Pick The Distance Metric
Select a vector distance metric from model documentation and ranking behavior instead of habit.
11% offline relevance drop after metric mismatch 3 metrics teams usually confuse: cosine, dot, Euclid 1 model card should be the source of truth A recommender's embedding model card says it was trained and evaluated with dot product, and vector magnitude is meaningful. What should Eli do first? Configure dot product and validate it against the labeled recommendation set Use cosine because normalization makes similarity comparisons more stable Use Euclidean distance because geometric distance is easier to explain Index all three metrics and let the application choose randomly per query The training objective is the contract. Validate against labels, but…
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