In this edition we are going to dive into a System Design Question asked at Amazon.
Here’s the question tile 👇
Source: Exponent
Design a system to track reviews abuse on Amazon.com
CATEGORY: SYSTEM DESIGN
+ Define the Problem:
• Review abuse includes fake reviews, review bombing, incentivized ratings, and coordinated manipulation.
• Goal: Detect and reduce inauthentic reviews to protect buyer trust and seller fairness.
+ Identify Actors & Motives:
• Honest customers (leave genuine reviews)
• Malicious reviewers (manipulate product rankings)
• Sellers (may benefit or suffer from fake reviews)
• Moderation teams (enforce review policies)
+ Design System Components:
• Input Signals: Review content, IP/device data, purchase history, reviewer behavior
• Detection Engine:
Rule-based filters (e.g. multiple reviews from same IP)
ML models (detect unnatural patterns & language)
Anomaly detection (review spikes, duplicate content)
• Action Layer:
Flag for manual review
Auto-suppress suspected reviews
Penalize accounts/sellers
Feedback loop for false positives
+ Handle Trade-offs:
• Accuracy (false positives vs. negatives)
• Real-time detection vs. batch processing
• Transparency for sellers vs. system integrity
+ Metrics for Success:
• % reduction in abusive reviews
• ML model precision/recall
• Customer trust score / NPS impact
• Time to action for flagged reviews
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