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c177af3feee1-0 | **InvalidInputException**
Provide a valid value for the field or parameter\.
HTTP Status Code: 400
**LimitExceededException**
The limit on the number of requests per second has been exceeded\.
HTTP Status Code: 400
**ResourceInUseException**
The specified resource is in use\.
HTTP Status Code: 400
**ResourceNotFoundException**
Could not find the specified resource\.
HTTP Status Code: 400 | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateSolutionVersion.md |
5738a915aea0-0 | For more information about using this API in one of the language\-specific AWS SDKs, see the following:
+ [AWS Command Line Interface](https://docs.aws.amazon.com/goto/aws-cli/personalize-2018-05-22/CreateSolutionVersion)
+ [AWS SDK for \.NET](https://docs.aws.amazon.com/goto/DotNetSDKV3/personalize-2018-05-22/CreateSolutionVersion)
+ [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/personalize-2018-05-22/CreateSolutionVersion)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/CreateSolutionVersion)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/CreateSolutionVersion)
+ [AWS SDK for JavaScript](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/personalize-2018-05-22/CreateSolutionVersion)
+ [AWS SDK for PHP V3](https://docs.aws.amazon.com/goto/SdkForPHPV3/personalize-2018-05-22/CreateSolutionVersion) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateSolutionVersion.md |
5738a915aea0-1 | + [AWS SDK for Python](https://docs.aws.amazon.com/goto/boto3/personalize-2018-05-22/CreateSolutionVersion)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/CreateSolutionVersion) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateSolutionVersion.md |
5e49dd73c249-0 | Use an asynchronous batch workflow to get recommendations from large datasets that do not require real\-time updates\. For instance, you might create a batch inference job to get product recommendations for all users on an email list, or to get [item\-to\-item similarities \(SIMS\)](native-recipe-sims.md) across an inventory\. To get batch recommendations, you can create a batch inference job by calling [CreateBatchInferenceJob](API_CreateBatchInferenceJob.md)\.
In order to get batch recommendations, the IAM user role that invokes the [CreateBatchInferenceJob](API_CreateBatchInferenceJob.md) operation must have read and write permissions to your input and output Amazon S3 buckets respectively\. For more information on bucket permissions, see [User Policy Examples](https://docs.aws.amazon.com/AmazonS3/latest/dev/example-policies-s3.html) in the *Amazon Simple Storage Service \(S3\) Developer Guide*\.
You can perform batch inference operations with any of the following tools:
+ [Amazon Personalize console](#batch-console)
+ [AWS CLI](#batch-cli)
+ [AWS SDK](#batch-sdk)
**How scoring works** | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
5e49dd73c249-1 | + [AWS SDK](#batch-sdk)
**How scoring works**
Item scores calculated by batch recommendation jobs are calculated the same ways as described in [Getting Real\-Time Recommendations](getting-real-time-recommendations.md), and can be viewed in the batch job's output JSON file\. Scores are only returned by models trained with the HRNN and Personalize\-Ranking recipes\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
1dca0c3b8fe7-0 | The [CreateBatchInferenceJob](API_CreateBatchInferenceJob.md) uses a chosen solution version to make recommendations based on data provided in an input JSON file\. The result is then returned as a JSON file to an Amazon S3 bucket\. The following tab list contains correctly formatted JSON input and output examples for each recipe type\.
**HRNN and USER\_PERSONALIZATION**
------ | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
1459afc0fdac-0 | ```
{"userId": "4638"}
{"userId": "663"}
{"userId": "3384"}
...
```
------ | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
6fd9b760a359-0 | ```
{"input":{"userId":"4638"}, "output": {"recommendedItems": ["296", "1", "260", "318"]}, {"scores": [0.0009785, 0.000976, 0.0008851]}}
{"input":{"userId":"663"}, "output": {"recommendedItems": ["1393", "3793", "2701", "3826"]}, {"scores": [0.00008149, 0.00007025, 0.000652]}}
{"input":{"userId":"3384"}, "output": {"recommendedItems": ["8368", "5989", "40815", "48780"]}, {"scores": [0.003015, 0.00154, 0.00142]}}
...
```
------
**Popularity\-Count**
------ | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
88e8c70535e1-0 | ```
{}
{"itemId": "105"}
{"itemId": "41"}
...
```
------ | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
ef3cc11e6391-0 | ```
{"input": {}, "output": {"recommendedItems": ["105", "106", "441"]}}
{"input": {"itemId": "105"}, "output": {"recommendedItems": ["105", "106", "441"]}}
{"input": {"itemId": "41"}, "output": {"recommendedItems": ["105", "106", "441"]}}
...
```
------
**Personalize\-Ranking**
------ | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
f5e2c48eb298-0 | ```
{"userId": "891", "itemList": ["27", "886", "101"]}
{"userId": "445", "itemList": ["527", "55", "901"]}
{"userId": "71", "itemList": ["27", "351", "101"]}
...
```
------ | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
ef8b5296d30a-0 | ```
{"input": {"userId": "891", "itemList": ["27", "886", "101"]}, "output": {"recommendedItems": ["27", "101", "886"]}, {"scores": [0.48421, 0.28133, 0.23446]}}
{"input": {"userId": "445", "itemList": ["527", "55", "901"]}, "output": {"recommendedItems": ["901", "527", "55"]}, {"scores": [0.46972, 0.31011, 0.22017]}}
{"input": {"userId": "71", "itemList": ["29", "351", "199"]}, "output": {"recommendedItems": ["351", "29", "199"]}, {"scores": [0.68937, 0.24829, 0.06232]}}
...
```
------
**SIMS**
------ | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
7bbf1a50bee6-0 | ```
{"itemId": "105"}
{"itemId": "106"}
{"itemId": "441"}
...
```
------ | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
6bfe81dcff58-0 | ```
{"input": {"itemId": "105"}, "output": {"recommendedItems": ["106", "107", "49"]}, }
{"input": {"itemId": "106"}, "output": {"recommendedItems": ["105", "107", "49"]}}
{"input": {"itemId": "441"}, "output": {"recommendedItems": ["2", "442", "435"]}}
...
```
------ | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
94f9e4554c5b-0 | The following procedure outlines the batch inference workflow using the Amazon Personalize console\. This procedure assumes that you have already created a solution that is properly formatted to perform the desired batch job on your dataset\.
1. Open the Amazon Personalize console at [https://console\.aws\.amazon\.com/personalize/home](https://console.aws.amazon.com/personalize/home) and sign in to your account\.
1. Choose **Batch inference jobs** in the navigation pane, then choose **Create batch inference job**\.
1. In **Batch inference job details**, in **Batch inference job name**, specify a name for your batch inference job\.
1. For **IAM service role**, choose the Amazon Personalize IAM service role that has read and write access to your input and output Amazon S3 buckets respectively\.
1. For **Solution**, choose the solution that you want to use to generate the recommendations The solution recipe must match the input data's format\.
1. In **Input data configuration**, specify the Amazon S3 path to your input file\. In **Output data configuration**, specify the path to your output Amazon S3 bucket\.
1. Choose **Create batch inference job**\. Batch inference job creation starts and the **Batch inference jobs** page appears with the **Batch inference job detail** section displayed\.
Your screen should look similar to the following: | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
94f9e4554c5b-1 | Your screen should look similar to the following:
![\[Image NOT FOUND\]](http://docs.aws.amazon.com/personalize/latest/dg/images/batch-job-result.png)
**Note**
Creating a batch inference job takes time\.
1. When the batch inference job's status changes to **Active**, you can retrieve the job's output from the designated output Amazon S3 bucket\. The output file's name will be of the format `input-name.out`\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
cedb0be4ec66-0 | The following is an example of a batch inference workflow using the AWS CLI for a solution trained using the the `USER_PERSONALIZATION` recipe\. A JSON file called `batch.json` is passed as input, and the output file, `batch.json.out`, is returned to an Amazon S3 bucket\.
For `batch-inference-job-config`, the example includes `USER_PERSONALIZE` recipe specific `itemExplorationConfig` hyperparameters: `explorationWeight` and `explorationItemAgeCutOff`\. Optionally include `explorationWeight` and `explorationItemAgeCutOff` values to configure exploration\. For more information, see [User\-Personalization Recipe](native-recipe-new-item-USER_PERSONALIZATION.md)\.
```
aws personalize create-batch-inference-job --job-name batchTest \
--solution-version-arn arn:aws:personalize:us-west-2:012345678901:solution/batch-test-solution-version/1234abcd \
--job-input s3DataSource={path=s3://personalize/batch/input/input.json} \
--job-output s3DataDestination={path=s3://personalize/batch/output/} \
--role-arn arn:aws:iam::012345678901:role/import-export-role \ | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
cedb0be4ec66-1 | --role-arn arn:aws:iam::012345678901:role/import-export-role \
--batch-inference-job-config itemExplorationConfig={explorationWeight=0.3, explorationItemAgeCutOff=30}
{
"batchInferenceJobArn": "arn:aws:personalize:us-west-2:012345678901:batch-inference-job/batchTest"
}
```
Once a batch inference job is created, you can inspect it further with the [DescribeBatchInferenceJob](API_DescribeBatchInferenceJob.md) operation\.
```
aws personalize describe-batch-inference-job --batch-inference-job-arn arn:aws:personalize:us-west-2:012345678901:batch-inference-job/batchTest
{
"jobName": "batchTest",
"batchInferenceJobArn": "arn:aws:personalize:us-west-2:012345678901:batch-inference-job/batchTest",
"solutionVersionArn": "arn:aws:personalize:us-west-2:012345678901:solution/batch-test-solution-version/1234abcd",
"jobInput": {
"s3DataSource": { | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
cedb0be4ec66-2 | "jobInput": {
"s3DataSource": {
"path": "s3://personalize/batch/input/batch.json"
}
},
"jobOutput": {
"s3DataDestination": {
"path": "s3://personalize/batch/output/"
}
},
"roleArn": "arn:aws:iam::012345678901:role/import-export-role",
"status": "ACTIVE",
"creationDateTime": 1542392161.837,
"lastUpdateDateTime: 1542393013.377
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
7112fa40ec35-0 | Use the following code to get batch recommendations using the AWS Python SDK\. The example includes `itemExplorationConfig` hyperparameters for solution versions trained using the `USER_PERSONALIZATION` recommendation recipe\. Optionally include the `itemExplorationConfig` hyperparameters to configure exploration\. For more information see [User\-Personalization Recipe](native-recipe-new-item-USER_PERSONALIZATION.md)\.
The operation reads an input JSON file from an Amazon S3 bucket and places an output JSON file \(`input-file-name.out`\) in an Amazon S3 bucket\.
The first item in the response file is considered by Amazon Personalize to be of most interest to the user\.
```
import boto3
personalize_rec = boto3.client(service_name='personalize')
personalize_rec.create_batch_inference_job (
solutionVersionArn = "Solution version ARN",
jobName = "Batch job name",
roleArn = "IAM role ARN",
batchInferenceJobConfig = { | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
0561120d2eba-0 | "itemExplorationConfig": {
"explorationWeight": "0.3",
"explorationItemAgeCutOff": "30"
}
},
jobInput =
{"s3DataSource": {"path": "S3 input path"}},
jobOutput =
{"s3DataDestination": {"path": "S3 output path"}}
)
```
The command returns the ARN for the batch job \(the `batchRecommendationsJobArn`\)\.
Processing the batch job might take a while to complete\. You can check a job's status by calling [DescribeBatchInferenceJob](API_DescribeBatchInferenceJob.md) and passing a `batchRecommendationsJobArn` as the input parameter\. You can also list all Amazon Personalize batch inference jobs in your AWS environment by calling [ListBatchInferenceJobs](API_ListBatchInferenceJobs.md)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/recommendations-batch.md |
f0a0282fa568-0 | A solution is created by calling the [CreateSolution](API_CreateSolution.md) API\. A condensed version of the `CreateSolution` request is shown, highlighting the `solutionConfig` object\. You use `solutionConfig` to override the default parameters of a recipe\. When `performAutoML` is `true`, all parameters of the `solutionConfig` object are ignored except for `autoMLConfig`\. The sub\-objects of `solutionConfig` are discussed in the following sections\.
```
{
"name": "string",
"performAutoML": boolean,
"recipeArn": "string",
"performHPO": boolean,
"eventType": "string",
"solutionConfig": {
"autoMLConfig": {
"metricName": "string",
"recipeList": [ "string" ]
},
"eventValueThreshold": "string",
"featureTransformationParameters": {
"string" : "string"
},
"algorithmHyperParameters": {
"string" : "string"
},
"hpoConfig": {
"algorithmHyperParameterRanges": {
...
},
"hpoResourceConfig": { | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/customizing-solution-config.md |
f0a0282fa568-1 | ...
},
"hpoResourceConfig": {
"maxNumberOfTrainingJobs": "string",
"maxParallelTrainingJobs": "string"
}
},
},
}
```
**Topics**
+ [Hyperparameters and HPO](customizing-solution-config-hpo.md) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/customizing-solution-config.md |
b40a4737f3e3-0 | Creates an Amazon Personalize schema from the specified schema string\. The schema you create must be in Avro JSON format\.
Amazon Personalize recognizes three schema variants\. Each schema is associated with a dataset type and has a set of required field and keywords\. You specify a schema when you call [CreateDataset](API_CreateDataset.md)\.
For more information on schemas, see [Datasets and Schemas](https://docs.aws.amazon.com/personalize/latest/dg/how-it-works-dataset-schema.html)\.
**Related APIs**
+ [ListSchemas](API_ListSchemas.md)
+ [DescribeSchema](API_DescribeSchema.md)
+ [DeleteSchema](API_DeleteSchema.md) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateSchema.md |
d844f8053e06-0 | ```
{
"name": "string",
"schema": "string"
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateSchema.md |
46a4d7679a86-0 | The request accepts the following data in JSON format\.
** [name](#API_CreateSchema_RequestSyntax) ** <a name="personalize-CreateSchema-request-name"></a>
The name for the schema\.
Type: String
Length Constraints: Minimum length of 1\. Maximum length of 63\.
Pattern: `^[a-zA-Z0-9][a-zA-Z0-9\-_]*`
Required: Yes
** [schema](#API_CreateSchema_RequestSyntax) ** <a name="personalize-CreateSchema-request-schema"></a>
A schema in Avro JSON format\.
Type: String
Length Constraints: Maximum length of 10000\.
Required: Yes | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateSchema.md |
929be6c42f13-0 | ```
{
"schemaArn": "string"
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateSchema.md |
8924c0cc87e6-0 | If the action is successful, the service sends back an HTTP 200 response\.
The following data is returned in JSON format by the service\.
** [schemaArn](#API_CreateSchema_ResponseSyntax) ** <a name="personalize-CreateSchema-response-schemaArn"></a>
The Amazon Resource Name \(ARN\) of the created schema\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateSchema.md |
a8c5d8360761-0 | **InvalidInputException**
Provide a valid value for the field or parameter\.
HTTP Status Code: 400
**LimitExceededException**
The limit on the number of requests per second has been exceeded\.
HTTP Status Code: 400
**ResourceAlreadyExistsException**
The specified resource already exists\.
HTTP Status Code: 400 | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateSchema.md |
67b05fceb8be-0 | For more information about using this API in one of the language\-specific AWS SDKs, see the following:
+ [AWS Command Line Interface](https://docs.aws.amazon.com/goto/aws-cli/personalize-2018-05-22/CreateSchema)
+ [AWS SDK for \.NET](https://docs.aws.amazon.com/goto/DotNetSDKV3/personalize-2018-05-22/CreateSchema)
+ [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/personalize-2018-05-22/CreateSchema)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/CreateSchema)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/CreateSchema)
+ [AWS SDK for JavaScript](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/personalize-2018-05-22/CreateSchema)
+ [AWS SDK for PHP V3](https://docs.aws.amazon.com/goto/SdkForPHPV3/personalize-2018-05-22/CreateSchema) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateSchema.md |
67b05fceb8be-1 | + [AWS SDK for Python](https://docs.aws.amazon.com/goto/boto3/personalize-2018-05-22/CreateSchema)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/CreateSchema) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateSchema.md |
d32beda35483-0 | Describes a custom algorithm\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_Algorithm.md |
cf1b93d9996a-0 | **algorithmArn** <a name="personalize-Type-Algorithm-algorithmArn"></a>
The Amazon Resource Name \(ARN\) of the algorithm\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: No
**algorithmImage** <a name="personalize-Type-Algorithm-algorithmImage"></a>
The URI of the Docker container for the algorithm image\.
Type: [AlgorithmImage](API_AlgorithmImage.md) object
Required: No
**creationDateTime** <a name="personalize-Type-Algorithm-creationDateTime"></a>
The date and time \(in Unix time\) that the algorithm was created\.
Type: Timestamp
Required: No
**defaultHyperParameterRanges** <a name="personalize-Type-Algorithm-defaultHyperParameterRanges"></a>
Specifies the default hyperparameters, their ranges, and whether they are tunable\. A tunable hyperparameter can have its value determined during hyperparameter optimization \(HPO\)\.
Type: [DefaultHyperParameterRanges](API_DefaultHyperParameterRanges.md) object
Required: No | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_Algorithm.md |
cf1b93d9996a-1 | Type: [DefaultHyperParameterRanges](API_DefaultHyperParameterRanges.md) object
Required: No
**defaultHyperParameters** <a name="personalize-Type-Algorithm-defaultHyperParameters"></a>
Specifies the default hyperparameters\.
Type: String to string map
Map Entries: Maximum number of 100 items\.
Key Length Constraints: Maximum length of 256\.
Value Length Constraints: Maximum length of 1000\.
Required: No
**defaultResourceConfig** <a name="personalize-Type-Algorithm-defaultResourceConfig"></a>
Specifies the default maximum number of training jobs and parallel training jobs\.
Type: String to string map
Map Entries: Maximum number of 100 items\.
Key Length Constraints: Maximum length of 256\.
Value Length Constraints: Maximum length of 1000\.
Required: No
**lastUpdatedDateTime** <a name="personalize-Type-Algorithm-lastUpdatedDateTime"></a>
The date and time \(in Unix time\) that the algorithm was last updated\.
Type: Timestamp
Required: No
**name** <a name="personalize-Type-Algorithm-name"></a>
The name of the algorithm\.
Type: String | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_Algorithm.md |
cf1b93d9996a-2 | The name of the algorithm\.
Type: String
Length Constraints: Minimum length of 1\. Maximum length of 63\.
Pattern: `^[a-zA-Z0-9][a-zA-Z0-9\-_]*`
Required: No
**roleArn** <a name="personalize-Type-Algorithm-roleArn"></a>
The Amazon Resource Name \(ARN\) of the role\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: No
**trainingInputMode** <a name="personalize-Type-Algorithm-trainingInputMode"></a>
The training input mode\.
Type: String
Length Constraints: Maximum length of 256\.
Required: No | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_Algorithm.md |
400b2d50a379-0 | For more information about using this API in one of the language\-specific AWS SDKs, see the following:
+ [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/personalize-2018-05-22/Algorithm)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/Algorithm)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/Algorithm)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/Algorithm) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_Algorithm.md |
0be750247852-0 | The following data types are supported by Amazon Personalize:
+ [Algorithm](API_Algorithm.md)
+ [AlgorithmImage](API_AlgorithmImage.md)
+ [AutoMLConfig](API_AutoMLConfig.md)
+ [AutoMLResult](API_AutoMLResult.md)
+ [BatchInferenceJob](API_BatchInferenceJob.md)
+ [BatchInferenceJobConfig](API_BatchInferenceJobConfig.md)
+ [BatchInferenceJobInput](API_BatchInferenceJobInput.md)
+ [BatchInferenceJobOutput](API_BatchInferenceJobOutput.md)
+ [BatchInferenceJobSummary](API_BatchInferenceJobSummary.md)
+ [Campaign](API_Campaign.md)
+ [CampaignConfig](API_CampaignConfig.md)
+ [CampaignSummary](API_CampaignSummary.md)
+ [CampaignUpdateSummary](API_CampaignUpdateSummary.md)
+ [CategoricalHyperParameterRange](API_CategoricalHyperParameterRange.md)
+ [ContinuousHyperParameterRange](API_ContinuousHyperParameterRange.md)
+ [Dataset](API_Dataset.md)
+ [DatasetGroup](API_DatasetGroup.md) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_Types.md |
0be750247852-1 | + [Dataset](API_Dataset.md)
+ [DatasetGroup](API_DatasetGroup.md)
+ [DatasetGroupSummary](API_DatasetGroupSummary.md)
+ [DatasetImportJob](API_DatasetImportJob.md)
+ [DatasetImportJobSummary](API_DatasetImportJobSummary.md)
+ [DatasetSchema](API_DatasetSchema.md)
+ [DatasetSchemaSummary](API_DatasetSchemaSummary.md)
+ [DatasetSummary](API_DatasetSummary.md)
+ [DataSource](API_DataSource.md)
+ [DefaultCategoricalHyperParameterRange](API_DefaultCategoricalHyperParameterRange.md)
+ [DefaultContinuousHyperParameterRange](API_DefaultContinuousHyperParameterRange.md)
+ [DefaultHyperParameterRanges](API_DefaultHyperParameterRanges.md)
+ [DefaultIntegerHyperParameterRange](API_DefaultIntegerHyperParameterRange.md)
+ [EventTracker](API_EventTracker.md)
+ [EventTrackerSummary](API_EventTrackerSummary.md)
+ [FeatureTransformation](API_FeatureTransformation.md) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_Types.md |
0be750247852-2 | + [FeatureTransformation](API_FeatureTransformation.md)
+ [Filter](API_Filter.md)
+ [FilterSummary](API_FilterSummary.md)
+ [HPOConfig](API_HPOConfig.md)
+ [HPOObjective](API_HPOObjective.md)
+ [HPOResourceConfig](API_HPOResourceConfig.md)
+ [HyperParameterRanges](API_HyperParameterRanges.md)
+ [IntegerHyperParameterRange](API_IntegerHyperParameterRange.md)
+ [Recipe](API_Recipe.md)
+ [RecipeSummary](API_RecipeSummary.md)
+ [S3DataConfig](API_S3DataConfig.md)
+ [Solution](API_Solution.md)
+ [SolutionConfig](API_SolutionConfig.md)
+ [SolutionSummary](API_SolutionSummary.md)
+ [SolutionVersion](API_SolutionVersion.md)
+ [SolutionVersionSummary](API_SolutionVersionSummary.md)
+ [TunedHPOParams](API_TunedHPOParams.md)
The following data types are supported by Amazon Personalize Events:
+ [Event](API_UBS_Event.md) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_Types.md |
0be750247852-3 | The following data types are supported by Amazon Personalize Events:
+ [Event](API_UBS_Event.md)
The following data types are supported by Amazon Personalize Runtime:
+ [PredictedItem](API_RS_PredictedItem.md) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_Types.md |
f734385a82a3-0 | Describes the given dataset\. For more information on datasets, see [CreateDataset](API_CreateDataset.md)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeDataset.md |
ff494f26fee0-0 | ```
{
"datasetArn": "string"
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeDataset.md |
5bc7733d06d3-0 | The request accepts the following data in JSON format\.
** [datasetArn](#API_DescribeDataset_RequestSyntax) ** <a name="personalize-DescribeDataset-request-datasetArn"></a>
The Amazon Resource Name \(ARN\) of the dataset to describe\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: Yes | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeDataset.md |
ab1004cdf846-0 | ```
{
"dataset": {
"creationDateTime": number,
"datasetArn": "string",
"datasetGroupArn": "string",
"datasetType": "string",
"lastUpdatedDateTime": number,
"name": "string",
"schemaArn": "string",
"status": "string"
}
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeDataset.md |
956c2ddb3fe9-0 | If the action is successful, the service sends back an HTTP 200 response\.
The following data is returned in JSON format by the service\.
** [dataset](#API_DescribeDataset_ResponseSyntax) ** <a name="personalize-DescribeDataset-response-dataset"></a>
A listing of the dataset's properties\.
Type: [Dataset](API_Dataset.md) object | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeDataset.md |
1b0c0ace9246-0 | **InvalidInputException**
Provide a valid value for the field or parameter\.
HTTP Status Code: 400
**ResourceNotFoundException**
Could not find the specified resource\.
HTTP Status Code: 400 | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeDataset.md |
a0dd43469df0-0 | For more information about using this API in one of the language\-specific AWS SDKs, see the following:
+ [AWS Command Line Interface](https://docs.aws.amazon.com/goto/aws-cli/personalize-2018-05-22/DescribeDataset)
+ [AWS SDK for \.NET](https://docs.aws.amazon.com/goto/DotNetSDKV3/personalize-2018-05-22/DescribeDataset)
+ [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/personalize-2018-05-22/DescribeDataset)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/DescribeDataset)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/DescribeDataset)
+ [AWS SDK for JavaScript](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/personalize-2018-05-22/DescribeDataset)
+ [AWS SDK for PHP V3](https://docs.aws.amazon.com/goto/SdkForPHPV3/personalize-2018-05-22/DescribeDataset) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeDataset.md |
a0dd43469df0-1 | + [AWS SDK for Python](https://docs.aws.amazon.com/goto/boto3/personalize-2018-05-22/DescribeDataset)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/DescribeDataset) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeDataset.md |
24a3bafea471-0 | If your Interactions dataset includes data that you don't want to use for training, you can filter out those records by setting a threshold for a value, such as price, or by specifying a type of event, such as purchase or click\. By filtering, you can train using only a relevant subset of your data or remove noise to train a more optimized model\.
You can filter records from an Interactions dataset two ways:
+ **Set a threshold to exclude records based on a specific value by specifying an event value in your recipe** \- If the records include an amount that is associated with a specific event—for example, the price a user paid is associated with the purchase of an item—you can set a specific value in a recipe as a threshold to exclude records from training\. The amount is called an *event value*\.
+ **Exclude records of a certain type by specifying an event type in your recipe** – A dataset often includes specific types of activities, for example, `“purchase”`, `“click”`, or `"wishlisted"`\. These are called *event types*\. To include only records for specific event types in training, filter your dataset by event type in your recipe\.
Likewise, an Interactions dataset often includes specific types of activities, for example, `“purchase”`, `“click”`, or `"wishlisted"`\. These are called *event types*\. To include only records for specific event types in training, filter your dataset by event type in your recipe\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
24a3bafea471-1 | To filter by event value or event type, you create an Interactions schema for the recipe that you use to create your solution\. To use a more specific subset in training, you can also filter a dataset by an event value and event type\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
6cac48aa55c5-0 | In the following procedure, you use the AWS SDK for Python \(Boto3\) to create an Interaction schema that filters a training dataset\. You can use a Jupyter \(iPython\) notebook to accomplish the same task\. For more information, see [Getting Started Using Amazon Personalize APIs with Jupyter \(iPython\) Notebooks](getting-started-python.md#gs-jupyter-notebook)\.
**Prerequisites: **Complete the prerequisites and verify that your Python environment is set up as described in [Getting Started \(AWS SDK for Python\)](getting-started-python.md)\.
**To filter records used in a training dataset by event value or event type**
1. Create an Interaction schema and include the `EVENT_TYPE` and `EVENT_VALUE` fields using `"name"` and `"type"` key\-value pairs as shown\.
```
import boto3
import json
personalize = boto3.client('personalize') | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
556727636f8a-0 | schema_name = 'YourSchemaName' | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
ebc205b6f62b-0 | schema = {
"type": "record",
"name": "Interactions",
"namespace": "com.amazonaws.personalize.schema",
"fields": [
{
"name": "USER_ID",
"type": "string"
},
{
"name": "ITEM_ID",
"type": "string"
},
{
"name": "EVENT_VALUE",
"type": "float"
},
{
"name": "EVENT_TYPE",
"type": "string"
},
{
"name": "TIMESTAMP",
"type": "long"
}
],
"version": "1.0"
} | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
cfb2cc3f5e27-0 | create_schema_response = personalize.create_schema(
name = schema_name,
schema = json.dumps(schema)
)
#To get the schema ARN, use the following lines
schema_arn = create_schema_response['schemaArn']
print('Schema ARN:' + schema_arn )
```
1. Format your input data to match your schema\. For a code sample, see [Formatting Your Input Data](data-prep-formatting.md)\.
1. Upload your data to an Amazon Simple Storage Service \(Amazon S3\) bucket\. For a code sample, see [Uploading to an S3 Bucket](data-prep-upload-s3.md)\.
1. Import your data into Amazon Personalize with the [CreateDatasetImportJob](API_CreateDatasetImportJob.md) API\. Be sure to record your dataset group Amazon Resource Name \(ARN\) because you will need it when you create the solution\. For a code sample, see [Import Your Data Using the AWS Python SDK](data-prep-importing.md#python-import-ex)\.
1. Get the ARN of the recipe that you want to use when you create your solution\. You'll need it when you create the solution\.
```
import boto3
personalize = boto3.client('personalize') | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
e84e4086ede6-0 | recipe_list = personalize.list_recipes()
for recipe in recipe_list['recipes']:
print(recipe['recipeArn']) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
793170523669-0 | recipe_arn = "arn:aws:personalize:::recipe/aws-recipe-name"
```
1. Call the [CreateSolution](API_CreateSolution.md) API\. If you want to specify the event type, for example `“purchase”`, set it in the `eventType` parameter\. If you want to specify an event value, for example `10`, set it in the `eventValueThreshold` parameter\. You can also specify both an event type and an event value\.
```
import boto3
personalize = boto3.client('personalize') | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
decc4478b298-0 | create_solution_response = personalize.create_solution(
name = "your-solution-name",
datasetGroupArn = dataset_group_arn,
recipeArn = recipe_arn,
"eventType": "purchase",
solutionConfig = {
"eventValueThreshold": "10"
}
) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
6140331070d4-0 | solution_arn = create_solution_response['solutionArn'] | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
5af1bfd320f2-0 | solution_description = personalize.describe_solution(solutionArn = solution_arn)['solution']
print('Solution status: ' + solution_description['status'])
```
1. When you have the solution, use it to train a model by specifying its solution ARN in a [CreateSolutionVersion](API_CreateSolutionVersion.md) request\.
```
import boto3
personalize = boto3.client('personalize') | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
26cd6fb140e8-0 | create_solution_version_response = personalize.create_solution_version(solutionArn = solution_arn) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
a0534494eaf0-0 | solution_version_arn = create_solution_version_response['solutionVersionArn'] | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
c5653294c75d-0 | solution_version_description = personalize.describe_solution_version(
solutionVersionArn = solution_version_arn)['solutionVersion']
print('Solution version status: ' + solution_version_description['status'])
```
Training is complete when the status is `ACTIVE`\. For more information, see [Creating a Solution](training-deploying-solutions.md)\.
After you train a model, you should evaluate its performance\. To optimize your model, you might want to adjust the `eventValueThreshold` or other hyperparameters\. For more information, see [Evaluating a Solution Version](working-with-training-metrics.md)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/event-values-types.md |
f9bffd719ad0-0 | Popularity\-count returns the top popular items from a dataset\. A popular item is defined by the number of times it occurs in the dataset\. The recipe returns the same popular items for all users\. Popularity\-count is a good starting point to compare against other recipes\.
This predefined recipe has the following properties:
+ **Name** – `aws-popularity-count`
+ **Recipe ARN** – `arn:aws:personalize:::recipe/aws-popularity-count`
+ **Algorithm ARN** – `arn:aws:personalize:::algorithm/aws-popularity-count`
+ **Feature Transformation ARN** – `arn:aws:personalize:::feature-transformation/sims`
+ **Recipe type** – `USER_PERSONALIZATION`
Popularity\-count has no exposed hyperparameters\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-popularity.md |
70eb6ab54fe7-0 | Describes the resource configuration for hyperparameter optimization \(HPO\)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_HPOResourceConfig.md |
13f04ab2694e-0 | **maxNumberOfTrainingJobs** <a name="personalize-Type-HPOResourceConfig-maxNumberOfTrainingJobs"></a>
The maximum number of training jobs when you create a solution version\. The maximum value for `maxNumberOfTrainingJobs` is `40`\.
Type: String
Length Constraints: Maximum length of 256\.
Required: No
**maxParallelTrainingJobs** <a name="personalize-Type-HPOResourceConfig-maxParallelTrainingJobs"></a>
The maximum number of parallel training jobs when you create a solution version\. The maximum value for `maxParallelTrainingJobs` is `10`\.
Type: String
Length Constraints: Maximum length of 256\.
Required: No | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_HPOResourceConfig.md |
7d7fd68f55c3-0 | For more information about using this API in one of the language\-specific AWS SDKs, see the following:
+ [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/personalize-2018-05-22/HPOResourceConfig)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/HPOResourceConfig)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/HPOResourceConfig)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/HPOResourceConfig) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_HPOResourceConfig.md |
88916ffb78ea-0 | When getting recommendations with Amazon Personalize, you can filter results based on custom criteria\. For example, you might not want to recommend products that a user has already purchased, or recommend movies that a user has already watched\. By filtering your recommendations, you can control the items that will be recommended to users\.
You create filters for a dataset group and apply filters to real\-time and batch recommendations at the campaign level\. To filter items, you first create a filter, which includes a SQL\-like filter expression based on a chosen event type or criteria\. You then apply the filter to a [GetRecommendations](API_RS_GetRecommendations.md), [CreateBatchInferenceJob](API_CreateBatchInferenceJob.md) or [GetPersonalizedRanking](API_RS_GetPersonalizedRanking.md) call, or apply it to a campaign on the **Test campaign results** panel in the console\. You can create, edit, delete, and apply filters using the Amazon Personalize console, the AWS Command Line Interface, and the AWS SDK\.
For information on the number of filters you can create see [Service Quotas](limits.md#limits-table)\.
**Note**
To filter recommendations using a campaign you deployed before July 30, 2020, you must re\-deploy the campaign by using the [UpdateCampaign](API_UpdateCampaign.md) call or by creating a new campaign\.
**Topics**
+ [Filter Expressions](filter-expressions.md)
+ [Filtering Real\-time Recommendations](filter-real-time.md) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/filter.md |
88916ffb78ea-1 | + [Filter Expressions](filter-expressions.md)
+ [Filtering Real\-time Recommendations](filter-real-time.md)
+ [Filtering Batch Recommendations](filter-batch.md) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/filter.md |
7c2770c7b856-0 | Specifies the hyperparameters and their default ranges\. Hyperparameters can be categorical, continuous, or integer\-valued\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DefaultHyperParameterRanges.md |
4f916a861ace-0 | **categoricalHyperParameterRanges** <a name="personalize-Type-DefaultHyperParameterRanges-categoricalHyperParameterRanges"></a>
The categorical hyperparameters and their default ranges\.
Type: Array of [DefaultCategoricalHyperParameterRange](API_DefaultCategoricalHyperParameterRange.md) objects
Array Members: Maximum number of 100 items\.
Required: No
**continuousHyperParameterRanges** <a name="personalize-Type-DefaultHyperParameterRanges-continuousHyperParameterRanges"></a>
The continuous hyperparameters and their default ranges\.
Type: Array of [DefaultContinuousHyperParameterRange](API_DefaultContinuousHyperParameterRange.md) objects
Array Members: Maximum number of 100 items\.
Required: No
**integerHyperParameterRanges** <a name="personalize-Type-DefaultHyperParameterRanges-integerHyperParameterRanges"></a>
The integer\-valued hyperparameters and their default ranges\.
Type: Array of [DefaultIntegerHyperParameterRange](API_DefaultIntegerHyperParameterRange.md) objects
Array Members: Maximum number of 100 items\.
Required: No | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DefaultHyperParameterRanges.md |
315e76b56de8-0 | For more information about using this API in one of the language\-specific AWS SDKs, see the following:
+ [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/personalize-2018-05-22/DefaultHyperParameterRanges)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/DefaultHyperParameterRanges)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/DefaultHyperParameterRanges)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/DefaultHyperParameterRanges) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DefaultHyperParameterRanges.md |
cbff8dd573e0-0 | The following list contains the parameters that all actions use for signing Signature Version 4 requests with a query string\. Any action\-specific parameters are listed in the topic for that action\. For more information about Signature Version 4, see [Signature Version 4 Signing Process](http://docs.aws.amazon.com/general/latest/gr/signature-version-4.html) in the *Amazon Web Services General Reference*\.
**Action** <a name="CommonParameters-Action"></a>
The action to be performed\.
Type: string
Required: Yes
**Version** <a name="CommonParameters-Version"></a>
The API version that the request is written for, expressed in the format YYYY\-MM\-DD\.
Type: string
Required: Yes
**X\-Amz\-Algorithm** <a name="CommonParameters-X-Amz-Algorithm"></a>
The hash algorithm that you used to create the request signature\.
Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header\.
Type: string
Valid Values: `AWS4-HMAC-SHA256`
Required: Conditional
**X\-Amz\-Credential** <a name="CommonParameters-X-Amz-Credential"></a> | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/CommonParameters.md |
cbff8dd573e0-1 | **X\-Amz\-Credential** <a name="CommonParameters-X-Amz-Credential"></a>
The credential scope value, which is a string that includes your access key, the date, the region you are targeting, the service you are requesting, and a termination string \("aws4\_request"\)\. The value is expressed in the following format: *access\_key*/*YYYYMMDD*/*region*/*service*/aws4\_request\.
For more information, see [Task 2: Create a String to Sign for Signature Version 4](http://docs.aws.amazon.com/general/latest/gr/sigv4-create-string-to-sign.html) in the *Amazon Web Services General Reference*\.
Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header\.
Type: string
Required: Conditional
**X\-Amz\-Date** <a name="CommonParameters-X-Amz-Date"></a>
The date that is used to create the signature\. The format must be ISO 8601 basic format \(YYYYMMDD'T'HHMMSS'Z'\)\. For example, the following date time is a valid X\-Amz\-Date value: `20120325T120000Z`\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/CommonParameters.md |
cbff8dd573e0-2 | Condition: X\-Amz\-Date is optional for all requests; it can be used to override the date used for signing requests\. If the Date header is specified in the ISO 8601 basic format, X\-Amz\-Date is not required\. When X\-Amz\-Date is used, it always overrides the value of the Date header\. For more information, see [Handling Dates in Signature Version 4](http://docs.aws.amazon.com/general/latest/gr/sigv4-date-handling.html) in the *Amazon Web Services General Reference*\.
Type: string
Required: Conditional
**X\-Amz\-Security\-Token** <a name="CommonParameters-X-Amz-Security-Token"></a>
The temporary security token that was obtained through a call to AWS Security Token Service \(AWS STS\)\. For a list of services that support temporary security credentials from AWS Security Token Service, go to [AWS Services That Work with IAM](http://docs.aws.amazon.com/IAM/latest/UserGuide/reference_aws-services-that-work-with-iam.html) in the *IAM User Guide*\.
Condition: If you're using temporary security credentials from the AWS Security Token Service, you must include the security token\.
Type: string
Required: Conditional
**X\-Amz\-Signature** <a name="CommonParameters-X-Amz-Signature"></a> | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/CommonParameters.md |
cbff8dd573e0-3 | **X\-Amz\-Signature** <a name="CommonParameters-X-Amz-Signature"></a>
Specifies the hex\-encoded signature that was calculated from the string to sign and the derived signing key\.
Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header\.
Type: string
Required: Conditional
**X\-Amz\-SignedHeaders** <a name="CommonParameters-X-Amz-SignedHeaders"></a>
Specifies all the HTTP headers that were included as part of the canonical request\. For more information about specifying signed headers, see [ Task 1: Create a Canonical Request For Signature Version 4](http://docs.aws.amazon.com/general/latest/gr/sigv4-create-canonical-request.html) in the * Amazon Web Services General Reference*\.
Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header\.
Type: string
Required: Conditional | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/CommonParameters.md |
23eecab2322e-0 | The Amazon Personalize hierarchical recurrent neural network \(HRNN\) recipe models changes in user behavior to provide recommendations during a session\. A session is a set of user interactions within a given timeframe with a goal of finding a specific item to fill a need, for example\. By weighting a user's recent interactions higher, you can provide more relevant recommendations during a session\.
HRNN accommodates user intent and interests, which can change over time\. It takes ordered user histories and automatically weights them to make better inferences\. HRNN uses a gating mechanism to model the discount weights as a learnable function of the items and timestamps\.
Amazon Personalize derives the features for each user from your dataset\. If you have done real\-time data integration, these features are updated in real time according to user activity\. To get a recommendation, you provide only the `USER_ID`\. If you also provide an `ITEM_ID`, Amazon Personalize ignores it\.
The HRNN recipe has the following properties:
+ **Name** – `aws-hrnn`
+ **Recipe Amazon Resource Name \(ARN\)** – `arn:aws:personalize:::recipe/aws-hrnn`
+ **Algorithm ARN** – `arn:aws:personalize:::algorithm/aws-hrnn`
+ **Feature transformation ARN** – `arn:aws:personalize:::feature-transformation/JSON-percentile-filtering`
+ **Recipe type** – `USER_PERSONALIZATION` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-hrnn.md |
23eecab2322e-1 | + **Recipe type** – `USER_PERSONALIZATION`
The following table describes the hyperparameters for the HRNN recipe\. A *hyperparameter* is an algorithm parameter that you can adjust to improve model performance\. Algorithm hyperparameters control how the model performs\. Featurization hyperparameters control how to filter the data to use in training\. The process of choosing the best value for a hyperparameter is called hyperparameter optimization \(HPO\)\. For more information, see [Hyperparameters and HPO](customizing-solution-config-hpo.md)\.
The table also provides the following information for each hyperparameter:
+ **Range**: \[lower bound, upper bound\]
+ **Value type**: Integer, Continuous \(float\), Categorical \(Boolean, list, string\)
+ **HPO tunable**: Can the parameter participate in HPO?
| Name | Description |
| --- | --- |
| Algorithm Hyperparameters | | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-hrnn.md |
23eecab2322e-2 | | Name | Description |
| --- | --- |
| Algorithm Hyperparameters |
| hidden\_dimension | The number of hidden variables used in the model\. *Hidden variables* recreate users' purchase history and item statistics to generate ranking scores\. Specify a greater number of hidden dimensions when your Interactions dataset includes more complicated patterns\. Using more hidden dimensions requires a larger dataset and more time to process\. To decide on the optimal value, use HPO\. To use HPO, set `performHPO` to `true` when you call [CreateSolution](API_CreateSolution.md) and [CreateSolutionVersion](API_CreateSolutionVersion.md) operations\. Default value: 43 Range: \[32, 256\] Value type: Integer HPO tunable: Yes |
| bptt | Determines whether to use the back\-propagation through time technique\. *Back\-propagation through time* is a technique that updates weights in recurrent neural network\-based algorithms\. Use `bptt` for long\-term credits to connect delayed rewards to early events\. For example, a delayed reward can be a purchase made after several clicks\. An early event can be an initial click\. Even within the same event types, such as a click, it’s a good idea to consider long\-term effects and maximize the total rewards\. To consider long\-term effects, use larger `bptt` values\. Using a larger `bptt` value requires larger datasets and more time to process\. Default value: 32 Range: \[2, 32\] Value type: Integer HPO tunable: Yes | | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-hrnn.md |
23eecab2322e-3 | | recency\_mask | Determines whether the model should consider the latest popularity trends in the Interactions dataset\. Latest popularity trends might include sudden changes in the underlying patterns of interaction events\. To train a model that places more weight on recent events, set `recency_mask` to `true`\. To train a model that equally weighs all past interactions, set `recency_mask` to `false`\. To get good recommendations using an equal weight, you might need a larger training dataset\. Default value: `True` Range: `True` or `False` Value type: Boolean HPO tunable: Yes |
| Featurization Hyperparameters |
| min\_user\_history\_length\_percentile | The minimum percentile of user history lengths to include in model training\. *History length* is the total amount of data about a user\. Use `min_user_history_length_percentile` to exclude a percentage of users with short history lengths\. Users with a short history often show patterns based on item popularity instead of the user's personal needs or wants\. Removing them can train models with more focus on underlying patterns in your data\. Choose an appropriate value after you review user history lengths, using a histogram or similar tool\. We recommend setting a value that retains the majority of users, but removes the edge cases\. For example, setting `min__user_history_length_percentile to 0.05` and `max_user_history_length_percentile to 0.95` includes all users except those with history lengths at the bottom or top 5%\. Default value: 0\.0 Range: \[0\.0, 1\.0\] Value type: Float HPO tunable: No | | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-hrnn.md |
23eecab2322e-4 | | max\_user\_history\_length\_percentile | The maximum percentile of user history lengths to include in model training\. *History length* is the total amount of data about a user\. Use `max_user_history_length_percentile` to exclude a percentage of users with long history lengths because data for these users tend to contain noise\. For example, a robot might have a long list of automated interactions\. Removing these users limits noise in training\. Choose an appropriate value after you review user history lengths using a histogram or similar tool\. We recommend setting a value that retains the majority of users but removes the edge cases\. For example, setting `min__user_history_length_percentile to 0.05` and `max_user_history_length_percentile to 0.95` includes all users except those with history lengths at the bottom or top 5%\. Default value: 0\.99 Range: \[0\.0, 1\.0\] Value type: Float HPO tunable: No | | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-hrnn.md |
c57f04fa910e-0 | The following Jupyter notebooks show how to use the HRNN recipe:
+ For a sample notebook that shows how to use HRNN with a hold\-out set, see [Amazon Personalize with temporal evaluation on hold\-out set](https://github.com/aws-samples/amazon-personalize-samples/blob/master/next_steps/core_use_cases/user_personalization/personalize_hrnn_metadata_example.ipynb)\.
+ For a sample notebook that shows how to use HRNN with the `PutEvents` API, see [Using the PutEvents API with Amazon Personalize](https://github.com/aws-samples/amazon-personalize-samples/blob/master/next_steps/core_use_cases/user_personalization/personalize_hrrn_example.ipynb)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-hrnn.md |
9c7acef26f7c-0 | Provides a summary of the properties of a dataset group\. For a complete listing, call the [DescribeDatasetGroup](API_DescribeDatasetGroup.md) API\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DatasetGroupSummary.md |
242b7d400c53-0 | **creationDateTime** <a name="personalize-Type-DatasetGroupSummary-creationDateTime"></a>
The date and time \(in Unix time\) that the dataset group was created\.
Type: Timestamp
Required: No
**datasetGroupArn** <a name="personalize-Type-DatasetGroupSummary-datasetGroupArn"></a>
The Amazon Resource Name \(ARN\) of the dataset group\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: No
**failureReason** <a name="personalize-Type-DatasetGroupSummary-failureReason"></a>
If creating a dataset group fails, the reason behind the failure\.
Type: String
Required: No
**lastUpdatedDateTime** <a name="personalize-Type-DatasetGroupSummary-lastUpdatedDateTime"></a>
The date and time \(in Unix time\) that the dataset group was last updated\.
Type: Timestamp
Required: No
**name** <a name="personalize-Type-DatasetGroupSummary-name"></a>
The name of the dataset group\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DatasetGroupSummary.md |
242b7d400c53-1 | The name of the dataset group\.
Type: String
Length Constraints: Minimum length of 1\. Maximum length of 63\.
Pattern: `^[a-zA-Z0-9][a-zA-Z0-9\-_]*`
Required: No
**status** <a name="personalize-Type-DatasetGroupSummary-status"></a>
The status of the dataset group\.
A dataset group can be in one of the following states:
+ CREATE PENDING > CREATE IN\_PROGRESS > ACTIVE \-or\- CREATE FAILED
+ DELETE PENDING
Type: String
Length Constraints: Maximum length of 256\.
Required: No | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DatasetGroupSummary.md |
65fa102b2cc0-0 | For more information about using this API in one of the language\-specific AWS SDKs, see the following:
+ [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/personalize-2018-05-22/DatasetGroupSummary)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/DatasetGroupSummary)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/DatasetGroupSummary)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/DatasetGroupSummary) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DatasetGroupSummary.md |
a2be32c5a844-0 | The following data types are supported by Amazon Personalize Events:
+ [Event](API_UBS_Event.md) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_Types_Amazon_Personalize_Events.md |
300fa0aad697-0 | Represents user interaction event information sent using the `PutEvents` API\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_UBS_Event.md |
85b22a2b9e9a-0 | **eventId** <a name="personalize-Type-UBS_Event-eventId"></a>
An ID associated with the event\. If an event ID is not provided, Amazon Personalize generates a unique ID for the event\. An event ID is not used as an input to the model\. Amazon Personalize uses the event ID to distinquish unique events\. Any subsequent events after the first with the same event ID are not used in model training\.
Type: String
Length Constraints: Minimum length of 1\. Maximum length of 256\.
Required: No
**eventType** <a name="personalize-Type-UBS_Event-eventType"></a>
The type of event, such as click or download\. This property corresponds to the `EVENT_TYPE` field of your Interactions schema and depends on the types of events you are tracking\.
Type: String
Length Constraints: Minimum length of 1\. Maximum length of 256\.
Required: Yes
**eventValue** <a name="personalize-Type-UBS_Event-eventValue"></a>
The event value that corresponds to the `EVENT_VALUE` field of the Interactions schema\.
Type: Float
Required: No
**impression** <a name="personalize-Type-UBS_Event-impression"></a> | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_UBS_Event.md |
85b22a2b9e9a-1 | Required: No
**impression** <a name="personalize-Type-UBS_Event-impression"></a>
A list of item IDs that represents the sequence of items you have shown the user\. For example, `["itemId1", "itemId2", "itemId3"]`\.
Type: Array of strings
Array Members: Minimum number of 1 item\. Maximum number of 25 items\.
Length Constraints: Minimum length of 1\. Maximum length of 256\.
Required: No
**itemId** <a name="personalize-Type-UBS_Event-itemId"></a>
The item ID key that corresponds to the `ITEM_ID` field of the Interactions schema\.
Type: String
Length Constraints: Minimum length of 1\. Maximum length of 256\.
Required: No
**properties** <a name="personalize-Type-UBS_Event-properties"></a>
A string map of event\-specific data that you might choose to record\. For example, if a user rates a movie on your site, other than movie ID \(`itemId`\) and rating \(`eventValue`\) , you might also send the number of movie ratings made by the user\.
Each item in the map consists of a key\-value pair\. For example,
`{"numberOfRatings": "12"}` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_UBS_Event.md |
85b22a2b9e9a-2 | Each item in the map consists of a key\-value pair\. For example,
`{"numberOfRatings": "12"}`
The keys use camel case names that match the fields in the Interactions schema\. In the above example, the `numberOfRatings` would match the 'NUMBER\_OF\_RATINGS' field defined in the Interactions schema\.
Type: String
Length Constraints: Minimum length of 1\. Maximum length of 1024\.
Required: No
**recommendationId** <a name="personalize-Type-UBS_Event-recommendationId"></a>
The ID of the recommendation\.
Type: String
Length Constraints: Minimum length of 1\. Maximum length of 40\.
Required: No
**sentAt** <a name="personalize-Type-UBS_Event-sentAt"></a>
The timestamp \(in Unix time\) on the client side when the event occurred\.
Type: Timestamp
Required: Yes | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_UBS_Event.md |
8f4f27b4229b-0 | For more information about using this API in one of the language\-specific AWS SDKs, see the following:
+ [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/personalize-events-2018-03-22/Event)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-events-2018-03-22/Event)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-events-2018-03-22/Event)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-events-2018-03-22/Event) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_UBS_Event.md |
dca5d4df1c41-0 | The following table describes important changes in each release of the *Amazon Personalize Developer Guide*\. For notification about updates to this documentation, you can subscribe to an RSS feed\.
+ **Latest documentation update:** July 10, 2020
| Change | Description | Date |
| --- |--- |--- |
| [New feature](#document-history) | Amazon Personalize now supports a new USER\_PERSONALIZATION recommendation recipe\. USER\_PERSONALIZATION features include modeling impression data, automatic item exploration, and automatic cold item selection\. For more information, see [USER\_PERSONALIZATION recipe](https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html)\. | August 5, 2020 |
| [New feature](#document-history) | Amazon Personalize can now filter recommendations based on item and user metadata using custom filter expressions\. For more information, see [Filtering Recommendations](https://docs.aws.amazon.com/personalize/latest/dg/filter.html)\. | July 31, 2020 |
| [New feature](#document-history) | Amazon Personalize now allows you to filter results based on which items a user has interacted with\. For more information, see [Filtering Recommendations](https://docs.aws.amazon.com/personalize/latest/dg/filter.html)\. | June 3, 2020 | | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/document-history.md |
dca5d4df1c41-1 | | [New feature](#document-history) | Amazon Personalize now exposes scores for recommended items\. Scores represent the Amazon Personalize model's certainty that a user will next choose a certain item\. For more information, see [Getting Recommendations](https://docs.aws.amazon.com/personalize/latest/dg/getting-recommendations.html)\. | April 3, 2020 |
| [New Region](#document-history) | Amazon Personalize adds support for the Asia Pacific \(Seoul\) Region\. For a complete list of the AWS Regions supported by Amazon Personalize, see the [AWS Region Table](https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/) or [AWS Regions and Endpoints](https://docs.aws.amazon.com/general/latest/gr/personalize.html) in the *Amazon Web Services General Reference*\. | January 21, 2020 |
| [New feature](#document-history) | Amazon Personalize can now get recommendations based on contextual metadata\. For more information, see [Getting Recommendations](https://docs.aws.amazon.com/personalize/latest/dg/getting-recommendations.html)\. | December 19, 2019 | | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/document-history.md |
dca5d4df1c41-2 | | [New Regions](#document-history) | Amazon Personalize adds support for the Asia Pacific \(Mumbai\), Asia Pacific \(Sydney\), and Canada \(Central\) Regions\. For a complete list of the AWS Regions supported by Amazon Personalize, see the [AWS Region Table](https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/) or [AWS Regions and Endpoints](https://docs.aws.amazon.com/general/latest/gr/personalize.html) in the *Amazon Web Services General Reference*\. | December 18, 2019 |
| [New feature](#document-history) | Amazon Personalize now supports batch recommendation workflows\. For more information, see [Get Batch Recommendations](https://docs.aws.amazon.com/personalize/latest/dg/getting-recommendations.html#recommendations-batch)\. | November 14, 2019 |
| [Amazon Personalize general availability](#document-history) | Amazon Personalize is now available for general use\. | June 10, 2019 |
| [Amazon Personalize preview release](#document-history) | This is the first preview release of the documentation for Amazon Personalize\. | November 28, 2018 | | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/document-history.md |
6792f54d0a7e-0 | This section lists the errors common to the API actions of all AWS services\. For errors specific to an API action for this service, see the topic for that API action\.
**AccessDeniedException**
You do not have sufficient access to perform this action\.
HTTP Status Code: 400
**IncompleteSignature**
The request signature does not conform to AWS standards\.
HTTP Status Code: 400
**InternalFailure** <a name="CommonErrors-InternalFailure"></a>
The request processing has failed because of an unknown error, exception or failure\.
HTTP Status Code: 500
**InvalidAction** <a name="CommonErrors-InvalidAction"></a>
The action or operation requested is invalid\. Verify that the action is typed correctly\.
HTTP Status Code: 400
**InvalidClientTokenId** <a name="CommonErrors-InvalidClientTokenId"></a>
The X\.509 certificate or AWS access key ID provided does not exist in our records\.
HTTP Status Code: 403
**InvalidParameterCombination** <a name="CommonErrors-InvalidParameterCombination"></a>
Parameters that must not be used together were used together\.
HTTP Status Code: 400
**InvalidParameterValue** <a name="CommonErrors-InvalidParameterValue"></a> | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/CommonErrors.md |
6792f54d0a7e-1 | **InvalidParameterValue** <a name="CommonErrors-InvalidParameterValue"></a>
An invalid or out\-of\-range value was supplied for the input parameter\.
HTTP Status Code: 400
**InvalidQueryParameter** <a name="CommonErrors-InvalidQueryParameter"></a>
The AWS query string is malformed or does not adhere to AWS standards\.
HTTP Status Code: 400
**MalformedQueryString** <a name="CommonErrors-MalformedQueryString"></a>
The query string contains a syntax error\.
HTTP Status Code: 404
**MissingAction** <a name="CommonErrors-MissingAction"></a>
The request is missing an action or a required parameter\.
HTTP Status Code: 400
**MissingAuthenticationToken** <a name="CommonErrors-MissingAuthenticationToken"></a>
The request must contain either a valid \(registered\) AWS access key ID or X\.509 certificate\.
HTTP Status Code: 403
**MissingParameter** <a name="CommonErrors-MissingParameter"></a>
A required parameter for the specified action is not supplied\.
HTTP Status Code: 400 | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/CommonErrors.md |
6792f54d0a7e-2 | A required parameter for the specified action is not supplied\.
HTTP Status Code: 400
**OptInRequired** <a name="CommonErrors-OptInRequired"></a>
The AWS access key ID needs a subscription for the service\.
HTTP Status Code: 403
**RequestExpired** <a name="CommonErrors-RequestExpired"></a>
The request reached the service more than 15 minutes after the date stamp on the request or more than 15 minutes after the request expiration date \(such as for pre\-signed URLs\), or the date stamp on the request is more than 15 minutes in the future\.
HTTP Status Code: 400
**ServiceUnavailable** <a name="CommonErrors-ServiceUnavailable"></a>
The request has failed due to a temporary failure of the server\.
HTTP Status Code: 503
**ThrottlingException** <a name="CommonErrors-ThrottlingException"></a>
The request was denied due to request throttling\.
HTTP Status Code: 400
**ValidationError** <a name="CommonErrors-ValidationError"></a>
The input fails to satisfy the constraints specified by an AWS service\.
HTTP Status Code: 400 | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/CommonErrors.md |
9d4a5747026a-0 | The files that you use to import data into Amazon Personalize must map to the schema that you are using\.
Amazon Personalize imports data only from files that are in the comma\-separated values \(CSV\) format\. Amazon Personalize requires the first row of your CSV file to contain column headers\. The column headers in your CSV file need to map to the schema to create the dataset\. Don't enclose headers in quotation marks \("\)\. `TIMESTAMP` and `CREATION_TIMESTAMP` data must be in *Unix epoch* time format\. For more information see [Timestamp Data](#timestamp-data)\.
**Important**
If your data includes any non\-ASCII encoded characters, your CSV file must be encoded in UTF\-8 format\.
The following CSV data sample maps to the Interactions schema shown in [Datasets and Schemas](how-it-works-dataset-schema.md)\. This data represents historical user activity from a website that sells movie tickets\. You can use the data to train a model that gives a user movie recommendations based on the activities of other users\.
```
USER_ID,ITEM_ID,EVENT_TYPE,EVENT_VALUE,TIMESTAMP
196,242,click,15,881250949
186,302,click,13,891717742
22,377,click,10,878887116
244,51,click,20,880606923 | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/data-prep-formatting.md |
9d4a5747026a-1 | 22,377,click,10,878887116
244,51,click,20,880606923
166,346,click,10,886397596
298,474,click,40,884182806
115,265,click,20,881171488
253,465,click,50,891628467
305,451,click,30,886324817
```
The associated Interactions schema is repeated below\.
```
{
"type": "record",
"name": "Interactions",
"namespace": "com.amazonaws.personalize.schema",
"fields": [
{
"name": "USER_ID",
"type": "string"
},
{
"name": "ITEM_ID",
"type": "string"
},
{ "name": "EVENT_TYPE",
"type": "string"
},
{
"name": "EVENT_VALUE",
"type": "float"
},
{
"name": "TIMESTAMP",
"type": "long"
}
], | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/data-prep-formatting.md |
9d4a5747026a-2 | {
"name": "TIMESTAMP",
"type": "long"
}
],
"version": "1.0"
}
```
Amazon Personalize requires the `USER_ID`, `ITEM_ID`, and `TIMESTAMP` fields\. `USER_ID` is the identifier for a user of your application\. `ITEM_ID` is the identifier for a movie\. `EVENT_TYPE` and `EVENT_VALUE` are the identifiers for user activities\. In the sample data, a `click` might represent a movie purchase event and `15` might be the purchase price of the movie\. `TIMESTAMP` represents the Unix epoch time that the movie purchase took place\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/data-prep-formatting.md |
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