students in danger of becoming lost in the large amount of information present at any time during the learning process [1]. In addition, the diversityof interactive objects enables relationships between students and students, students and courses, and courses and courses, even if these relationships are indirect or potential. The network in the real world is complex, with maany different elements affecting each other. The traditional methods of modeling the real world with homogeneous networks neglect the impact of multi-source heterogeneous elements, while heterogeneous graphs (abbr. HG) have proved to be an effective method for modeling graph structures composed of multiple entities and relationships [4; 5]. The advantage of HG is that it fully and intuitively uses the heterogeneous network structure in the dataset [6]. However, the main disadvantage is that it needs to manually design the meta-path, which is difficult to achieve optimally in real practice. Unfortunately, most existing methods do not solve these problems. Faced with the aforementioned problems and issues, in this paper we propose a course recommendation method based on an enhancement of HG meta-path embedding (abbr. HGE-CRec). First, we use the skip-gram algorithm to generate the original meta-path embedding. Then, Graph Convolutional Networks (abbr. GCN) are utilized to generate simulated meta-path embedding based on the construction of the original meta-paths, enriching the dataset used for HG meta-path embedding. Third, we adopt a Graph Attention Network (abbr.GAT) to aggregate the neighboring node information of each node on the meta-path in order to enrich the semantics of node embedding. Fourth, a nonlinear method is adopted to fuse all information in order to enhance the node embedding of the meta-paths. Finally, the enhanced embedding is combined with a Matrix Factorization-based model to predict ratings for students and courses. We compare and analyze HGE-CRec with other existing Matrix Factorization-based recommendation models using two real-world online course datasets and two general open datasets dealing with HIN embedding-based recommendation research. In brief, the contributions of this paper can be summarized based on the following three perspectives:  
* We propose a novel course recommendation method based on heterogeneous graph embedding, and our experiments prove that the performance of this method is better than existing methods. * We propose a novel solution for enhancing the embedding of the meta-path in HG.