Asked about The recommender system design interview
Design the recommendation system for a short-video app's home feed.
An answer that works
Clarify the objective and scale. Then describe multi-source candidate generation, including two-tower retrieval with approximate nearest neighbour search, a ranking model predicting several engagement signals combined by weights tied to the goal, re-ranking for diversity and freshness, logging of impressions and outcomes, cold-start handling, exploration, and evaluation with time-split offline metrics and online A/B tests.
Then they push further
Engagement went up but users report the feed feels repetitive. What do you change?
Add diversity in re-ranking, penalise near-duplicates and over-represented creators, include longer-term signals such as return visits in the objective, and track a diversity metric and user surveys as guardrails.
How do you handle a brand-new video with no interactions?
Embed it from content features such as visuals, audio, text and creator, give it a small exploration budget of impressions, and let early engagement quickly update its ranking.
What gets marked up
Strong answers start from the objective, separate retrieval from ranking, and address feedback loops and cold start without prompting.
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