88. Design a Reviews and Ratings System
Dish reviews only from customers who ordered them, votes counted from a change stream, reviews ranked by the Wilson bound, payouts made exactly once.
Pick a system. Work through the problem. Compare your approach.
Company tags are community-reported. Counts on cards show how many people reported that design.
Dish reviews only from customers who ordered them, votes counted from a change stream, reviews ranked by the Wilson bound, payouts made exactly once.
Choose 20 videos out of 10 million for 100 million daily users: implicit labels, two-tower retrieval, a multi-task ranker, bias and exploration.
Predict the chance a person clicks an ad, calibrated for the auction, at 10 B requests a day: sampling and its correction, DCN-V2, delayed clicks.
Order each home feed so the time is worth it: candidate sources, a multi-task ranker and value model, integrity re-ranking and feedback loops.
Rank upcoming events when every event is new and expires: geo and time candidates, live features, calibrated ranking, a two-sided market.
Find the right videos for a typed query among billions: BM25 and a dual encoder over text, speech and frames, LambdaMART on debiased clicks, human raters.