Thesis Abstract

Abstract
This thesis explores the implementation of artificial intelligence (AI) for personalized recommendations in library catalogs. With the advancement of technology and the increasing volume of information available, traditional methods of information retrieval in libraries are becoming inefficient. AI-powered recommendation systems offer a promising solution to this challenge by providing users with personalized recommendations based on their preferences and behavior. This study aims to investigate the design, development, and evaluation of an AI-driven recommendation system tailored for library catalogs. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, research objectives, limitations, scope, significance, and structure of the thesis. The chapter also includes a definition of key terms relevant to the research. Chapter 2 comprises a comprehensive literature review on AI-based recommendation systems, library catalogs, user preferences, and information retrieval techniques. The review examines existing studies, methodologies, and technologies related to personalized recommendations in library settings. Chapter 3 outlines the research methodology employed in this study. It includes details on the research design, data collection methods, algorithm selection, system development process, evaluation metrics, and ethical considerations. The chapter also discusses the challenges encountered during the research process and the strategies adopted to address them. Chapter 4 presents a detailed discussion of the findings obtained from the implementation and evaluation of the AI-driven recommendation system in library catalogs. The chapter analyzes the effectiveness, accuracy, user satisfaction, and practical implications of the recommendation system. Chapter 5 concludes the thesis by summarizing the key findings, highlighting the contributions of the study, discussing implications for practice and future research directions, and providing recommendations for the successful implementation of AI for personalized recommendations in library catalogs. Overall, this thesis contributes to the field of library and information science by demonstrating the potential of AI technologies to enhance information retrieval and user experience in library settings. The findings and insights from this research can inform librarians, information professionals, and technology developers on the design and implementation of personalized recommendation systems to meet the evolving needs of library users in the digital age.

Thesis Overview