Title: Book classification recommendation method for university libraries based on collaborative filtering algorithm

Authors: Yina Liu

Addresses: Library, Hunan College of Information, ChangSha, 410000, China

Abstract: The recommendation of book classification in university libraries is of great significance for improving information retrieval efficiency and optimising book resource management. In order to solve the problems of low accuracy, long time, and low user satisfaction in traditional book classification recommendation methods for university libraries, a book classification recommendation method for university libraries based on collaborative filtering algorithm is proposed. Firstly, the fuzzy C-means clustering algorithm is used to cluster the data of university library platforms, completing the collection of library platform data. Secondly, determine the user characteristics of university libraries based on the collected data. Finally, the collaborative filtering algorithm calculates the predicted scores of university library books based on user characteristics, and implements book classification recommendations for university libraries. Experimental results show that the maximum classification recommendation accuracy of the proposed method is 97.6%, the average recommendation time is 0.52 s, and the average user satisfaction is 94.71.

Keywords: collaborative filtering algorithm; university library; book classification recommendation; fuzzy C-means clustering algorithm; user characteristics.

DOI: 10.1504/IJBIDM.2025.149066

International Journal of Business Intelligence and Data Mining, 2025 Vol.27 No.2/3/4, pp.121 - 138

Received: 07 Nov 2024
Accepted: 16 Jan 2025
Published online: 13 Oct 2025 *

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