Product design question

Design a product for Podcast recommendations.

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

Tests recommendation-product design, identifying discovery pain points distinct from music recommendation.

Explain why podcast recs differ from music: episodes are long-form, often one-time listens, so cold-start and topic-matching matter more than taste-matching alone.
Segment casual listeners wanting topic-based discovery from loyal fans wanting more of a specific show.
Identify the pain point: users don't know what to try next, and podcast ratings are sparser than music ratings.
Design episode-level, not just show-level, recommendations based on topic and guest overlap, plus a starter-episode surfacing for new shows and social proof from followed accounts.
Define success as new-show trial rate from recommendations, completion rate of recommended episodes, and follows generated per session.

Explains why episode-level granularity matters specifically for podcasts, unlike already-granular song-level music recs.
Addresses the cold-start problem given sparse podcast rating data compared to music.
Proposes a concrete mechanism, starter episodes and social proof, rather than a generic similarity model.

Treating podcast recs identically to music recs without addressing episode-level and cold-start differences.
Ignoring that discovery, not repeat listening, is the core job for most podcast users.

How would you cold-start recommendations for a brand-new show with no listen history?
How would you weight a followed friend's listens against algorithmic similarity?