Step 1: Choosing a recipe - Amazon Personalize
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Step 1: Choosing a recipe

Amazon Personalize provides recipes, based on common use cases, for training models. Recipes are Amazon Personalize algorithms that are prepared for specific use cases. With recipes, you can create a personalization system without prior machine learning experience.

Amazon Personalize 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.

Amazon Personalize recipes

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 Recipes API API requirements



HRNN recipe (legacy)

HRNN-Metadata recipe (legacy)

HRNN-Coldstart recipe (legacy)


userId: Required

itemId: Not used

inputList: NA




userId: Required

itemId: NA

inputList: list of itemId's



SIMS (legacy)


userId: Not used

itemId: Required

inputList: NA





For batch workflow requirements, see Creating a batch segment job.

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 Amazon SDK for Python (Boto3), call the ListRecipes API.

  • With the Amazon 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 Amazon CLI, use the following command.

aws personalize describe-recipe --recipe-arn recipe_arn