Design a game recommendation modeling approach
Quick Overview
This question evaluates proficiency in end-to-end machine learning system design for recommender systems, including product goal and target definition, feature and training data construction, bias and leakage considerations, model selection, loss/optimization choices, and offline/online evaluation within a personalized game recommendation context.
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:
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Product goal & target definition
- What is the objective (e.g., installs, D1/D7 retention, playtime, purchases), and what label(s) would you predict?
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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)?
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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?
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Model choice
- What baseline(s) would you start with?
- What more advanced models would you consider for retrieval and ranking?
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Loss functions & optimization
- What loss would you use (pointwise/pairwise/listwise)?
- How would you train at scale (negative sampling, mini-batching), and what optimizer?
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Offline evaluation
- What metrics would you report, and how would you do validation splits over time?
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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).