SAVDO PLATFORMASIDA FOYDALANUVCHI TAJRIBASINI YAXSHILASH UCHUN INNOVATSION TAVSIYA VA SHAXSIYLASHTIRISH MEXANIZMLARINI ISHLAB CHIQISH

Maqola yon paneli

PDF (inglizcha)
Nashr qilingan: 2022-10-05
DOI: https://doi.org/10.66073/researchfocus.v2i9.758
Kalit so‘zlar:
Tavsiya mexanizmi, shaxsiylashtirish, foydalanuvchi tajribasi, savdo platformasi

Maqolaning asosiy mazmuni

Rajabov Narzullo Agzamovich
Fizika-matematika fanlari nomzodi, "Perspective team" MChJ ilmiy bo'limi bosh ilmiy xodimi
Azamov Temur Narzullayevich
Toshkent axborot texnologiyalari universiteti mustaqil tadqiqotchisi

Abstrak

Ushbu maqola savdo platformasida foydalanuvchi tajribasini yaxshilash uchun innovatsion tavsiyalar va shaxsiylashtirish mexanizmlarini ishlab chiqishga bag'ishlangan. Maqsad foydalanuvchilarga moslashtirilgan va tegishli mahsulot tavsiyalarini taqdim etish, shu orqali foydalanuvchilarning jalb etilishi va mamnunligini oshirishdan iborat. Foydalanuvchilarning afzal ko'rishlari, xaridlar bo'yicha tarixiy ma'lumotlar va kontekstual axborotni tahlil qilish uchun ilg'or ma'lumotlar tahlili usullari hamda mashinali o'qitish algoritmlari qo'llaniladi. Ushbu axborotdan foydalangan holda tavsiya mexanizmi har bir foydalanuvchining qiziqishlari va afzal ko'rishlariga mos keladigan shaxsiylashtirilgan tavsiyalarni shakllantiradi. Bundan tashqari, maqolada uzluksiz va intuitiv xarid tajribasini yaratish uchun moslashuvchan foydalanuvchi interfeyslari va real vaqtdagi yangilanishlar kabi dinamik shaxsiylashtirish mexanizmlarini joriy etish o'rganiladi. Natijalar ushbu innovatsion yondashuvlarning savdo platformalarida foydalanuvchilar jalb etilishini sezilarli oshirish, konversiya ko'rsatkichlarini ko'paytirish va mijozlarning uzoq muddatli sodiqligini mustahkamlash borasidagi salohiyatini ko'rsatadi.

Maqola tafsilotlari

Nashr

Jild 2 Nomeri 9 (2023): Research Focus International Scientific Journal. 2-tom, 9-son (2023)

Bo'lim

Maqolalar
Creative Commons litsenziyasi

Ushbu asar Creative Commons Attribution 4.0 International litsenziyasi asosida litsenziyalangan.

Qanday iqtibos keltirish kerak

SAVDO PLATFORMASIDA FOYDALANUVCHI TAJRIBASINI YAXSHILASH UCHUN INNOVATSION TAVSIYA VA SHAXSIYLASHTIRISH MEXANIZMLARINI ISHLAB CHIQISH. (2022). Research Focus International Scientific Journal, 2(9), 24-31. https://doi.org/10.66073/researchfocus.v2i9.758

Adabiyotlar

Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749.

Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370.

Cremonesi, P., Koren, Y., & Turrin, R. (2010). Performance of recommender algorithms on top-n recommendation tasks. Proceedings of the fourth ACM conference on Recommender systems, 39-46.

Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5-53.

Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: An introduction. Cambridge University Press.

Konstan, J. A., Riedl, J., & Herlocker, J. (2012). Recommender systems: From algorithms to user experience. User Modeling and User-Adapted Interaction, 22(1-2), 101-123.

Ricci, F., Rokach, L., & Shapira, B. (2015). Introduction to recommender systems handbook. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), Recommender Systems Handbook (2nd ed., pp. 1-35). Springer.

Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1-2), 115-153.

Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, Article ID 421425.

Zhang, Y., & Hurley, N. (2016). Deep learning for recommender systems: A rigorous introduction. arXiv preprint arXiv:1701.00160.