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

Shixin Xu

doi:10.31449/inf.v44i3.3295

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.

Highlights

The arrival of the Internet era has made great changes in our lives, the most intuitive expression of which is that the amount of information that can be obtained far exceeds the era before the emergence of the Internet [1]
According to the book classification number, it was found that the books recommended by the collaborative filtering recommendation algorithm were mostly irrelevant, there were books related to the major; only one or two books recommended by the traditional association rule algorithm were irrelevant; the books recommended by the improved association rule algorithm were basically relevant to the major
The vertical comparison of the recommendation results of different people under the same algorithm showed that the result types under the collaborative filtering algorithm were messy and nearly involved all the majors; the results under the traditional association rule algorithm had overlapping, i.e., high similarity; the results under the improved association rule algorithm involved different types, but different from the collaborative filtering algorithm, they were relevant to the major of the borrower

Summary

Introduction

The arrival of the Internet era has made great changes in our lives, the most intuitive expression of which is that the amount of information that can be obtained far exceeds the era before the emergence of the Internet [1]. In order to meet the needs of university teachers and students, the selection of books is often very rich [2]. Similar to the Internet described before, the amount of book information in university library cannot be compared with the amount of data in the whole Internet, it is still a huge amount of data for university teachers and students. Zhang [4] proposed a personalized book recommendation algorithm based on time series collaborative filtering recommendation and found through experiment that the book recommendation algorithm met the professional learning needs of college students. In order to verify the effectiveness of the improved algorithm, it was compared with the traditional association rule algorithm and collaborative filtering algorithm

Methods

Results

Conclusion