{"type":"ScholarlyArticle","title":"DFRP: A Dual-Track Feedback Recommendation System for Educational Resources","authors":[{"name":"ChaoJun Meng","slug":"chaojun-meng","given":"ChaoJun","family":"Meng","affiliation":"School of Computer Science and Technology , Zhejiang Normal University"},{"name":"Changfan Pan","slug":"changfan-pan","given":"Changfan","family":"Pan","affiliation":"School of Computer Science and Technology , Zhejiang Normal University"},{"name":"Zilong Li","slug":"zilong-li","given":"Zilong","family":"Li","affiliation":"School of Computer Science and Technology , Zhejiang Normal University"},{"name":"Cong Zhou","slug":"cong-zhou","given":"Cong","family":"Zhou","affiliation":"School of Computer Science and Technology , Zhejiang Normal University"},{"name":"Xinran Cao","slug":"xinran-cao","given":"Xinran","family":"Cao","affiliation":"School of Computer Science and Technology , Zhejiang Normal University"},{"name":"Jia Zhu","slug":"jia-zhu","given":"Jia","family":"Zhu","affiliation":"Zhejiang Key Laboratory of Intelligent Education Technology and Application , Zhejiang Normal University"}],"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.","doi":"10.24963/ijcai.2024/1022","url":"https://ijcai.org/proceedings/2024/1022","pdf":"https://ijcai.org/proceedings/2024/1022.pdf","markdown":"https://ijcai.org/proceedings/2024/1022.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/1022","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":1022,"pages":{"first":8741,"last":8744},"keywords":["Data Mining: DM: Recommender systems","Multidisciplinary Topics and Applications: MDA: Education","Search: S: Applications","Humans and AI: HAI: Computer-aided education"],"videos":[],"references":[{"position":1,"raw":"[Balabanović and Shoham, 1997] Marko Balabanović and Yoav Shoham. 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