AI-Powered Visual E-Commerce

Abstract

The explosion of social media and artificial intelligence (AI) has turned online shopping more interactive and engaging. This research introduces AI-based Visual E-Commerce, a novel platform that combines short-form video content (reels) with AI-based personalized product recommendations. Drawing inspiration from Instagram and Flipkart, this system facilitates users to browse clothing items through dynamic reels, using computer vision and machine-learning algorithms to interpret user preferences and make suitable apparel recommendations. The design increases user engagement by offering an immersive, scroll-driven shopping experience where customers can effortlessly switch from watching reels to buying products. The recommendation engine employs deep learning concepts to forecast user interests by analyzing watching patterns, interaction behavior, and purchasing history. Besides, the platform integrates natural language processing (NLP) and sentiment analysis to enhance product recommendations and enhance customer satisfaction. The outcome of prototype deployment reveals higher user interaction and conversion rates than standard image-based e-commerce sites. This research presents the capabilities of AI-based visual commerce to revolutionize digital retail with an intelligent and engaging shopping experience.

Introduction

???? Project Overview

The project introduces a visual e-commerce platform that combines the product browsing features of traditional shopping sites (like Flipkart) with the interactive, short-video style of social media platforms (like Instagram), targeting the fashion/clothing retail sector.

• Products are displayed through reels (short videos), offering an engaging, realistic view of apparel (material, fit, style).

• Built using the MERN stack (MongoDB, Express.js, React.js, Node.js) with cloud integration for media storage, scalability, and fast reel delivery.

???? Literature Review Highlights

• AI Personalization (Widayanti, Hosanagar): Enhances user experience by tailoring product suggestions.

• Visual Content (Fischer): Reduces return rates by helping users make informed choices.

• Hyper-personalization (Zhang): Incorporates user behavior to dynamically personalize content.

• E-Commerce Trends (Ahmad): Emphasize the shift toward immersive and interactive interfaces.

???? Methodology

1. Requirement Analysis

• Found user demand for visual, engaging interfaces over static product listings.

2. System Design & Tech Stack

• MongoDB: Stores products, users, filters.

• Express.js/Node.js: Backend logic and API handling.

• React: Front-end UI with smooth reel playback.

3. Reel-Based Interface

• Scrollable video feed of products with category/size filters.

• Mimics familiar social media design for high user engagement.

4. AI Personalization

• Uses content-based and collaborative filtering.

• Learns from user interactions (e.g., viewed reels, likes) to suggest relevant products.

5. Cloud Services

• AWS/Firebase for storage.

• Cloud deployment for scalable, low-latency performance.

• AI models hosted on cloud for fast personalization.

6. Testing & Optimization

• Involved unit, integration, and user testing.

• Feedback refined UI/UX, reel performance, and recommendation accuracy.

???? Results & Features

• Reel-Based Shopping: Increased engagement by mimicking social media browsing.

• Seamless Checkout: Purchase directly within reels.

• Dynamic Admin Panel: Real-time control of text, categories, and offers.

• User Verification: Ensures platform security with manual verification.

• Flexible Inventory Management: Admin can enable/disable categories as needed.

???? User Feedback

• 78% preferred reels over traditional listings.

• 90% found filters easy to use.

• 85% relevance in personalized recommendations.

• Fast loading, even under high traffic, due to optimized cloud deployment.

Conclusion

The AI-powered visual e-commerce platform seamlessly closes the gap between traditional online buying and modern media-intensive social networks. With the implementation of the MERN stack, cloud platforms, and AI-based personalization techniques, the system provides a new way of shopping in which customers engage with products through dynamic reels tailored to their preferences and size. The project did not only grow user engagement, but it also applied a cutting-edge way for discovering and deciding to buy within web-based fashion purchasing. Using visual content merged with a core shop interface along with having clever filtering and recommendation capabilities is a decent step in the direction of e-commerce tomorrow as well as an improved individualized immersive shopping experience. Future development can include the inclusion of augmented reality (AR) try-on functionality, voice search, and more advanced behavior prediction algorithms to further enhance user satisfaction and retention.

References

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Copyright

Copyright © 2025 Dr. Pradeep Fale, Aditya Zanzad, Aachal Bansod, Sanket Shendre, Snehal Waghmare, Sanket Choudhary. 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.