Meta concept recommendation based on knowledge graph

Massive Open Online Courses (MOOCs) are playing a key role in improving educational ways. Abundant learning resources make it difficult for online users to find suitable learning content. The current personalized service in the field of online education relies more on course recommendations. However, coarse-grained recommendations cannot help users discover the defects of their knowledge network effectively. In this paper, we propose a Knowledge-Aware Meta-Concept (KAMC) framework to provide fine-grained recommendation services. We innovatively incorporate Knowledge Graph (KG) into the field of educational recommendation to provide abundant auxiliary information. However, simply combining knowledge graphs with educational recommender systems cannot improve the performance of existing recommendation models, and may even weaken the performance of the models. Because the modeled KG ignores the enhancements on the user side and only considers the enhancements on the item side. We further propose to enrich the semantic representation of users with collaborative information in user-item interactions, and at the same time enrich the semantic representation of items with information in KG. Furthermore, to provide users with more accurate and fine-grained personalized recommendation services, we propose a user-based attention mechanism to capture users' fine-grained semantic information. Our method is experimentally validated on three real-world datasets. In the three datasets, KAMC's AUC evaluation index is 6.2%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6.2\%$$\end{document}, 6.9%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6.9\%$$\end{document}, and 2.2%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.2\%$$\end{document} higher than the latest baseline method (KGAN), respectively. Experimental results show that the KAMC method outperforms the current state-of-the-art baseline methods.