intermediate~7h
Recommendation System Components
Foundational building blocks reused across recommendation and ranking systems: candidate generation (two-tower models, collaborative filtering), scoring and ranking (learning-to-rank, cross-features), and re-ranking with position bias correction, calibration, and exploration-vs-exploitation. Grounded in Khang Pham's ML Primer and Recommendation System Components chapters.
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Subtopics
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Exercises
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Projects
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Quiz Qs
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Flashcards
▶📚 Prerequisites(3)
🎓 Learning objectives
- •Describe the three-stage recommendation pipeline (candidate generation → ranking → re-ranking) and what each stage optimizes for
- •Explain how a two-tower model works and why it enables fast candidate retrieval at scale
- •Distinguish collaborative filtering from content-based filtering and identify when each is appropriate
- •Describe the features used in a learning-to-rank model and how pointwise, pairwise, and listwise loss functions differ
- •Explain position bias and describe two methods for debiasing training data
- •Define calibration in the context of click-through rate (CTR) prediction and explain why miscalibrated scores cause poor decisions
- •Compare greedy exploitation vs. exploration strategies (epsilon-greedy, UCB, Thompson sampling) and their trade-offs
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📂 Subtopics
Related concepts
two-tower modelcollaborative filteringcontent-based filteringmatrix factorizationlearning-to-rankNDCGposition biasinverse propensity scoringcalibrationepsilon-greedyUCBThompson samplingMMRcold startFAISSANNcross featuresWide & Deepfeature store
Next to learn
ai-system-architecture-patterns →embedding-model-finetuning →eval-metrics-fundamentals →