Portfolio Project
AI-Powered Content Engine
Machine learning-powered recommendation engine increasing user engagement by 45%
Project Gallery
About This Project
Developed a sophisticated content recommendation engine using machine learning algorithms to personalize user experiences. The system analyzes user behavior, preferences, and engagement patterns to deliver highly relevant content recommendations.
Technical implementation:
• Collaborative filtering and content-based algorithms
• Real-time recommendation updates
• A/B testing framework for optimization
• Scalable architecture handling 10M+ daily requests
• 45% increase in user engagement
• 30% increase in time spent on platform
Technical implementation:
• Collaborative filtering and content-based algorithms
• Real-time recommendation updates
• A/B testing framework for optimization
• Scalable architecture handling 10M+ daily requests
• 45% increase in user engagement
• 30% increase in time spent on platform
Technologies Used
Docker
FastAPI
PostgreSQL
Python
Redis
scikit-learn
TensorFlow