Thesis Abstract

Abstract
The utilization of artificial intelligence (AI) in academic libraries has become increasingly prevalent in recent years, particularly in the development of personalized recommender systems. This thesis explores the application of AI techniques to enhance the recommendation process in academic libraries, with a focus on providing personalized recommendations to users based on their individual preferences and needs. The research investigates the current state of recommender systems in academic libraries, identifies key challenges and limitations, and proposes innovative solutions leveraging AI technologies. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter 2 presents a comprehensive literature review, analyzing existing studies on AI-based recommender systems in academic libraries, highlighting key concepts, methodologies, and findings. Chapter 3 details the research methodology employed in this study, including research design, data collection methods, sampling techniques, data analysis procedures, and ethical considerations. The methodology aims to gather insights into user preferences, behavior, and feedback to develop an effective personalized recommender system. Chapter 4 presents a detailed discussion of the research findings, including the development and evaluation of the AI-based personalized recommender system. The chapter explores the design, implementation, and testing of the system, as well as the analysis of user feedback and system performance metrics. Chapter 5 concludes the thesis by summarizing the key findings, discussing implications for future research and practice, and highlighting the significance of utilizing AI for personalized recommender systems in academic libraries. The study contributes to the advancement of AI technologies in library settings and provides valuable insights for improving user experience and information discovery. Overall, this thesis demonstrates the potential of AI in transforming the recommendation process in academic libraries, offering personalized services that enhance user satisfaction and engagement. The research underscores the importance of leveraging AI technologies to support information retrieval, knowledge discovery, and user interaction in library environments, paving the way for more intelligent and user-centric library services.

Thesis Overview

The project titled "Utilizing Artificial Intelligence for Personalized Recommender Systems in Academic Libraries" aims to explore the integration of artificial intelligence (AI) technology to enhance personalized recommender systems within academic library settings. This research seeks to address the growing need for more efficient and tailored library services to meet the diverse information needs of users in educational institutions. The utilization of AI in personalized recommender systems offers the potential to revolutionize the way academic libraries deliver content and services to their patrons. By leveraging machine learning algorithms and data analytics, AI can analyze user behavior, preferences, and interactions with library resources to provide customized recommendations that align with individual needs and interests. The research will delve into the background of the study, highlighting the evolution of recommender systems and the adoption of AI in library settings. This will provide a comprehensive understanding of the current landscape and the potential benefits and challenges associated with implementing AI-powered personalized recommender systems in academic libraries. The primary objective of this project is to design and develop an AI-based personalized recommender system prototype tailored specifically for academic libraries. Through a systematic methodology, the research will explore various AI techniques, algorithms, and data sources to optimize the recommendation process and enhance user experience within the library environment. Furthermore, the study will address key research questions related to the effectiveness, usability, and user acceptance of AI-driven personalized recommender systems in academic libraries. By evaluating the system performance, user feedback, and impact on information retrieval, the research aims to provide valuable insights into the practical implications and future potential of AI technology in library services. The significance of this research lies in its contribution to advancing library services through innovative technology solutions. By enhancing the personalized recommendation capabilities of academic libraries, this project seeks to improve information access, user satisfaction, and overall efficiency in serving the diverse needs of students, faculty, and researchers. In conclusion, "Utilizing Artificial Intelligence for Personalized Recommender Systems in Academic Libraries" represents a pioneering effort to harness the power of AI for enhancing user experiences and information discovery in educational settings. Through systematic research, innovation, and collaboration, this project aims to pave the way for a more intelligent and user-centric approach to library services, ultimately benefiting the academic community and shaping the future of information access and retrieval.