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.

数据挖掘现代技术在图书馆管理系统和信息中心的应用非常重要。随着大量信息的可用性越来越高，不浪费时间和成本的传统工具和做法无法准确快速地响应用户。本研究旨在分析图书流通交易并发现用户的图书借阅模式以开发推荐系统。数据包括来自 8636 条用户记录的 109,639 笔交易和信息。应用微软 SQL Server 和 Matlab 软件对数据进行分析。还应用了基于项目的协同过滤算法和决策树方法。结果导致提取规则以向用户推荐书籍。流通数据的分析可用于解决评估等许多问题，