DFRP: A Dual-Track Feedback Recommendation System for Educational Resources

DFRP: A Dual-Track Feedback Recommendation System for Educational Resources

ChaoJun Meng, Changfan Pan, Zilong Li, Cong Zhou, Xinran Cao, Jia Zhu

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8741-8744. https://doi.org/10.24963/ijcai.2024/1022

The educational disparities among different regions are remarkably significant. The educational resource platform can effectively bridge the educational capability gap between regions. Most of the existing recommendation algorithms only consider interaction history, while we argue that the dependencies between knowledge points and education-related features are crucial for education resource recommendations. To address this, we propose DFRP, an educational resource recommendation platform based on knowledge graphs(KGs) and educational scale feedback. DFRP employs a recommendation algorithm based on teaching pathways and educational dimensions to achieve accurate recommendations and active feedback on educational resources. We also provide a detailed description of the system framework and present a demonstration scenario that uses educational scales for active feedback and KGs to show knowledge point dependencies.
Keywords:
Data Mining: DM: Recommender systems
Humans and AI: HAI: Computer-aided education
Multidisciplinary Topics and Applications: MDA: Education
Search: S: Applications