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 2025-03-25 UTC."],[[["This page provides reference documentation for the `FeatureView.Types.IndexConfig.Types.DistanceMeasureType` enum within the Google Cloud AI Platform v1 API."],["The content lists available versions of the documentation, ranging from version 1.0.0 to the latest version 3.22.0, all relating to the same DistanceMeasureType."],["The `DistanceMeasureType` enum defines the methods for calculating distance in nearest neighbor searches, with options including `CosineDistance`, `DotProductDistance`, `SquaredL2Distance`, and `Unspecified`."],["The documentation clarifies that `DotProductDistance` combined with `UNIT_L2_NORM` is the preferred method over `CosineDistance` due to optimizations."],["The documentation spans across different versions, and each one of them includes the same fields and descriptions for `DistanceMeasureType`."]]],[]]