Design a recommendation system

Q: Design a recommendation system

This is a System Design interview question from Meta for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

System Design: Large-Scale Home-Feed Recommendation System

Problem

Design a large-scale recommendation system for a consumer app's home feed. Describe the end-to-end architecture and address the following topics:

Core architecture
- Candidate generation
- Feature stores (offline/online) and point-in-time correctness
- Real-time signals ingestion
- Ranking and re-ranking
- Online exploration (e.g., multi-armed bandits)
Strategies
- Cold start for new users and new items
- Feedback loops and bias mitigation
Experimentation and operations
- A/B testing approach
- Latency and throughput targets
- Data privacy and governance
- Fallback behavior during outages

Assumptions (to scope the design)

Consumer social app with hundreds of millions of MAU, peak 100–200k QPS feed requests.
Each request returns ~20–40 items, drawn from a few thousand candidates.
Multi-objective goals: short-term engagement (CTR, dwell, watch time) and long-term value (retention, creator health, diversity, safety).

Design a recommendation system

System Design: Large-Scale Home-Feed Recommendation System

Problem

Assumptions (to scope the design)

Solution (Locked)

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