An assessment of AI-driven personalized reading suggestions in Niger State Polytechnic Library, Zungeru, Niger State
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Background of the study
Personalized reading suggestion systems leverage AI algorithms—such as collaborative filtering, content-based filtering, and hybrid approaches—to recommend books and articles tailored to individual user preferences and reading histories (Nguyen, 2024). These systems enhance user engagement, promote serendipitous discovery, and increase circulation of underutilized materials (Okafor, 2023). In vocational and technical libraries, personalized recommendations can expose users to relevant case studies, technical manuals, and professional development resources. Niger State Polytechnic Library in Zungeru recently introduced an AI-driven recommendation feature within its discovery platform, using borrowing data and user profiles to suggest resources (Eze, 2025). While initial uptake is promising, systematic assessment of recommendation accuracy, user satisfaction, and impact on reading behaviors has not been conducted. Factors influencing system effectiveness include the diversity of technical subject matter, data sparsity for new users, and user trust in algorithmic suggestions (Smith, 2023). This assessment evaluates the performance of personalized reading suggestions, measuring click-through and checkout rates, surveying user perceptions, and identifying improvement areas.
Statement of the problem
Despite the rollout of AI-driven suggestions, many patrons report that recommendations often reflect general popularity rather than individual interests, leading to low click-through rates and skepticism about algorithmic curation (Ibrahim, 2024). Without empirical evaluation, the library cannot refine recommendation algorithms or develop user guidance to enhance personalization effectiveness.
Objectives of the study
To measure the accuracy and relevance of AI-generated reading suggestions based on user feedback and usage metrics.
To assess the impact of personalized recommendations on circulation patterns and resource discovery.
To identify system and user factors that influence recommendation acceptance and trust.
Research questions
What proportion of AI-generated recommendations result in resource checkouts?
How do users rate the relevance and usefulness of personalized suggestions?
What algorithmic or interface improvements can enhance recommendation accuracy?
Significance of the study
The assessment will guide library technologists and collection managers in optimizing recommendation algorithms, curating user profiles, and designing interfaces that foster trust and engagement. Improved personalization can increase resource utilization and support continuous learning among polytechnic students.
Scope and limitations of the study
This study focuses on AI-driven reading suggestions within the online catalog of Niger State Polytechnic Library, Zungeru. It excludes manual staff recommendations and external reading platforms. Limitations include the cold-start problem for new users and variability in borrowing data quality.
Definitions of terms
Hybrid recommendation: AI approach combining collaborative and content-based filtering for improved accuracy.
Click-through rate (CTR): Proportion of recommended items that users click on.
Cold-start problem: Challenge in recommendation systems when insufficient data exists for new users or items.
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