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Case Study: Social Feed Ranking System Design
End-to-end design of a social feed ranking system — the pattern behind Twitter/X, LinkedIn, Facebook News Feed, and Instagram. Covers the fan-out problem, multi-signal ranking with multi-objective optimization, time-decay, diversity, author concentration, and the unique tradeoff between engagement and user trust. Grounded in Khang Pham's feed ranking case study.
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▶📚 Prerequisites(3)
🎓 Learning objectives
- •Explain the fan-out problem in social feeds and describe the push-on-write vs. pull-on-read architecture tradeoff
- •Identify the three categories of signals used in feed ranking: network signals, content signals, and engagement signals
- •Design a multi-task ranking model for social feeds that jointly optimizes for multiple engagement types
- •Describe how time-decay is incorporated into feed ranking and why recency matters differently in social vs. content recommendation
- •Explain the author concentration problem and describe the diversity mechanisms that address it
- •Distinguish between feed ranking for social networks and content recommendation, identifying where they share patterns and where they diverge
- •Design an evaluation framework for feed ranking that balances engagement metrics with trust and well-being metrics
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📂 Subtopics
Related concepts
fan-out problempush vs. pull architecturemulti-task learningtime-decayauthor diversitynetwork signalsengagement signalscontent signalsfeed cachededuplicationMinHashfilter bubblewell-being metricsuser-author affinityrecommendation-system-componentscase-study-recommendation
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recommendation-system-components →case-study-recommendation →ai-system-architecture-patterns →eval-metrics-fundamentals →