Association Rule Model of On-demand Lending Recommendation for University Library

Abstract

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.