基于Hadoop和Python的高校图书馆个性化服务的研究与应用

Research and application of personalized service in university library based on Hadoop and Python

大数据背景下,如何利用信息化技术,掌握在校学生的阅读习惯,在海量的图书资源中,快速有效地找到需要的书籍,为学生提供个性化服务推荐,是文章研究的主要目的。主要研究方法是通过Sqoop工具将北京中医药大学数据中心8 304名学生的图书借阅信息同步到Hadoop平台并进行整理,以Python作为数据处理语言,通过K-means聚类算法获取有相似阅读习惯的书籍。处理结果可以将有相似阅读习惯的学生挖掘出来,将他们所阅读的书籍作为互相推荐的依据,实现了个性化的图书服务。但当前的研究也存在局限性,即不包括半结构化的电子阅读信息的采集和分析。文章通过对研究进行结果分析,提供了一种个性化图书服务的框架和处理方法,并实现了个性化图书推荐,为提供个性化服务的研究提供了一种思路和方法。

Under the background of big data, how to use the information technology to master the reading habits of the students in school, in the mass of book resources, quickly and effectively find the required books, and provide a bio-chemical service recommendation for the students. This is the main purpose of the study. The main research method is to synchronize the book borrowing information of 8 304 students in the data center of Beijing University of Chinese Medicine to the Hadoop platform and organize it through Sqoop tool, and use Python as the data processing language. K-means clustering algorithm is used to obtain books with similar reading habits. The results allow students with similar reading habits to be dug up and the books read by them are dug out as a recommendation, and the personalized book service is realized. However, the current research also has limitations, that is, it does not include the collection and analysis of the semi-structured electronic reading information. Through the analysis of the results of the research, this paper provides a kind of framework and processing method for personalized book service, and realizes the recommendation of personalized book, and provides a kind of thought and method for the research of providing the personalized service.