AI-Powered Personal Stylist: An Integrated Multimodal Architecture for Context-Aware Fashion Recommendation, Computer Vision Style Analysis, and Catalogue-Driven Outfit Composition
Keywords:
Catalogue-based constraint satisfaction, Computer vision in e-commerce, Context-aware personalization, Decision support systems, Fashion recommendation systems, Multimodal recommender, Outfit compatibility modelingAbstract
Online fashion retail offers consumers extensive product choices, yet assembling coordinated outfits that align with individual body characteristics, stylistic preferences, budget limits, and contextual occasions remains challenging. Unsuitable apparel selections contribute substantially to e-commerce return rates, which reached approximately 17.3% in retail reporting. This study presents the AI-Powered Personal Stylist, a modular web-based decision-support system designed to assist users in discovering cohesive fashion ensembles. The architecture couples a FastAPI backend and interactive frontend with a structured recommendation pipeline and a computer-vision-based image analysis module. Users provide explicit profile attributes, including sizing, preferred palettes, body silhouette, and financial boundaries, which are supplemented by an automated photo-analysis endpoint that extracts dominant clothing colors, garment patterns, lighting conditions, and aesthetic cues. To maintain pricing integrity and inventory feasibility, candidate garments are retrieved directly from a structured catalogue rather than generated through synthetic virtual try-on models. Furthermore, contextual parameters such as real-time weather and occasion-specific constraints are incorporated to refine outfit compositions. Uncertain visual estimates remain fully editable by the user, ensuring algorithmic transparency and personal agency. Functional testing demonstrates that integrating multi-attribute user profiles with catalogue-grounded filtering reliably produces balanced, four-piece coordinated looks with transparent styling rationale. The system provides an extensible foundation for transparent, context-aware digital fashion assistance while mitigating the operational and computational challenges associated with unconstrained generative styling.
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