面向MOOCs的个性化知识概念推荐  

Personalized knowledge concept recommendation for massive open online courses

作　　者：孔超 陈家会 孟丹 刁华彬 王维 张丽平 刘涛 KONG Chao;CHEN Jiahui;MENG Dan;DIAO Huabin;WANG Wei;ZHANG Liping;LIU Tao(School of Computer and Information,Anhui Polytechnic University,Wuhu,Anhui 241000,China;OPPO Research Institute,Shenzhen,Guangdong 518000,China)

机构地区：[1]安徽工程大学计算机与信息学院,安徽芜湖241000 [2]OPPO研究院,广东深圳518000

出　　处：《华东师范大学学报（自然科学版）》2024年第5期32-44,共13页Journal of East China Normal University(Natural Science)

基　　金：安徽省高等学校科学研究项目(自然科学类)(2023AH050914);安徽省高等学校省级质量工程项目(2023zybj018);安徽省教育厅重大教学研究项目(2023jyxm0451);芜湖市科技计划项目(2023pt07,2023ly13);安徽工程大学本科教学质量提升计划项目(2022lzyybj02)。

摘　　要：近年来,大规模开放在线课程(massive open online courses,MOOCs)已成为获取知识和技能的重要途径.然而,因课程数量激增导致信息过载的问题日益严重.知识概念推荐旨在识别并向学生推荐需要掌握的特定知识点.现有研究通过建立异质信息网络应对数据稀疏性,但在充分挖掘异质信息网络数据和考虑学习者与知识概念之间多样互动方面存在局限性.为了解决这些问题,本文提出了一种名为融合异质信息网络与行为感知的知识概念推荐(heterogeneous learning behavior-aware knowledge concept recommendation,HLB-KCR)的新方法.首先,使用基于元路径的随机游走和skip-gram算法生成富含语义信息的元路径嵌入,并通过两阶段元路径嵌入增强模块优化嵌入效果;其次,构建融入时间上下文信息的多类型交互图,利用图神经网络(graph neural network,GNN)进行消息传递,更新节点嵌入,获得包含时间和交互类型信息的深度嵌入表示;再次,引入语义注意力模块,将元路径嵌入与多类型交互嵌入相融合;最后,使用扩展的矩阵分解评分预测模块优化推荐算法.在大型公开的MOOCCubeX数据集上进行大量的实验证明了HLB-KCR的有效性与合理性.In recent years,massive open online courses(MOOCs)have become a significant pathway for acquiring knowledge and skills.However,the increasing number of courses has led to severe information overload.Knowledge concept recommendation aims to identify and recommend specific knowledge points that students need to master.Existing research addresses the challenge of data sparsity by constructing heterogeneous information networks;however,there are limitations in fully leveraging these networks and considering the diverse interactions between learners and knowledge concepts.To address these issues,this study proposes a novel method,heterogeneous learning behavior-aware knowledge concept recommendation(HLB-KCR).First,it uses metapath-based random walks and skip-gram algorithms to generate semantically rich metapath embeddings and optimizes these embeddings through a two-stage enhancement module.Second,a multi-type interaction graph incorporating temporal contextual information is constructed,and a graph neural network(GNN)is employed for message passing to update the nodes,obtaining deep embedded representations that include time and interaction type information.Third,a semantic attention module is introduced to integrate meta-path embeddings with multi-type interaction embeddings.Finally,an extended matrix factorization rating prediction module is used to optimize the recommendation algorithm.Extensive experiments on the large-scale public MOOCCubeX dataset demonstrate the effectiveness and rationality of the HLB-KCR method.

关 键 词：在线学习 知识概念推荐 图神经网络 异质信息网络 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]