## Scenario You are designing the **modeling approach** for an ads ranking system in a feed/search product. ## Requirements - For each ad impression opportunity, choose and rank candidate ads. - Optimize business value (e.g., revenue) while maintaining user experience. - Account for auction/bidding constraints (e.g., advertisers bid per click or per conversion). ## What to cover - What labels you would predict (CTR/CVR/expected value), and how you combine them into a final ranking score. - Feature sets (user, ad, context) and handling sparse/categorical data. - Training data generation, delayed feedback, and bias (position bias, selection bias). - Calibration, evaluation metrics, and online experimentation. - Cold start for new ads/advertisers and exploration.