Users dataset schema requirements (custom) - Amazon Personalize
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Users dataset schema requirements (custom)

A Users dataset stores metadata about your users. This might include information such as age, gender, and loyalty membership for each item. For information on the types of user data you can import into Amazon Personalize, see Users dataset.

The data you provide for each user must match your schema. At minimum, you must provide a User ID for each user (max length 256 characters). Depending on your schema, user metadata can include empty/null values. Your Users schema must have minimum one metadata field, but if you add a null type, this value can be null for the user. You are free to add additional fields depending on your use case and your data. As long as the fields aren't listed as required or reserved, and the data types are listed in Schema data types, the field names and data types are up to you.

To use categorical data, add a field of type string and set the field's categorical attribute to true in your schema. Then include the categorical data in your bulk CSV file and individual item imports. For users with multiple categories, separate each value using the vertical bar, '|'. For example, for a SUBSCRIPTION_MODEL field, your data for a user might be student|monthly|discount.

Categorical values can have at most 1000 characters. If you have a user with a categorical value with more than 1000 characters, your dataset import job will fail.

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

Users schema example (custom)

The following example shows how to structure a Users schema. 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 25 metadata fields. For information about schema requirements see Custom 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" }

For this schema, the first few lines of historical data in a CSV file might look like the following.

USER_ID,AGE,GENDER 5,34,Male 6,56,Female 8,65,Male ... ...