## Scenario You are building a **personalized game recommender** for a consumer app/store. The goal is to recommend a ranked list of games to each user to increase engagement and/or revenue. ## Task Explain, at a practical interview level, how you would design the **end-to-end ML modeling approach**: 1. **Product goal & target definition** - What is the objective (e.g., installs, D1/D7 retention, playtime, purchases), and what label(s) would you predict? 2. **Data & features** - What user/game/context features would you use? - How would you handle sparse/categorical features, text/image metadata, and missing values? - How do you prevent leakage (e.g., future info)? 3. **Training data construction** - How do you build positive and negative samples from logs? - How do you handle exposure bias (only shown items can be clicked) and position bias? 4. **Model choice** - What baseline(s) would you start with? - What more advanced models would you consider for retrieval and ranking? 5. **Loss functions & optimization** - What loss would you use (pointwise/pairwise/listwise)? - How would you train at scale (negative sampling, mini-batching), and what optimizer? 6. **Offline evaluation** - What metrics would you report, and how would you do validation splits over time? 7. **Online evaluation / A/B testing** - How would you design an A/B test, guardrail metrics, and iterate safely? Assume you have standard event logs (impressions, clicks, installs, sessions, purchases) and game metadata (genre, tags, price, ratings).