I started with engagement metrics and the interviewer kind of just waited, like they wanted more.
Start by clarifying the goal of the recommendation engine—likely to maximize member engagement and satisfaction while supporting business objectives like retention. Then, structure your answer around a metrics framework that includes engagement, satisfaction, and business impact, and discuss how you would validate these metrics through A/B testing and guardrail metrics.
Pro tip: Emphasize that the ultimate measure of success is long-term member retention and happiness, not just short-term clicks or viewing hours. Mention how you would balance different metrics to avoid optimizing for one at the expense of others.
Confirm that the recommendation engine's primary goal is to help members discover content they love, leading to increased engagement and retention. Also consider secondary goals like promoting diverse content or supporting content investment.
Identify key metrics across three categories: engagement (e.g., viewing hours, completion rate), satisfaction (e.g., thumbs up/down ratio, survey responses), and business impact (e.g., retention, churn reduction).
Explain how you would prioritize metrics based on company objectives and avoid over-optimizing for a single metric. Discuss the importance of guardrail metrics to ensure the system doesn't harm user experience.
Describe how you would run controlled experiments to measure the impact of changes to the recommendation engine, ensuring statistical significance and accounting for novelty effects.
Outline a process for ongoing monitoring, including dashboards and alerts, and how you would use insights to iterate on the algorithm and strategy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.