A Course Recommendation Method Based on the Integration of Curriculum Knowledge Graph and Collaborative Filtering

• Published in: International journal of advanced network, monitoring, and controls Vol. 10; no. 2; pp. 94 - 100
• Main Authors: Hu, Jingyi, Wang, Qingqing
• Format: Journal Article
• Language: English
• Published: Xi'an Sciendo 16.06.2025; De Gruyter Poland
• Subjects: Accuracy; Algorithms; Collaboration; Core curriculum; Course Attributes; Data mining; Data Sparsity; Datasets; Graphs; Information sources; Knowledge Graph; Knowledge representation; Learning; Propagation; Ratings & rankings; Recommender systems; Semantics; Students
• ISSN: 2470-8038; 2470-8038
• DOI: 10.2478/ijanmc-2025-0020

To address the problems of data sparsity and cold start in collaborative filtering algorithms, this paper proposes an improved course recommendation method that integrates knowledge graphs and collaborative filtering. First, the RippleNet model is used to construct a knowledge graph based on course-attribute-relation triples and generate a recommendation list. Then, an item-based collaborative filtering algorithm utilizes users’ historical interaction behavior to produce another recommendation list. Finally, a weighted linear method is employed to fuse the recommendation list generated by the RippleNet-based course knowledge graph and the one generated by collaborative filtering, resulting in the final course recommendation list. Experiments conducted on the public dataset MOOCCube demonstrate that the RippleNet-CF method improves precision, recall, and F1-score, while also effectively mitigating the issue of data sparsity.