Product design question

What parameters will you take into consideration while designing a recommendation engine for Netflix?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Technical and product understanding of recommendation systems, checking you know the real inputs and tradeoffs, not just 'AI picks good shows'.

Clarify the objective the recommendation engine should optimize for, such as watch time, completion rate, or long-term retention, since these can conflict.
List input signals: explicit signals like ratings and watchlist adds, and implicit signals like watch history, completion rate, and time of day.
Consider content-based features, genre, cast, and metadata, alongside collaborative filtering based on similar users' behavior.
Address cold start, both for new users with no history and new content with no engagement data yet.
Discuss diversity and exploration, ensuring recommendations do not over-narrow into a filter bubble that reduces long-term satisfaction.
Mention evaluation, proposing offline metrics like precision and recall alongside online A/B testing on watch time and retention.

Distinguishes optimizing for short-term watch time versus long-term retention explicitly, noting these can produce different, sometimes conflicting recommendations.
Names both content-based and collaborative filtering approaches and explains why a hybrid handles cold start better than either alone.
Addresses filter bubble risk directly, proposing a diversity or exploration mechanism to avoid narrowing recommendations too much over time.
Proposes a concrete evaluation plan, combining an offline metric with an online A/B test on retention, not just accuracy.

Treating this as a purely technical ML question with no product framing of what to optimize for.
Ignoring cold start for new users or new content entirely.
Not mentioning diversity or filter bubble risk at all.

How would you handle cold start for a brand-new piece of content with no watch history?
How would you detect if recommendations were creating a filter bubble?