The way people shop for fashion online has shifted quite dramatically over the last few years. Consumers today no longer simply look for clothes — they want the right clothes, at the right price, from the right platform, all without switching between multiple apps. Yet, when one looks at the landscape of existing fashion e-commerce platforms, a rather glaring problem becomes visible: personalization, price discovery, and user experience are each handled in isolation, never together. In this paper, we take up exactly this challenge and propose a unified platform architecture that brings all three dimensions under one roof. In this research, the proposed system integrates an AI-based recommendation engine built on CLIP embeddings and ResNet50-based collaborative filtering, a real-time multi-vendor price aggregation module powered by web scraping and RESTful microservices, and an adaptive UI/UX layer designed around user-centered principles. The platform targets fashion-conscious digital consumers who demand both personalization and value transparency. In this paper, we draw upon thirteen existing peer-reviewed research works to map the current state of the field, analyze their individual strengths and weaknesses, and identify the precise gap that this research occupies. The findings of this research suggest that fusing these three sub-systems within a single scalable platform represents a novel and practically significant contribution to fashion e-commerce. In this paper, no prior work was found that addresses all three dimensions simultaneously, which establishes the originality of this research.
Fashion E-Commerce, Personalized Recommendation System, Real-Time Price Aggregation, Adaptive UI/UX Design, Deep Learning, Collaborative Filtering, CLIP Model, Web Scraping, Multi-Vendor Platform, Next-Generation Shopping Platform.
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