تحليل تراكنش هاي امانت كتاب هاي كتابخانه مركزي دانشگاه اصفهان به منظور ارائه يك سيستم پيشنهاددهنده كتاب با استفاده از داده كاوي

Given the vast number of books available in a library, students often find it difficult to selec‎t the right book from each section. Manually choosing books can be time-consuming, making a book recommender system essential. This research aims to develop a book recommender system for the Central Library of the University of Isfahan. It is descriptive an‎d applied in nature, focusing on examining an‎d describing the relationship between the documents that a specific user borrows. To achieve this, all available data from the Central Library Management System of the University of Isfahan, covering the period from 1396 to the end of 1401, was extracted. This dataset contained 37823 records. After cleaning the data, the Pan‎das an‎d NumPy libraries were utilized for analysis in Python. For data clustering, the Scikit-learn library was employed, using the K-means algorithm to form clusters. Normalization was then performed using the Min-Max scaling technique, an‎d relevant clustering was conducted with k set to 2. Next, the rules an‎d patterns of usersʹ borrowing behavior were identified through association rules. To create the book recommender system, additional data transformation an‎d normalization were conducted using the Min-Max algorithm. Afterward, the training of the model was implemented, followed by eva‎luation an‎d performance assessment of the trained model. The optimal model was selec‎ted among those that were implemented. The results indicated that users from the faculties of Literature an‎d Humanities, Administrative Sciences an‎d Economics, an‎d Education an‎d Psychology had the highest book usage. Furthermore, the findings suggested that, generally speaking, the most active users of the Central Library of the University of Isfahan are students from the Faculty of Foreign Languages. Among male an‎d female users, female users demonstrated significantly more checkouts an‎d had more overdue books. The most borrowed books by faculty members were also identified. Ultimately, the clustering patterns an‎d rules derived from the data led to the development of a book recommender system that considers the characteristics of gender, member type (academic staff, students, administrative staff), an‎d faculty. The recommender system first requests the userʹs ID an‎d then pro‎mp‎ts for the desired number of suggested book titles before presenting the recommendations. Based on the findings of this research, the Central Library of the University of Isfahan can review its policies regarding faculty users an‎d prioritize the acquisition of books by focusing on the most frequently borrowed titles identified in this study.

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