University library which is connected with the Internet is more convenient to search, but the huge amount of data is not convenient for users who lack precise target. In this study, the traditional association rule algorithm was improved by Bayesian algorithm, and then simulation experiment was carried out taking borrowing records of 1000 students as examples. In order to verify the effectiveness of the improved algorithm, it was compared with the traditional association rule algorithm and collaborative filtering algorithm. The results showed that the recommendation results of the improved association rule recommendation algorithm were more relevant to students’ majors, and the coincidence degree of different students was low. In the objective evaluation of the performance of the algorithm, the accuracy, recall rate and F value showed that the personalized recommendation performance of the improved association rule algorithm was better and the improved association rule algorithm could recommend users with the book type that they need.

中文翻译：

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高校图书馆按需借阅推荐关联规则模型

与互联网相连的大学图书馆更便于搜索，但庞大的数据量对于缺乏精准目标的用户来说并不方便。本研究利用贝叶斯算法对传统的关联规则算法进行改进，并以1000名学生的借阅记录为例进行了仿真实验。为了验证改进算法的有效性，将其与传统的关联规则算法和协同过滤算法进行了比较。结果表明，改进关联规则推荐算法的推荐结果与学生专业的相关性更高，不同学生的重合度较低。在客观评价算法的性能、准确率、