Design a recommendation system end-to-end

Q: Design a recommendation system end-to-end

This question evaluates a candidate's competency in large-scale machine learning system design, including architecture for candidate generation and ranking, feature engineering across real-time and batch pipelines, training and serving workflows, and monitoring for fairness and safety.

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Question

Question

Design a large-scale recommendation system (e.g., short videos or e-commerce items).

Requirements

Personalized feed ranking for hundreds of millions of users.
Handle cold start for new users/items.
Near real-time adaptation to user actions.
Optimize for long-term engagement while controlling for harmful feedback loops.

Deliverables

Candidate generation + ranking architecture.
Feature pipelines (real-time and batch).
Training, serving, and evaluation (metrics + experimentation).
Monitoring and guardrails (fairness, spam, safety).

Design a recommendation system end-to-end

Question

Requirements

Deliverables

Solution

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Design a recommendation system end-to-end

Overview

Question

Requirements

Deliverables

Solution

Comments (0)