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LinkedIn·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior
May 2026

Summary

One product sense question about fake review detection, framed as a Yelp PM scenario but asked in a LinkedIn interview. Short and focused, no behavioral rounds from what I could tell.

Questions Asked (1)

Q1

You're a PM at Yelp. How would you build a system to detect fake reviews?

Product Sense & IdeationProduct Analytics & MetricsRoot Cause Analysis
Author's notes

I started with signals I could actually defend: review velocity spikes, account age, reviewer history, linguistic patterns.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal: reduce fake reviews to maintain trust and platform integrity. Then outline a system that combines detection (using signals and models), enforcement (actions on fake reviews), and continuous improvement (feedback loops). Emphasize trade-offs between false positives and false negatives, and how you'd measure success.

Pro tip: Frame the solution as a trust and safety system, not just a classifier. Show you understand adversarial dynamics: fraudsters adapt, so the system must evolve. Also, consider the reviewer and business perspectives to avoid unintended consequences.

1. Clarify Goals and Constraints

Define what 'fake' means (e.g., incentivized, malicious, bot-generated) and the primary objective (e.g., reduce prevalence, minimize user impact). Identify constraints like scalability, latency, and fairness.

2. Identify Signals and Data Sources

List potential signals: review content (language, sentiment), reviewer behavior (frequency, burstiness), business patterns (sudden spikes), and network connections (IP, device). Consider both supervised and unsupervised approaches.

3. Design Detection and Enforcement

Propose a multi-layered system: rule-based filters for obvious cases, ML models for nuanced detection, and human review for edge cases. Define enforcement actions: removal, downranking, warning, or account suspension.

4. Define Metrics and Evaluation

Choose metrics: precision/recall, false positive rate, prevalence of fake reviews over time, and impact on user trust (e.g., via surveys). Set up A/B tests to measure effectiveness and unintended consequences.

5. Plan Iteration and Adaptation

Establish feedback loops: user reports, model retraining, and adversarial testing. Monitor for drift and new fraud patterns. Consider transparency and appeals process.

Key Points to Mention

  • Trade-off between false positives (legitimate reviews removed) and false negatives (fake reviews allowed), and how to balance based on business impact.
  • Use of both content-based and behavioral signals; no single signal is sufficient.
  • Importance of human-in-the-loop for ambiguous cases and to generate labeled data.
  • Metrics should include both model performance (precision/recall) and business outcomes (user trust, review quality).
  • Adversarial nature: fraudsters adapt, so system needs continuous monitoring and retraining.
  • Consideration of fairness and bias: ensure detection doesn't disproportionately affect certain businesses or reviewers.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.