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Case Study: Ad Click-Through Rate Prediction System Design
End-to-end design of an ad click-through rate (CTR) prediction system — the foundational ML system for every ad-supported platform (Google, Meta, Twitter/X, TikTok). Covers feature engineering for ads, the Wide & Deep architecture, GBDT + logistic regression, probability calibration, cold start for new ads, and real-time serving requirements. Grounded in Khang Pham's ad click prediction case study and Google's published production systems.
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Quiz Qs
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▶📚 Prerequisites(3)
🎓 Learning objectives
- •Explain what CTR prediction is and why it is the foundational ML problem for ad-supported platforms
- •Identify the four feature categories used in CTR prediction (user, ad, context, interaction) and explain the outsized importance of cross-features
- •Describe the GBDT + logistic regression architecture and explain why it became the standard before deep learning
- •Explain the Wide & Deep architecture (Google, 2016) and describe what each component contributes
- •Explain the calibration problem in CTR prediction: why predicted probabilities must match actual click rates, and how Platt scaling fixes miscalibration
- •Describe the cold-start problem for new ads and the two standard mitigation strategies
- •Design an evaluation framework that distinguishes calibration quality from ranking quality
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📂 Subtopics
Related concepts
click-through ratead auctioneCPMVickrey auctionWide & DeepGBDT + LRDINDCNcalibrationPlatt scalingECEreliability diagramnegative samplingcross-featuressparse featurescold startexploration-exploitationThompson samplingAUC-ROCfeature storerecommendation-system-components
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