Online Clothing Recommendations and Virtual Trials using AI

Abstract

This project uses computer vision and machine learning to create a virtual trial room and a recommendation system that will improve the e-commerce fashion experience. CNNs are utilized to forecast body form for more precise recommendations, and the recommendation engine uses collaborative and content-based filtering to offer fashion items based on user preferences, past purchases, and style. To provide clothing recommendations based on each user\'s tastes, style, and body type, the recommendation engine will make use of collaborative filtering and content-based filtering algorithms. At the same time, a computer vision-powered virtual trial room lets customers see how clothes fit by superimposing ensembles on user-provided images or avatars and modifying dimensions according to body measurements to create a realistic fit simulation. By analyzing user- provided pictures, we can enhance body form detection, improving fit accuracy and recommendation precision. Users will be able to upload photos, see suggestions, and virtually try on clothing in real time thanks to aresponsive web interface. Data processing will be handled by the Flask or Django-built backend, which will also effortlessly interact with a PostgreSQL or MySQL database to store user and suggestion data. The system is built for high performance and scalability and is hosted on cloud infrastructure. By offering personalized recommendations and lowering returns with precise fit visualization, this integrated system seeks to increase user happiness. This website allows users to digitally try on clothing and make purchases, which eventually improves consumer happiness and lowers return rates.

Introduction

This project presents an AI-driven Virtual Trial Room and Fashion Recommendation System designed to improve the online fashion shopping experience. It addresses challenges such as lack of physical interaction, fit uncertainty, high return rates, and poor personalization.

Key Features:

• Fashion Recommendation System: Uses TensorFlow, ResNet-50, and Scikit-learn's Nearest Neighbor algorithm to extract visual features (color, texture, style) from a dataset of 45,000 fashion images and deliver personalized clothing suggestions.

• Virtual Trial Room: Employs computer vision, pose recognition, and JavaScript overlays to let users visualize how garments look on digital mannequins, enhancing realism and confidence in purchasing decisions.

• Technical Stack: Built using HTML, CSS, Bootstrap (frontend), Flask (backend), and PostgreSQL (database), ensuring scalability, performance, and secure user data management.

• Performance: Achieves 93% accuracy in recommendations and 95% alignment precision in the virtual try-on system. Handles 500+ simultaneous queries with minimal latency.

Benefits:

• Reduces return rates by improving visualization and fit accuracy.

• Enhances user engagement through interactive and inclusive design (separate mannequins for men and women).

• Assists businesses in optimizing inventory, marketing, and customer satisfaction.

• Encourages sustainable fashion e-commerce by lowering packaging waste and environmental impact.

Future Scope:

Plans include augmented reality (AR) integration, real-time try-on feedback, user-uploaded image support, and mobile app development to expand accessibility and personalization.

Conclusion

By offering precise suggestions and interactive visualization, the Fashion Recommendation System and Virtual Trial Room effectively improved online buying.Using a dataset of 45,000 photos, the recommendation engine identified visually related goods with 93% accuracy by utilizing ResNet50 and the Nearest Neighbour algorithm.By enabling real- time apparel visualization on mannequins with accurate overlay and dynamic scaling, the Virtual Trial Room helped to close the gap between online and in-store buying. Smooth operation, effective login authentication, and suggestion retrieval were guaranteed by the smooth integration of Flask and PostgreSQL for the backend with an easy-to-use interface.While usability testing revealed excellent user satisfaction with the UI, fast reaction times, and pertinent suggestions, scalability testing validated the system\'s resilience.All things considered, the project produced a creative, scalable, and user-centric solution that enhances the online fashion buying experience.

References

[1] Wangetal.(2022):\"Pose-AwareVirtualTry-OnforE-Commerce\". [2] Singh and Sharma (2022): \"Inclusivity in Virtual Shopping Platforms\". [3] Ramesh and Tan (2021): \"Scalable Fashion Recommendation Systems\". [4] Das et al. (2021): \"Deep Learning Architectures for Visual Similarity\". [5] Nguyen et al. (2021): \"Integration of AI and E-commerce for Enhanced User Experience\" [6] Luo and Feng (2020): \"Overcoming Challenges in Virtual Fashion Trials\". [7] Johnson and Lee (2020): \"Improving Customer Retention with Personalized Recommendations\". [8] Zhang et al. (2020): \"Virtual Try-On Systems Using Computer Vision\". [9] Lee and Park (2019): \"AI-Driven Personalization in E-commerce\". [10] Ahmed et al. (2019):\"E-commerceandConsumerTrust\".

Copyright

Copyright © 2025 Mrs. Sindhu Jain A M, Anusha K, Tanushree N, Thushara K, Varalakshmi D R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.