Design a Recommendation Service
Last updated: January 25, 2026
Quick Overview
Design a distributed recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
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Design a distributed recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
LinkedIn asks this during the System Design Round to assess your understanding of the full ML lifecycle. They want to see how you translate a business problem into an ML objective, design the feature pipeline, and plan for model monitoring and retraining.
What the Interviewer Expects
- Design the full ML lifecycle from data collection to model monitoring
- Address cold start, exploration/exploitation, and model freshness
- Discuss multi-objective optimization and ranking systems
- Plan for model debugging, fairness, and bias mitigation
- Design the feature store and training pipeline for scale
- Address model versioning, canary deployments, and rollback strategies
- Discuss the data flywheel and long-term system evolution
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you handle the cold start problem?
- How would you ensure fairness and reduce bias in the model?
- How would you run A/B tests on different model versions?
- What would you do if model performance degrades over time?
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