Datasets and schemas - Amazon Personalize
Schema and data formatting requirements
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Datasets and schemas

Amazon Personalize datasets are containers for data. There are three types of datasets:

  • Users – This dataset stores metadata about your users. This might include information such as age, gender, or loyalty membership, which can be important signals in personalization systems.

  • Items – This dataset stores metadata about your items. This might include information such as price, SKU type, or availability.

  • Interactions – This dataset stores historical and real-time data from interactions between users and items. In Amazon Personalize, an interaction is an event that you record and then import as training data. For both Domain dataset groups and Custom dataset groups, you must at minimum create an Interactions dataset.

Amazon Personalize stores your data in datasets until you delete the datasets. For all use cases (Domain dataset groups) and recipes (Custom dataset groups), your interactions data must have the following:

  • At minimum 1000 interactions records from users interacting with items in your catalog. These interactions can be from bulk imports, or streamed events, or both.

  • At minimum 25 unique user IDs with at least two interactions for each.

For quality recommendations, we recommend that you have at minimum 50,000 interactions from at least 1,000 users with two or more interactions each.

Domain dataset groups and Custom dataset groups can have only one of each type of dataset. Before you create a dataset, you define a schema for that dataset. A schema tells Amazon Personalize about the structure of your data and allows Amazon Personalize to parse the data. A schema has a name key whose value must match the dataset type. After you create a schema, you can't make changes to the schema.

For Domain dataset groups, each dataset type has a default schema with required fields and reserved keywords. Each time you create a dataset, you can either use the existing domain schema or create a new one by modifying the existing default schema. Use the default schema as a guide for what data to import for your domain. Once you define the schema and create the dataset, you can't make changes to the schema.

If you import data in bulk, your data must be stored in comma-separated values (CSV) format. The first row of your CSV file must contain column headers, which must match your schema.

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Schema and data formatting requirements

When you create a schema for either dataset in a Domain dataset group or Custom dataset group, you must follow these guidelines:

  • You must define the schema in Avro format. For information on the Avro data types we support, see Schema data types.

  • The schema fields can appear in any order, but they must match the order of the corresponding column headers in your CSV file.

  • Schemas must be flat JSON files without nested structures. For example, a field cannot be the parent of multiple sub-fields.

  • Amazon Personalize schemas don't support complex types such as arrays and maps.

  • Schema fields must have unique alphanumeric names. For example, you can't add both a GENRES_FIELD_1 field and a GENRESFIELD1 field.

  • You must define required fields as their required data types. Reserved categorical string fields must have the categorical attribute set to true, while reserved string fields can't be categorical. The keywords can't be in your data.

  • If you add your own metadata field of type string, it must include the categorical attribute or the textual attribute (only Items schemas support fields with the textual attribute). Otherwise, Amazon Personalize won't use the field when training a model.

  • Amazon Personalize doesn't use boolean type data when training or filtering recommendations. To have Amazon Personalize use boolean data when training or filtering, use a field of type String and use the values "True" and "False" in your data. Or you can use type int or long and values 0 and 1.

  • Textual fields must be of the type string and must have the textual attribute set to true. For more information about unstructured text data, see Unstructured text metadata.

  • For fields with multiple values, including categorical metadata and impressions data, use the data type string and separate each value using the vertical bar, '|', character. For categorical fields, add "categorical": true.

Domain dataset group datasets have additional requirements based on both domain and dataset type. Custom dataset group datasets have additional requirements depending on type.

Schema data types

Amazon Personalize schemas support the following Avro types for fields:

  • float

  • double

  • int

  • long

  • string

  • boolean

  • null

Some required and reserved fields support null data. Adding a null type to a field allows you to use imperfect data (for example, metadata with blank values) to generate recommendations. For information on which fields support null data, see Domain datasets and schemas or Custom datasets and schemas. The following example shows how to add a null type for a GENDER field.

{
  "name": "GENDER",
  "type": [
    "null",
    "string"
  ],
  "categorical": true
}