改进Apriori算法的高校图书馆图书智能推荐系统

Intelligent Book Recommendation System in University Libraries Based on Improved Apriori Algorithms

为了提高高校图书馆图书推荐的准确性,设计了基于改进Apriori算法的高校图书馆图书智能推荐系统。首先描述高校图书馆图书智能推荐系统的基本架构,并通收集高校图书智能推荐系统中的借阅者和图书相关信息,然后引入Apriori算法对借阅者和图书之间的关联进行分析和挖掘,建立两者之间的关联规则,并针对Apriori算法的不足进行相应的改进,最后根据建立的关联规则进行借阅者的个性化图书信息推荐,采用具体仿真实验分析了与其它高校图书馆图书智能推荐系统的性能。结果表明,改进Apriori算法的高校图书馆图书推荐精度超过95%,推荐时间控制在有效范围内,提高了高校图书馆图书智能推荐效率,获得了比较其它高校图书馆图书智能推荐系统更加理想的结果。

In order to improve the accuracy of the recommendation system of university library,an intelligent recommendation system of university library based on improved Apriori algorithm is designed.This paper first describes the basic structure of the intelligent recommendation system,and collects the relevant information of the borrowers and the books in the intelligent recommendation system of university library,then introduces the Apriori algorithm to analyze and mine the association between the borrowers and books,establishes the association rules between the two,and improves the Apriori algorithm accordingly.Finally,association rules are established andused to recommend personalized book information for readers.The performance of the system is analyzed by simulation experiments.The results show that the recommended accuracy of the improved Apriori algorithm is more than 95%,and the recommended time is controlled in an effective range,which improves the efficiency of the intelligent recommendation of the university library,and obtains a more ideal result compared with other intelligent recommendation systems of university libraries.