Overview
Built a hybrid recommendation engine that combines collaborative filtering, content-based filtering, and deep learning to provide personalized product recommendations.
Key Features
- Hybrid recommendation approach
- Real-time personalization
- Cold-start problem handling
- A/B testing framework
- Diversity and serendipity optimization
Technical Stack
- Deep Learning: PyTorch, TensorFlow Recommenders
- Algorithms: Matrix Factorization, Neural Collaborative Filtering
- Data Processing: Apache Spark, Redis
- Serving: TensorFlow Serving, AWS SageMaker
Results
- 28% increase in click-through rate
- 35% boost in conversion rate
- 22% improvement in average order value
- Reduced bounce rate by 15%
Implementation
The system uses a three-tier approach:
Collaborative Filtering
- Matrix factorization for user-item interactions
- Neural networks for complex pattern learning
Content-Based Filtering
- Product attribute similarity
- Category and brand affinity
Deep Learning
- Neural Collaborative Filtering
- Wide & Deep networks
- Attention mechanisms for context
Features include:
- Session-based recommendations for anonymous users
- Multi-armed bandit for exploration vs exploitation
- Real-time model updates with online learning
- Contextual awareness (time, device, location)
Processes 50M+ events daily and serves recommendations with sub-100ms latency.