Implement A/B testing framework for recommendation algorithms

Overview

Build an A/B testing framework to compare different recommendation algorithms in production.

Features

• Multi-armed bandit allocation
• Traffic splitting (50/50, 90/10, etc.)
• Statistical significance testing
• Experiment tracking dashboard

Metrics to Track

• Click-through rate (CTR)
• Conversion rate
• Time on page
• Revenue per user

Tech Stack

• Python experimentation library
• PostgreSQL for experiment config
• Grafana for visualization

Acceptance Criteria

• Support for 5+ concurrent experiments
• Automatic winner selection (p-value < 0.05)
• Rollback capability within 5 minutes