Project Overview
Developed for StyleCart Retail, this machine learning system analyzes user behavior, purchase history, and product attributes to deliver highly personalized product recommendations in real-time.
Challenge
StyleCart was struggling with low conversion rates and cart abandonment. Their existing recommendation system provided generic suggestions that didn't resonate with individual customers.
Solution
We implemented a hybrid recommendation system combining collaborative filtering, content-based filtering, and deep learning models to understand user preferences and product relationships.
Technology Stack
- Python with Scikit-learn and TensorFlow
- Apache Spark for data processing
- Redis for real-time caching
- React.js for frontend integration
- AWS SageMaker for model deployment
Results & Impact
The system processes over 2 million user interactions daily and has become a critical component of StyleCart's revenue growth strategy.