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Recommendation System Design Interview Interview Guide

10 interview questions with sample answers

16-20 hours
Prep Time
$170K-$300K+
Salary
10
Questions

About This Role

Master recommendation system design: two-stage retrieval-ranking, feature engineering, personalization, and scaling to billions of items.

Behavioral Questions (2)

Q1

Tell me about a recommendation system you designed. What was the biggest challenge?

Sample Answer:

Designed YouTube-like recommendation system for 100M videos, 1B users. Challenge: cold-start (new users, new videos). Solved: content-based retrieval for new items, exploration arms in ranking.

Q2

How have you handled recommendation diversity? When does it matter?

Sample Answer:

Diversity prevents stale recommendations and improves long-term engagement. Implemented: diversity constraint in ranking, content-based candidate mixing, user feedback integration.

Technical & Situational Questions (4)

Q3

Design a two-stage recommendation system (retrieval + ranking) for e-commerce.

Sample Answer:

Retrieval (user-to-item embeddings, item similarity, popularity): 1000 candidates in <100ms. Ranking (CTR model with features): user, item, context. Output: reranked items with diversity.

Q4

How do you handle cold-start problems in recommendations?

Sample Answer:

For new users: show popular items, use content-based features. For new items: similarity to existing items, editorial placement. Hybrid: blend collaborative and content-based.

Q5

What features would you use in a recommendation ranking model?

Sample Answer:

User features: history, profile, location. Item features: category, popularity, freshness. Interaction features: distance, temporal proximity. Context: time of day, device.

Q6

How do you optimize recommendations for engagement vs revenue?

Sample Answer:

Multi-objective optimization: weight engagement + monetization. Use Pareto frontier to find good trade-offs. A/B test to find user-acceptable balance.

FAQ

Should I optimize for CTR or engagement?
CTR is short-term. Optimize for engagement (time spent, watch time). Long-term business impact > short-term clicks.
How do I handle popularity bias?
Popular items get more interactions, dominate recommendations. Mitigate: explore new content, inverse popularity weighting, diversity constraints.
What&apos;s the role of exploration in recommendations?
Exploration (showing new items) enables: discovering new preferences, testing new content, improving long-term satisfaction. Balance exploration (10-20%) vs exploitation.
How do I A/B test recommendation changes?
Test on slices (new vs established users), monitor metrics holistically (engagement, satisfaction, diversity). Run long enough for learning effects (2+ weeks).

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Last updated on 2026-03-07