Sort Similarity Metrics by Use Case
Classify retrieval scenarios by the similarity metric assumption they most likely need.
Sort each scenario by the metric assumption it most likely needs. Use the model documentation when in doubt. Cosine-style semantic direction Inner-product scoring L2 geometric distance Text passage search where vector length should not dominate semantic closeness Question-to-policy retrieval using normalized text embeddings A recommender model trained to rank user and item vectors by dot product Sparse-vector ranking that uses inner product over weighted token dimensions Feature vectors where absolute geometric distance between points is the intended signal Image embedding duplicate check validated with nearest Euclidean neighbors
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