139. Design Similar Listings for a Vacation Rental Platform
Pick 12 homes a guest could book instead, from listing embeddings learned on browsing sessions, filtered by dates and party size, then ranked.
Start with a template. Work through each step. Ask Coach when you need a second opinion.
Company tags are community-reported. Counts on cards show how many people reported that design.
Pick 12 homes a guest could book instead, from listing embeddings learned on browsing sessions, filtered by dates and party size, then ranked.
Suggest people a member knows out of a billion: bounded friends of friends, affiliations and contacts, two ranking heads, GNN embeddings, privacy and abuse.
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.
Selfies in, professional headshots out: per-order LoRA on SDXL, an identity-encoder preview, face-match ranking, GPU pools, consent and deletion.
Images at 2K–4K without paying for every pixel: latent diffusion, an SR cascade with noise augmentation, tiled decoding, distilled drafts, cost per megapixel: one-hour boards for junior, senior and staff, with the theory.
Short clips from a prompt: latent video diffusion, recaptioned data, spacetime attention, cascades, multi-GPU jobs, previews, fair queues, provenance.
Blur every face and licence plate in billions of street panoramas: tiles for tiny faces, recall-first detection, a batch pipeline that fails closed.
The URL shortener with a payload: the same id generation and cache-first read path, but the value is kilobytes of text, so it moves out of the database and into object storage.