“Application of Artificial Intelligence to Transform Library Management Systems for Tailored Recommendations – A Review”

Abstract

Traditional library management systems primarily focus on cataloguing and organizing resources, which creates challenges in fulfilling modern expectations for personalized user experiences. These systems depend on standardized classification and metadata-based search mechanisms, leading to limited support for personalized recommendations. As a result, users often encounter static interactions that are less engaging. The heavy reliance on librarian- mediated assistance, while beneficial, lacks scalability and the ability to provide real-time, tailored recommendations. As the demand for user-centric experiences increases, libraries are increasingly adopting Artificial Intelligence (AI) technologies, such as machine learning (ML) and natural language processing (NLP), to enhance resource discoverability and offer personalized recommendations. This paper reviews existing literature on both traditional and AI-driven library management systems, highlighting the transformative potential of AI in delivering tailored recommendations through advanced algorithms and behavioural data analysis. AI enables libraries to adapt dynamically to user preferences, academic focuses, and interaction patterns, ultimately enhancing user satisfaction and engagement. The review also addresses challenges, such as data privacy concerns and resource constraints, faced by libraries in implementing AI-based solutions.