Generative AI Meets Cross-Brand Fit Intelligence: A User-Centric Framework for Outfit Recommendation with Occasion and Weather Awareness

Online fashion retail suffers from inconsistent sizing across brands and difficulty in composing context-appropriate outfits. Existing recommender systems address either fit or style, but not both in an integrated manner. We present FitGen, a generative AI framework that combines cross-brand size intelli-gence with occasion- and weather-aware outfit recommendation. FitGen collects user body measurements and style preferences, maps them to brand-specific sizes using a weighted Euclidean distance heuristic, and generates personalized outfit descriptions via GPT-4o-mini and corresponding visualizations via DALL-E-3. All interactions occur within a privacy-first Streamlit dashboard. A controlled user study (N=100) demonstrates that FitGen achieves 78.3% fit accuracy across five brands, with precision and recall values of 0.79 and 0.78 respectively, a 4.6/5 user satisfaction score, and a 92% reduction in self-reported size anxiety. The end-to-end latency is 3.2 seconds. We compare our results with existing commercial solutions and academic models, highlighting the trade-offs between transparency and accuracy. While limitations exist, including synthetic measurement distributions, a heuristic size mapper, and the absence of live e-commerce APIs, the results indicate that combining generative AI with explicit fit modeling significantly enhances the online fashion shopping experience. The prototype, source code, and a demo video are made publicly available to facilitate further research.