Design a recommendation system
Overview
This question evaluates a candidate's competency in ML system design for recommendation systems, including choices of recommendation models, handling implicit feedback and cold-start, low-latency serving architecture, and storage/schema design for users, items, and interactions.
Design a User–Item Recommendation System
Context
You are asked to design an end-to-end recommendation service that suggests items to users. The service should include choices for the recommendation model, the serving architecture with multiple application servers behind a reverse proxy, and a storage schema for users, items, and interactions.
Assume: millions of users and items, primarily implicit feedback (views, clicks, purchases), and a p95 online latency target under 150 ms for the recommendation endpoint.
Tasks
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Model choice and approach
- Explain a reasonable baseline (e.g., NMF/matrix factorization) and alternatives.
- Discuss handling implicit vs. explicit feedback, cold start, and ranking.
- Describe training cadence and evaluation.
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Data fetching and serving architecture
- Describe how multiple stateless servers behind a reverse proxy will serve recommendations.
- Cover caching, timeouts/retries, fallbacks, and model/feature serving calls.
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Data storage design
- Propose schemas for user, item, and interaction data.
- Include any derived tables (e.g., learned embeddings/factors) and indexing/partitioning choices.
Solution
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