Model an ads ranking system

Q: Model an ads ranking system

This question evaluates machine learning modeling, feature engineering, and systems-level ranking competencies for ad selection and monetization, covering prediction targets (CTR/CVR/expected value), handling sparse and categorical features, delayed feedback and bias, calibration, evaluation metrics, and online experimentation.

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Question

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Scenario

You are designing the modeling approach for an ads ranking system in a feed/search product.

Requirements

For each ad impression opportunity, choose and rank candidate ads.
Optimize business value (e.g., revenue) while maintaining user experience.
Account for auction/bidding constraints (e.g., advertisers bid per click or per conversion).

What to cover

What labels you would predict (CTR/CVR/expected value), and how you combine them into a final ranking score.
Feature sets (user, ad, context) and handling sparse/categorical data.
Training data generation, delayed feedback, and bias (position bias, selection bias).
Calibration, evaluation metrics, and online experimentation.
Cold start for new ads/advertisers and exploration.

Model an ads ranking system

Quick Overview

Scenario

Requirements

What to cover

Solution

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