Utilizing Artificial Intelligence for Personalized Information Retrieval in Libraries

Thesis Abstract

Abstract
This thesis explores the application of artificial intelligence (AI) in enhancing personalized information retrieval within library settings. The increasing volume and complexity of information available in libraries necessitate innovative solutions to ensure efficient access and retrieval. AI technologies offer promising capabilities to tailor information retrieval processes to individual user preferences and needs. This study aims to investigate the potential of AI in transforming traditional library services, focusing on personalized information retrieval. The introduction provides an overview of the research background, highlighting the challenges faced in conventional information retrieval methods within libraries. The background of the study delves into the evolution of AI technologies and their relevance to information science. The problem statement identifies the gaps and limitations in existing library information retrieval systems, emphasizing the need for personalized approaches. The objectives of the study outline the specific goals to be achieved through this research, including exploring AI applications, evaluating user preferences, and enhancing information retrieval efficiency. The literature review chapter presents an in-depth analysis of existing studies and frameworks related to AI in information retrieval and personalized services in libraries. Key themes include AI algorithms, user modeling, recommendation systems, and user experience enhancement. The review synthesizes current knowledge and identifies areas for further research and development. The research methodology chapter describes the design and implementation of the study, including data collection methods, AI tools utilized, and evaluation criteria. The methodology incorporates both qualitative and quantitative approaches to ensure comprehensive analysis of user preferences and system performance. Key components include user surveys, AI algorithm testing, and performance metrics assessment. The findings chapter presents the results of the study, highlighting the effectiveness of AI technologies in improving personalized information retrieval in libraries. Key outcomes include enhanced search accuracy, personalized recommendations, user satisfaction levels, and system performance metrics. The discussion section interprets the findings in the context of existing literature, emphasizing the implications for library services and future research directions. The conclusion chapter summarizes the key findings and contributions of the study, emphasizing the significance of AI-driven personalized information retrieval in libraries. The thesis concludes with recommendations for implementing AI technologies in library settings and enhancing user experiences. Overall, this research contributes to the growing body of knowledge on AI applications in information science and provides valuable insights for practitioners and researchers in the field. Keywords Artificial Intelligence, Information Retrieval, Libraries, Personalization, User Preferences, Recommendation Systems, User Experience.