Design a short-video recommender system

Q: Design a short-video recommender system

This question evaluates a data scientist's competency in end-to-end machine learning system design for recommender systems, including retrieval and ranking architecture, feature and label design, offline and online evaluation, serving and latency constraints, and monitoring for safety and robustness.

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Q: What difficulty level is this interview question?

This is a easy difficulty Machine Learning question, commonly asked during Technical Screen rounds at LinkedIn.

Q: What role is this question designed for?

This question is commonly asked for Data Scientist candidates at LinkedIn during technical interviews.

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ML System Design — Short-video recommendation

Design an end-to-end recommendation system for a short-video feed (TikTok/Reels-style). Walk through the full pipeline:

1) Objective and constraints

Define the product goal (e.g., maximize long-term user value).
State constraints: latency budget, freshness, exploration, safety/compliance.

2) Labels and training data

What labels would you use (watch time, completion, likes, shares, follows, skips)?
How do you handle delayed feedback , position bias , and negative sampling ?
How do you construct training examples (impressions, sessions, user-video pairs)?

3) Features

User features (history, embeddings, time-of-day, locale)
Item/video features (content, creator, freshness)
Context features (device, network, entry point)
Cross features / interactions

4) “Three-stage” recommendation architecture

Describe a standard 3-stage system:

Candidate generation (retrieval)
Ranking
Re-ranking / post-processing (diversity, constraints, business rules)

For each stage:

Model family choices (e.g., two-tower, GBDT, deep ranker)
Serving architecture and latency considerations
How you ensure freshness and handle cold start

5) Offline evaluation and model selection

Which offline metrics would you use and why?
How do you validate correlation with online metrics?
How do you avoid offline-to-online mismatch?

6) Online experimentation

A/B test design, primary/guardrail metrics, ramp strategy
Debugging when offline improves but online regresses

7) Safety, robustness, and monitoring

How to prevent harmful content amplification
Monitoring (data drift, performance drift, quality regressions)

Be specific and justify trade-offs.