# Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
## 5 Experiments
### Datasets  
In order to verify the effective of our proposed approach, the experiments are carried on four real datasets: two large-scale online courses datasets, Scholat and CNPC, and two other datasets, Yelp and Movielens, which are commonly used in research on HIN embedding-based recommendation. CNPC, Yelp, and Movielens are public datasets. Table 2 presents statistical information about the nodes and relationships in these datasets. * Scholat This dataset is from a real academic social course platform (scholat.com) which provides courses offered by Chinese universities, including undergraduate and graduate courses. The courses involve computer science, economics, pedagogy, and other disciplines. The student profiles include the school, grade, major, courses learned, etc. The dataset used in our experiment contains 3168 courses, 150,563 users, and 1,237,485 course visit records for the 2020-2021 academic year. The frequency of students' attendance of courses represents information about student interest in the course. In this experiment, we scaled the attendance frequency to an interval. * CNPC ([[https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/26147](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/26147)]([https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/26147](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/26147)), accessed on 28 September 2022) This dataset consists of the Canvas Network Open Course (canvas.net), which hosts open online courses, including Massive Open Online Courses (MOOCs) that are freely available to participants around the world. The dataset used in our experiments is from January 2014 to September 2015, including 224,914 users and 238 courses as well as various attribute information on users' social relations, forums, users, and courses. The courses include ten disciplines, e.g., mathematics, statistics, and education. * Yelp ([[https://www.yelp.com/dataset/documentation/main](https://www.yelp.com/dataset/documentation/main)]([https://www.yelp.com/dataset/documentation/main](https://www.yelp.com/dataset/documentation/main)), accessed on 30 September 2022) This dataset comes from the largest merchant rating website in the United States, yelp.com. The dataset records user ratings of merchants, the users' social relationship, and attribute information on users and merchants, including 16,239 users, 14,282 merchants, and 198,397 ratings. * Movielens ([[http://files.grouplens.org/datasets/movielens/ml-100k-README.txt](http://files.grouplens.org/datasets/movielens/ml-100k-README.txt)]([http://files.grouplens.org/datasets/movielens/ml-100k-README.txt](http://files.grouplens.org/datasets/movielens/ml-100k-README.txt)), accessed on 30 September 2022) The Movielens dataset is a classic movie recommendation dataset from movielens.org. Movielens-100k was selected for this experiment. This dataset has 943 users, 1682 movies, and 100,000 scores, and contains social relationship and attribute information between users and movies. For the proposed model, the interactive data between students and courses is the most important. In addition, we are concerned about the characteristics that are conducive to the formation of the rich meta-paths from datasets, such as course type and teacher or student school and type. Based on the statistical analysis in Table 2, we defined the meta-paths in all four datasets, as shown in Table 3.