Question: A/B Test to Improve YouTube’s Recommendations
Explore how to design a clean and interpretable A/B test for YouTube's recommendation algorithm. Learn to apply the CLEAR TEST framework, select meaningful success metrics, and use guardrails to balance user engagement with platform health. This lesson guides you in analyzing experimental results and improving recommendation quality with long-term user value in mind.
We'll cover the following...
We'll cover the following...
- Interview question
- Scenario overview
- Step-by-Step framework: CLEAR TEST for recommendation experiments
- Clarify the purpose
- Limit to one primary metric
- Establish counter metrics
- Align variants tightly
- Run for statistical power
- Timebox thoughtfully
- Evaluate cohorts
- Synthesize into a Decision
- Translate the learning
- Success metrics
- Key takeaways
...