Design query generation to maximize CTR
Overview
This question evaluates a candidate's competency in end-to-end ML system design for query generation and CTR optimization, covering candidate generation and ranking, exploration–exploitation, real-time inference, personalization, safety/privacy, evaluation, and scalability.
TikTok
Jul 31, 2025, 12:00 AM
Machine Learning Engineer
Technical Screen
ML System Design
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System Design: End-to-End Query Generation to Maximize CTR
Context
You are designing the query-generation layer for a consumer search/recommendation product with a search box and a highly dynamic content feed. The system should:
- Generate user-facing query suggestions (e.g., autocomplete, related queries) that users can click.
- Optionally rewrite/expand the user’s typed query into internal retrieval queries to fetch better candidates.
Assume scale is very large, traffic is global and mobile-first, and content is short-form, fast-evolving, and safety-sensitive.
Task
Design an end-to-end system that maximizes click-through rate (CTR) for query suggestions while protecting user experience and safety. Address:
- Goals, constraints, and metrics
- Data sources and feature engineering
- Candidate generation and ranking models
- Exploration–exploitation (e.g., bandits), feedback loops, and debiasing
- Offline evaluation (splits, counterfactual evaluation) and online A/B testing
- Real-time inference architecture, latency/throughput/SLA, and scalability
- Personalization, cold start, and diversity controls
- Safety/abuse and privacy compliance
- Diagrams or pseudocode for critical components
- How you would iterate after launch
Solution
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