一种快速的个性化书目推荐方法

A Fast Personalized Bibliographic Recommendation Method

针对高校数字图书馆现有书目推荐方法存在的不足,提出一种快速的个性化书目推荐方法。该方法利用矩阵向量技术和压缩技术对Apriori算法进行改进,以提高数据资源的挖掘效率,然后利用改进的Apriori算法从读者的借阅记录中挖掘出图书之间的关联关系,以此为读者的借阅提供个性化的书目推荐服务。仿真结果能够证明该方法的有效性。

Aimed at the shortcomings of the bibliographic recommended methods in digital library, a Fast Personalized Bibliographic Recommendation Method （FPBRM） is proposed. By using the technologies of matrix vector and compression, the method improves Apriori algorithm that it can enhance the efficiency of data mining. Then the correlation between the books can be mined from the loan records by using the Improved Apriori Algorithm（IAA） ,which can provide personalized bibliographic recommendation for the readers. Finally, the simulation results show the effectiveness of the method.