基于数据挖掘技术的图书馆个性化快速推荐算法研究

Research on library personalized fast-recommendation algorithm based on data mining technology

随着高校图书管理系统建设的不断发展,广大师生的图书借阅活动产生了大量的浏览数据。为了对以上借阅信息进行数据挖掘以便为读者提供更高水平的服务,提出一种基于数据挖掘技术的图书馆个性化快速推荐算法。首先对数据挖掘的主要方法和组织结构进行了介绍;然后对经典关联规则挖掘算法中的Apriori算法进行改进,提高了关联规则的运算效率;最后采用改进的Apriori算法对图书借阅历史数据进行关联分析,从而对读者做出个性化的推荐。实验结果表明,提出的图书馆个性化快速推荐算法具有较高的准确度和运行效率。

With the continuous development of the construction of university book management system,the book borrowing activities of teachers and students have generated a large amount of browsing data.In order to mine the above borrowing information to provide a higher level of service for readers,a library personalized fast.recommendation algorithm based on data mining technology is proposed.The main methods and organizational structure of data mining are introduced.The Apriori algorithm as a classical association rule mining algorithm is modified to improve the computing efficiency of association rules.The improved Apriori algorithm is used to perform the association analysis for the historical data of the book borrowing,so as to make the personalized recommendations for readers.The experimental results show that the proposed library personalized fast.recommendation algorithm has high accuracy and operational efficiency.