THE ROLE OF ARTIFICIAL INTELLIGENCE IN CREATING A PERSONALIZED EXPERIENCE IN A CHILDREN'S GOODS E-COMMERCE PLATFORM

A. Brilliantova, L. Mandrikova, A. Zelenkov

Abstract


 

The application of artificial intelligence in e-commerce offers new avenues for enhancing personalization, resulting in more relevant and user-centered shopping experiences. This study introduces an AI-driven recommendation system within a children’s goods e-commerce platform, developed to analyze both individual user preferences and collective consumer behavior patterns. By leveraging this data, the platform provides personalized product suggestions and assists users in selecting appropriate clothing sizes, optimal delivery options, and real-time stock availability. These features simplify the decision-making process, enhance convenience, and increase user engagement. The growing demand for adaptive e-commerce platforms highlights the relevance of this research, as personalized recommendations foster customer satisfaction and encourage repeat usage. Moreover, this platform's AI integration provides a solution tailored to the children's product market, where reliability, safety, and precision are crucial. This study contributes significant insights to the field by demonstrating the role of AI in improving the quality of digital consumer interactions, particularly within specialized retail environments. This project exemplifies how AI can dynamically respond to both individual and broader consumer data, presenting a valuable framework for future developments in adaptive e-commerce solutions and marking a step forward in the intelligent, responsive design of personalized shopping experiences.


Keywords


Artificial Intelligence; E-commerce; Platform User-Centered Shopping Experience; Consumer Behavior Analysis; Intelligent Personalized Recommendations

Full Text:

PDF

References


Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022). Artificial intelligence in e-commerce: A bibliometric study and literature review. Electronic Markets, 32(1), 297–338. https://doi.org/10.1007/s12525-022-00537-z.

De, U. C., Banerjee, S., Rath, M. K., Swain, T., & Samant, T. (2022). Content based apparel recommendation for e-commerce stores. In 2022 3rd International Conference for Emerging Technology (INCET (pp. 1–6). https://doi.org/10.1109/INCET54531.2022.9824870

Karn, A. L., Karna, R. K., Kondamudi, B. R., Bagale, G., Pustokhin, D. A., Pustokhina, I. V., et al. (2023). Customer centric hybrid recommendation system for E Commerce applications by integrating hybrid sentiment analysis. Electronic Commerce Research, 23(1), 279–314. https://doi.org/10.1007/s10660-022-09630-z.

Khan, Z., Hussain, M. I., Iltaf, N., Kim, J., & Jeon, M. (2021). Contextual recommender system for E-commerce applications. Applied Soft Computing, 109, Article 107552. https://doi.org/10.1016/j.asoc.2021.107552.

Kottage, G. N., Jayathilake, D. K., Chankuma, K. C., Ganegoda, G. U., & Sandanayake, T. (2018). Preference based recommendation system for apparel e commerce sites. In 2018 IEEE/ACIS 17th international conference on computer and information science (ICIS (pp. 122–127). https://doi.org/10.1109/ICIS.2018.8466382.

Loukili, M., Messaoudi, F., & El Ghazi, M. (2023). Machine learning based recommender system for e-commerce. IAES International Journal of Artificial Intelligence, 12(4), 1803–1811. https://doi.org/10.11591/ijai.v12.i4.pp1803-1811.

Necula, S. C., & Pav˘ ˘ aloaia, V. D. (2023). AI-Driven Recommendations: A Systematic review of the state of the art in E-Commerce. Applied Sciences, 13(9), 5531. https://doi.org/10.1016/j.jretconser.2022.103003, 10.3390/app13095531.

Tahir, M., Enam, R. N., & Mustafa, S. M. N. (2021). E-commerce platform based on machine learning recommendation system. In 2021 6th International Multi-Topic ICT Conference (IMTIC (pp. 1–4). https://doi.org/10.1109/

IMTIC53841.2021.9719822..

Vanesa Aciar, S., Serarols-Tarres, C., Royo-Vela, M., & Esteva, J. L. (2007). Increasing effectiveness in e-commerce: Recommendations applying intelligent agents. International Journal of Business and Systems Research, 1(1), 81–97. https://doi.org/ 10.1504/IJBSR.2007.014774.

Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in recommender systems. Complex & Intelligent Systems, 7(1), 439–457. https://doi.org/10.1007/s40747-020- 00212-w.

Zhao, Q., Zhang, Y., Friedman, D., & Tan, F. (2015). E-commerce recommendation with personalized promotion. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 219–226). https://doi.org/

1145/2792838.2800178.




DOI: https://doi.org/10.32620/oikit.2025.103.09

Refbacks

  • There are currently no refbacks.