Abstract
The traditional Apriori applied in books management system causes slow system operation due to frequent scanning of database and excessive quantity of candidate item-sets, so an information recommendation book management system based on improved Apriori data mining algorithm is designed, in which the C/S (client/server) architecture and B/S (browser/server) architecture are integrated, so as to open the book information to library staff and borrowers. The related information data of the borrowers and books can be extracted from books lending database by the data preprocessing sub-module in the system function module. After the data is cleaned, converted and integrated, the association rule mining sub-module is used to mine the strong association rules with support degree greater than minimum support degree threshold and confidence coefficient greater than minimum confidence coefficient threshold according to the processed data and by means of the improved Apriori data mining algorithm to generate association rule database. The association matching is performed by the personalized recommendation sub-module according to the borrower and his selected books in the association rule database. The book information associated with the books read by borrower is recommended to him to realize personalized recommendation of the book information. The experimental results show that the system can effectively recommend book related information, and its CPU occupation rate is only 6.47% under the condition that 50 clients are running it at the same time. Anyway, it has good performance.
Highlights
Data mining is an algorithm that mines hidden laws from a large amount of data for effective analysis, which can efficiently calculate data statistics, pattern processing and other related issues
The traditional Apriori applied in books management system causes slow system operation due to frequent scanning of database and excessive quantity of candidate item-sets, so an information recommendation book management system based on improved Apriori data mining algorithm is designed, in which the C/S architecture and B/S architecture are integrated, so as to open the book information to library staff and borrowers
The book information associated with the books read by borrower is recommended to him to realize personalized recommendation of the book information
Summary
Data mining is an algorithm that mines hidden laws from a large amount of data for effective analysis, which can efficiently calculate data statistics, pattern processing and other related issues. In order to solve these problems, this paper designs a book management system for information recommendation based on Apriori data mining algorithm. Using the Shanghai Maritime University Library borrowing statistics as the data source, the experimental results show that the scheme can accurately and effectively recommend related books to borrowers. The following paper is arranged as below: The second section describes the Overall structure and functional modules of the book management system; The third section introduces the traditional Apriori data mining algorithm and improves it. The fifth section makes an experimental analysis of the book recommendation system based on the improved Apriori algorithm, which verifies that the book recommendation system in this paper can accurately mine book related information and effectively reduce the memory occupied by the computer. The Overall Structure of Book Management System for Information Recommendation
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