摘要
图书馆用户信息挖掘是提升图书馆管理效率,实现高质量用户管理与用户服务的有效方法。文中研究大数据环境下基于云计算的图书馆用户信息挖掘技术,构建图书馆用户信息挖掘技术结构体系。数据层包括用户基本信息、图书借阅历史数据、图书信息咨询记录等,采用降噪处理、遗漏数据处理等方式预处理用户信息数据;云计算层基于计算资源与存储资源,采用K-means聚类算法从用户借阅情况和用户兴趣类型两方面划分图书馆用户群体类型,依照划分后的用户借阅信息采用蚁群规则挖掘算法,获取用户信息关联规则,形成规则库;应用层依照云计算层的挖掘结果执行个性化推荐。用户信息挖掘结果显示,所研究技术能有效挖掘图书馆用户兴趣类型,以及用户群体信息和时间序列信息。
Library user information mining is an effective method to promote library management efficiency,and realize high quality user management and user service.The library user information mining technology based on cloud computing in the big data environment is researched to build the structural system of the library user information mining technology.The data layer of the system includes user’s basic information,book borrowing historical data,book information consultation records and so on.The user information data is preprocessed with the modes of noise reduction processing and missing data processing.The cloud computing layer is based on computing resource and storage resource,in which the type of library user groups are divided into two aspects of users borrowing situation and users’interest type by means of the k-means clustering algorithm.According to the divided users borrowing information,the association rules of user information are obtained to form the rule base by means of the colony rule mining algorithm.The personalized recommendation is performed by the application layer on the basis of the mining results of the cloud computing layer.The mining results of user information show that the researched technology can effectively mine the interest types of library users,user group information and time series information.
作者
主雪梅
杨洪秀
魏荣华
许雅涵
ZHU Xuemei;YANG Hongxiu;WEI Ronghua;XU Yahan(Hebei University of Water Resources and Electric Engineering,Cangzhou 061001,China;Cangzhou Normal University,Cangzhou 061001,China)
出处
《现代电子技术》
北大核心
2020年第6期168-170,174,共4页
Modern Electronics Technique
基金
河北省科技厅自然科学项目(17K55403D)
河北省教育厅人文社会科学研究项目(SQ171140)。
关键词
图书馆用户
信息挖掘
云计算
大数据环境
用户分类
个性化推荐
library user
information mining
cloud computing
big data environment
user classification
personalized recommendation
