1 Mentor, Graduate Studies Department, La Consolacion University, Bulihan, City of Malolos, Bulacan, Philippines.
2 Mentor, Graduate Studies Department, Quezon City University, Sanbartolome, Quezon City, Philippines.
3 Mentor, Graduate Studies Department, Bulacan State University, Malolos, Bulacan, Philippines.
4 Mentor, Graduate Studies Department, Far Eastern University, Sampaloc, Manila, Philippines.
5 Mentor, Graduate Studies Department, Bulacan State University, Malolos, Bulacan, Philippines.
6 Mentor, Graduate Studies Department, La Consolacion University, Bulihan, City of Malolos, Bulacan, Philippines.
7 Mentor, Graduate Studies Department, Bulacan State University, Malolos, Bulacan, Philippines.
World Journal of Advanced Research and Reviews, 2025, 25(03), 645-657
Article DOI: 10.30574/wjarr.2025.25.3.0779
Received on 30 January 2025; revised on 06 March 2025; accepted on 08 March 2025
This study, titled "Unlocking Insights from Academic Library Data using Clustering and Recommender Dashboard Analytics for Enhanced Book Collection Management: UST Perspective," explores data-driven strategies to enhance the University of Santo Tomas (UST) library’s collection management. The K-Means clustering algorithm was used to analyze library collection data, identifying patterns based on book titles, publication years, authors, and categories. The clustering results revealed high-demand clusters, including Social Sciences, Health Sciences, Humanities, and Science and Technology, while low-usage clusters highlighted underutilized resources such as Senior High School (SHS), Heritage, Junior High School (JHS), Education High School, and Music collections. Acquisition patterns showed peaks in specific years and emerging categories, particularly in Science and Technology. Data visualization tools like Tableau and JupyterLab were used to present these insights. Despite challenges, such as handling interdisciplinary overlaps and managing data inconsistencies, the K-Means algorithm effectively uncovered meaningful patterns. To enhance user experience, a personalized recommender system using collaborative filtering was developed. This system provides offered book suggestions based on users’ interests, reviews, and ratings by analyzing similar users’ interactions. The recommender system is accessible via www.ustlibrary.online. This study highlights the potential of combining clustering algorithms and recommender systems to support data-informed decision-making, ultimately fostering a responsive and user-centric academic library service.
Academic Library Analytics; Clustering Algorithms; Data-Driven Insights; K-Means Clustering, Library Collection Management; Recommender Systems; Library Collection Management
Preview Article PDF
Edwin Santos de Guzman, Isagani Mirador Tano, Keno Piad, Ace Lagman, Joseph Espino, Jonilo Mababa and Jayson Victoriano. Unlocking insights from academic library data using clustering and recommender dashboard analytics for enhanced book collection management. World Journal of Advanced Research and Reviews, 2025, 25(03), 645-657. Article DOI: https://doi.org/10.30574/wjarr.2025.25.3.0779.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0