In order to more accurately capture the dynamic change of users' learning interests, especially for C language courses, where there is a clear order and dependence between knowledge points, it is recommended to not only consider users' interests, but also combine the rationality of knowledge structure. In this paper, a hybrid recommendation algorithm combining user attributes and RippleNet is proposed. By introducing the knowledge graph, the algorithm uses the multi-hop propagation mechanism to dig the potential correlation of knowledge points in the learning path, and combines the collaborative filtering algorithm to provide personalized recommendation from the two dimensions of user interest and knowledge structure. The research results show that this method can improve the logic and consistency of learning path while ensuring interest matching, and achieve the expected effect.
Key words
knowledge graph /
personalized recommendation /
C language
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Funding
黑龙江省高等教育教学改革研究项目“‘教材、资源、方法’游戏化智慧课程的立体资源建设与研究”; 哈尔滨师范大学教育教学改革项目,项目编号：XJGZ202501; 黑龙江省线上线下混合式一流课程“高级语言程序设计”
