Smart Clothing Fit Recommendation Using E-Commerce Big Data

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 1, pp. 298-305, January 2026.

https://doi.org/10.58482/ijersem.v2i1.40

This work is licensed under a Creative Commons Attribution 4.0 International License.

Smart Clothing Fit Recommendation Using E-Commerce Big Data

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S Shilpa

V. D. Rekha

K. Reddemma

M. Ramya Sree

T. Manohar

T. Vamsi Krishna

Department of CSE, Siddharth Institute of Engineering and Technology, Puttur, India.

Abstract: Smart Clothing Fit Recommendation Using E-Commerce Big Data addresses the critical retail challenge of inconsistent sizing across brands, which drives high return rates and customer dissatisfaction. This paper develops an intelligent framework that leverages Big Data analytics and Machine Learning to provide personalized size predictions. By synthesizing historical purchase patterns, granular body measurements, customer feedback, and technical product specifications, the system identifies the optimal fit for individual users. The solution utilizes predictive modeling to bridge the gap between static size charts and diverse human proportions. Integrating this AI-driven engine into e-commerce platforms minimizes “reproducibility debt” in sizing choices, significantly reducing logistical costs associated with returns. Ultimately, the framework facilitates a Smart Retail transformation, enhancing user trust and operational sustainability by delivering a precise, data-backed online shopping experience.

Keywords: Clothing Fit Recommendation, E-Commerce Big Data, Machine Learning, Personalized Size Prediction, Smart Retail Systems.

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This article is part of Volume 2, Issue 1 (January 2026)