Cold-start recommendations
Design the cold-start strategy for a recommendation system on two fronts at once: brand-new *users* (no history) and brand-new *items* (no engagement data, e.g. a just-published article or a just-listed product). The collaborative-filtering / engagement-trained ranker is useless for both. How do you give new users decent recommendations from minute one, how do you give new items a fair chance to be discovered without flooding everyone with untested content, and how do you stop the rich-get-richer feedback loop where popular items always win?
What a strong answer looks like
Clarify scale and constraints first. Propose a clean component breakdown, then go deep on the hard parts (data model, bottlenecks, consistency, failure modes) and name the trade-offs you are making.
Clarify5:00 left
Estimate5:00 planned
Design15:00 planned
Deep dive12:00 planned
Failure8:00 planned
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.