Design a video recommendation system

Q: Design a video recommendation system

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

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Question

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Scenario

Design an end-to-end video recommendation system for a short-video or spotlight-style feed.

Requirements

Product goals
- Personalized ranked feed for each user.
- Support both "For You" (personalized) and "Following" (simpler) feeds.
Core interactions / signals
- Impressions, clicks/plays, watch time, completion rate, likes, comments, shares, hides, follows.
System constraints
- Low latency for feed generation (e.g., p95 < 200 ms for ranking service; end-to-end may be higher with caching).
- Handle cold-start users and new videos.
- Avoid spam/low-quality content; respect safety/policy constraints.
Scale (assume)
- Tens of millions of DAU, millions of new videos/day, heavy read traffic.

What to cover

Candidate generation vs ranking vs re-ranking.
Feature store and training data generation.
Online serving architecture and caching.
Evaluation: offline metrics, online A/B, guardrails.
Feedback loops, bias, exploration, and debiasing.

Design a video recommendation system

Scenario

Requirements

What to cover

Solution

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