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Chen, C.Y., Hong, J.M., Zhou, W.L., Lin, G.H., Wang, Z.F., Zhang, Q.F., et al. (2017) The Method and Application to Construct Experience Recommendation Platform of Acupuncture Ancient Books Based on Data Mining Technology. Chinese Acupuncture & Moxibustion, 37, 768-772.

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• TITLE: Design and Implementation of Book Recommendation Management System Based on Improved Apriori Algorithm

  AUTHORS: Yingwei Zhou

  KEYWORDS: Information Recommendation, Book Management, Apriori Algorithm, Data Mining, Association Rule, Personalized Recommendation

  JOURNAL NAME: Intelligent Information Management, Vol.12 No.3, April 7, 2020

  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.