AXBOROT-KUTUBXONA TIZIMLARIDA MATRITSANI FAKTORIZATSIYA QILISH ALGORITMI YORDAMIDA FOYDALANUVCHILARGA RESURSLARNI TAVSIYA ETISH

Authors

  • Normatov Sherbek Baxtiyorovich Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti
  • Sharifov Yashin Xusnitdin o‘g‘li Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti
  • Bekkamov Fayzi Absoatovich Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti

Keywords:

Axborot-kutubxona tizimi, foydalanuvchi, resurs, axborot, tavsiya etish tizimi, hamkorlik asosida filterlash, matritsani faktorizatsiya qilish algoritm

Abstract

Hozirda axborot-kutubxona tizimlarida foydalanuvchilar uchun kerakli axborotlarni hamda resurslarni taqdim etish dolzarb hisoblanadi. Chunki ko‘plab axborot-kutubxona tizimlari foydalanuvchilarning demografik kelib chiqishi, qiziqishlari, afzalliklari, bilim darajasi kabi ma’lumotlarni hisobga olmaydi. Shu sababli foydalanuvchilarga kerakli resurslarni taqdim etishda bir qancha noqulayliklarni keltirib chiqaradi. Ushbu jarayon foydalanuvchilarning kitobxonlik xususiyatiga hamda bilim olishiga salbiy ta’sir ko‘rsatishi mumkin. Ushbu muammoni sun’iy intellekt asosida tavsiya etish tizimlari yordamida bartaraf etish mumkin. Tavsiya etish tizimining hamkorlik asosida filterlash usuli yordamida foydalanuvchilarga kerakli resurslarni hamda axborotlarni taqdim etish muammosini bartaraf etish mumkin. Ushbu maqolada axborot-kutubxona tizimlarida foydalanuvchilarga axborot ehtiyojlariga mos resurslarni taqdim etish, hamkorlik usuli asosida filterlash va matritsani faktorizatsiya qilish algoritmi yordamida amalga oshiriladi.

References

Shardanand U, Maes P (1995) Social information filtering: algorithms for automating ‘word of mouth’. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1995, pp 210–217

Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1):1–47

Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66– 72

Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:1–19

Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-adapt Interact 12(4):331–370

Felfernig A, Burke R (2008) Constraint- based recommender systems: technologies and research issues. In: Proceedings of the 10th International Conference on Electronic Commerce, 2008, p 3

R. A. Djamal, W. Maharani, and P. Kurniati, “Analisis Dan Implementasi metode Item-Based Clustering Hybrid Pada Recommender System,” Konf. Nas. Sist. dan Inform., no. November, pp. 216- 222, 2010.

ahmet uyar, f. m. a. Evaluating search features of google knowledge graph and bing satori. Online Information Review (2015).

Tiddi, I., d'Aquin, M., and Motta, E. Using linked data traversal to label academic communities. In Proceedings of the 24th International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee (2015), 1029--1034.

F. Leal et al. Scalable modelling and recommendation using wiki-based crowdsourced repositories Electron. Commer. Res. Appl.(2019)

H. Liu et al.Collaborative filtering with a deep adversarial and attention network for cross-domain recommendation Information Sciences (2021)

P. Bai et al. Joint interaction with context operation for collaborative filteringPattern Recogn.(2019)

A. I.Kapterev, Cognitive management and artificial intellect in libraries:Possibilities and highlights // Scientific and technical libraries. 2023. No. 6. P. 113–137. https://doi.org/10.33186/1027-3689-2023-6-113-137.

M. Rakhmatullaev, Sh. Normatov, and F. Bekkamov, “FUZZY RELATIONS BASED INTELLIGENT INFORMATION RETRIEVAL FOR DIGITAL LIBRARY USERS”, ETR, vol. 2, pp. 80–83, Jun. 2023, doi: 10.17770/etr2023vol2.7218.

M. Musaev, S. Mussakhojayeva, I. Khujayorov, Y. Khassanov, M.Ochilov, H.A. Varol, USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments. Speech and Computer 23rd International Conference, SPECOM 2021. St. Petersburg, Russia, September 27–30, 2021, Proceedings. – рр. 437-447.

A.V. Sozykin, An Overview of Methods for Deep Learning in Neural Networks.. Bulletin of the South Ural State University. Series: Computational Mathematics and Software Engineering. 2017. vol. 6, no. 3. pp. 28–59. (in Russian) DOI: 10.14529/cmse170303.

Elahi, Mehdi; Ricci, Francesco; Rubens, Neil. A survey of active learning in collaborative filtering recommender systems. Computer Science Review – via Elsevier 2016

Madadipouya K., Chelliah S., A Literature Review on Recommender Systems Algorithms, Techniques and Evaluations. BRAIN: Broad Research in Artificial Intelligence and Neuroscience, ročník 8, č. 2, July 2017

Абрамян М. Э., Козак М. В. Электронный задачник Programming Taskbook: реализация 64-разрядной версии и адаптация к новым средам программирования / Современные информационные технологии: тенденции и перспективы развития. Материалы XXVIII научной конференции. Ростов н/Д, Таганрог: Изд-во ЮФУ, 2021. С. 23–25.

Bokde, Dheeraj kumar. Girase, Sheetal. Mukhopadhyay, Debajyoti. 2015. “An Item-Based Collaborative Filtering using Dimensionality Reduction Techniques on Mahout Framework”. Published in WWW10.

Gabrani, Goldie, Sangeeta Sabharwal, and Viomesh Kumar Singh. "Artificial intelligence based recommender systems: A survey." International Conference on Advances in Computing and Data Sciences. Springer, Singapore, 2016.

Y.X.Sharifov. (2022). SUN’IY INTELLEKTGA ASOSLANGAN TAVSIYA ETISH TIZIMLARI. Journal of Integrated Education and Research, 1(6), 225–228. Retrieved from https://ojs.rmasav.com/index.php/ojs/article/view/602

Published

2025-02-28

How to Cite

Normatov , S., Sharifov , Y., & Bekkamov, F. (2025). AXBOROT-KUTUBXONA TIZIMLARIDA MATRITSANI FAKTORIZATSIYA QILISH ALGORITMI YORDAMIDA FOYDALANUVCHILARGA RESURSLARNI TAVSIYA ETISH. DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 3(1), 181–190. Retrieved from https://dtai.tsue.uz/index.php/dtai/article/view/v3i128