Actions dataset schema requirements (custom)

An action is an engagement activity that you might want to recommend to your customers. Actions might include installing your mobile app, completing a membership profile, joining your loyalty program, or signing up for promotional emails. An Actions dataset stores data about your actions. For information about the types of action data you can import into Amazon Personalize, see Actions dataset.

The data you provide for each action must match your Actions dataset schema. Depending on your schema, action metadata can include empty/null values.

At minimum, you must provide an Action ID for each item (max length 256 characters). Your schema must have an minimum one metadata field, but if you add a null type, this value can be null for the action. You can add additional fields depending on your use case and your data. You can choose the field names and data types unless the fields are listed as required or reserved, and the data types are listed in Schema data types.

To add a categorical field, 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 action imports. Categorical values can have at most 1000 characters. If you have an action 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 an Actions dataset, see Service quotas.

Actions dataset schema example (custom)

The following example shows how to structure an Actions schema. The ACTION_ID field is required. The MEMBERSHIP_LEVEL field is a categorical string field. The VALUE, CREATION_TIMESTAMP, and REPEAT_FREQUENCY fields are reserved keywords with the required types. You can add a maximum of 10 columns. For information about schema requirements, see Custom dataset and schema requirements.

{
  "type": "record",
  "name": "Actions",
  "namespace": "com.amazonaws.personalize.schema",
  "fields": [
    {
      "name": "ACTION_ID",
      "type": "string"
    },
    {
      "name": "VALUE",
      "type": [
        "null",
        "long"
      ]
    },
    
    {
      "name": "MEMBERSHIP_LEVEL",
      "type": [
        "null",
        "string"
      ],
      "categorical": true
    },
    
    {
      "name": "CREATION_TIMESTAMP",
      "type": "long"
    },
    {
      "name": "REPEAT_FREQUENCY",
      "type": [
        "long",
        "null"
      ]
    }
  ],
  "version": "1.0"
}

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

ACTION_ID,VALUE,MEMBERSHIP_LEVEL,CREATION_TIMESTAMP,REPEAT_FREQUENCY
1,10,Deluxe|Premium,1510003267,7
2,5,Basic,1580003267,7
3,5,Preview,1590003267,3
4,10,Deluxe|Platinum,1560003267,4
...
...