摘要
文章针对当前研究成果存在的用户满意度和响应效率较低等问题,文章提出了基于个性化推送服务的数字图书馆学习资源提取方法。对用户资源下载信息、访问URL内容以及系统访问时间等数据进行采集,选择小型日志文件解析工具直接针对采集到的数据进行相应解析,将解析得到的数据保存至MySQL或Oracle数据库;依据读者个性化行为数据采集与处理结果,利用等价矩阵下的聚类法对数字图书馆访问用户开展模糊聚类,利用最优聚类中用户浏览与借阅及资源利用情况为用户提供个性化推送服务;利用模糊识别方法识别目标用户个体状况,根据识别结果作为用户检索资源反馈给用户,实现学习资源提取。实验测试结果表明,该数字图书馆学习资源提取方法的性能较为完善,用户满意度较高,且响应速度更快,提取准确性高,表现出了良好的应用性能。
Aiming at the problems of low user satisfaction and response efficiency existing in current research results,this paper proposes a method of extracting learning resources of digital library based on personalized push service. Collect data such as user resource download information, access URL content and system access time, select small log file parsing tool to directly analyze the collected data, and save the analyzed data to MySQL or Oracle database. According to the data collection and processing results of personalized behavior of readers, fuzzy clustering is carried out for visiting users of digital library by using the clustering method under the equivalence matrix, and personalized push service is provided for users by using the optimal clustering of browsing, borrowing and resource utilization. The fuzzy recognition method is used to identify the individual situation of target users, and the retrieval resources are fed back to users according to the recognition results to achieve the extraction of learning resources. The experimental results show that the extraction method of learning resources in the digital library has better performance, higher user satisfaction, faster response time and higher extraction accuracy, showing good application performance.
作者
萨支斌
许震
Sa Zhibin;Xu Zhen
出处
《图书与情报》
CSSCI
北大核心
2019年第5期103-108,共6页
Library & Information
基金
中国工程科技知识中心建设项目子项目“知识组织体系建设2018”(项目编号：CKCEST2018-1-26)研究成果之一
关键词
个性化推送
数字图书馆
资源提取
个性化行为数据
模糊识别
personalized push
digital library
resource extraction
personalized behavioral data
fuzzy recognition
作者简介
通讯作者:萨支斌(1972-),男,闽江学院图书馆副研究馆员,896309261@qq.com;许震(1976-),男,中国科学技术信息研究所馆员。
