ranking
6 free lessons tagged ranking across AI, Computer Science, Business. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Ranking and Objectives: What Should the Model Optimise?
The ranker is a prediction machine, and the hard question is what it should predict. Clicks are plentiful and poisonous, watch time bends toward length, likes are rare and unrepresentative. This lesson covers implicit feedback, the position bias baked into every training log, multi-objective ranking, and calibration.
The Two-Stage Machine: Why No Model Ranks the Whole Catalogue
A recommender has milliseconds to pick ten items from millions, and no model good enough to rank them all is cheap enough to run on them all. The industry's answer is a funnel: cheap candidate generation cuts millions to hundreds, an expensive ranker orders those hundreds. This lesson builds that architecture, its latency arithmetic, and the multi-source retrieval layer real systems run.
BM25: How Lexical Relevance Is Actually Computed
Matching finds candidates; scoring orders them, and the ordering is the product. This lesson builds BM25, the default ranking function of Lucene, Elasticsearch and OpenSearch, from its three ingredients: rare terms count more, repeated terms saturate, and long documents get discounted. With the formula, the two tuning knobs, and the saturation curve computed by hand.
Why one page beats another
Thousands of pages match your query; the engine picks ten and orders them. Learn the four questions ranking answers, why links became a proxy for trust, what PageRank actually measured, how the query itself is interpreted before anything is scored, and why chasing individual ranking factors is the wrong mental model.
What the algorithm optimizes, and why it drifts
A feed does exactly what it was told to maximize, which is rarely what anyone wanted. Learn which signals rankers actually weight and why implicit ones beat likes, how proxy objectives produce clickbait and rage-bait as correct answers to badly posed questions, and the mechanisms platforms use to pull an optimizer back toward what people value.
The two-stage funnel: retrieval then ranking
Every feed has the same impossible job: pick ten items out of millions, in under a tenth of a second. The answer is a funnel. Learn why recommenders split into a cheap retrieval stage and an expensive ranking stage, how two-tower models make retrieval possible, and why nearest-neighbour search is the trick that makes the whole thing fit in a budget.