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E-Commerce AI Recommendation Engine
A sophisticated recommendation engine designed for e-commerce platforms that uses collaborative filtering, content-based filtering, and deep learning to deliver highly personalized product suggestions. The system processes user behavior data in real-time, builds dynamic user profiles, and generates recommendations that significantly improve conversion rates and customer engagement.
🛠️ Tech Stack
PythonPyTorchDjangoRedisKafkaPostgreSQLCeleryDocker
✨ Key Features
- Hybrid recommendation model (collaborative + content-based)
- Real-time user behavior tracking and profiling
- A/B testing framework for recommendation strategies
- Cold-start handling for new users and products
- Contextual recommendations based on time and trends
- Performance dashboard with conversion tracking
- REST API with sub-50ms response time
- Scalable to millions of products and users
🧩 Technical Challenges
- Solving the cold-start problem for new users effectively
- Balancing recommendation diversity with relevance
- Processing real-time user events at scale using streaming
- Preventing recommendation bubbles and ensuring discovery