Paper Details

Digital libraries have transformed into essential platforms for knowledge storage, access, and dissemination in the digital age. However, the exponential growth of digital content has made information retrieval, personalization, and resource management increasingly complex. Traditional keyword-based systems often fail to meet user expectations, leading to information overload and reduced efficiency. This paper presents a framework for integrating Machine Learning (ML) into digital library systems to enable intelligent services. By applying Natural Language Processing (NLP) for semantic search, recommendation systems for personalized content delivery, predictive analytics for resource management, and anomaly detection for digital preservation, libraries can become adaptive and user-centered platforms. Experimental results demonstrate that NLP-based retrieval improves precision and recall significantly, hybrid recommendation models enhance user satisfaction, and predictive analytics accurately forecast resource demand. The study also emphasizes ethical concerns such as privacy, algorithmic bias, and transparency in ML-driven libraries. The findings suggest that ML integration can move digital libraries toward the concept of Library 4.0, where intelligent services enhance accessibility, personalization, and long-term sustainability of knowledge resources.

Digital Libraries, Machine Learning, Intelligent Services, Information Retrieval, Natural Language Processing, Recommendation Systems, Predictive Analytics, Library 4.0