Product design question

Develop a solution for Airbnb to help guests find properties that match their preferences.

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

Product design for search and discovery personalization, a core but often generically answered Airbnb problem, requiring a specific mechanism.

Identify the current gap: filters today are mostly explicit and miss implicit preferences like design style or neighborhood vibe.
Propose combining explicit filters with implicit signal: past bookings, saved listings, and time spent viewing certain photos.
Propose a preference quiz at signup or first search to bootstrap for new users with no history.
Design the surfacing mechanism: a matched for you ranking layer on top of standard search results, not replacing filters entirely.
Define success: booking conversion rate for guests using personalized results versus standard search.

Combines explicit and implicit signals instead of just adding more filter options.
Addresses cold start directly with an onboarding preference quiz, a practical fix for new users.
Uses booking conversion and session length as metrics, tying personalization to an actual outcome.

Proposing only more explicit filters, which does not solve for preferences users cannot easily articulate.
No plan for new users with no booking history to personalize against.

How would you avoid personalization narrowing results too much and hiding good options?
How would you validate that matched for you listings actually perform better?