ROME: a graph contrastive multi-view framework from hyperbolic angular space for MOOCs recommendation
As Massive Open Online Courses (MOOCs) expand and diversify, more and more researchers study recommender systems that take advantage of interaction data to keep students interested and boost their performance. In a typical roadmap, courses and videos are recommended using a graph model, but this d...
Autori principali: | Luo, Hao, Husin, Nor Azura, Mohd Aris, Teh Noranis |
---|---|
Natura: | Articolo |
Pubblicazione: |
Institute of Electrical and Electronics Engineers
2023
|
Documenti analoghi
Documenti analoghi
-
Multi-view graph contrastive learning for social recommendation
di: Rui Chen, et al.
Pubblicazione: (2024-09-01) -
Object-oriented online course recommendation systems based on deep neural networks
di: Luo, Hao, et al.
Pubblicazione: (2024) -
H-MOOC framework: reusing MOOCs for hybrid education
di: Pérez-Sanagustín, Mar, et al.
Pubblicazione: (2017) -
Prototypical Graph Contrastive Learning for Recommendation
di: Tao Wei, et al.
Pubblicazione: (2025-02-01) -
ConceptGCN: Knowledge concept recommendation in MOOCs based on knowledge graph convolutional networks and SBERT
di: Rawaa Alatrash, et al.
Pubblicazione: (2024-06-01)