recommender-systems
4 free lessons tagged recommender-systems across AI. 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.
The three jobs AI does in social media
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.
Cold start, interest graphs, and why feeds differ
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.
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.