Dot Product Similarity
Dot Product Similarity is a metric that measures the alignment of two vectors in a high-dimensional space by multiplying corresponding elements and summing the products. Unlike Cosine Similarity, it is sensitive to vector magnitude.
Frequently Asked Questions
When should dot product be used over cosine similarity?▼
When the length or magnitude of the vector carries information (e.g. term frequency or item popularity) that should influence the ranking score.
How do model builders optimize embeddings for dot product search?▼
By normalizing all vectors during training, which makes the dot product mathematically equivalent to cosine similarity but faster to calculate.
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationDense vector search, attention matrix calculations, and recommender scoring.
Coverage Trend12 Weeks
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