Here’s something they don’t teach you in ML courses: a perfectly relevant recommendation list is usually a terrible one. You spend months training a ranking model. Features, architectures, multi-task objectives — the works. Then the product team walks in: “Can you make sure we don’t show 5 horror movies in a row? And boost new releases? Oh, and reserve slot 3 for promoted content.” Each request costs you relevance. The question isn’t whether to spend — it’s how much. Think of it as a budget. Your ranking model gives you relevance scores for every item. Re-ranking is the art of spending that relevance wisely — trading some accuracy for diversity, freshness, fairness, and business value.