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An examination of AI-driven personalized e-book recommendations in Bauchi State Polytechnic Library, Bauchi, Bauchi State

  • Project Research
  • 1-5 Chapters
  • Abstract : Available
  • Table of Content: Available
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  • Recommended for :
  • NGN 5000

Background of the study
Personalized e‑book recommendation engines use AI to analyze user reading histories, subject interests, and content metadata to suggest digital titles tailored to individual preferences (Nguyen, 2024). Such systems can increase e‑book circulation, promote underused digital collections, and support remote learning (Perez, 2024). At Bauchi State Polytechnic Library, an AI module was integrated into the e‑library portal, recommending textbooks, reference works, and vocational guides based on user profiles (Eze, 2025). While initial adoption metrics show increased click‑through rates, systematic evaluation of recommendation relevance, impact on e‑book usage patterns, and user satisfaction is needed. Considerations include handling multi‑disciplinary users, cold‑start challenges for new patrons, and ensuring recommendation diversity.

Statement of the problem
Despite personalized e‑book recommendations, many users at Bauchi Polytechnic report repetitive suggestions and lack of transparency in how recommendations are generated, leading to low trust and minimal engagement with the feature. Without empirical analysis, library staff cannot refine recommendation algorithms or provide guidance on leveraging e‑book suggestions effectively.

Objectives of the study

 

To measure the relevance and diversity of AI-driven e‑book recommendations.

 

 

To assess changes in e‑book access and download rates following recommendation deployment.

 

 

To identify system and user interface enhancements that improve recommendation acceptance.

 

Research questions

 

What proportion of recommended e‑books are accessed or downloaded by users?

 

 

How do users rate the relevance and novelty of personalized recommendations?

 

 

What algorithmic adjustments and interface features can enhance e‑book recommendation effectiveness?

 

Significance of the study
Findings will inform library and IT teams on optimizing e‑book recommendation engines—through algorithm tuning, profile enrichment, and user interface design—to boost digital resource engagement and support remote learning at Bauchi State Polytechnic Library.

Scope and limitations of the study
This examination focuses on AI-driven e‑book recommendations within the digital portal of Bauchi State Polytechnic Library. It excludes physical book recommendations and external e‑book platforms. Limitations include the cold‑start problem and evolving e‑book collections.

Definitions of terms

 

E‑library portal: Online platform providing access to digital library resources.

 

 

Recommendation diversity: The range of topics and formats presented in personalized suggestions.

 

 

Profile enrichment: Process of augmenting user profiles with additional data to improve recommendations.

 


 





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