Enhancing library book recommendations using clustering and association rule mining

Information from the abstract

Introduction. This study investigates library user clusters to improve book recommendations. Data on daily borrowing behaviours from 2020 to 2025 were collected from the Prince of Songkla University, Pattani Campus library. Method. Users were clustered using K-means. The optimal number of clusters was selected via the elbow method combined with silhouette scores. Borrowing-pattern relationships were analysed using association rule mining with the FP-Growth algorithm (minimum support 15%, minimum confidence 90%). Analysis. The study examined the distribution of borrowed book categories and evaluated clustering performance using precision, recall, F-measure, and hit rate. Association rules were extracted to identify co-borrowing trends among categories. Results. Languages was the most borrowed category, followed by History & Geography and Science. The best user clustering solution was k = 3, achieving precision 82.98%, recall 98.55%, F-measure 0.8815, and hit rate 97.57%. The strongest rule showed that users borrowing Economics [330], Social Sciences [300], and History of Europe [940] were likely to also borrow History of Asia [950]. Associations were also found among life sciences, biology, physics, and chemistry. Conclusions. The study results indicate patterns of interest and knowledge connections derived from borrowing behaviour, which can be used to improve library services and serve as a model for data-driven library management.

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Related topics: Data Mining and Machine Learning Applications · Data Mining Algorithms and Applications · Recommender Systems and Techniques