recommender-systems

A deployed recommender chooses its own future training data: it shows items, users respond to what was shown, and those responses become the next model's ground truth. This lesson maps the loop's consequences, exposure bias, popularity compounding, narrowing candidate pools, explains why offline metrics reward imitation of the loop, and covers the exploration budget that keeps the system learning.

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.

Candidate generation's workhorse is the two-tower model: one network embeds users, another embeds items, and relevance becomes a dot product in shared space. This lesson traces the idea from collaborative filtering through matrix factorization to trained towers, why the towers must never mix features, and the cold-start problem geometry alone cannot solve.

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.

Before the tools, the map. AI does three separate jobs on a social platform: it decides what you see, it helps make the content, and it decides what stays up. Learn what each job actually is, why they are built and judged differently, and why the same word covering all three causes most of the confusion about AI and social media.

Why a brand new post can reach millions on one platform and nothing on another. Learn the cold-start problem and why collaborative filtering cannot solve it alone, how content embeddings let a system rank an item nobody has touched, the difference between a social graph and an interest graph, and what actually separates YouTube, TikTok, Instagram and Pinterest.

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.

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.