How would you design a Shop Ads ranking algorithm?

Q: How would you design a Shop Ads ranking algorithm?

This question evaluates a candidate's understanding of machine learning-driven ad ranking, auction mechanics, multi-stakeholder objective formulation, predictive modeling for CTR/CVR/value, and measurement challenges such as delayed feedback, selection bias, and fairness in online advertising.

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Question

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Context

An ads ranking system serves ads via an auction. You want to uprank Shop Ads relative to Website Ads to improve user conversion and help certain advertisers.

Task

Design (at a high level) an algorithmic approach for ranking that incorporates the “Shop Ads” preference.

Cover:

Objective function : What are you optimizing (e.g., expected value, user utility, revenue)? How do you handle multiple stakeholders (users, advertisers, platform)?
Modeling approach : What predictions do you need (CTR, CVR, purchase value)? Any special considerations for Shop Ads vs Website Ads?
How to incorporate uprank without breaking the auction (e.g., boost factor, constraint, or multi-objective optimization).
Training data & labels : delayed conversions, missing data (privacy), selection bias from prior ranking.
Offline + online evaluation : what offline metrics, counterfactual evaluation methods, and online tests would you run?
Key risks : feedback loops, calibration issues, fairness across advertiser sizes, cold start.

You may assume the platform currently ranks ads by something like score = bid * pCTR * pCVR * value (or a learned equivalent).

How would you design a Shop Ads ranking algorithm?

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Context

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