Thesis Abstract

Abstract
This thesis explores the implementation of Artificial Intelligence (AI) technology to develop personalized recommender systems for enhancing library services. The rapid advancement of AI has revolutionized various industries, and the field of library and information science stands to benefit significantly from its application. Traditional library services often struggle to meet the diverse and evolving needs of users, leading to inefficiencies in information retrieval and resource utilization. By leveraging AI algorithms and techniques, libraries can offer tailored recommendations to users, improving the overall user experience and increasing engagement with library resources. The thesis begins with an introduction that sets the context for the study, highlighting the importance of personalized recommender systems in modern library services. The background of the study explores the evolution of AI technology and its potential impact on library operations. The problem statement identifies the challenges faced by traditional library services and the need for personalized recommendations to address these issues. The objectives of the study are outlined to guide the research process, focusing on the development and implementation of AI-based recommender systems in libraries. The limitations of the study are acknowledged, including constraints related to data availability, algorithm complexity, and user privacy concerns. The scope of the study is defined to clarify the specific aspects of library services that will be addressed through the implementation of personalized recommender systems. The significance of the study lies in its potential to transform library services, making them more efficient, user-centric, and responsive to individual preferences and needs. The literature review provides a comprehensive examination of existing research and developments in AI-based recommender systems, focusing on their applications in various domains and industries. Key concepts and methodologies related to AI, machine learning, and recommendation algorithms are explored to inform the design and implementation of personalized recommender systems in libraries. The research methodology outlines the approach taken to develop and evaluate the AI-based recommender systems, including data collection, preprocessing, algorithm selection, model training, and performance evaluation. The discussion of findings presents the results of the implementation process, highlighting the effectiveness and usability of the personalized recommender systems in enhancing library services. In conclusion, the thesis summarizes the key findings and contributions of the study, emphasizing the potential of AI technologies to revolutionize library services through personalized recommendations. The implications of the research are discussed, along with recommendations for future work and areas for further exploration in the field of AI-driven library services. Overall, this thesis demonstrates the transformative potential of AI for personalized recommender systems in library services, offering new insights and opportunities for improving information access and user engagement in the digital age.

Thesis Overview