An appraisal of AI-based book recommendation systems in the Federal Polytechnic, Ilaro Library, Ogun State
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- 1-5 Chapters
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- NGN 5000
Background of the study
AI-based book recommendation systems are transforming how library users discover and engage with reading materials. At the Federal Polytechnic, Ilaro Library, such systems leverage machine learning algorithms to analyze user preferences and reading habits, thereby offering personalized book suggestions. This innovation enhances user experience by reducing the time spent searching for relevant resources and promoting a more tailored approach to learning (Akinola, 2023). The recommendation system continuously learns from user interactions, improving its suggestions over time and contributing to increased circulation and reader satisfaction. However, issues such as algorithm transparency, data privacy, and the potential for reinforcing narrow reading habits remain a concern. The study appraises the effectiveness of AI-based recommendation systems in driving book circulation and enhancing reading experiences, while also addressing the challenges that limit their broader application in library services (Balogun, 2024).
Statement of the problem
Although AI-based recommendation systems offer personalized reading suggestions, their effectiveness in the Federal Polytechnic, Ilaro Library is not fully understood. Challenges such as data privacy concerns, algorithmic biases, and limited system transparency can impede user trust and engagement. These issues may result in suboptimal recommendations and reduced overall user satisfaction. The study seeks to identify and address these challenges to ensure that the recommendation system optimally supports users’ literary interests (Oluwatobi, 2023).
Objectives of the study
To evaluate the impact of AI-based recommendation systems on book circulation.
To identify challenges limiting the effectiveness of these systems.
To propose strategies to improve personalized recommendation outcomes.
Research questions
How do AI-based systems affect user engagement and book circulation?
What challenges hinder the optimal performance of these systems?
What improvements can enhance the effectiveness of AI-driven recommendations?
Significance of the study
This study is significant as it provides insights into how AI-based recommendation systems can enhance user experience in library settings. The findings will inform strategies to refine these systems, ensuring they offer more accurate and personalized recommendations, thereby boosting overall library usage at the Federal Polytechnic, Ilaro Library (Adeyemi, 2024).
Scope and limitations of the study
Limited to the topic only.
Definitions of terms
Book Recommendation Systems: AI-driven tools that suggest reading materials based on user preferences.
Machine Learning: A branch of AI that enables systems to learn from data and improve over time.
User Personalization: Tailoring services to meet individual user needs and preferences.
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