Storm分布式计算框架下基于知识图谱的快速学习资源推荐
Recommendation of fast learning resources based on knowledge map in storm distributed computing framework
刘莹 1杨淑萍 1张治国2
作者信息
- 1. 辽宁师范大学 教育学院,辽宁 大连 116000
- 2. 河海大学 机电工程学院,江苏 南京 211100
- 折叠
摘要
针对在线学习资源推荐存在精度较低或实时性较差的问题,采用知识图谱进行用户及资源的知识表示,并采用长短时间记忆网络对用户资源特征差进行优化,从而将与用户特征差最小的资源推送给用户.首先,在获得在线学习记录样本后,利用知识图谱进行实体特征关系的知识表示,并借助Storm分布式框架生成知识图谱中头尾实体及关系特征向量.接着,建立用户-资源实体的最小特征差目标函数,并采用长短时间记忆网络对最小特征差目标函数进行优化.最后,通过Storm分布式平台进行长短时间记忆网络的参数求解,从而快速生成稳定的相关资源推荐模型.实验结果表明,在Storm分布式框架下采用知识图谱和长短时间记忆网络实现在线资源推荐,可获得较高准确率及运行效率,在应对大规模资源的实时推荐方面具有较强的适应度.
Abstract
Since the online learning resource recommendation holds low accuracy or poor real-time performance,this paper presents the knowledge representation of users and resources using knowledge graphs,and uses the long-short time memory network to optimize user resource feature differences.Thus,the resources with the smallest feature differences with the users can be pushed to the users.First,a sample of online learning records is obtained,thus the knowledge representation of entity feature relationships is performed using the knowledge graph,and the head and tail entity and relationship feature vectors in the knowledge graph are generated with the help of the Storm distributed framework.Second,the minimum feature difference objective function of user-resource entities is established,and the minimum feature difference objective function is optimized by using long and short time memory networks.The parameters of the long-short time memory network are again solved by the Storm distributed platform,so as to quickly generate a stable and relevant resource recommendation model.The experimental results show that the online resource recommendation using the knowledge graph and the long-short time memory network under the distributed framework of Storm can achieve high accuracy and operational efficiency,and has strong adaptability in dealing with real-time recommendation of large-scale resources.
关键词
资源推荐/知识图谱/Storm框架/长短时间记忆/TransD模型Key words
resource recommendation/knowledge graph/Storm framework/long and short term memory/TransD model引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据