Product design question

Design a content recommendation engine for New York Times to improve content/news recommendation to users.

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

Recommendation system product design for a news context, balancing personalization with editorial values like diversity and trust.

Identify the user: readers who want relevant, high quality journalism without being trapped in a narrow filter bubble.
Name the tension: pure engagement-optimized recommendation (more of what you already click) risks narrowing exposure and undermining a news organization's public interest mission.
Propose a design: a hybrid ranking that blends personalized topic relevance with editorial signals (story importance, diversity of topics and viewpoints) rather than pure click-through optimization.
Add a diversity mechanism: ensure a minimum share of recommended stories comes from outside the user's usual topic clusters, to avoid a narrow filter bubble.
Prioritize this editorial balance explicitly, since NYT's brand and subscription value depend on trust and quality, not just engagement volume.
Define success as subscriber retention and reading depth (articles read per session) rather than raw click-through rate alone, since clicks alone can reward sensational, low quality content.

Names the real tension explicitly, pure engagement optimization versus editorial diversity and trust, which is central to news recommendation design.
Proposes a concrete diversity mechanism, a minimum share of out-of-cluster stories, rather than just saying 'balance relevance and diversity'.
Picks subscriber retention over click-through rate as the primary success metric, correctly reflecting NYT's subscription, not ad-clicks, business model.

Optimizing purely for click-through rate, which risks rewarding sensational content over quality journalism.
Ignoring the reputational and mission-related risk of a narrow, engagement-only recommendation approach for a news organization.

How would you measure filter bubble risk quantitatively?
How would this recommendation approach differ for a free reader versus a paying subscriber?