31How would you use a user's booking history to personalize search: fine-tune embeddings or build a reranker?▼hardOpenAIGlean1 replies◆ premiumA real interview question with a trap in its framing: it offers you two expensive options and hides the cheap one. The escalation ladder from feature-based reranking to two-tower fine-tuning, with the cold-start and position-bias answers ready.Open full answer →
12Design learning-to-rank for product/marketplace search.▼hardAmazonDoorDashEtsy2 replies○ sign inA search box is a recommender with a strong prior: the query. The hard parts are understanding ambiguous intent, retrieving in two stages under a tight latency budget, and learning from clicks without teaching the model that whatever you showed first is best.Open full answer →