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随着数据库技术的迅速发展以及数据库管理系统在图书馆的广泛应用,在图书馆内积累了大量的借阅数据,然而目前的图书馆管理系统无法发现这些数据中存在的关系和规则,无法预测读者的信息需求,缺乏挖掘数据背后隐藏的知识的手段,读者仍然很难找到所需要的信息,这将会给图书馆实现个性服务带来困难。为了改变这种现状,国内外学者做了很多相关调查研究,并提出了很多解决该问题的方法和手段,但这些方法各有利弊。现阶段据有关对图书馆业务流通数据进行数据挖掘的文献研究主要集中在关于图书馆读者借阅需求与兴趣领域的研究、基于数据挖掘的图书馆读借阅行为的研究以及图书推荐模型的构建与算法的研究三个方面。
With the rapid development of database technology and the extensive application of database management system in the library, a great deal of loan data has been accumulated in the library. However, the current library management system can not find the relationship and rules existing in these data and can not predict the reader The lack of information mining hidden data behind the means, the reader is still difficult to find the information they need, which will give the library to achieve personalized services difficult. In order to change the status quo, many scholars at home and abroad have done a lot of related investigations and studies and put forward many methods and means to solve the problem. However, these methods have their own advantages and disadvantages. At this stage, the literature research on data mining of circulation data in library business mainly focuses on the study of library readers’ borrowing needs and interests, the study of library reading and borrowing based on data mining and the construction and algorithm of book recommendation model Three aspects of research.