MASHINALI O‘QITISHDA TAVSIYALAR TIZIMLARINING AMALIY QOʻLLANILISHI VA KELAJAK TENDENSIYALARI
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Kalit soʻzlar: Tavsiyalar tizimlari, elektron tijorat, taʼlimda personalizatsiya, personalizatsiya, cheklovlar, kelajak tendensiyalari, texnologik asoslar, graph-based modeling, explainable AI.Abstrak
Annotatsiya
Mazkur maqola mashinali o‘qitish tizimlari tavsiyalarni amaliy qoʻllanilishi va
kelajakdagi rivojlanish yoʻnalishlarini tahlil qiladi. Ushbu tizimlarning elektron tijorat,
platformalar va taʼlim sohasida qoʻllanilishi yoritiladi. Kelajakda sunʼiy intellekt va
mashinani oʻqitish orqali tavsiyalar tizimlari yanada yuqori darajada
personalizatsiyaga erishishi kutilmoqda.
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Foydalanilgan adabiyotlar:
1. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for
recommender systems. Computer, 42(8), 30-37.
2. Tang, J., & Liu, H. (2017). Graph-based learning in recommendation systems:
A survey. ACM Transactions on Knowledge Discovery from Data, 11(4), 48.
3. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017). Neural
collaborative filtering. In Proceedings of the 26th International Conference on
World Wide Web (pp. 173-182).
4. Wang, J., & Benitez, J. (2020). The recommendation system based on deep
learning techniques: A comprehensive review. Artificial Intelligence Review,
53(3), 2019-2054.
5. Gedikli, F., Ge, M., & Jannach, D. (2014). How should I explain? A comparison
of different explanation types for recommender systems. International Journal
of Human-Computer Studies, 72(4), 367-382.
6. Bellogin, A., & Parapar, J. (2019). Cross-domain collaborative filtering in
recommender systems. Information Processing & Management, 55(1), 18-26.
7. Feder, A., & Greene, D. (2019). Explainable artificial intelligence in
recommender systems. IEEE Access, 7, 130655-130666.
8. B.Umarov, M.Hakimov., “International journal of scientific researchers”,
“Su’niy intelekt tizimlarida qayta tiklashga asoslangan o‘qitish” 2024y.
