Strategy question
How do you build people you may know recommendation for LinkedIn?
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests systems thinking for a graph-based recommendation product, balancing relevance signals with privacy and spam risk.
How to approach it
- Clarify the goal: People You May Know should grow the professional graph and drive downstream engagement, not just show any known face.
- List candidate-generation signals: mutual connection count, shared employer or school, imported contacts, profile views, shared groups or events.
- Rank by combining mutual-connection count with recency of shared context, since a job overlap from 10 years ago should fade.
- Add spam guardrails: cap daily suggestions, support a not-interested action, and dedupe against pending requests.
- Define success as connection acceptance rate and downstream messaging or engagement after connecting, not raw suggestion volume.
What a strong answer includes
- Separates candidate generation, mutual connections and imported contacts, from ranking, the weighted signal combination.
- Flags the privacy sensitivity of using imported phone contacts as a signal.
- Picks acceptance rate over impressions as the north star, since suggestion volume alone is a vanity metric.
Common mistakes
- Optimizing for suggestion volume instead of acceptance quality.
- Ignoring unwanted-contact or harassment risk, such as surfacing an ex-coworker or ex-partner repeatedly.
Likely follow-up questions
- How would you handle someone who keeps rejecting the same suggested connection?
- How would you weight a 10-year-old mutual connection versus a fresh one?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop