failure:feedback-loop
Feedback Loop
Feedback Loop in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Recommendation, search & ranking
From corpus to slate: retrieve, score, re-rank
Diagnose recommendation quality stage by stage: coverage first, ordering second, slate policy third.
Recommendation, search & rankingClicks are not labels
A click is an exposed, policy-shaped outcome—not a universal relevance label.
Recommendation, search & rankingBM25, factorization, two towers, and ANN
Hybrid retrieval works because lexical, collaborative, and content representations fail in different places.
Recommendation, search & rankingPointwise, pairwise, and listwise ranking
Pointwise learns values, pairwise learns preferences, and listwise learns positions—choose the supervision that matches the slate.
Recommendation, search & rankingServing freshness, diversity, and experiments
A recommendation ships only when relevance, freshness, diversity, and system guardrails succeed together.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.