Choosing a Recipe

Amazon Personalize provides recipes, based on common use cases, for training models. A recipe is a machine learning algorithm or algorithm variant that you use with settings, or hyperparameters, and a dataset group to train an Amazon Personalize model. With recipes, you can create a personalization system without prior machine learning experience.

The predefined recipes use the following during training:

• Predefined attributes of your data

• Predefined feature transformations

• Predefined algorithms

• Initial parameter settings for the algorithms

To optimize your model, you can override many of these parameters when you create a solution. For more information, see Hyperparameters and HPO.

Choose a specific recipe based on what you want to accomplish and how familiar you are with the recipes. Each recipe is designed for a specific use case. When creating a solution, choose the recipe that best fits your needs. See Amazon Personalize Recipe Categories for a list of Amazon Personalize recipes by category.

Amazon Personalize Recipe Categories

Amazon Personalize provides three types of recipes. Besides behavioral differences, each type has different requirements for getting recommendations, as shown in the following table.

Recipe type	API	Requirements	Recipes
USER_PERSONALIZATION	GetRecommendations	userId: Required itemId: Optional inputList: NA	User-Personalization HRNN HRNN-Metadata HRNN-Coldstart Popularity-Count
PERSONALIZED_RANKING	GetPersonalizedRanking	userId: Required itemId: NA inputList: list of itemId's	Personalized-Ranking
RELATED_ITEMS	GetRecommendations	userId: Not used itemId: Required inputList: NA	SIMS

Viewing Available Amazon Personalize Recipes

To see a list of available recipes:

• In the Amazon Personalize console, choose a dataset group. From the navigation pane, choose Solutions and recipes, and choose the Recipes tab.

• With the AWS SDK for Python (Boto3), call the ListRecipes API.

• With the AWS CLI, use the following command.

  aws personalize list-recipes

To get information about a recipe using the SDK for Python (Boto3), call the DescribeRecipe API. To get information about a recipe using the AWS CLI, use the following command.

aws personalize describe-recipe --recipe-arn recipe_arn

Using AutoML to Choose an HRNN Recipe (API Only)

Amazon Personalize can automatically choose the most appropriate hierarchical recurrent neural network (HRNN) recipe based on its analysis of the input data. This option is called AutoML. To perform AutoML, set the performAutoML parameter to true when you call the CreateSolution API.

You can also specify the list of recipes that Amazon Personalize examines to determine the optimal recipe, based on a metric you specify. In this case, you call the CreateSolution operation, specify true for the performAutoML parameter, omit the recipeArn parameter, and include the solutionConfig parameter, specifying the metricName and recipeList as part of the autoMLConfig object.

How a recipe is chosen is shown in the following table. Either performAutoMLor recipeArn must be specified but not both. AutoML is only performed using the HRNN recipes.

performAutoML	recipeArn	solutionConfig	Result
true	omit	omitted	Amazon Personalize chooses the recipe
true	omit	autoMLConfig: metricName and recipeList specified	Amazon Personalize chooses a recipe from the list that optimizes the metric
omit	specified	omitted	You specify the recipe
omit	specified	specified	You specify the recipe and override the default training properties

Note

When performAutoML is true, all parameters of the solutionConfig object are ignored except for autoMLConfig.