The effect of personalized recommendation systems on student engagement in Delta State University Library, Abraka, Ethiope East LGA, Delta State
- Project Research
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- Table of Content: Available
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- NGN 5000
Background of the Study
Personalized recommendation systems are becoming an integral part of digital library services, designed to tailor resource suggestions based on individual user preferences. At Delta State University Library, such systems aim to increase student engagement by presenting customized reading lists and relevant academic materials (Olufemi, 2023). The utilization of these systems has been linked with higher rates of resource discovery and improved learning outcomes as students receive guidance aligned with their research interests (Akinlade, 2024). By analyzing user behavior and borrowing patterns, personalized recommendation algorithms contribute to a more dynamic and interactive digital library environment. Nevertheless, challenges exist in ensuring that recommendations accurately reflect user needs and do not lead to informational echo chambers. Various factors such as data privacy, algorithm bias, and the need for continuous system updates are central to their effectiveness (Babatunde, 2025). This investigation explores the extent to which personalized recommendation systems affect student engagement and overall satisfaction, assessing both the benefits and potential drawbacks in the context of academic research and study.
Statement of the Problem
Despite the promising potential of personalized recommendation systems at Delta State University Library, issues persist regarding their accuracy and overall impact on student engagement. Students have reported instances of irrelevant recommendations and a lack of diversity in suggested resources, which may hinder exploratory research (Olufemi, 2023). The system’s heavy reliance on historical data sometimes results in recommendations that do not adapt to rapidly evolving academic interests. Furthermore, concerns about data privacy and the ethical use of student information have surfaced, complicating the balance between personalization and user trust (Babatunde, 2025). These challenges necessitate a thorough investigation into the system’s operational mechanisms and its real-world effectiveness in enhancing the academic experience.
Objectives of the Study:
To evaluate the accuracy of personalized recommendations.
To assess the impact on student engagement and resource usage.
To identify strategies to overcome existing system biases.
Research Questions:
How effectively do the recommendations match student research interests?
What impact do personalized suggestions have on resource engagement?
How can algorithm biases be mitigated to enhance diversity?
Significance of the Study
This study is significant as it evaluates the role of personalized recommendation systems in elevating student engagement, thereby informing library management on strategic improvements. The research findings will contribute to the optimization of digital recommendation algorithms, support enhanced academic research, and provide insight into balancing personalization with ethical considerations, ultimately leading to a more engaging library experience (Akinlade, 2024).
Scope and Limitations of the Study:
The study focuses exclusively on the personalized recommendation system in Delta State University Library and does not address other aspects of digital library services.
Definitions of Terms:
Personalized Recommendation System: A digital tool that customizes resource suggestions based on user behavior and preferences.
Student Engagement: The level of active involvement and participation in academic resource usage.
Algorithm Bias: A systematic error in recommendations due to skewed data or design flaws.
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