CMAB Recommendation Engine
Contextual multi-armed bandit recommendation engine with XGBoost, handling 5K requests/second for real-time ML predictions.
Overview
A contextual multi-armed bandit recommendation engine powering Optimizely’s experimentation platform with XGBoost, enabling personalized A/B test variations based on individual user context.
How It Works
- Contextual Bandits personalize user experience by considering context-specific factors
- XGBoost powers the prediction layer for real-time decision making
- Large-scale data feed with regular interval training
- Context-aware bandit system that reduces regret and improves experiment outcomes
Tech Stack
Python XGBoost FastAPI GKE BigQuery Datadog
Impact
- Handling 5,000 requests/second for real-time ML predictions
- Reduced regret by 15% and boosted conversions by 8%
- Secured a major US financial services client with tens of millions of customers
- Notable clients: Discovery, Atlassian, IBM, Salesforce, Capital One