What features and feature selection would you use?

## Context You are building an ML system to rank/promote **shop ads** in an e-commerce feed/search page. At serving time, the system may score candidate shop ads for a given user and context. Assume you have access to: - User events (impressions, clicks, purchases, shop follows) - Shop metadata (category, price bands, inventory signals) - Query/context (search query, time, device) - Ad/auction signals (bid, budget pacing) ## Questions 1. If you were to build the **shop-ads ranking model**, what feature families would you use? (Give examples.) 2. You have “a ton” of candidate features. How would you identify which ones are **useful**? - Include at least one **offline** approach and one **online**/production-safe approach. 3. If you were **not allowed to use a model-based importance method** (e.g., no SHAP/GBDT gain/permutation importance), how would you still find the key useful features? 4. Call out common pitfalls: leakage, feedback loops, cold start, and feature drift.