AI System Case Studies

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.

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.

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.

End-to-end design of a search and listing ranking system — the pattern behind e-commerce search (Amazon, eBay), marketplace search (Airbnb listings), and enterprise search. Covers query understanding, hybrid retrieval (BM25 + semantic), learning-to-rank, personalization, position bias, and multi-objective ranking that balances relevance, quality, and revenue. Grounded in Khang Pham's search/listing ranking case study.

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.