DFRP: A Dual-Track Feedback Recommendation System for Educational Resources
ChaoJun Meng, Changfan Pan, Zilong Li, Cong Zhou, Xinran Cao, Jia Zhu
Abstract
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.
BibTeX
@inproceedings{ijcai2024p1022,
title = {DFRP: A Dual-Track Feedback Recommendation System for Educational Resources},
author = {Meng, ChaoJun and Pan, Changfan and Li, Zilong and Zhou, Cong and Cao, Xinran and Zhu, Jia},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8741--8744},
year = {2024},
month = {8},
note = {Demo Track},
doi = {10.24963/ijcai.2024/1022},
url = {https://doi.org/10.24963/ijcai.2024/1022},
}