AI engineering glossary · NLP embeddings
What is dot product similarity?
Dot product similarity (also called Inner product similarity): Scoring two vectors by multiplying matching entries and summing them: a . b = a1 b1 + a2 b2 + ... . For vectors of unit length it equals cosine similarity, but for other vectors it also grows with their lengths.
Dot product similarity: an example
[1, 2, 3] . [4, 0, 1] = 4 + 0 + 3 = 7.
Related terms
More NLP embeddings terms
Where you learn it
Dot product similarity is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.