# Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
## 2 Related Work
### Course Recommendation System  
In the educational domain, personalized learning recommender system have been a research hotspot. Traditional recommendation methods have applications in personalized course recommendation, such as content-based filtering [7; 8], collaborative filtering [9; 10], and Hybrid-based course recommendation systems [11]. Due to the hierarchical structure of knowledge concepts in curricula, research into ontology-based course recommendations [1; 12; 13] have received extensive attention. Generally, an administrator is required to operate an operational course recommender system. The administrator can adjust the algorithm parameters to generate more accurate learning log-based recommendations, monitor user feedback on the courses recommended by the system, and adjust the algorithm parameters according to actual situation to ensure the normal operation of the system.  
Traditional recommendation methods mostly come from the field of e-commerce. They regard courses as commodities, students as customers, and course learning behaviors as commodity purchase behaviors. However, course selection behavior is essentially different from commodity purchase behavior. When comparing learning content to movies or books, the cognitive state of the student and the learning content may change over time and context [14; 15; 16]. Learning a course generally takes a relatively long period of time, and constantly consumes students' attention and energy. In addition, students intend to achieve good grades through a course, which strongly affects students' level of satisfaction towards the course. Furthermore, due to changes in their level of knowledge mastery, students' preference for courses is not persistent; that is, after completing a course and mastering relevant knowledge, their preference for courses with similar knowledge concepts decreases. Student satisfaction with a course should not be equated with preference, as course recommendation methods based on student preference have certain limitations. In [17], the authors pointed out that student satisfaction with course learning is significantly affected by teachers, course content, curriculum, and grades. Recently, the satisfaction of students has become an important factor in research on course recommendation. In [18], course recommendation based on learning performance prediction was proposed.  
A number of course recommendation methods focus on the characteristics and behavior data of students in the learning system. For example, [19] proposed a recommendation method for learning objects based on the integration of social signals, interests, and learner user preferences in an e-learning system. The authors of [20] proposed a course recommender system that uses extracted rules to find suitable courses according to student behaviors and preferences. In [21], a path that satisfies students' limited time while maximizing their grades is used to make lesson-by-lesson recommendations.  
Similar to other recommendation systems, course recommendation systems must face the cold start problem, meaning that it is difficult to recommend courses for new students. The literature [22] points out that when there are insufficient data in one domain and relatively rich data in another, transfer learning can be used to overcome the cold-start problem when the two domains are explicitly or implicitly related. In addition, with sufficient data from the source domain, transfer learning and collaborative filtering can be used in combination to extract knowledge in order to improve the accuracy of recommendations in the target domain [23].