Design an ad recommendation ranking approach
Overview
This question evaluates competency in machine-learning driven ad ranking and recommendation systems, including objective formulation, modeling strategy (feature and label design, candidate generation versus ranking), offline and online evaluation, experimentation, and production challenges such as cold start, budget pacing, feedback loops, and calibration. It is commonly asked because it assesses the ability to balance long-term business value with user experience, reason about metrics and failure modes, and design reliable evaluation and experimentation pipelines; the domain is Machine Learning and the level of abstraction spans both conceptual understanding and practical application.
You are designing an ad recommendation (ad ranking) system for a consumer app.
Goal
Maximize long-term business value while maintaining a good user experience.
Prompt
Describe how you would:
- Formulate the ranking objective (e.g., revenue vs engagement tradeoff)
- Build a modeling approach (features, labels, candidate generation vs ranking)
- Evaluate offline (datasets, metrics, counterfactual concerns)
- Run online experiments (A/B design, guardrails)
- Handle practical challenges: cold start, budget/pacing, feedback loops, and calibration
Be specific about key metrics and failure modes.
Solution
Login required