Optimization of Library Book Recommendation Systems Using Big Data in Federal University Dutsin-Ma, Katsina State
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- NGN 5000
Background of the Study
In university libraries, book recommendation systems play a crucial role in facilitating efficient information retrieval and enhancing the user experience. At Federal University Dutsin-Ma in Katsina State, traditional recommendation methods often rely on static algorithms that fail to capture the evolving interests and academic needs of students. Big data analytics offers a transformative solution by processing vast amounts of user interaction data, borrowing histories, and search patterns to deliver personalized recommendations (Ibrahim, 2023). By leveraging machine learning techniques such as collaborative filtering and content-based filtering, big data-based recommendation systems can analyze patterns in user behavior and suggest relevant resources with greater accuracy. This data-driven approach not only improves resource discoverability but also enhances user engagement by aligning recommendations with individual academic pursuits (Chinwe, 2024). Furthermore, real-time analytics enable continuous updates to the recommendation engine, ensuring that the system adapts to changing trends and preferences. The implementation of such a system can lead to improved academic outcomes by ensuring that students have timely access to pertinent literature and research materials. However, challenges such as data privacy, integration of heterogeneous data sources, and the computational complexity of processing large datasets need to be addressed. This study aims to optimize the library book recommendation system at Federal University Dutsin-Ma using big data analytics, providing a framework that enhances the overall effectiveness and efficiency of library services (Olufemi, 2025).
Statement of the Problem
Federal University Dutsin-Ma currently utilizes conventional book recommendation systems that are limited by their static nature and inability to adapt to users' evolving preferences. These traditional systems often result in irrelevant suggestions, thereby diminishing user satisfaction and hindering academic research. The lack of a dynamic, data-driven recommendation system means that the library is not effectively supporting the diverse and changing needs of its student body (Adebola, 2023). Additionally, the fragmented storage of user interaction data and borrowing histories hampers the integration necessary for advanced analytics. This deficiency leads to missed opportunities for providing personalized recommendations that could enhance students' learning experiences. Moreover, without real-time updates, the system cannot adjust to emerging trends or shifting academic interests, further reducing its utility. These challenges underscore the need for an optimized recommendation system that leverages big data analytics to deliver accurate, personalized, and adaptive suggestions. This study seeks to address these issues by developing a big data-based recommendation framework that integrates multiple data sources and utilizes machine learning algorithms to analyze and predict user preferences. The goal is to improve resource discovery, increase user engagement, and ultimately enhance the overall effectiveness of the library services provided at the university.
Objectives of the Study:
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To develop an optimized book recommendation system using big data analytics.
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To evaluate the system’s ability to provide personalized and relevant recommendations.
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To propose strategies for integrating real-time analytics into the library recommendation process.
Research Questions:
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How can big data analytics improve the accuracy of library book recommendations?
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What are the key factors that influence user preferences in a library setting?
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How can the system be integrated into existing library management practices for continuous improvement?
Significance of the Study
This study is significant as it demonstrates how big data analytics can transform library book recommendation systems, enhancing user engagement and resource discoverability at Federal University Dutsin-Ma. The findings will provide actionable insights for librarians and administrators to implement data-driven solutions that personalize user experiences and improve academic research outcomes. The research supports the digital transformation of library services, contributing to improved educational quality (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to the optimization of library book recommendation systems using big data at Federal University Dutsin-Ma, Katsina State, and does not extend to other library services or institutions.
Definitions of Terms:
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Big Data Analytics: The use of advanced computational methods to analyze large datasets.
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Book Recommendation System: A digital tool that suggests library resources to users based on their preferences and behavior.
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Personalization: The customization of services to meet individual user needs.
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