advanced~7h
Case Study: Content Recommendation System Design
End-to-end design of a large-scale video/content recommendation system — the pattern behind YouTube, Netflix, and TikTok. Covers scoping and metric selection, two-tower retrieval architecture, deep ranking with multi-objective optimization, cold-start handling, and the full evaluation + feedback loop. Grounded in Khang Pham's video recommendation case study.
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▶📚 Prerequisites(3)
🎓 Learning objectives
- •Scope a content recommendation system: define the problem, scale assumptions, and success metrics before touching model design
- •Explain why watch time or long-term engagement is a better optimization target than raw click-through rate for video recommendation
- •Design the two-stage retrieval-then-ranking pipeline and explain the role of each stage at the scale of millions of items
- •Describe the cold-start problem for both new users and new content, and explain at least two mitigation strategies for each
- •Identify the key feature categories used in a video ranking model and explain why cross-features between user and item matter
- •Explain the filter bubble problem and describe how diversity and exploration mechanisms counteract it
- •Design an A/B testing framework for a recommendation system, including what metrics to track and for how long
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📂 Subtopics
Related concepts
two-tower modelcandidate generationlearning-to-rankcollaborative filteringcontent-based filteringcold startposition biasmulti-task learningFAISSANN retrievalfeature storeA/B testingwatch time optimizationfilter bubbleexploration-exploitationrecommendation-system-components
Next to learn
case-study-feed-ranking →recommendation-system-components →eval-metrics-fundamentals →ai-system-architecture-patterns →