数据挖掘在数字图书馆个性化服务中的应用

【作者】 熊拥军；

【导师】 刘卫国； 李静；

【作者基本信息】 中南大学 ， 计算机应用技术， 2005， 硕士

【摘要】 本文针对现有数字图书馆个性化服务系统存在着数据分析能力欠缺、信息孤岛现象严重和个性化服务单一等问题,将数据仓库、联机分析和数据挖掘技术引入到数字图书馆个性化服务系统,并结合计算机网络技术,进行了深入的理论和实践研究。 论文运用数据仓库、联机分析处理和数据挖掘技术通过对读者、资源以及读者对资源的访问等数据对象进行分析和挖掘,设计和实现了一种基于关联挖掘和读者兴趣模型的个性化服务系统。该系统通过对数据的关联分析和挖掘以及自动地识别读者兴趣来为读者提供个性化的服务。论文的主要内容如下: (1) 采用新型决策支持系统的思想,设计出数字图书馆个性化服务系统模型,模型中突出了对数据的分析和挖掘以及读者兴趣的自动识别。 (2) 采用维度建模的方法,对读者、资源以及读者对资源的访问三个主题进行了数据仓库逻辑模型设计。 (3) 对AprioriTid算法进行了改进,算法采用事务压缩和项目压缩相结合方法,候选项目集及支持度的计算是在每条事务压缩后通过联接产生,候选项目集采用关键字识别,省去了AprioriTid算法中的剪枝和字符串模式匹配步骤,实验结果表明,改进的算法执行效率明显优于AprioriTid算法。 (4) 建立了资源访问的多维数据立方体,并利用AprioriTid改进算法对文献资源进行关联分析和挖掘,来分析资源的访问情况以及读者对资源的访问关联。 (5) 设计与实现了基于资源多层分类树的读者兴趣模型,该模型通过与读者的主动和被动交互,不断地接收读者的兴趣和推测读者的兴趣,积累读者信息需求的偏好,从而实现自适应的个性化服务。 本文所作的研究工作,对数字图书馆个性化服务系统的建设和发展打下了良好的基础。更多还原

【Abstract】 To solve the problems in the traditional personalized service system of digital library, such as deficiency of data analysis, phenomena of information alone islands, and singleness of personalized services modal, etc, the paper, introducing data warehouse (DW), on-line analytical processing (OLAP) and data mining (DM) concepts together with network technology, carries through a comprehensive theoretical and empirical study on digital library.The paper, with DW, OLAP and DM techniques adopted, has analyzed and delved into the data of reader, resources, and the interviews between them, and then designed and realized an individual service system of university digital library based on the association analysis and reader interest models. The system analyzes the data and identifies readers’ interests before providing them with personalized services. The main contents of this dissertation are summarized as follows:(1) Adopt a new Decision Support System (DSY) to design the model of the personalized service system, which emphasizes analyzing resources and identifying readers’ interests, in a digital library.(2) Adopt a dimension-modeling method to design the data warehouse logical mode based on such three subjects as reader analysis, resource analysis and interview analysis.(3) Put forward an enhanced algorithm, which associates AprioriTid with transaction reduction and item reduction technique. In this algorithm, candidate set generation and the support calculation of each item set is created after each transaction is compressed and connected. As the candidate set adopts key words to identify in this algorithm, thus the process of pruning and string pattern matching is removed from AprioriTid algorithm. It is shown, from the results of tests, that the algorithm outperforms AprioriTid algorithm.(4) Establish a multidimensional data cube of information and use the AprioriTid enhanced algorithm to analyze document resources for analyzing resource interviews and the association between readers and resources.(5) Design and realize a reader interest model based on a resource classification tree, which, through active and passive interaction with a reader, constantly receives and speculates the reader’s interest, and accumulates the reader’s information requirement bias, thus realizing self-adaptive personalized service in digital library.The research work in this dissertation has laid a good foundation for the construction and development of the personalized service system of a digital library.更多还原