PreBiGE: Course Recommendation Using Course Prerequisite Relation Embedding and Bipartite Graph Embedding

Hafsa Kabir Ahmad, Bo Liu

¹ Department of Computer Science, Bayero University, Kano 700241, Nigeria

² School of Computer Science, Shaanxi Normal University, Xi’an 710062, China

A growing number of students enrol in online education to improve their skills. However, students are faced with the challenge of finding courses that meet their individual needs. Recommender systems were introduced to help students choose the courses that best meet their needs. To learn better representations of students and courses for improved recommendation results, existing graph-based recommender systems utilize the high-order collaborative signals between set of students or set of courses from a bipartite graph. However, courses also have prerequisite dependency between them, which when utilized together with collaborative relations can improve recommendation results. On this basis, we propose a model that utilizes the high-order relation between set of courses, the prerequisite dependency between courses, as well as the direct relation between students and courses. Using meta-paths generated from the knowledge graph, our model extracts the prerequisite dependency between courses, which is then used to generate a course prerequisite graph. The course prerequisite graph and the student-course bipartite graph are used to learn the representation of the students and courses, jointly capturing the prerequisite dependency, high-order collaborative relations as well as direct relations. The learned representations are used for recommendation. The experiments on real-world dataset show the superiority of our proposed method, achieving 3.61% on F1@10 and 1.38% on Mrr@10.

Course recommendation, prerequisite dependency, MOOCs, knowledge graph

Hafsa Kabir Ahmad, Bo Liu, Bello Ahmad Muhammad, Mubarak Umar (2022). PreBiGE: Course Recommendation Using Course Prerequisite Relation Embedding and Bipartite Graph Embedding. Journal of Networking and Network Applications, Volume 2, Issue 4, pp. 161–171. https://doi.org/10.33969/J-NaNA.2022.020404.

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