摘要
个性化学习资源推荐以提取学习者兴趣为基础,为学习者推荐感兴趣的学习资源.然而,学习者的兴趣不仅受知识点、学习资源、课程等因素影响导致其兴趣难表征,而且其会随时间推移动态变化使得学习兴趣模式难捕获.针对此,提出基于时空多粒度兴趣建模的学习资源推荐方法,其特点在于:设计并实现了一种融合学习空间和时间维度的学习兴趣表征学习架构,其中,首先提出基于异构图的学习空间及其多粒度兴趣表征,即用节点表示知识点、学习资源、课程、教师和学校等实体,边表示实体间关系,用此异构图表示学习空间,再通过图神经网络表征学习节点嵌入来表达节点上的多粒度兴趣;然后提出时间维度多粒度兴趣模式表征方法,即结合时间、学习空间和课程偏好等多维度,切分学习者历史行为序列,用于挖掘学习者近期课程内、中期跨课程和长期跨课程等不同粒度的兴趣模式,并设计多粒度兴趣自监督任务,破解时空多粒度兴趣缺少监督信号问题;最后,提出多粒度兴趣自适应融合层,将多粒度兴趣表征和兴趣模式融合,获得最终的学习者兴趣,经预测层为学习者推荐感兴趣的学习资源.实验结果表明,在MOOCCube数据集上,所提算法较最优对比算法HinCRec,在Recall@20和NDCG@20指标上分别提升了3.13%,7.45%;在MOOPer数据集上,所提算法较最优对比算法HinCRec在Recall@20和NDCG@20指标上分别提升了4.87%,7.03%.
Personalized learning resource recommendation is derived from identifying learners’interests,recomm-ending interesting and relevant learning resources accordingly.However,learners’interests are influenced by various factors such as knowledge points,learning resources,and courses,which makes it a challenging task to accurately represent their interests.Additionally,these interests evolve dynamically over time,complicating the task of identifying learning interest patterns.To address this challenge,we propose a learning resource recommendation method based on spatio-temporal multi-granularity interest modeling,which is characterized as follow:An innovative architecture is designed and implemented for learning interest representation that integrates the learning space and temporal dimension in a heterogeneous graph-based learning space and the multi-granularity interest representation.The nodes in this graph represent entities,such as knowledge points,learning resources,courses,teachers,and schools;and the edges of the graph represent the inter-entity relationships.A graph neural network is utilized to express the multi-granularity interest in these nodes.Moreover,we propose a temporal multi-granularity interest pattern representation method by combining multi-dimensionality of time,learning space,and course preference,and slicing through the sequence of learner’s historical behaviors is used to mine the learner’s different granularity of interest patterns in the near-term within-course,mid-term across-course,and long-term across-course.Then,a multi-granularity interest adaptive fusion layer is proposed to fuse multi-granularity interest representations and interest patterns.Based on this method,a multi-granularity interest self-supervision task is designed to solve the problem of lack of supervised signaling for spatio-temporal multi-granularity interests,and recommend relevant learning resources for learners via prediction layer.Our experimental results show that the proposed method outperforms the optimal comparison algorithms HinCRec in Recall@20 and NDCG@20 metrics by 3.13%and 7.45%on MOOCCube dataset,respectively,and outperforms optimal comparison algorithm HinCRec in Recall@20 and NDCG@20 metrics by 4.87%and 7.03%on MOOPer dataset,respectively.
作者
朱海萍
王子瑜
赵成成
陈妍
刘均
田锋
Zhu Haiping;Wang Ziyu;Zhao Chengcheng;Chen Yan;Liu Jun;Tian Feng(School of Computer Science and Technology,Xi’an Jiaotong University,Xi’an 710049;Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering(Xi’an Jiaotong University),Xi’an 710049;National Engineering Lab for Big Data Analytics(Xi’an Jiaotong University),Xi’an 710049)
出处
《计算机研究与发展》
北大核心
2025年第8期1884-1901,共18页
Journal of Computer Research and Development
基金
国家重点研发计划项目(2022YFC3303600)
国家自然科学基金项目(62277042,62293551,62177038,62377038)
中国工程科技知识中心项目
联想-西安交通大学智慧行业联合实验室项目。
关键词
推荐系统
学习资源推荐
多粒度兴趣建模
图神经网络
序列建模
recommendation system
learning resource recommendation
multi-granularity interest modeling
graph neural network
sequential modeling
