Top 4 Recommenders Interview Questions
Browse 4 real-world technical and behavioral interview questions about Recommenders. Review scenarios, edge cases, and architectural best practices.
The recommender is good for users with history and useless for the ones who just signed up. What do you serve them?
Serve context instead of history: popularity conditioned on the country, device and referrer you already have, then re-rank on the first two clicks of the session with a model that needs no user identity. Reserve slots for exploration, and segment every offline metric by history length, because a metric averaged over users with history cannot see this cohort at all.
Why is a recommender built as candidate generation followed by ranking rather than as one model?
Because you cannot score a million-item catalogue per request inside a few tens of milliseconds. Retrieval reduces millions to hundreds with a cheap model judged on recall, then a far more expensive ranker orders those hundreds and is judged on the ordering it produces at the top of the list.
How do you handle cold start for a brand new user and a brand new item?
Fall back to content features and segment popularity while behavioural signal is absent, then blend towards collaborative scores as interactions accumulate. New items also need guaranteed exploration impressions, or the already-popular items take all the traffic and nothing can dislodge them.
Your offline ranking metrics improved but engagement dropped in the A/B test. What happened?
Almost always the offline evaluation was measured on logs the old ranker produced. Clicks are confounded with position, the training data only contains items the old system chose to show, and a candidate that reproduces the old ordering scores well offline while adding nothing live.