Instruction: Describe how you would measure and attribute changes in user engagement directly to algorithm adjustments.
Context: This question challenges the candidate to apply causal inference methods to a complex system like a recommendation engine, where multiple confounding factors might be present.
Official answer available
Preview the opening of the answer, then unlock the full walkthrough.
First, let's clarify what we mean by "user engagement." For the purposes of this analysis, I would define user engagement based on metrics such as daily active users (DAU), which refers to the number of unique users who logged on at least once on our platform during a calendar day, average session duration, and the click-through rate (CTR) for recommended content. These metrics provide a holistic view of how engaged users are with the content being recommended.
To effectively measure and attribute changes in user engagement to our algorithm adjustments, I would employ a controlled experiment, specifically, an A/B testing framework. Here’s how I would structure it:...