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Reputation: 23

Azure Recommendations API's Parameter

I would like to make a recommendation model using Recommendations API on Azure MS Cognitive Services. I can't understand three API's parameters below for "Create/Trigger a build." What do these parameters mean?

https://westus.dev.cognitive.microsoft.com/docs/services/Recommendations.V4.0/operations/56f30d77eda5650db055a3d0

EnableModelingInsights
Allows you to compute metrics on the recommendation model.
Valid Values: True/False

AllowColdItemPlacement
Indicates if the recommendation should also push cold items via feature similarity.
Valid Values: True/False

ReasoningFeatureList
Comma-separated list of feature names to be used for reasoning sentences (e.g. recommendation explanations).
Valid Values: Feature names, up to 512 chars

Thank you!

Upvotes: 1

Views: 299

Answers (1)

Craig Vandenberg
Craig Vandenberg

Reputation: 46

That page is missing references to content mentioned at other locations. See this page for a more complete guide...

https://azure.microsoft.com/en-us/documentation/articles/machine-learning-recommendation-api-documentation/

It describes Cold Items in the Rank Build section in the document as...

Features can enhance the recommendation model, but to do so requires the use of meaningful features. For this purpose a new build was introduced - a rank build. This build will rank the usefulness of features. A meaningful feature is a feature with a rank score of 2 and up. After understanding which of the features are meaningful, trigger a recommendation build with the list (or sublist) of meaningful features. It is possible to use these feature for the enhancement of both warm items and cold items. In order to use them for warm items, the UseFeatureInModel build parameter should be set up. In order to use features for cold items, the AllowColdItemPlacement build parameter should be enabled. Note: It is not possible to enable AllowColdItemPlacement without enabling UseFeatureInModel.

It also describes the ReasoningFeatureList in the Recommendation Reasoning section as...

Recommendation reasoning is another aspect of feature usage. Indeed, the Azure Machine Learning Recommendations engine can use features to provide recommendation explanations (a.k.a. reasoning), leading to more confidence in the recommended item from the recommendation consumer. To enable reasoning, the AllowFeatureCorrelation and ReasoningFeatureList parameters should be setup prior to requesting a recommendation build.

Upvotes: 3

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