Meta-relationship for course recommendation in MOOCs

Course recommendations are used to help students with different needs to choose courses. However, students’ needs are not always determined by their personal interests, they are also influenced by different curriculum settings, different teacher teams and other factors. Current course recommendation...

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Bibliographic Details
Published inMultimedia systems Vol. 29; no. 1; pp. 235 - 246
Main Authors Hao, Pengyi, Li, Yali, Bai, Cong
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023
Springer Nature B.V
Subjects
Algorithms
Computer Communication Networks
Computer Graphics
Computer Science
Cryptology
Data Storage Representation
Datasets
Multimedia Information Systems
Operating Systems
Recommender systems
Regular Paper
Semantics
Students
Correlation measure
Course recommendation
Semantic correlational information
Meta-relationship
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Summary:Course recommendations are used to help students with different needs to choose courses. However, students’ needs are not always determined by their personal interests, they are also influenced by different curriculum settings, different teacher teams and other factors. Current course recommendation methods lack the consideration of complex relational semantic information that affects students’ needs, resulting in unsatisfied recommendation. To address this issue, we propose Meta-Relationship Course Recommendation (MRCRec) to enrich the expression of relational information. Focusing on complex semantic information of multi-entity relationship and entity association, we construct creatively the multi-entity relational self-symmetric meta-path (MSMP) and associative relational self-symmetric meta-graph (ASMG), which are referred as meta-relationship (MR). We also design an algorithm of meta-relationship correlation measure (MRCor) to obtain semantic correlational information. Then, we adopt the graph embedding to mine and fuse the latent representations of users and that of courses as user preference and course characteristic, respectively. Finally, we optimize matrix factorization to complete recommended task. Comprehensive experiments are conducted on the MOOCCube dataset and XuetangX dataset. The results show that MRCRec can effectively recommend courses for users.
ISSN:0942-4962
1432-1882
DOI:10.1007/s00530-022-00989-5