Neural information retrieval in digital libraries for enhancing the quality of library and information services

The digital transformation of the library industry entails intensive integration of artificial intelligence (AI), with the focus on neural information retrieval (or neural search), based on artificial neural networks (ANN). Neural search (NS) unlocks new opportunities for enhancing the efficiency of library and information services (LIS), providing more accurate, faster, and personalized access to knowledge in digital libraries (DL). The application of NS transforms the paradigm of library and information services (LIS), changes the user – content interaction. The author analyzes how NS addresses the problems of low relevance of search results, limited semantic query processing, and insufficient personalization. The research methods include theoretical analysis of scientific literature, regulatory documents, and generalization of ANN implementation experience. The research findings evidence that NS significantly improves service quality by processing complex queries, adapting to individual user needs, and supporting inclusive access to library collections. Despite its high technological potential, NS does not replace but rather complements traditional methods, automates routine processes and allows librarians to focus on complex and creative tasks. The implementation of NS aligns with the digital transformation strategies of the library sector.