Case Study
Vision-Based Outfit Recommendation & Styling Engine
Built a computer-vision and recommendation platform that combines clothing embeddings, attribute tagging, and hybrid ranking logic for personalized outfit generation.
Challenge
AI Wardrobe needed a recommendation system that could reason across visual similarity, style constraints, and user-specific preferences. Rule-only systems lacked personalization depth, while pure embedding similarity produced stylistically inconsistent combinations for real-world occasions.
Solution
Helios developed a hybrid recommendation architecture combining vision embeddings, attribute extraction, FAISS-based nearest-neighbor retrieval, and rule-aware outfit composition. The system tagged catalog items across category, color, material, and occasion dimensions, then merged embedding relevance with business and style rules. A feedback loop captured user interactions to incrementally refine recommendation ranking and personalization quality over time.
Technical Responsibilities
- Built clothing embedding and feature extraction pipelines for visual understanding
- Implemented attribute tagging across category, color, material, and occasion
- Integrated FAISS-based retrieval for high-speed similarity search
- Designed hybrid ranking combining rule-based logic with embedding relevance
- Developed personalization feedback loop for ranking refinement
- Implemented scalable serving APIs for recommendation workflows
Outcomes
- Consistent and scalable outfit recommendation generation
- Improved recommendation relevance through hybrid ranking logic
- Better personalization via iterative user-feedback incorporation
- Operationally efficient retrieval and serving pipeline for large catalogs