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|---|---|---|
8f1fb39bae97-1 | + Items
+ Users
Type: String
Length Constraints: Maximum length of 256\.
Required: No
**lastUpdatedDateTime** <a name="personalize-Type-Dataset-lastUpdatedDateTime"></a>
A time stamp that shows when the dataset was updated\.
Type: Timestamp
Required: No
**name** <a name="personalize-Type-Dataset-name"></a>
The name of the dataset\.
Type: String
Length Constraints: Minimum length of 1\. Maximum length of 63\.
Pattern: `^[a-zA-Z0-9][a-zA-Z0-9\-_]*`
Required: No
**schemaArn** <a name="personalize-Type-Dataset-schemaArn"></a>
The ARN of the associated schema\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: No
**status** <a name="personalize-Type-Dataset-status"></a>
The status of the dataset\.
A dataset can be in one of the following states: | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_Dataset.md |
8f1fb39bae97-2 | The status of the dataset\.
A dataset can be in one of the following states:
+ CREATE PENDING > CREATE IN\_PROGRESS > ACTIVE \-or\- CREATE FAILED
+ DELETE PENDING > DELETE IN\_PROGRESS
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_Dataset.md |
b320b4d2827d-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/Dataset)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/Dataset)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/Dataset)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/Dataset) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_Dataset.md |
c2f03658c1e2-0 | The metric to optimize during hyperparameter optimization \(HPO\)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_HPOObjective.md |
8a32dc1745c4-0 | **metricName** <a name="personalize-Type-HPOObjective-metricName"></a>
The name of the metric\.
Type: String
Length Constraints: Maximum length of 256\.
Required: No
**metricRegex** <a name="personalize-Type-HPOObjective-metricRegex"></a>
A regular expression for finding the metric in the training job logs\.
Type: String
Length Constraints: Maximum length of 256\.
Required: No
**type** <a name="personalize-Type-HPOObjective-type"></a>
The type of the metric\. Valid values are `Maximize` and `Minimize`\.
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_HPOObjective.md |
aadec5bd7127-0 | 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/HPOObjective)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/HPOObjective)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/HPOObjective)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/HPOObjective) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_HPOObjective.md |
2e3f76afaedd-0 | Creates a batch inference job\. The operation can handle up to 50 million records and the input file must be in JSON format\. For more information, see [Getting Batch Recommendations](recommendations-batch.md)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateBatchInferenceJob.md |
639c11749746-0 | ```
{
"batchInferenceJobConfig": {
"itemExplorationConfig": {
"string" : "string"
}
},
"filterArn": "string",
"jobInput": {
"s3DataSource": {
"kmsKeyArn": "string",
"path": "string"
}
},
"jobName": "string",
"jobOutput": {
"s3DataDestination": {
"kmsKeyArn": "string",
"path": "string"
}
},
"numResults": number,
"roleArn": "string",
"solutionVersionArn": "string"
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateBatchInferenceJob.md |
ade42bed53a5-0 | The request accepts the following data in JSON format\.
** [batchInferenceJobConfig](#API_CreateBatchInferenceJob_RequestSyntax) ** <a name="personalize-CreateBatchInferenceJob-request-batchInferenceJobConfig"></a>
The configuration details of a batch inference job\.
Type: [BatchInferenceJobConfig](API_BatchInferenceJobConfig.md) object
Required: No
** [filterArn](#API_CreateBatchInferenceJob_RequestSyntax) ** <a name="personalize-CreateBatchInferenceJob-request-filterArn"></a>
The ARN of the filter to apply to the batch inference job\. For more information on using filters, see Using Filters with Amazon Personalize\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: No
** [jobInput](#API_CreateBatchInferenceJob_RequestSyntax) ** <a name="personalize-CreateBatchInferenceJob-request-jobInput"></a>
The Amazon S3 path that leads to the input file to base your recommendations on\. The input material must be in JSON format\.
Type: [BatchInferenceJobInput](API_BatchInferenceJobInput.md) object | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateBatchInferenceJob.md |
ade42bed53a5-1 | Type: [BatchInferenceJobInput](API_BatchInferenceJobInput.md) object
Required: Yes
** [jobName](#API_CreateBatchInferenceJob_RequestSyntax) ** <a name="personalize-CreateBatchInferenceJob-request-jobName"></a>
The name of the batch inference job to create\.
Type: String
Length Constraints: Minimum length of 1\. Maximum length of 63\.
Pattern: `^[a-zA-Z0-9][a-zA-Z0-9\-_]*`
Required: Yes
** [jobOutput](#API_CreateBatchInferenceJob_RequestSyntax) ** <a name="personalize-CreateBatchInferenceJob-request-jobOutput"></a>
The path to the Amazon S3 bucket where the job's output will be stored\.
Type: [BatchInferenceJobOutput](API_BatchInferenceJobOutput.md) object
Required: Yes
** [numResults](#API_CreateBatchInferenceJob_RequestSyntax) ** <a name="personalize-CreateBatchInferenceJob-request-numResults"></a>
The number of recommendations to retreive\.
Type: Integer
Required: No | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateBatchInferenceJob.md |
ade42bed53a5-2 | The number of recommendations to retreive\.
Type: Integer
Required: No
** [roleArn](#API_CreateBatchInferenceJob_RequestSyntax) ** <a name="personalize-CreateBatchInferenceJob-request-roleArn"></a>
The ARN of the Amazon Identity and Access Management role that has permissions to read and write to your input and out Amazon S3 buckets respectively\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):iam::\d{12}:role/?[a-zA-Z_0-9+=,.@\-_/]+`
Required: Yes
** [solutionVersionArn](#API_CreateBatchInferenceJob_RequestSyntax) ** <a name="personalize-CreateBatchInferenceJob-request-solutionVersionArn"></a>
The Amazon Resource Name \(ARN\) of the solution version that will be used to generate the batch inference recommendations\.
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_CreateBatchInferenceJob.md |
a82ba33a1a72-0 | ```
{
"batchInferenceJobArn": "string"
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateBatchInferenceJob.md |
456391258ac7-0 | If the action is successful, the service sends back an HTTP 200 response\.
The following data is returned in JSON format by the service\.
** [batchInferenceJobArn](#API_CreateBatchInferenceJob_ResponseSyntax) ** <a name="personalize-CreateBatchInferenceJob-response-batchInferenceJobArn"></a>
The ARN of the batch inference job\.
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_CreateBatchInferenceJob.md |
e9501e0a8795-0 | **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
**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_CreateBatchInferenceJob.md |
64bbcb8317b0-0 | 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/CreateBatchInferenceJob)
+ [AWS SDK for \.NET](https://docs.aws.amazon.com/goto/DotNetSDKV3/personalize-2018-05-22/CreateBatchInferenceJob)
+ [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/personalize-2018-05-22/CreateBatchInferenceJob)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/CreateBatchInferenceJob)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/CreateBatchInferenceJob)
+ [AWS SDK for JavaScript](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/personalize-2018-05-22/CreateBatchInferenceJob) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateBatchInferenceJob.md |
64bbcb8317b0-1 | + [AWS SDK for PHP V3](https://docs.aws.amazon.com/goto/SdkForPHPV3/personalize-2018-05-22/CreateBatchInferenceJob)
+ [AWS SDK for Python](https://docs.aws.amazon.com/goto/boto3/personalize-2018-05-22/CreateBatchInferenceJob)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/CreateBatchInferenceJob) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_CreateBatchInferenceJob.md |
9fd6d52a86ef-0 | Describes a job that imports training data from a data source \(Amazon S3 bucket\) to an Amazon Personalize dataset\. For more information, see [CreateDatasetImportJob](API_CreateDatasetImportJob.md)\.
A dataset import job can be in one of the following states:
+ CREATE PENDING > CREATE IN\_PROGRESS > ACTIVE \-or\- CREATE FAILED | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DatasetImportJob.md |
ce6936499c12-0 | **creationDateTime** <a name="personalize-Type-DatasetImportJob-creationDateTime"></a>
The creation date and time \(in Unix time\) of the dataset import job\.
Type: Timestamp
Required: No
**datasetArn** <a name="personalize-Type-DatasetImportJob-datasetArn"></a>
The Amazon Resource Name \(ARN\) of the dataset that receives the imported data\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: No
**datasetImportJobArn** <a name="personalize-Type-DatasetImportJob-datasetImportJobArn"></a>
The ARN of the dataset import job\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: No
**dataSource** <a name="personalize-Type-DatasetImportJob-dataSource"></a>
The Amazon S3 bucket that contains the training data to import\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DatasetImportJob.md |
ce6936499c12-1 | The Amazon S3 bucket that contains the training data to import\.
Type: [DataSource](API_DataSource.md) object
Required: No
**failureReason** <a name="personalize-Type-DatasetImportJob-failureReason"></a>
If a dataset import job fails, provides the reason why\.
Type: String
Required: No
**jobName** <a name="personalize-Type-DatasetImportJob-jobName"></a>
The name of the import job\.
Type: String
Length Constraints: Minimum length of 1\. Maximum length of 63\.
Pattern: `^[a-zA-Z0-9][a-zA-Z0-9\-_]*`
Required: No
**lastUpdatedDateTime** <a name="personalize-Type-DatasetImportJob-lastUpdatedDateTime"></a>
The date and time \(in Unix time\) the dataset was last updated\.
Type: Timestamp
Required: No
**roleArn** <a name="personalize-Type-DatasetImportJob-roleArn"></a>
The ARN of the AWS Identity and Access Management \(IAM\) role that has permissions to read from the Amazon S3 data source\.
Type: String | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DatasetImportJob.md |
ce6936499c12-2 | Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: No
**status** <a name="personalize-Type-DatasetImportJob-status"></a>
The status of the dataset import job\.
A dataset import job can be in one of the following states:
+ CREATE PENDING > CREATE IN\_PROGRESS > ACTIVE \-or\- CREATE FAILED
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_DatasetImportJob.md |
d6e8f0e51a1c-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/DatasetImportJob)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/DatasetImportJob)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/DatasetImportJob)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/DatasetImportJob) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DatasetImportJob.md |
4ee88644ae78-0 | Removes a campaign by deleting the solution deployment\. The solution that the campaign is based on is not deleted and can be redeployed when needed\. A deleted campaign can no longer be specified in a [GetRecommendations](https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html) request\. For more information on campaigns, see [CreateCampaign](API_CreateCampaign.md)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DeleteCampaign.md |
5f761e0905cb-0 | ```
{
"campaignArn": "string"
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DeleteCampaign.md |
3a34e3245153-0 | The request accepts the following data in JSON format\.
** [campaignArn](#API_DeleteCampaign_RequestSyntax) ** <a name="personalize-DeleteCampaign-request-campaignArn"></a>
The Amazon Resource Name \(ARN\) of the campaign to delete\.
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_DeleteCampaign.md |
3642d8223892-0 | If the action is successful, the service sends back an HTTP 200 response with an empty HTTP body\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DeleteCampaign.md |
385414d97271-0 | **InvalidInputException**
Provide a valid value for the field or parameter\.
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_DeleteCampaign.md |
fe4822574acf-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/DeleteCampaign)
+ [AWS SDK for \.NET](https://docs.aws.amazon.com/goto/DotNetSDKV3/personalize-2018-05-22/DeleteCampaign)
+ [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/personalize-2018-05-22/DeleteCampaign)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/DeleteCampaign)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/DeleteCampaign)
+ [AWS SDK for JavaScript](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/personalize-2018-05-22/DeleteCampaign) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DeleteCampaign.md |
fe4822574acf-1 | + [AWS SDK for PHP V3](https://docs.aws.amazon.com/goto/SdkForPHPV3/personalize-2018-05-22/DeleteCampaign)
+ [AWS SDK for Python](https://docs.aws.amazon.com/goto/boto3/personalize-2018-05-22/DeleteCampaign)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/DeleteCampaign) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DeleteCampaign.md |
1a8815adf6d2-0 | Use the HRNN\-Coldstart recipe to predict the items that a user will interact with when you frequently add new items and interactions and want to get recommendations for those items immediately\. The HRNN\-Coldstart recipe is similar to the [HRNN\-Metadata](native-recipe-hrnn-metadata.md) recipe, but it allows you to get recommendations for new items\.
In addition, you can use the HRNN\-Coldstart recipe when you want to exclude from training items that have a long list of interactions either because of a recent popularity trend or because the interactions might be highly unusual and introduce noise in training\. With HRNN\-Coldstart, you can filter out less relevant items to create a subset for training\. The subset of items, called *cold items*, are items that have related interaction events in the Interactions dataset\. An item is considered a cold item when it has the following:
+ Fewer interactions than a specified number of maximum interactions\. You specify this value in the recipe's `cold_start_max_interactions` hyperparameter\.
+ A shorter relative duration than the maximum duration\. You specify this value in the recipe's `cold_start_max_duration` hyperparameter\.
To reduce the number of cold items, set a lower value for `cold_start_max_interactions` or `cold_start_max_duration`\. To increase the number of cold items, set a greater value for `cold_start_max_interactions` or `cold_start_max_duration`\.
HRNN\-Coldstart has the following cold item limits:
+ `Maximum cold start items`: 80,000
+ `Minimum cold start items`: 100 | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-hrnn-coldstart.md |
1a8815adf6d2-1 | + `Maximum cold start items`: 80,000
+ `Minimum cold start items`: 100
If the number of cold items is outside this range, attempts to create a solution will fail\.
The HRNN\-Coldstart recipe has the following properties:
+ **Name** – `aws-hrnn-coldstart`
+ **Recipe Amazon Resource Name \(ARN\)** – `arn:aws:personalize:::recipe/aws-hrnn-coldstart`
+ **Algorithm ARN** – `arn:aws:personalize:::algorithm/aws-hrnn-coldstart`
+ **Feature transformation ARN** – `arn:aws:personalize:::feature-transformation/featurize_coldstart`
+ **Recipe type** – `USER_PERSONALIZATION`
For more information, see [Choosing a Recipe](working-with-predefined-recipes.md)\.
The following table describes the hyperparameters for the HRNN\-Coldstart 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)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-hrnn-coldstart.md |
1a8815adf6d2-2 | 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 |
| 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: 149 Range: \[32, 256\] Value type: Integer HPO tunable: Yes | | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-hrnn-coldstart.md |
1a8815adf6d2-3 | | 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 |
| 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 |
| cold\_start\_max\_interactions | The maximum number of user\-item interactions an item can have to be considered a cold item\. Default value: 15 Range: Positive integers Value type: Integer HPO tunable: No | | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-hrnn-coldstart.md |
1a8815adf6d2-4 | | cold\_start\_max\_duration | The maximum duration in days relative to the starting point for a user\-item interaction to be considered a cold start item\. To set the starting point of the user\-item interaction, set the `cold_start_relative_from` hyperparameter\. Default value: 5\.0 Range: Positive floats Value type: Float HPO tunable: No |
| cold\_start\_relative\_from | Determines the starting point for the HRNN\-Coldstart recipe to calculate `cold_start_max_duration`\. To calculate from the current time, choose `currentTime`\. To calculate `cold_start_max_duration` from the timestamp of the latest item in the Interactions dataset, choose `latestItem`\. This setting is useful if you frequently add new items\. Default value: `latestItem` Range: `currentTime`, `latestItem` Value type: String HPO tunable: No | | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-hrnn-coldstart.md |
1a8815adf6d2-5 | | 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-coldstart.md |
1a8815adf6d2-6 | | 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-coldstart.md |
c32e55e67fa0-0 | For a sample Jupyter notebook that shows how to use the HRNN\-Coldstart recipe, see [Handling cold or new items with Amazon Personalize](https://github.com/aws-samples/amazon-personalize-samples/blob/master/next_steps/core_use_cases/user_personalization/personalize_hrnn_coldstart_example.ipynb)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-hrnn-coldstart.md |
7b7ae98ce3de-0 | Filtering batch recommendations works nearly the same as filtering real\-time recommendations\. For both, you [create a filter](filter-real-time.md) and then apply it to a [CreateBatchInferenceJob](API_CreateBatchInferenceJob.md) operation create a batch inference job using the console \(see [Getting Batch Recommendations \(Amazon Personalize Console\)](recommendations-batch.md#batch-console)\)\. Amazon Personalize removes filtered recommendations from the batch job’s output JSON file\.
You can use either the console or the AWS SDKs to filter batch recommendations\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/filter-batch.md |
8223ea3c37f2-0 | 1. Use the console or the SDK to [create a filter](filter-real-time.md)\.
1. When you create the batch recommendation job, on the **Create batch inference job** page, for **Filter configuration \- optional**, **Filter name**, choose the filter\.
![\[Image NOT FOUND\]](http://docs.aws.amazon.com/personalize/latest/dg/images/batch_filter.PNG) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/filter-batch.md |
d1d43e97b63f-0 | 1. Use the console or the SDK to [create a filter](filter-real-time.md)\.
1. Include the `FilterArn` parameter in the [CreateBatchInferenceJob](API_CreateBatchInferenceJob.md) request\.
```
import boto3
personalize = boto3.client('personalize')
personalize_rec.create_batch_inference_job (
solutionVersionArn = "Solution version ARN",
jobName = "Batch job name",
roleArn = "IAM role ARN",
filterArn = "Filter ARN",
jobInput =
{"s3DataSource": {"path": "S3 input path"}},
jobOutput =
{"S3DataDestination": {"path": "S3 output path"}}
)
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/filter-batch.md |
d08fbef9a80e-0 | When the solution performs AutoML \(`performAutoML` is true in [CreateSolution](API_CreateSolution.md)\), specifies the recipe that best optimized the specified metric\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_AutoMLResult.md |
d2601281427d-0 | **bestRecipeArn** <a name="personalize-Type-AutoMLResult-bestRecipeArn"></a>
The Amazon Resource Name \(ARN\) of the best recipe\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: No | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_AutoMLResult.md |
d9d093622dd2-0 | 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/AutoMLResult)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/AutoMLResult)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/AutoMLResult)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/AutoMLResult) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_AutoMLResult.md |
fb260eea4750-0 | Contains information on a batch inference job\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_BatchInferenceJob.md |
63fc10de7a57-0 | **batchInferenceJobArn** <a name="personalize-Type-BatchInferenceJob-batchInferenceJobArn"></a>
The Amazon Resource Name \(ARN\) of the batch inference job\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: No
**batchInferenceJobConfig** <a name="personalize-Type-BatchInferenceJob-batchInferenceJobConfig"></a>
A string to string map of the configuration details of a batch inference job\.
Type: [BatchInferenceJobConfig](API_BatchInferenceJobConfig.md) object
Required: No
**creationDateTime** <a name="personalize-Type-BatchInferenceJob-creationDateTime"></a>
The time at which the batch inference job was created\.
Type: Timestamp
Required: No
**failureReason** <a name="personalize-Type-BatchInferenceJob-failureReason"></a>
If the batch inference job failed, the reason for the failure\.
Type: String
Required: No
**filterArn** <a name="personalize-Type-BatchInferenceJob-filterArn"></a> | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_BatchInferenceJob.md |
63fc10de7a57-1 | **filterArn** <a name="personalize-Type-BatchInferenceJob-filterArn"></a>
The ARN of the filter used on the batch inference job\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: No
**jobInput** <a name="personalize-Type-BatchInferenceJob-jobInput"></a>
The Amazon S3 path that leads to the input data used to generate the batch inference job\.
Type: [BatchInferenceJobInput](API_BatchInferenceJobInput.md) object
Required: No
**jobName** <a name="personalize-Type-BatchInferenceJob-jobName"></a>
The name of the batch inference job\.
Type: String
Length Constraints: Minimum length of 1\. Maximum length of 63\.
Pattern: `^[a-zA-Z0-9][a-zA-Z0-9\-_]*`
Required: No
**jobOutput** <a name="personalize-Type-BatchInferenceJob-jobOutput"></a>
The Amazon S3 bucket that contains the output data generated by the batch inference job\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_BatchInferenceJob.md |
63fc10de7a57-2 | The Amazon S3 bucket that contains the output data generated by the batch inference job\.
Type: [BatchInferenceJobOutput](API_BatchInferenceJobOutput.md) object
Required: No
**lastUpdatedDateTime** <a name="personalize-Type-BatchInferenceJob-lastUpdatedDateTime"></a>
The time at which the batch inference job was last updated\.
Type: Timestamp
Required: No
**numResults** <a name="personalize-Type-BatchInferenceJob-numResults"></a>
The number of recommendations generated by the batch inference job\. This number includes the error messages generated for failed input records\.
Type: Integer
Required: No
**roleArn** <a name="personalize-Type-BatchInferenceJob-roleArn"></a>
The ARN of the Amazon Identity and Access Management \(IAM\) role that requested the batch inference job\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):iam::\d{12}:role/?[a-zA-Z_0-9+=,.@\-_/]+`
Required: No | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_BatchInferenceJob.md |
63fc10de7a57-3 | Required: No
**solutionVersionArn** <a name="personalize-Type-BatchInferenceJob-solutionVersionArn"></a>
The Amazon Resource Name \(ARN\) of the solution version from which the batch inference job was created\.
Type: String
Length Constraints: Maximum length of 256\.
Pattern: `arn:([a-z\d-]+):personalize:.*:.*:.+`
Required: No
**status** <a name="personalize-Type-BatchInferenceJob-status"></a>
The status of the batch inference job\. The status is one of the following values:
+ PENDING
+ IN PROGRESS
+ ACTIVE
+ CREATE FAILED
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_BatchInferenceJob.md |
a46d59b3a6af-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/BatchInferenceJob)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/BatchInferenceJob)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/BatchInferenceJob)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/BatchInferenceJob) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_BatchInferenceJob.md |
5ff37e599d1e-0 | The AWS global infrastructure is built around AWS Regions and Availability Zones\. AWS Regions provide multiple physically separated and isolated Availability Zones, which are connected with low\-latency, high\-throughput, and highly redundant networking\. With Availability Zones, you can design and operate applications and databases that automatically fail over between zones without interruption\. Availability Zones are more highly available, fault tolerant, and scalable than traditional single or multiple data center infrastructures\.
For more information about AWS Regions and Availability Zones, see [AWS Global Infrastructure](http://aws.amazon.com/about-aws/global-infrastructure/)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/disaster-recovery-resiliency.md |
f0fc5da12482-0 | Deletes a schema\. Before deleting a schema, you must delete all datasets referencing the schema\. For more information on schemas, see [CreateSchema](API_CreateSchema.md)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DeleteSchema.md |
86a9bbf0dc8b-0 | ```
{
"schemaArn": "string"
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DeleteSchema.md |
70e5fef45142-0 | The request accepts the following data in JSON format\.
** [schemaArn](#API_DeleteSchema_RequestSyntax) ** <a name="personalize-DeleteSchema-request-schemaArn"></a>
The Amazon Resource Name \(ARN\) of the schema to delete\.
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_DeleteSchema.md |
2b18b01a5b8f-0 | If the action is successful, the service sends back an HTTP 200 response with an empty HTTP body\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DeleteSchema.md |
ac3fd42e42db-0 | **InvalidInputException**
Provide a valid value for the field or parameter\.
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_DeleteSchema.md |
134e408f85b1-0 | 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/DeleteSchema)
+ [AWS SDK for \.NET](https://docs.aws.amazon.com/goto/DotNetSDKV3/personalize-2018-05-22/DeleteSchema)
+ [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/personalize-2018-05-22/DeleteSchema)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/DeleteSchema)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/DeleteSchema)
+ [AWS SDK for JavaScript](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/personalize-2018-05-22/DeleteSchema)
+ [AWS SDK for PHP V3](https://docs.aws.amazon.com/goto/SdkForPHPV3/personalize-2018-05-22/DeleteSchema) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DeleteSchema.md |
134e408f85b1-1 | + [AWS SDK for Python](https://docs.aws.amazon.com/goto/boto3/personalize-2018-05-22/DeleteSchema)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/DeleteSchema) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DeleteSchema.md |
8dd76643f73c-0 | Amazon Personalize recognizes three types of historical datasets\. Each type has an associated schema with a *name* key whose value matches the dataset type\. The three types are:
+ **Users** – This dataset is intended to provide metadata about your users\. This might include information such as age, gender, or loyalty membership, which can be important signals in personalization systems\.
+ **Items** – This dataset is intended to provide metadata about your items\. This might include information such as price, SKU type, or availability\.
+ **Interactions** – This dataset is intended to provide historical interaction data between users and items\. It can also provide metadata on your user's browsing context, such as their location or device \(mobile, tablet, desktop, and so on\)\.
The Users and Items dataset types are known as metadata types and are used only by certain recipes\. For more information, see [Choosing a Recipe](working-with-predefined-recipes.md)\.
If you are using the AWS console, you create a new schema when you create a dataset for your input data\. You can also choose an existing schema\. For more information, see [Step 1: Import Training Data](getting-started-console.md#getting-started-console-import-dataset)\.
If you are using the AWS CLI, see [Step 1: Import Training Data](getting-started-cli.md#gs-create-ds) for an example\.
**Note**
A dataset group can contain only one of each type of dataset\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/how-it-works-dataset-schema.md |
f5c164f249ea-0 | Each dataset has a set of required fields, reserved keywords, and their required datatypes, as shown in the following table\.
| Dataset Type | Required Fields | Reserved Keywords |
| --- | --- | --- |
| Users | USER\_ID \(`string`\) 1 metadata field | |
| Items | ITEM\_ID \(`string`\) 1 metadata field | CREATION\_TIMESTAMP \(`long`\) |
| Interactions | USER\_ID \(`string`\) ITEM\_ID \(`string`\) TIMESTAMP \(`long`\) | EVENT\_TYPE \(`string`\) IMPRESSION \(`string`\) EVENT\_VALUE \(`float`, `null`\) |
Before you add a dataset to Amazon Personalize, you must define a schema for that dataset\. Once you define the schema and create the dataset, you can't make changes to the schema\. Schemas in Amazon Personalize are defined in the Avro format\. For more information, see [Apache Avro](https://avro.apache.org/docs/current/)\.
When you create a schema, you must follow these guidelines:
+ The schema fields can appear in any order, but they must match the order of the corresponding column headers in the data file\.
+ Each dataset type requires specific non\-metadata fields in its schema \(see the preceding table\)\. You must define required fields as their required datatypes\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/how-it-works-dataset-schema.md |
f5c164f249ea-1 | + `EVENT_VALUE` data and Interactions, User, and Item metadata can be a `null` type\. Adding a `null` type to a field in your schema allows you to use imperfect data \(for example, metadata with blank values\), to generate personalized recommendations\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/how-it-works-dataset-schema.md |
de19eb2037c3-0 | 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 and Items schemas require at least one metadata field,
+ Users and Interactions datasets can contain up to five metadata fields\. An Items dataset can contain up to 50 metadata fields\.
+ If you add your own metadata field of type `string`, it must include the `categorical` attribute\. Otherwise, Amazon Personalize won't use the field when training a model\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/how-it-works-dataset-schema.md |
ec1b80654c97-0 | Reserved keywords are optional, non\-metadata fields\. You must define reserved keywords as their required datatype\. The following are reserved keywords:
+ EVENT\_TYPE: Use an `EVENT_TYPE` field for Interactions datasets with one or more event types, such as Click and Download\. You must define an EVENT\_TYPE field as a `string`\.
+ EVENT\_VALUE: Use an `EVENT_VALUE` field for Interactions datasets that include value data for events, such as `perecent_watched`\. You must define an `EVENT_VALUE` field only as a `float` or `null`\.
+ CREATION\_TIMESTAMP: Use a `CREATION_TIMESTAMP` field for Items datasets with a timestamp for each item’s creation date\. Amazon Personalize uses `CREATION_TIMESTAMP` data to calculate the age of an item and adjust recommendations accordingly\. See [Creation Timestamp Data](data-prep-formatting.md#creation-timestamp-data)\.
+ IMPRESSION: Use an `IMPRESSION` field for Interactions datasets with impressions data\. 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](data-prep-formatting.md#data-prep-impressions-data)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/how-it-works-dataset-schema.md |
3f2e3106a55f-0 | The following example schemas are organized by dataset type\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/how-it-works-dataset-schema.md |
b10d4e352705-0 | The following example shows an Interactions schema\. The `EVENT_TYPE`, `EVENT_VALUE`, and `IMPRESSION` fields are optional reserved keywords recognized by Amazon Personalize\. `LOCATION` and `DEVICE` are optional contextual metadata fields\.
```
{
"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",
"null"
]
},
{
"name": "LOCATION",
"type": "string",
"categorical": true
},
{
"name": "DEVICE",
"type": [
"string",
"null"
],
"categorical": true
},
{ | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/how-it-works-dataset-schema.md |
b10d4e352705-1 | "string",
"null"
],
"categorical": true
},
{
"name": "TIMESTAMP",
"type": "long"
},
{
"name": "IMPRESSION",
"type": "string"
}
],
"version": "1.0"
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/how-it-works-dataset-schema.md |
b734d01751a7-0 | The following example shows a Users schema in Avro format\. Only the `USER_ID` field is required\. The `AGE` and `GENDER` fields are metadata\.
```
{
"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"
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/how-it-works-dataset-schema.md |
e5c088169e69-0 | The following example shows an Items schema\. Only the `ITEM_ID` field is required\. The `GENRE` field is metadata\. The `CREATION_TIMESTAMP` is a reserved keyword\.
```
{
"type": "record",
"name": "Items",
"namespace": "com.amazonaws.personalize.schema",
"fields": [
{
"name": "ITEM_ID",
"type": "string"
},
{
"name": "GENRES",
"type": [
"null",
"string"
],
"categorical": true
},
{
"name": "CREATION_TIMESTAMP",
"type": "long"
}
],
"version": "1.0"
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/how-it-works-dataset-schema.md |
a919ed6b4e8c-0 | 1. Define the Avro format schema that you want to use\.
1. Save the schema in a JSON file in the default Python folder\.
1. Create the schema using the following code\.
```
import boto3
personalize = boto3.client('personalize')
with open('schema.json') as f:
createSchemaResponse = personalize.create_schema(
name = 'YourSchema',
schema = f.read()
)
schema_arn = createSchemaResponse['schemaArn']
print('Schema ARN:' + schema_arn )
```
1. Amazon Personalize returns the ARN of the new schema\. Store it for later use\.
Amazon Personalize provides operations for managing schemas\. For example, you can use the [ListSchemas](API_ListSchemas.md) API to get a list of the available schemas\.
After you create a schema, use it with datasets that match the schema\. For more information, see [Formatting Your Input Data](data-prep-formatting.md)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/how-it-works-dataset-schema.md |
4586eaa0a7c2-0 | The User\-Personalization \(aws\-user\-personalization\) recipe is optimized for all USER\_PERSONALIZATION recommendation scenarios\. When recommending items, it uses automatic item exploration\.
With automatic exploration, Amazon Personalize automatically tests different item recommendations, learns from how users interact with these recommended items, and boosts recommendations for items that drive better engagement and conversion\. This improves item discovery and engagement when you have a fast\-changing catalog, or when new items, such as news articles or promotions, are more relevant to users when fresh\.
You can balance how much to explore \(where items with less interactions data or relevance are recommended more frequently\) against how much to exploit \(where recommendations are based on what we know or relevance\)\. Amazon Personalize automatically adjusts future recommendations based on implicit user feedback\.
**Working with Impressions Data**
Unlike other recipes, which solely use positive interactions \(clicking, watching, or purchasing\), the User\-Personalization recipe can also use impressions data\. Impressions are lists of items that were visible to the user when they interacted with \(clicked, watched, purchased, and so on\) a particular item\.
Using this information, a solution trained with the User\-Personalization recipe can quickly infer the suitability of new items based on how frequently an item has been ignored, and change recommendation accordingly\. For more information see [Impressions Data](data-prep-formatting.md#data-prep-impressions-data)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-new-item-USER_PERSONALIZATION.md |
187d1af67cee-0 | The User\-Personalization recipe has the following properties:
+ **Name** – `aws-user-personalization`
+ **Recipe Amazon Resource Name \(ARN\)** – `arn:aws:personalize:::recipe/aws-user-personalization`
+ **Algorithm ARN** – `arn:aws:personalize:::algorithm/aws-user-personalization`
For more information, see [Choosing a Recipe](working-with-predefined-recipes.md)\.
The following table describes the hyperparameters for the User\-Personalization 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-new-item-USER_PERSONALIZATION.md |
187d1af67cee-1 | | 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: 149 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-new-item-USER_PERSONALIZATION.md |
187d1af67cee-2 | | 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-new-item-USER_PERSONALIZATION.md |
187d1af67cee-3 | | 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 |
| Item Exploration Configuration Hyperparameters |
| exploration\_weight | Determines how frequently recommendations include items with less interactions data or relevance\. The closer the value is to 1\.0, the more exploration\. At zero, no exploration occurs and recommendations are based on current data \(relevance\)\. Default value: 0\.3 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-new-item-USER_PERSONALIZATION.md |
187d1af67cee-4 | | exploration\_item\_age\_cut\_off | Determines items to be explored based on time frame since latest interaction\. Provide the maximum item age, in days since the latest interaction, to define the scope of item exploration\. The larger the value, the more items are considered during exploration\. Default value: 30\.0 Range: Positive floats Value type: Float HPO tunable: No | | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-new-item-USER_PERSONALIZATION.md |
963b5a3738c5-0 | To use the User\-Personalization recipe to generate recommendations in the console, first train a new solution version using the recipe\. Then deploy a campaign using the solution version and use the campaign to get recommendations\.
**Training a new solution version with the User\-Personalization recipe \(console\)**
1. Open the Amazon Personalize console at [https://console\.aws\.amazon\.com/personalize/](https://console.aws.amazon.com/personalize/) and sign into your account\.
1. Create a dataset group with a new schema and upload your dataset with impressions data\. Optionally include [CREATION\_TIMESTAMP](data-prep-formatting.md#creation-timestamp-data) data in your Items dataset so Amazon Personalize can more accurately calculate the age of an item and identify cold items\.
For more information on creating dataset groups and uploading training data, see [Step 1: Import Training Data](getting-started-console.md#getting-started-console-import-dataset) in the Getting Started tutorial\.
1. On the **Dataset groups** page, choose the new dataset group that contains the dataset or datasets with impressions data\.
1. In the navigation pane, choose **Solutions and recipes** and choose **Create solution**\.
1. On the **Create solution** page, for the **Solution name**, enter the name of your new solution\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-new-item-USER_PERSONALIZATION.md |
963b5a3738c5-1 | 1. On the **Create solution** page, for the **Solution name**, enter the name of your new solution\.
1. For **Recipe**, choose **aws\-user\-personalization**\. The **Solution configuration** section appears providing several configuration options\.
1. Specify the following:
+ **Event type:** If your data has multiple event types, enter the event type, such as click or download, to use for training\.
+ **Event value threshold:** Enter a value to define which events to use for training\. Only events with a value greater than or equal to this value will be used for training\.
For example, suppose that your application rates user interest in videos based on the percentage of a video watched by the user, with values greater than 70 indicating high interest\. If you want to use only high\-interest events for training, for the **Event type** enter *watched* and for **Event value threshold** enter *70*\. Only videos with a *watched* value greater than *70* will be used for training\.
1. Optionally configure hyperparameters for your solution\. For a list of User\-Personalization recipe properties and hyperparameters, see [Properties and Hyperparameters](#bandit-hyperparameters)\.
1. Choose **Next**\. You can review your settings on the **Create solution version** page\.
1. Choose **Finish** to create the solution version\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-new-item-USER_PERSONALIZATION.md |
963b5a3738c5-2 | 1. Choose **Finish** to create the solution version\.
On the solution details page, you can track training progress in the **Solution versions** section\. When training is complete, the status is **Active**\.
**Creating a campaign and getting recommendations \(console\)**
When your solution version status is **Active** you are ready to create your campaign and get recommendations as follows:
1. On either the solution details page or the **Campaigns** page, choose **Create new campaign**\.
1. On the **Create new campaign** page, for **Campaign details**, provide the following information:
+ **Campaign name:** Enter the name of the campaign\. The text you enter here appears on the Campaign dashboard and details page\.
+ **Solution:** Choose the solution that you just created\.
+ **Solution version ID:** Choose the ID of the solution version that you just created\.
+ **Minimum provisioned transactions per second:** Set the minimum provisioned transactions per second that Amazon Personalize supports\. For more information, see the [CreateCampaign](API_CreateCampaign.md) operation\.
1. For **Campaign configuration**, provide the following information:
+ **Exploration weight:** Configure how much to explore, where recommendations include items with less interactions data or relevance more frequently the more exploration you specify\. The closer the value is to 1, the more exploration\. At zero, no exploration occurs and recommendations are based on current data \(relevance\)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-new-item-USER_PERSONALIZATION.md |
963b5a3738c5-3 | + **Exploration item age cut off**: Enter the maximum item age, in days since the latest interaction, to define the scope of item exploration\. To increase the number of items Amazon Personalize considers during exploration, enter a greater value\.
For example, if you enter 10, only items with interactions data from the 10 days since the latest interaction in the dataset are considered during exploration\.
**Note**
Recommendations might include items without interactions data from outside this time frame\. This is because these items are relevant to the user's interests, and exploration wasn't required to identify them\.
1. Choose **Create campaign**\.
1. On the campaign details page, when the campaign status is **Active**, you can use the campaign to get recommendations and record impressions\. For more information, see [Step 4: Get Recommendations](getting-started-console.md#getting-started-console-get-recommendations) in "Getting Started\." | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-new-item-USER_PERSONALIZATION.md |
eadee4b0f458-0 | When you have created a dataset group and uploaded your dataset\(s\) with impressions data, you can train a solution with the User\-Personalization recipe\. Optionally include [CREATION\_TIMESTAMP](data-prep-formatting.md#creation-timestamp-data) data in your dataset so Amazon Personalize can more accurately calculate the age of an item and identify cold start items\. For more information on creating dataset groups and uploading training data see [Datasets and Schemas](how-it-works-dataset-schema.md)\.
**To train a solution with the User\-Personalization recipe using the AWS SDK**
1. Create a new solution using the `create_solution` method\.
Replace `solution name` with your solution name and `dataset group arn` with the Amazon Resource Name \(ARN\) of your dataset group\.
```
import boto3
personalize = boto3.client('personalize')
print('Creating solution')
create_solution_response = personalize.create_solution(name='solution name',
recipeArn= arn:aws:personalize:::recipe/aws-user-personalization,
datasetGroupArn = 'dataset group arn',
)
solution_arn = create_solution_response['solutionArn']
print('solution_arn: ', solution_arn)
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-new-item-USER_PERSONALIZATION.md |
eadee4b0f458-1 | print('solution_arn: ', solution_arn)
```
For a list of aws\-user\-personalization recipe properties and hyperparameters, see [Properties and Hyperparameters](#bandit-hyperparameters)\.
1. Create a new *solution version* with the updated training data and set `trainingMode` to `FULL` using the following code snippet\. Replace the `solution arn` with the ARN of your solution\.
```
import boto3
personalize = boto3.client('personalize')
create_solution_version_response = personalize.create_solution_version(solutionArn = 'solution arn',
trainingMode='FULL')
new_solution_version_arn = create_solution_version_response['solutionVersionArn']
print('solution_version_arn:', new_solution_version_arn)
```
1. When Amazon Personalize is finished creating your solution version, create your campaign with the following parameters:
+ Provide a new `campaign name` and the `solution version arn` generated in step 2\.
+ Modify the `explorationWeight` item exploration configuration hyperparameter to configure how much to explore\. Items with less interactions data or relevance are recommended more frequently the closer the value is to 1\.0\. The default value is 0\.3\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-new-item-USER_PERSONALIZATION.md |
eadee4b0f458-2 | + Modify the `explorationItemAgeCutOff` item exploration configuration hyperparameter parameter to provide the maximum duration, in days relative to the latest interaction, for which items should be explored\. The larger the value, the more items are considered during exploration\.
Use the following Python snippet to create a new campaign with an emphasis on exploration with exploration cut\-off at 30 days\.
```
import boto3
personalize = boto3.client('personalize')
create_campaign_response = personalize.create_campaign(
name = 'campaign name',
solutionVersionArn = 'solution version arn',
minProvisionedTPS = 1,
campaignConfig = {"itemExplorationConfig": {"explorationWeight": "0.3", "explorationItemAgeCutOff": "30"}}
)
campaign_arn = create_campaign_response['campaignArn']
print('campaign_arn:', campaign_arn)
```
**Note**
Creating a campaign usually takes a few minutes but can take over an hour\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-new-item-USER_PERSONALIZATION.md |
ae2625c19467-0 | When your campaign is created, you can use it to get recommendations for a user and record impressions\. For information on getting batch recommendations using the AWS SDK see [Getting Batch Recommendations \(AWS Python SDK\)](recommendations-batch.md#batch-sdk)\.
**To get recommendations and record impressions**
1. Call the `get_recommendations` method\. Change the `campaign arn` to the ARN of your new campaign and `user id` to the userId of the user\.
```
import boto3
rec_response = personalize_runtime.get_recommendations(campaignArn = 'campaign arn', userId = 'user id')
print(rec_response['recommendationId'])
```
1. Create a new event tracker for sending PutEvents requests\. Replace `event tracker name` with the name of your event tracker and `dataset group arn` with the ARN of your dataset group\.
```
import boto3
personalize = boto3.client('personalize')
event_tracker_response = personalize.create_event_tracker(
name = 'event tracker name',
datasetGroupArn = 'dataset group arn'
)
event_tracker_arn = event_tracker_response['eventTrackerArn']
event_tracking_id = event_tracker_response['trackingId'] | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-new-item-USER_PERSONALIZATION.md |
ae2625c19467-1 | event_tracking_id = event_tracker_response['trackingId']
print('eventTrackerArn:{},\n eventTrackingId:{}'.format(event_tracker_arn, event_tracking_id))
```
1. Use the `recommendationId` from step 1 and the `event tracking id` from step 2 to create a new `PutEvents` request\. This request logs the new impression data from the user’s session\. Change the `user id` to the ID of the user\.
```
import boto3
personalize_events.put_events(
trackingId = 'event tracking id',
userId= 'user id',
sessionId = '1',
eventList = [{
'sentAt': datetime.now().timestamp(),
'eventType' : 'click',
'itemId' : rec_response['itemList'][0]['itemId'],
'recommendationId': rec_response['recommendationId'],
'impression': [item['itemId'] for item in rec_response['itemList']],
}]
)
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-new-item-USER_PERSONALIZATION.md |
35e22c65e434-0 | For a sample Jupyter notebook that shows how to use the User\-Personalization recipe, see [User Personalization with Exploration](https://github.com/aws-samples/amazon-personalize-samples/blob/master/next_steps/core_use_cases/user_personalization/user-personalization-with-exploration.ipynb)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/native-recipe-new-item-USER_PERSONALIZATION.md |
f8eb14935a11-0 | Describes the given feature transformation\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeFeatureTransformation.md |
a8fed9fcc177-0 | ```
{
"featureTransformationArn": "string"
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeFeatureTransformation.md |
44c106b37533-0 | The request accepts the following data in JSON format\.
** [featureTransformationArn](#API_DescribeFeatureTransformation_RequestSyntax) ** <a name="personalize-DescribeFeatureTransformation-request-featureTransformationArn"></a>
The Amazon Resource Name \(ARN\) of the feature transformation 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_DescribeFeatureTransformation.md |
359024eb044a-0 | ```
{
"featureTransformation": {
"creationDateTime": number,
"defaultParameters": {
"string" : "string"
},
"featureTransformationArn": "string",
"lastUpdatedDateTime": number,
"name": "string",
"status": "string"
}
}
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeFeatureTransformation.md |
d6f8b51595fa-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\.
** [featureTransformation](#API_DescribeFeatureTransformation_ResponseSyntax) ** <a name="personalize-DescribeFeatureTransformation-response-featureTransformation"></a>
A listing of the FeatureTransformation properties\.
Type: [FeatureTransformation](API_FeatureTransformation.md) object | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeFeatureTransformation.md |
26e3a3d7ced7-0 | **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_DescribeFeatureTransformation.md |
5f3f781c379c-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/DescribeFeatureTransformation)
+ [AWS SDK for \.NET](https://docs.aws.amazon.com/goto/DotNetSDKV3/personalize-2018-05-22/DescribeFeatureTransformation)
+ [AWS SDK for C\+\+](https://docs.aws.amazon.com/goto/SdkForCpp/personalize-2018-05-22/DescribeFeatureTransformation)
+ [AWS SDK for Go](https://docs.aws.amazon.com/goto/SdkForGoV1/personalize-2018-05-22/DescribeFeatureTransformation)
+ [AWS SDK for Java](https://docs.aws.amazon.com/goto/SdkForJava/personalize-2018-05-22/DescribeFeatureTransformation)
+ [AWS SDK for JavaScript](https://docs.aws.amazon.com/goto/AWSJavaScriptSDK/personalize-2018-05-22/DescribeFeatureTransformation) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeFeatureTransformation.md |
5f3f781c379c-1 | + [AWS SDK for PHP V3](https://docs.aws.amazon.com/goto/SdkForPHPV3/personalize-2018-05-22/DescribeFeatureTransformation)
+ [AWS SDK for Python](https://docs.aws.amazon.com/goto/boto3/personalize-2018-05-22/DescribeFeatureTransformation)
+ [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/personalize-2018-05-22/DescribeFeatureTransformation) | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_DescribeFeatureTransformation.md |
c85e1329c938-0 | With Amazon CloudWatch, you can get metrics associated with Amazon Personalize\. You can set up alarms to notify you when one or more of these metrics fall outside a defined threshold\. To see metrics, you can use [Amazon CloudWatch](https://console.aws.amazon.com/cloudwatch/), [Amazon AWS Command Line Interface](https://docs.aws.amazon.com/AmazonCloudWatch/latest/cli/), or the [CloudWatch API](https://docs.aws.amazon.com/AmazonCloudWatch/latest/APIReference/)\.
You can also see aggregated metrics, for a chosen period of time, by using the Amazon Personalize console\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/personalize-monitoring.md |
bb64c4a83e08-0 | To use metrics, you must specify the following information:
+ The metric name\.
+ The metric dimension\. A *dimension* is a name\-value pair that helps you to uniquely identify a metric\.
You can get monitoring data for Amazon Personalize using the AWS Management Console, the AWS CLI, or the CloudWatch API\. You can also use the CloudWatch API through one of the Amazon AWS Software Development Kits \(SDKs\) or the CloudWatch API tools\. The console displays a series of graphs based on the raw data from the CloudWatch API\. Depending on your needs, you might prefer to use either the graphs displayed in the console or retrieved from the API\.
The following list shows some common uses for the metrics\. These are suggestions to get you started, not a comprehensive list\.
| How Do I? | Relevant Metric |
| --- | --- |
| How do I track the number of events that have been recorded? | Monitor the `PutEventsRequests` metric\. |
| How can I monitor the DatasetImportJob errors? | Use the `DatasetImportJobError` metric\. |
| How can I monitor the latency of `GetRecommendations` calls? | Use the `GetRecommendationsLatency` metric\. | | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/personalize-monitoring.md |
bb64c4a83e08-1 | | How can I monitor the latency of `GetRecommendations` calls? | Use the `GetRecommendationsLatency` metric\. |
You must have the appropriate CloudWatch permissions to monitor Amazon Personalize with CloudWatch\. For more information, see [Authentication and Access Control for Amazon CloudWatch](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/auth-and-access-control-cw.html)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/personalize-monitoring.md |
9ebc452f5ca9-0 | The following examples show how to access Amazon Personalize metrics using the CloudWatch console, the AWS CLI, and the CloudWatch API\.
**To view metrics \(console\)**
1. Sign in to the AWS Management Console and open the CloudWatch console at [https://console\.aws\.amazon\.com/cloudwatch/](https://console.aws.amazon.com/cloudwatch/)\.
1. Choose **Metrics**, choose the **All metrics** tab, and then choose **AWS/Personalize**\.
1. Choose the metric dimension\.
1. Choose the desired metric from the list, and choose a time period for the graph\.
**To view metrics for events received over a period of time \(CLI\)**
+ Open the AWS CLI and enter the following command:
```
aws cloudwatch get-metric-statistics \
--metric-name PutEventsRequests \
--start-time 2019-03-15T00:00:20Z \
--period 3600 \
--end-time 2019-03-16T00:00:00Z \
--namespace AWS/Personalize \
--dimensions Name=EventTrackerArn,Value=EventTrackerArn \
--statistics Sum
``` | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/personalize-monitoring.md |
9ebc452f5ca9-1 | --dimensions Name=EventTrackerArn,Value=EventTrackerArn \
--statistics Sum
```
This example shows the events received for the given event tracker ARN over a period of time\. For more information, see [get\-metric\-statistics](https://docs.aws.amazon.com/cli/latest/reference/cloudwatch/get-metric-statistics.html)\.
**To access metrics \(CloudWatch API\)**
+ Call `[GetMetricStatistics](https://docs.aws.amazon.com/AmazonCloudWatch/latest/APIReference/API_GetMetricStatistics.html)`\. For more information, see the [Amazon CloudWatch API Reference](https://docs.aws.amazon.com/AmazonCloudWatch/latest/APIReference/)\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/personalize-monitoring.md |
e2f5b39f9564-0 | You can create a CloudWatch alarm that sends an Amazon Simple Notification Service \(Amazon SNS\) message when the alarm changes state\. An alarm watches a single metric over a time period you specify\. The alarm performs one or more actions based on the value of the metric relative to a given threshold over a number of time periods\. The action is a notification sent to an Amazon SNS topic or an AWS Auto Scaling policy\.
Alarms invoke actions for sustained state changes only\. CloudWatch alarms do not invoke actions simply because they are in a particular state\. The state must have changed and been maintained for a specified number of time periods\.
**To set an alarm \(console\)**
1. Sign in to the AWS Management Console and open the CloudWatch console at [https://console\.aws\.amazon\.com/cloudwatch/](https://console.aws.amazon.com/cloudwatch/)\.
1. In the navigation pane, Choose **Alarms**, and then choose **Create alarm**\. This launches the **Create Alarm Wizard**\.
1. Choose **Select metric**\.
1. In the **All metrics** tab, choose **AWS/Personalize**\.
1. Choose **EventTrackerArn**, and then choose **PutEventsRequests** metrics\.
1. Choose the **Graphed metrics** tab\.
1. For **Statistic** choose **Sum**\.
1. Choose **Select metric**\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/personalize-monitoring.md |
e2f5b39f9564-1 | 1. For **Statistic** choose **Sum**\.
1. Choose **Select metric**\.
1. Fill in the **Name** and **Description**\. For **Whenever**, choose **>**, and then enter a maximum value of your choice\.
1. If you want CloudWatch to send you email when the alarm state is reached, for **Whenever this alarm:**, choose ** State is ALARM**\. To send alarms to an existing Amazon SNS topic, for **Send notification to:**, choose an existing SNS topic\. To set the name and email addresses for a new email subscription list, choose **New list**\. CloudWatch saves the list and displays it in the field so you can use it to set future alarms\.
**Note**
If you use **New list** to create a new Amazon SNS topic, the email addresses must be verified before the intended recipients receive notifications\. Amazon SNS sends email only when the alarm enters an alarm state\. If this alarm state change happens before the email addresses are verified, intended recipients do not receive a notification\.
1. Choose **Create alarm**\.
**To set an alarm \(AWS CLI\)**
+ Open the AWS CLI, and then enter the following command\. Change the value of the `alarm-actions` parameter to reference an Amazon SNS topic that you previously created\.
```
aws cloudwatch put-metric-alarm \
--alarm-name PersonalizeCLI \ | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/personalize-monitoring.md |
e2f5b39f9564-2 | ```
aws cloudwatch put-metric-alarm \
--alarm-name PersonalizeCLI \
--alarm-description "Alarm when more than 10 events occur" \
--metric-name PutEventsRequests \
--namespace AWS/Personalize \
--statistic Sum \
--period 300 \
--threshold 10 \
--comparison-operator GreaterThanThreshold \
--evaluation-periods 1 \
--unit Count \
--dimensions Name=EventTrackerArn,Value=EventTrackerArn \
--alarm-actions SNSTopicArn
```
This example shows how to create an alarm for when more than 10 events occur for the given event tracker ARN within 5 minutes\. For more information, see [put\-metric\-alarm](https://docs.aws.amazon.com/cli/latest/reference/cloudwatch/put-metric-alarm.html)\.
**To set an alarm \(CloudWatch API\)**
+ Call `[PutMetricAlarm](https://docs.aws.amazon.com/AmazonCloudWatch/latest/APIReference/API_PutMetricAlarm.html)`\. For more information, see *[Amazon CloudWatch API Reference](https://docs.aws.amazon.com/AmazonCloudWatch/latest/APIReference/)*\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/personalize-monitoring.md |
3a5e5986e047-0 | Provides the name and range of an integer\-valued hyperparameter\. | https://github.com/siagholami/aws-documentation/tree/main/documents/amazon-personalize-developer-guide/doc_source/API_IntegerHyperParameterRange.md |
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