USING BAYESIAN METHODS TO PREDICT USERS’ INFORMATION NEEDS IN A DIGITAL LIBRARY

Authors

  • Fayzi Bekkamov Library information systems department, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi (UZ)
  • Mumin Babajanov Modern Information and Communication Technologies department, International Islamic Academy of Uzbekistan (UZ)
  • Mansur Berdimurodov Modern Information and Communication Technologies department, International Islamic Academy of Uzbekistan (UZ)

DOI:

https://doi.org/10.17770/etr2025vol2.8586

Keywords:

digital library, Bayes methods, personal preferences, information requirements, predict, resource, recommender systems, rating, metadata similarity, probability

Abstract

In digital library, identifying users’ information needs and providing them with relevant resources is of critical importance. Users' characteristics, personal preferences, and demographic information play a key role in determining their information needs. Machine learning based methods are widely utilized to recommend resources that align with users' information requirements, significantly enhancing the efficiency of information retrieval systems. The Bayesian method is one of the most effective methods for predicting users' information needs. This approach is based on the principle of conditional probabilities and enables the prediction of future information needs by analyzing users' past search behaviors and interaction patterns. The Bayesian model offers several advantages, including the ability to process large datasets, high classification accuracy, and computational efficiency. Due to these benefits, it is extensively applied in digital library to optimize information retrieval and recommend relevant resources to users. This article examines the fundamental principles of the Bayesian method in the context of digital library, highlighting its advantages and limitations. Furthermore, the effectiveness of this model in identifying users' information needs and recommending appropriate resources is analyzed. The findings of this research contribute to the improvement of digital library, enhancing user search experiences and optimizing resource recommendations.

 

References

M. Condliff, D. Lewis, D. Madigan, and C. Posse, Bayesian mixed-effects models for recommender systems. ACM SIGIR Workshop on Recommender Systems: Algorithms and Evaluation, 1999

W.Jin, User interest modeling and collaborative filtering algorithms application in English personalized learning resource recommendation. Soft Comput 2023. https://doi.org/10.1007/s00500-023-08700-0 DOI: https://doi.org/10.1007/s00500-023-08700-0

M. Rakhmatullaev, S. Normatov, F. Bekkamov, Fuzzy Relations Based Intelligent Information Retrieval for Digital Library Users, Environment. Technology. Resources. 14th international scientific and practical conference. June 15-16, 2023, Rezekne Academy Of Technologies, Rezekne, Latvia DOI: https://doi.org/10.17770/etr2023vol2.7218

A.Rustamov, F.Bekkamov, Recommender systems: an overview, Scientific reports of Bukhara State University, 2021/3(85)

C.Yang, Q. Fan, T. Wang et al., RepoLike: a multi-feature-based personalized recommendation approach for open-source repositories. Front Inf Technol Electron Eng 20(02):86–101, Springer 2019 DOI: https://doi.org/10.1631/FITEE.1700196

K. Miyahara, and M. J. Pazzani, Collaborative filtering with the simple Bayesian classifier. Pacific Rim International Conference on Artificial Intelligence, 2000 DOI: https://doi.org/10.1007/3-540-44533-1_68

F. Bekkamov, Y. Sharifov, Extended User Profiling Approaches for Recommendation Systems, Environment. Technology. Resources, 15th International Scientific and Practical Conference. June 27-28, 2024, ‘’Vasil Levski’’ National Military University, Veliko Tarnovo, Bulgaria

S. Farshidi, K. Rezaee, S., Mazaheri . et al. Understanding user intent modeling for conversational recommender systems: a systematic literature review. 2024. https://doi.org/10.1007/s11257-024-09398-x DOI: https://doi.org/10.21203/rs.3.rs-3238230/v1

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, BPR: Bayesian personalized ranking from implicit feedback. Uncertainty in Artificial Intelligence (UAI), pp. 452–451, 2009.

Ф.А. Беккамов, М.А. Рахматуллаев, Система поиска научной и образовательной информации с анализом сведений о пользователях, Информатизация образования и методика электронного обучения: цифровые технологии в образовании, Материалы VIII Международной научной конференции, 24-27 сентября 2024 г. Красноярск

M. Rakhmatullaev, S, Normatov, F. Bekkamov, Tavsiya etish tizimlarining umumiy tahlili, Raqamli Transformatsiya va Sun’iy Intellekt ilmiy jurnali, Volume 1, Issue 4, December 2023

T. Ha, S. Lee, Item-network-based collaborative filtering: a personalized recommendation method based on a user’s item network. Inf Process Manage 53(5):1171–1184, Springer 2017 DOI: https://doi.org/10.1016/j.ipm.2017.05.003

Downloads

Published

08.06.2025

Issue

Vol. 2 (2025): Environment. Technology. Resources. Proceedings of the 16th International Scientific and Practical Conference. Volume 2

Section

Information Technologies

License

Copyright (c) 2025 Fayzi Bekkamov, Mumin Babajanov, Mansur Berdimurodov

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
F. Bekkamov, M. Babajanov, and M. Berdimurodov, “USING BAYESIAN METHODS TO PREDICT USERS’ INFORMATION NEEDS IN A DIGITAL LIBRARY”, ETR, vol. 2, pp. 37–41, Jun. 2025, doi: 10.17770/etr2025vol2.8586.