Designing A/B Tests for Video Recommendation Algorithms

Recommendation algorithms drive user engagement, but changing them without rigorous testing can hurt user retention. Understanding how to design and evaluate A/B tests is essential for building reliable, high-performing recommendation systems. This text-based course guides you through the process of setting up safe, effective experiments for recommendation engines. You will transition from understanding basic statistical concepts to structuring complex metrics and guardrails that protect the user experience while driving engagement. What you'll learn: - Understand foundational A/B testing terminology, hypothesis testing, and statistical significance. - Define key recommendation metrics, including engagement, retention, and quality-focused guardrails. - Design experimental frameworks specifically tailored for dynamic video recommendation feeds. - Apply modern testing patterns such as sequential analysis and basic multi-armed bandit concepts to minimize user risk. - Analyze test results to make data-driven decisions without compromising platform stability. You will start with core testing terminology and statistical foundations before moving into recommendation-specific metrics and experimental design. Through written examples and scenarios, you will learn to interpret data and safely roll out algorithmic updates. This course is designed for beginner data analysts, product managers, and software engineers who are new to experimentation. No prior experience with advanced statistics or machine learning is required. Start reading today to master the fundamentals of recommendation system experimentation.