LARAVEL PLATFORMASIDA NEYROTARMOQLARNI QO‘LLASH ORQALI FOYDALANUVCHI XATTIHARAKATLARINI BASHORAT QILISH VA TAVSIYA TIZIMLARINI YARATISH
DOI:
https://doi.org/10.5281/zenodo.21007186Keywords:
Laravel, neyrotarmoqlar, tavsiya tizimlari, foydalanuvchi xatti-harakatlari, kollaborativ filtrlash, bashoratlash, NCF, veb-arxitektura, shaxsiylashtirilgan tajriba, mashinali o‘qitishAbstract
Ushbu tadqiqotda Laravel (PHP) veb-freymvorki asosida qurilgan raqamli platformalarda foydalanuvchi
xatti-harakatlarini neyrotarmoq algoritmlari yordamida tahlil qilish va shaxsiylashtirilgan tavsiyalarni (recommendations)
generatsiya qilish arxitekturasi taklif etilgan. Tadqiqot doirasida foydalanuvchi sessiyalari ma’lumotlari, kliklar tarixi hamda
vaqt qatorlari asosida xatti-harakatlarni bashorat qiluvchi Neural Collaborative Filtering (NCF) modeli ishlab chiqilgan va
uni Laravel ekotizimiga integratsiya qilish metodologiyasi bayon etilgan. Amaliy sinov natijalari shuni ko‘rsatdiki, taklif etilgan
gibrid arxitektura an’anaviy qoida asosidagi (rule-based) tavsiya tizimlariga nisbatan tavsiya aniqligi (Precision@10)
bo‘yicha 34 % yuqori natija bergan. Tizim real vaqt rejimida ishlash imkoniyatiga ega bo‘lib, kichik va o‘rta biznes subyektlari
uchun ochiq manbali yechim sifatida taqdim etiladi
References
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017). Neural Collaborative Filtering. Proceedings of the 26th
International Conference on World Wide Web (WWW), 173–182. https://doi.org/10.1145/3038912.3052569
Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. IEEE Computer,
(8), 30–37. https://doi.org/10.1109/MC.2009.263
Burke, R. (2007). Hybrid Web Recommender Systems. The Adaptive Web, LNCS 4321, 377–408. https://doi.
org/10.1007/978-3-540-72079-9_12
Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-Based Recommender Systems: State of the Art and Trends.
Recommender Systems Handbook, 73–105. https://doi.org/10.1007/978-0-387-85820-3_3
Raschka, S., & Mirjalili, V. (2019). Python Machine Learning (3rd ed.). Packt Publishing.
Newman, S. (2021). Building Microservices: Designing Fine-Grained Systems (2nd ed.). O’Reilly Media.
Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender Systems Handbook (2nd ed.). Springer. https://doi.
org/10.1007/978-1-4899-7637-6
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Harper, F. M., & Konstan, J. A. (2015). The MovieLens Datasets: History and Context. ACM Transactions on Interactive
Intelligent Systems, 5(4), 1–19. https://doi.org/10.1145/2827872
Covington, P., Adams, J., & Sargin, E. (2016). Deep Neural Networks for YouTube Recommendations. Proceedings of
the 10th ACM Conference on Recommender Systems (RecSys), 191–198. https://doi.org/10.1145/2959100.2959190
Otwell, T. (2023). Laravel Documentation: Events, Queues and Broadcasting. Laravel LLC. https://laravel.com/docs
Paszke, A., Gross, S., Massa, F., et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library.
Advances in Neural Information Processing Systems (NeurIPS), 32. https://doi.org/10.48550/arXiv.1912.01703
Al-Safi, Y., & Al-Qurabat, A. K. (2020). Performance Evaluation of Asynchronous Task Queues in Web Applications.
International Journal of Computer Applications, 176(31), 12–18. https://doi.org/10.5120/ijca2020920198
Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2009). BPR: Bayesian Personalized Ranking from
Implicit Feedback. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI), 452–461. https://
doi.org/10.48550/arXiv.1205.2618
Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep Learning Based Recommender System: A Survey and New
Perspectives. ACM Computing Surveys, 52(1), 1–38. https://doi.org/10.1145/3285029
Yusupov, R. A., & Toshmatov, N. B. (2022). Veb-tizimlarda sun’iy intellekt algoritmlarini integratsiya qilish muammolari
va yechimlari. Axborot Texnologiyalari va Telekommunikatsiyalar, 3(2), 45–52.
Karimov, A. J., & Nazarov, B. T. (2023). Mashinali o‘rganish algoritmlarini veb-platformalarda qo‘llash: imkoniyatlar va
cheklovlar. O‘zbekiston Milliy Universiteti Xabarlari, 2(1), 88–97.
Sidorov, V. A., & Petrov, I. M. (2021). Нейросетевые модели рекомендательных систем в веб-приложениях.
Programmnaya Inzheneriya, 12(5), 198–207. https://doi.org/10.17587/prin.12.198-207
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-Based Collaborative Filtering Recommendation
Algorithms. Proceedings of the 10th International Conference on World Wide Web (WWW), 285–295. https://doi.
org/10.1145/371920.372071
Wang, X., He, X., Wang, M., Feng, F., & Chua, T.-S. (2019). Neural Graph Collaborative Filtering. Proceedings of the
nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 165–174. https://
doi.org/10.1145/3331184.3331267
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