You are building an **ad recommendation/ranking** system for a content feed (e.g., short-form videos). At each feed position, you may show either an organic item or an ad. Design the end-to-end approach: 1) Define the **objective(s)** (e.g., revenue, user experience) and how you would combine them (constraints vs. weighted objective). 2) Propose modeling choices for predicting key events (CTR, CVR, p(revenue), long-term value), including labels, features, and handling delayed feedback. 3) Explain how you would address common issues: - selection bias / counterfactual evaluation (logged policy) - exploration vs. exploitation - cold start (new ads, new users) - advertiser budget/pacing constraints - fairness/diversity constraints (optional but discuss if relevant) 4) Provide an evaluation plan: offline metrics + online A/B metrics and guardrails.