Users Dataset

A Users dataset stores metadata about your users. This might include information such as age, gender, or loyalty membership.

When you create a Users dataset, you must also create a schema for the dataset. A schema tells Amazon Personalize about the structure of your data and allows Amazon Personalize to parse the data. For an example of a Users schema, see Users Schema Example. For information on schema requirements see Dataset and Schema Requirements.

This section provides information about required user data and the kinds of user data you can upload for training. It also includes a Users Schema Example. For information about importing user data into a Users dataset, see Preparing and Importing Data.

Once you create a Users dataset and add user data, you can then filter recommendations to include or exclude items based on specific user conditions. For more information see Filtering Recommendations.

Note

RELATED_ITEMS recipes, such as item-to-item similarities (SIMS), do not use Users datasets.

Required User Data

The training data you provide for each user must match your schema. At minimum, you must provide a User ID for each user. Depending on your schema, user metadata can include empty/null values.

For more information on minimum requirements and maximum data limits for a Users dataset, see Service Quotas.

Users Schema Example

The following example shows a Users schema in Avro format. The USER_ID field is required and the AGE and GENDER fields are metadata. At least one metadata field is required and you can add at most 5 metadata fields. For information on schema requirements see Dataset and Schema Requirements.

{
  "type": "record",
  "name": "Users",
  "namespace": "com.amazonaws.personalize.schema",
  "fields": [
      {
          "name": "USER_ID",
          "type": "string"
      },
      {
          "name": "AGE",
          "type": "int"
      },
      {
          "name": "GENDER",
          "type": "string",
          "categorical": true
      }
  ],
  "version": "1.0"
}