# Custom datasets and schemas
<a name="custom-datasets-and-schemas"></a>

When you create a Custom dataset group, you create your own schemas from scratch. Custom dataset group datasets and schemas have fewer required fields and more flexibility. The following topics explain the schema and data requirements for datasets a Custom dataset group. Each dataset section lists the required data for the dataset type and provides a JSON example of a schema. 

For information on the types of data you can import into Amazon Personalize see [Types of data Amazon Personalize can use](datasets.md). For information about general Amazon Personalize schema requirements, such as formatting requirements and available field data types, see [Creating schema JSON files for Amazon Personalize schemas](how-it-works-dataset-schema.md). These requirements apply to all Amazon Personalize schemas.

**Topics**
+ [Custom dataset and schema requirements](#dataset-requirements)
+ [Item interactions dataset schema requirements (custom)](interactions-dataset-requirements.md)
+ [Users dataset schema requirements (custom)](user-dataset-requirements.md)
+ [Items dataset schema requirements (custom)](item-dataset-requirements.md)
+ [Actions dataset schema requirements (custom)](action-dataset-requirements.md)
+ [Action interactions dataset schema requirements (custom)](action-interactions-dataset-requirements.md)

## Custom dataset and schema requirements
<a name="dataset-requirements"></a>

When you create a dataset for a Custom dataset group, each dataset type has the following required fields and reserved keywords with required data types.


| Dataset type | Required fields | Reserved keywords | 
| --- | --- | --- | 
| Item interactions ([schema example](interactions-dataset-requirements.md#schema-examples-interactions)) |  USER\$1ID (`string`) ITEM\$1ID (`string`) TIMESTAMP (`long`)  |  EVENT\$1TYPE (`string`) EVENT\$1VALUE (`float`, `null`) IMPRESSION (`string`, `null`) RECOMMENDATION\$1ID (`string`, `null`) EVENT\$1ATTRIBUTION\$1SOURCE (`string`, `null`)  | 
| Users ([schema example](user-dataset-requirements.md#schema-examples-users)) |  USER\$1ID (`string`) 1 metadata field (categorical `string` or numerical)  |  | 
| Items ([schema example](item-dataset-requirements.md#schema-examples-items)) |  ITEM\$1ID (`string`) 1 metadata field (categorical or textual `string` field or numerical field)  |  CREATION\$1TIMESTAMP (`long`)  | 
| Actions ([schema example](action-dataset-requirements.md#schema-examples-actions)) |  ACTION\$1ID (`string`) 1 metadata field (categorical `string` or numerical)  |  CREATION\$1TIMESTAMP (`long`) VALUE (`long`, `null`) TYPE (`string`, `null`) EXPIRATION\$1TIMESTAMP (`long`, `null`) REPEAT\$1FREQUENCY (`long`, `null`)  | 
| Action interactions ([schema example](action-interactions-dataset-requirements.md#schema-examples-action-interactions)) |  USER\$1ID (`string`) ACTION\$1ID (`string`) EVENT\$1TYPE (`string`) TIMESTAMP (`long`)  |  IMPRESSION (`string`, `null`) RECOMMENDATION\$1ID (`string`, `null`)  | 

### Metadata fields
<a name="metadata-fields"></a>

Metadata includes string or non-string fields that aren't required or don't use a reserved keyword. Metadata schemas have the following restrictions: 
+ Users, Items, and Actions schemas require at least one metadata field.
+ You can add at most 25 metadata fields for a Users schema, 100 metadata fields for an Items schema, and 10 metadata fields for an Actions schema.
+ 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.

### Reserved keywords
<a name="reserved-keywords"></a>

Reserved keywords are optional, non-metadata fields. These fields are considered reserved because you must define the fields as their required data type when you use them, and the keywords can't be used as values in your data. Reserved categorical string fields must have `categorical` set to `true`, while reserved string fields can't be categorical. The following are reserved keywords:
+ EVENT\$1TYPE: For Item interactions datasets with one or more event types, such as both *click* and *download*, use an `EVENT_TYPE` field. You must define an EVENT\$1TYPE field as a `string` and can't be set as categorical.
+ EVENT\$1VALUE: For Item interactions datasets that include value data for events, such as the percentage of a video a user watched, use an `EVENT_VALUE` field with type `float` and optionally `null`.
+  CREATION\$1TIMESTAMP: For Items or Actions datasets with a timestamp for each item’s creation date, use a `CREATION_TIMESTAMP` field with a type `long`. Amazon Personalize uses `CREATION_TIMESTAMP` data to calculate the age of an item and adjust recommendations accordingly. See [Creation timestamp data](items-datasets.md#creation-timestamp-data). 
+  IMPRESSION: For Item interactions datasets with explicit impressions data, use an `IMPRESSION` field with type `String` and optionally type `null`. Impressions are lists of items that were visible to a user when they interacted with (for example, clicked or watched) a particular item. For more information, see [Impressions data](interactions-datasets.md#interactions-impressions-data). 
+  RECOMMENDATION\$1ID: For Item interactions datasets that use previous recommendations as implicit impressions data, optionally use a `RECOMMENDATION_ID` field with type `String` and optionally type `null`. 

  You don't need to add a `RECOMMENDATION_ID` field for Amazon Personalize to use implicit impressions when generating recommendations. You can pass a `recommendationId` in a [PutEvents](API_UBS_PutEvents.md) operation without it. For more information, see [Impressions data](interactions-datasets.md#interactions-impressions-data). 
+ VALUE: For Actions datasets, if you have value you data for some or all of your actions, add a `VALUE` field to your schema. For its type, use `long` and optionally type `null`. For more information about actions and their value, see [Value data](actions-datasets.md#action-value-data). 
+ ACTION\$1EXPIRATION\$1TIMESTAMP: For Actions datasets, if you have an expiration timestamp for some or all of your actions, add a `ACTION_EXPIRATION_TIMESTAMP` field to your schema. For its type, use `long` and optionally type `null`. For more information about expiration timestamps, see [Action expiration timestamp data](actions-datasets.md#action-expiration-timestamp-data). 
+ REPEAT\$1FREQUENCY: For Actions datasets, if you have repeat frequency data for some or all of your actions, add a `REPEAT_FREQUENCY` field to your schema. For its type, use `long` and optionally type `null`. For more information about repeat frequency data, see [Repeat frequency data](actions-datasets.md#action-repeat-frequency).