Explainable exercise recommendation with knowledge graph.

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Networks Pub Date : 2024-12-05 DOI:10.1016/j.neunet.2024.106954
Quanlong Guan, Xinghe Cheng, Fang Xiao, Zhuzhou Li, Chaobo He, Liangda Fang, Guanliang Chen, Zhiguo Gong, Weiqi Luo
{"title":"Explainable exercise recommendation with knowledge graph.","authors":"Quanlong Guan, Xinghe Cheng, Fang Xiao, Zhuzhou Li, Chaobo He, Liangda Fang, Guanliang Chen, Zhiguo Gong, Weiqi Luo","doi":"10.1016/j.neunet.2024.106954","DOIUrl":null,"url":null,"abstract":"<p><p>Recommending suitable exercises and providing the reasons for these recommendations is a highly valuable task, as it can significantly improve students' learning efficiency. Nevertheless, the extensive range of exercise resources and the diverse learning capacities of students present a notable difficulty in recommending exercises. Collaborative filtering approaches frequently have difficulties in recommending suitable exercises, whereas deep learning methods lack explanation, which restricts their practical use. To address these issue, this paper proposes KG4EER, an explainable exercise recommendation with a knowledge graph. KG4EER facilitates the matching of various students with suitable exercises and offers explanations for its recommendations. More precisely, a feature extraction module is introduced to represent students' learning features, and a knowledge graph is constructed to recommend exercises. This knowledge graph, which includes three primary entities - knowledge concepts, students, and exercises - and their interrelationships, serves to recommend suitable exercises. Extensive experiments conducted on three real-world datasets, coupled with expert interviews, establish the superiority of KG4EER over existing baseline methods and underscore its robust explainability.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"183 ","pages":"106954"},"PeriodicalIF":6.0000,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1016/j.neunet.2024.106954","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Recommending suitable exercises and providing the reasons for these recommendations is a highly valuable task, as it can significantly improve students' learning efficiency. Nevertheless, the extensive range of exercise resources and the diverse learning capacities of students present a notable difficulty in recommending exercises. Collaborative filtering approaches frequently have difficulties in recommending suitable exercises, whereas deep learning methods lack explanation, which restricts their practical use. To address these issue, this paper proposes KG4EER, an explainable exercise recommendation with a knowledge graph. KG4EER facilitates the matching of various students with suitable exercises and offers explanations for its recommendations. More precisely, a feature extraction module is introduced to represent students' learning features, and a knowledge graph is constructed to recommend exercises. This knowledge graph, which includes three primary entities - knowledge concepts, students, and exercises - and their interrelationships, serves to recommend suitable exercises. Extensive experiments conducted on three real-world datasets, coupled with expert interviews, establish the superiority of KG4EER over existing baseline methods and underscore its robust explainability.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用知识图谱提出可解释的练习建议。
推荐合适的练习并说明推荐理由是一项非常有价值的工作,因为它可以显著提高学生的学习效率。然而,练习资源的广泛性和学生学习能力的多样性给推荐练习带来了明显的困难。协作过滤方法经常难以推荐合适的练习,而深度学习方法则缺乏解释,这限制了其实际应用。针对这些问题,本文提出了一种可解释的知识图谱练习推荐方法--KG4EER。KG4EER便于为不同学生匹配合适的练习,并为其推荐提供解释。更确切地说,本文引入了一个特征提取模块来表示学生的学习特征,并构建了一个知识图谱来推荐练习。该知识图谱包括三个主要实体--知识概念、学生和练习--以及它们之间的相互关系,用于推荐合适的练习。在三个真实世界数据集上进行的广泛实验以及专家访谈证明了 KG4EER 优于现有的基线方法,并强调了其强大的可解释性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
期刊最新文献
Enabling deformation slack in tracking with temporally even correlation filters. Outer synchronization and outer H synchronization for coupled fractional-order reaction-diffusion neural networks with multiweights. Corrigendum to "Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning" [Neural Networks Volume 178, October (2024), 1-11/106414]]. Adaptive discrete-time neural prescribed performance control: A safe control approach. Disentangled latent energy-based style translation: An image-level structural MRI harmonization framework.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1