Re-ranking, Multi-Objective Scoring, and Evaluation
~40 min read
Multi-objective re-ranking that blends relevance with quality and business signals, A/B testing design for search, and how to close the training feedback loop.
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Key points
- •Multi-objective re-ranking blends LTR relevance score with quality and business signals using policy-set weights — separating what the model learns (relevance) from what business strategy controls (revenue, quality)
- •Zero-result rate is the most critical guardrail metric for search: a high NDCG@10 system with a 10% zero-result rate is failing one in ten users completely
- •The LTR feedback loop: click logs → IPS bias correction → LTR retraining (weekly); human labels → validation set (monthly). Using click data without IPS correction produces a model that optimizes for position-1 popularity, not relevance