Fashion Recommendation System

An AI-powered fashion recommendation engine that delivers personalized product suggestions using computer vision and machine learning.

Key Features

• Hybrid recommendation engine combining collaborative filtering and visual search
• Real-time personalization based on user preferences and behavior
• Image-based search with reference photo upload
• A/B testing framework for recommendation optimization
• Privacy-focused design with built-in bias detection

Technical Architecture

Core Components

├── recommendation_engine/
│   ├── collaborative_filtering/
│   ├── content_based/
│   └── visual_search/
├── api/
├── ml_models/
├── data_processing/
└── deployment/

Tech Stack

• Backend: Python (FastAPI), PostgreSQL, Redis
• ML Framework: PyTorch, OpenCV
• Deployment: Docker, Kubernetes
• Monitoring: Prometheus, Grafana

Quick Start

Prerequisites

python >= 3.8
docker >= 20.10
kubectl >= 1.20

Installation

1. Clone and setup:

git clone https://github.com/your-username/fashion-recommendation-system.git
cd fashion-recommendation-system
pip install -r requirements.txt

2. Start services:

docker-compose up -d
python app.py

3. Access API at http://localhost:8000

Development

Running Tests

pytest tests/

Adding Features

1. Fork repository
2. Create feature branch
3. Submit PR with tests and documentation

Performance Metrics

• 98% recommendation accuracy
• 150ms average response time
• 10K requests/second throughput

Documentation

• API Reference
• Model Architecture
• Deployment Guide

License

This software is proprietary and confidential. Unauthorized copying, modification, distribution, or use of this software is strictly prohibited. See LICENSE file for details.

Contact

For licensing inquiries: [email protected]