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.

1. GOST R 7.0.103-2018 SIBID. Bibliotechno-informatcionnoe obsluzhivanie. Terminy` i opredeleniia. Moskva : Standartinform, 2018. 29 s.

2. Strategiia razvitiia bibliotechnogo dela v Rossii`skoi` Federatcii na period do 2030 goda. Utverzhdena rasporiazheniem Pravitel`stva Rossii`skoi` Federatcii ot 13 marta 2021 g. № 608r. URL: http://government.ru/docs/all/133337/ (data obrashcheniia: 04.03.2026).

3. Ukaz Prezidenta RF ot 28 fevralia 2024 g. № 145 «O Strategii nauchno-tekhnologicheskogo razvitiia Rossii`skoi` Federatcii». URL: https://www.garant.ru/products/ipo/prime/doc/408518353/ (data obrashcheniia: 04.03.2026).

4. Neshcheret M. Iu. Nei`roseti v biblioteke: novoe v bibliograficheskom obsluzhivanii // Nauchny`e i tekhnicheskie biblioteki. 2024. № 1. S. 105–128.

5. Nuzhdova D. A. Nei`roseti v bibliotechnom dele: opy`t proekta «Novy`e bibliotekari» // Korporativny`e bibliotechny`e sistemy`: tekhnologii i innovatcii: materialy` Mezhdunarodnoi` nauchno-prakticheskoi` konferentcii. 2023. S. 59–65. URL: https://elib.spbstu.ru/dl/2/k236.pdf/download/k23-6.pdf (data obrashcheniia: 04.03.2026).

6. Stepanov V. K., Madzhumder M. Sh., Begunova D. D. Metodika primeneniia bol`shoi` iazy`kovoi` modeli ChatGPT v bibliotechno-bibliograficheskoi` deiatel`nosti // Nauchny`e i tekhnicheskie biblioteki. 2024. № 4. S. 86–108.

7. Stepanov V. K., Liutetckii` V. M. Iskusstvenny`e nei`roseti v rossii`skikh bibliotekakh: sovremennoe sostoianie i programma vnedreniia // Nauchno-tekhnicheskaia informatciia. Seriia 1: Organizatciia i metodika informatcionnoi` raboty`. 2025. № 1. S. 7–12. DOI: 10.36535/0548-0019-2025-01-2.

8. Kovalevskii` A. V. Model` kommunikatcii s iskusstvenny`m intellektom DRUG kak metodologicheskii` podhod k sostavleniiu i ocenke promptov // Nauchny`e i tekhnicheskie biblioteki. 2025. № 7. S. 142–163.

9. Mel`nikov E. V. Vozmozhnosti nei`rosetei` dlia bibliotek // Informatcionny`i` biulleten` RNTB. 2024. № 1. S. 21–23.

10. Horoshavina U. V. Generativny`i` iskusstvenny`i` intellekt v biblioteke: obzor professional`noi` periodicheskoi` pechati // Informatcionny`i` biulleten` RNTB. 2024. № 2. S. 20–25.

11. Das R., Islam M. Application of Artificial Intelligence and Machine Learning in Libraries: A Systematic Review. 2021. DOI: 10.48550/arXiv.2112.04573.

12. Doszkocs T. E. Neural networks in libraries: the Potential of a New Information Technology. URL: http://web.simmons.edu/~chen/nit/NIT'91/027-dos.htm

13. Frické M. Artificial Intelligence and Librarianship: Notes for Teaching 3rd Edition. The University of Arizona : SoftOption, 2024.

14. The Role of Artificial Intelligence in Enhancing Information Retrieval Systems in Academic Libraries / K. S. Kini, K. Ranjan, V. A. Avula et al. // Library Progress International. 2024. Vol. 44, No. 3. P. 22526–22541.

15. Kong J. Application and Research of Artificial Intelligence in Digital Library. In M. Atiquzzaman, N. Yen, & Z. Xu (Eds.). Big Data Analytics for Cyber-Physical System in Smart City. 2021. P. 318–325. DOI: 10.1007/978-981-33-4572-0_47.

16. Kwok K. L. A neural network for probabilistic information retrieval In ACM SIGIR Proceedings, 1989. P. 21–30.

17. Scholtes J. C. Brussels Artificial neural networks for information retrieval in a libraries context. Luxembourg, 1995.

18. A Survey on Artificial Intelligence Aided Internet-of-Things Technologies in Emerging Smart Libraries / S. Bi, C. Wang, J. Zhang et al. // Sensors, 22 (8). Article 8. 2022. DOI: 10.3390/s22082991.

19. Nauchnaia e`lektronnaia biblioteka rasshiriaet vozmozhnosti poiska blizkikh po tematike publikatcii` s pomoshch`iu vstroennoi` nei`roseti // Nauchnaia e`lektronnaia biblioteka – eLIBRARY.RU. URL: https://elibrary.ru/projects/news/neural_search.asp? (data obrashcheniia: 04.03.2026).

20. Le KH. Issledovanie i razrabotka e`lektronny`kh bibliotek na osnove iavnogo opisaniia semantiki resursov s ispol`zovaniem tekhnologii` Semantic Web: spetcial`nost` 05.13.11 «Matematicheskoe i programmnoe obespechenie vy`chislitel`ny`kh mashin, kompleksov i komp`iuterny`kh setei`» : dis. … kand. tekhn. nauk. Tomsk, 2013. 181 s.

21. Semantic search in digital library semantic technology / O. Nurul, M. Noah, Sh. Azman, O. Nazlia // 2010 International Symposium on Information Technology, Kuala Lumpur, Malaysia, 2010. P. 1504–1507. DOI: 10.1109/ITSIM.2010.5561476.

22. Mitra B., Craswell N. An Introduction to Neural Information Retrieval, Now Foundations and Trends, 2018. DOI: 10.1561/1500000061.

23. Harnessing AI for Enhanced Searching in Digital Libraries: Transforming Research Practices / M. Shamsitdinova, D. Khashimova, U. Nasirova, N. Khikmatov // Indian Journal of Information Sources and Services. 2024. 14 (3). P. 102–109. DOI: 10.51983/ijiss-2024.14.3.14.

24. Xinjun K. Personalized Recommendation System of Smart Library Based on Deep Learning. In book: Application of Intelligent Systems in Multi-modal Information Analytics. 2022. P. 1011–1016. DOI: 10.1007/978-3-031-05484-6_136.

25. Xue T., Xianqing W., Tingting L. Research on Personalized Recommendation System of Library Collection Based on Deep Learning. IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA), Shenyang, China. 2024. P. 850–854. DOI: 10.1109/ICIPCA61593.2024.10709150.

26. Implementation of Voice Search Technology in Digital Library Systems / M. H. Sallaah, S. Kumar, M. A. P. Manimekalai et al. // Indian Journal of Information Sources and Services. 2025. 15 (2). P. 110–115. DOI: 10.51983/ijiss-2025.IJISS.15.2.15.

27. Vijay K. S., Sheshadri Kn. The Voice Assistants that connect you to your library, whether it is Alexa, Google, or Siri. Annals of Library and Information Studies. 2024. 71 (3). P. 272–278. DOI: 10.56042/alis.v71i3.8342.

28. Zeng Y., Zhou J., Xu T. Research on the Construction of Multimodal Datasets for Digital Libraries. 5th International Conference on Computer Science and Technologies in Education (CSTE), Xi'an, China, 2023. P. 330–334. DOI: 10.1109/CSTE59648.2023.00064.

29. Retrieval-Augmented Generation for Large Language Models: A Survey / Y. Gao, Y. Xion, X. Gao et al. 2023. ArXiv, abs/2312.10997.