I went straight into talking about average star ratings and engagement drop-off, which felt fine in the moment but I think I missed the more interesting angle around genre affinity versus individual title ratings.
Start by clarifying the goal of the rating system—whether it's for personalization, content discovery, or quality assessment—and then outline a design that balances user effort with data quality. Propose a multi-faceted approach (e.g., explicit ratings, implicit signals, and genre-specific weighting) and define success metrics that tie back to business objectives like engagement and retention.
Pro tip: Emphasize the importance of avoiding rating bias and cold-start problems by incorporating implicit signals and using techniques like Bayesian averaging; this shows you understand real-world ML challenges beyond just collecting stars.
Ask clarifying questions to understand whether the rating system is for improving recommendations, measuring content quality, or both. Define the target users and how ratings will be consumed (e.g., by algorithms or displayed to users).
Propose a combination of explicit ratings (e.g., thumbs up/down, 5-star) and implicit signals (watch time, completion rate, re-watches). Consider genre-specific scales or weighting to account for varying expectations across genres.
Discuss strategies to mitigate biases such as popularity bias, user bias, and genre bias. Mention techniques like normalization, Bayesian averaging, and using implicit signals to complement sparse explicit ratings.
Select metrics that measure both system performance and business impact. Include offline metrics (e.g., RMSE, precision@k) and online metrics (e.g., click-through rate, watch time, retention, diversity of recommendations).
Outline an A/B testing framework to validate the rating system's effectiveness. Discuss how to monitor for unintended consequences (e.g., filter bubbles) and iterate based on user feedback and metric trends.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.