Design an ad-selection system across objectives

Q: Design an ad-selection system across objectives

This question evaluates expertise in machine learning system design for ad selection and real-time bidding, covering predictive modeling and calibration, value unification across objectives, auction mechanics, budget pacing, exploration strategies, guardrails, and evaluation methodologies.

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Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Technical Screen rounds at TikTok.

Q: What role is this question designed for?

This question is commonly asked for Data Scientist candidates at TikTok during technical interviews.

Question

End-to-End Ad-Selection System Design

Context

You must choose, at impression time, which advertiser type to show to a user. There are three advertiser objectives with fixed value schemes (values reflect the platform's objective weights per action):

Gaming (CPI): $10 per install
E‑commerce (CPA): $5 per purchase
Branding (CPM): $3 per 1000 impressions

Design a system that maximizes expected advertiser value per impression while respecting budgets and pacing.

Requirements

Specify the following components:

Prediction
- Features and models to estimate P(install | ad, user, context) and P(purchase | ·).
- Calibration/debiasing for auction-time use.
- Updating cadence.
Value Unification and Constraints
- How to make scores commensurate across objectives (e.g., expected value per impression; convert CPM to per‑impression value).
- Incorporate predicted win rate, quality thresholds, and user experience constraints.
Exploration, Cold Start, Guardrails
- Exploration vs. exploitation (e.g., contextual bandits).
- Cold‑start handling.
- Guardrails (frequency capping, diversity, fairness).
Auction and Budgeting
- Auction mechanics (second‑price/VCG), bid shading, pacing control.
- Multi‑objective bidding interactions.
Evaluation
- Offline/online evaluation: counterfactual replay, lift tests.
- Success metrics (incremental ROAS, retention, long‑term engagement).
- Detecting/mitigating feedback loops or gaming.

Design an ad-selection system across objectives

Quick Overview

End-to-End Ad-Selection System Design

Context

Requirements

Solution

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