Cosine Distance. Defined as 1 - cosine similarity.
We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead
of COSINE distance. Our algorithms have been more optimized for
DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is
mathematically equivalent to COSINE distance and results in the same
ranking.
DotProductDistance
Dot Product Distance. Defined as a negative of the dot product.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-03-31 UTC."],[[["This document provides reference information for the `FeatureView.Types.IndexConfig.Types.DistanceMeasureType` enum within the Google Cloud AI Platform v1beta1 API."],["The `DistanceMeasureType` enum defines the different types of distance measures that can be used in nearest neighbor searches."],["Available distance types include `CosineDistance`, `DotProductDistance`, `SquaredL2Distance`, and `Unspecified`, each with its own mathematical definition and use cases."],["The documentation advises to use `DOT_PRODUCT_DISTANCE` and `UNIT_L2_NORM` instead of `COSINE_DISTANCE`, as they are mathematically equivalent and the algorithms have been optimized for the former."]]],[]]
