Analysis of book circulation data and a book recommendation system in academic libraries using data mining techniques

• Published in: Library & information science research Vol. 44; no. 4; p. 101191
• Main Authors: Khademizadeh, Shahnaz, Nematollahi, Zahra, Danesh, Farshid
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2022
• Subjects: Academic library; Association rule; Collaborative filtering algorithm; Data mining; Information resource circulation; Recommendation system; User access pattern; Collaborative filtering algorithm; Information resource circulation; Association rule; User access pattern; Academic library; Data mining; Recommendation system
• ISSN: 0740-8188; 1873-1848
• DOI: 10.1016/j.lisr.2022.101191

The use of data mining modern technology in library management systems and information centers is of great importance. With the increasing availability of a large quantity of information, traditional tools and practices without wasting time and cost cannot respond to users accurately and quickly. The present study aims to analyze book circulation transactions and discover the user's book loan patterns to develop a recommender system. The data included 109,639 transactions and information from 8636 user records. Microsoft SQL Server and Matlab software were applied to analyze the data. Item-based collaborative filtering algorithms and decision tree methods were also applied. The results led to the extraction of rules for suggesting books to users. Analysis of the circulation data could be applied to address many issues like evaluation, collection acquisition policies, allocating funding for materials, and suggesting approaches to deselecting and allocating physical space for materials. •The collaboration of university librarians and data scientists is essential.•Data mining can affect the resource selection and circulation process in university libraries.•Data mining techniques can be used in university library sections, especially in the collection development.•The use of association rules and collaborative filtering algorithms can help predict library resources.•The high capabilities of the association rules algorithm predict library resources for users.