Design a game recommendation modeling approach

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).