AI Implementation
E-commerce Recommendation Engine
Personalized product recommendation system using collaborative filtering and deep learning, driving 35% increase in conversion rates.
Technologies Used
Python PyTorch Apache Spark Redis PostgreSQL FastAPI React AWS
Business Impact
Increased average order value by 28% and conversion rates by 35% through personalized product recommendations.
E-commerce Recommendation Engine
Overview
An advanced recommendation system that combines collaborative filtering, content-based filtering, and deep learning techniques to provide personalized product recommendations for an e-commerce platform with over 1M daily active users.
Key Features
Hybrid Recommendation Algorithm
- Collaborative filtering for user-item interactions
- Content-based filtering using product attributes
- Neural collaborative filtering for complex patterns
- Real-time A/B testing of recommendation strategies
Real-Time Processing
- Stream processing for user behavior events
- Online learning for model updates
- Low-latency recommendation serving (U+0035;50ms)
- Scalable architecture handling 10K+ RPS
Analytics & Optimization
- Recommendation performance metrics
- User engagement tracking
- Automated algorithm selection
- Business impact measurement
Technical Architecture
The system is built on a lambda architecture:
- Speed Layer: Real-time recommendations using Redis
- Batch Layer: Daily model training on historical data
- Serving Layer: REST API for recommendation retrieval
Business Impact
- 35% increase in conversion rates
- 28% increase in average order value
- 45% improvement in user engagement
- $3.2M additional revenue generated annually
- 60% reduction in cart abandonment
Technologies Used
- ML Framework: PyTorch, scikit-learn
- Data Processing: Apache Spark, Python
- Database: PostgreSQL, Redis
- API: FastAPI, React
- Infrastructure: AWS (EC2, S3, Lambda)