基于聚类优化的数字图书馆协同过滤个性化推荐服务研究
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摘要
随着近年来计算机技术快速发展,数字图书馆有必要基于聚类优化对多种文献数据资源协同过滤,面向用户提供个性化推荐服务,以提高服务效率。文章在介绍聚类优化概念及含义的基础上,分析了数字图书馆协同过滤个性化推荐服务的优势,包括优化图书馆数字信息服务机制、为用户提供优质文献资源、提高数字资源利用率等。探究了协同过滤个性化推荐服务模式,包括个性化定制服务、个性化推送服务、数据挖掘与决策服务。最后归纳出数字图书馆协同过滤个性化推荐服务构建策略,即利用智能搜索技术、精确搜索资源,应用数据挖掘技术、挖掘数字信息价值,优化聚类算法、制定精准服务方案,完善系统平台、构建资源过滤服务机制。
With the rapid development of computer technology in recent years, it is necessary for digital libraries to collaboratively filter a variety of literature and data resources based on clustering optimization and provide personalized recommendation services for users in order to improve service efficiency. On the basis of introducing the concept and meaning of clustering optimization, this article analyzed the advantages of collaborative filtering personalized recommendation services in digital libraries, including optimizing the mechanism of digital information services in libraries, providing high-quality literature resources for users, and improving the utilization rate of resources, explored the personalized recommendation service mode, including personalized customization services, personalized push services, and data mining and decision making services,and finally concluded the construction strategies of collaborative filtering personalized recommendation services in digital libraries, that is to use intelligent search technology to accurately search resources, apply data mining technology to mining digital information value, optimize clustering algorithms to develop precise service plans,and improve system platform to construct resource filtering service mechanism.
With the rapid development of computer technology in recent years, it is necessary for digital libraries to collaboratively filter a variety of literature and data resources based on clustering optimization and provide personalized recommendation services for users in order to improve service efficiency. On the basis of introducing the concept and meaning of clustering optimization, this article analyzed the advantages of collaborative filtering personalized recommendation services in digital libraries, including optimizing the mechanism of digital information services in libraries, providing high-quality literature resources for users, and improving the utilization rate of resources, explored the personalized recommendation service mode, including personalized customization services, personalized push services, and data mining and decision making services,and finally concluded the construction strategies of collaborative filtering personalized recommendation services in digital libraries, that is to use intelligent search technology to accurately search resources, apply data mining technology to mining digital information value, optimize clustering algorithms to develop precise service plans,and improve system platform to construct resource filtering service mechanism.
引文
[1]陈臣.大数据时代一种基于用户行为分析的图书馆个性化智慧服务模式[J].图书馆理论与实践,2015(2):96-99.
[2]姚钱,温嵘生.人工智能技术在图书馆中的应用[J].科技资讯,2017(24):222,224.
[3]陆婷婷.从智慧图书馆到智能图书馆:人工智能时代图书馆发展的转向[J].图书与情报,2017(3):98-101,140.
[4]张兴旺.以信息推荐为例探讨图书馆人工智能体系的基本运作模式[J].情报理论与实践,2017,40(12):69-74.
[5]韦美峰,王亚民.基于后缀树聚类的主题搜索引擎研究[J].情报理论与实践,2017,40(12):123-127,62.
[6]赵蓉英,陈必坤.基于Nutch的图情博客搜索引擎的设计与实现[J].情报科学,2012,30(4):486-491.
[7]胡秀云.云计算环境下数字图书馆个性化信息服务研究[D].武汉:湖北工业大学,2014.
[2]姚钱,温嵘生.人工智能技术在图书馆中的应用[J].科技资讯,2017(24):222,224.
[3]陆婷婷.从智慧图书馆到智能图书馆:人工智能时代图书馆发展的转向[J].图书与情报,2017(3):98-101,140.
[4]张兴旺.以信息推荐为例探讨图书馆人工智能体系的基本运作模式[J].情报理论与实践,2017,40(12):69-74.
[5]韦美峰,王亚民.基于后缀树聚类的主题搜索引擎研究[J].情报理论与实践,2017,40(12):123-127,62.
[6]赵蓉英,陈必坤.基于Nutch的图情博客搜索引擎的设计与实现[J].情报科学,2012,30(4):486-491.
[7]胡秀云.云计算环境下数字图书馆个性化信息服务研究[D].武汉:湖北工业大学,2014.