Module Awso_personalize_syncSource

Provides the name and range of a categorical hyperparameter.

Provides the name and range of a continuous hyperparameter.

Provides the name and range of an integer-valued hyperparameter.

Describes the parameters of events, which are used in solution creation.

The training data configuration to use when creating a domain recommender or custom solution version (trained model).

The metric to optimize during hyperparameter optimization (HPO). Amazon Personalize doesn't support configuring the hpoObjective at this time.

Describes the resource configuration for hyperparameter optimization (HPO).

Specifies the hyperparameters and their ranges. Hyperparameters can be categorical, continuous, or integer-valued.

The automatic training configuration to use when performAutoTraining is true.

Describes the configuration of events, which are used in solution creation.

Provides the name and default range of a categorical hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).

Provides the name and default range of a continuous hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).

Provides the name and default range of a integer-valued hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).

The configuration details of the recommender.

When the solution performs AutoML (performAutoML is true in CreateSolution), Amazon Personalize determines which recipe, from the specified list, optimizes the given metric. Amazon Personalize then uses that recipe for the solution.

Describes the properties for hyperparameter optimization (HPO).

Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.

The configuration details of the solution update.

The configuration details of an Amazon S3 input or output bucket.

The configuration details of a campaign.

A string to string map of the configuration details for theme generation.

Contains information on a metric that a metric attribution reports on. For more information, see Measuring impact of recommendations.

The optional metadata that you apply to resources to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define. For more information see Tagging Amazon Personalize resources.

Provides a summary of the properties of a solution. For a complete listing, call the DescribeSolution API.

Provides a summary of the properties of a solution version. For a complete listing, call the DescribeSolutionVersion API.

Provides a summary of the properties of a dataset schema. For a complete listing, call the DescribeSchema API.

Provides a summary of the properties of the recommender.

Provides a summary of the properties of a recipe. For a complete listing, call the DescribeRecipe API.

Provides a summary of the properties of a metric attribution. For a complete listing, call the DescribeMetricAttribution.

A short summary of a filter's attributes.

Provides a summary of the properties of an event tracker. For a complete listing, call the DescribeEventTracker API.

Provides a summary of the properties of a dataset. For a complete listing, call the DescribeDataset API.

Provides a summary of the properties of a dataset import job. For a complete listing, call the DescribeDatasetImportJob API.

Provides a summary of the properties of a dataset group. For a complete listing, call the DescribeDatasetGroup API.

Provides a summary of the properties of a dataset export job. For a complete listing, call the DescribeDatasetExportJob API.

Provides a summary of the properties of a data deletion job. For a complete listing, call the DescribeDataDeletionJob API operation.

Provides a summary of the properties of a campaign. For a complete listing, call the DescribeCampaign API.

A truncated version of the BatchSegmentJob datatype. ListBatchSegmentJobs operation returns a list of batch segment job summaries.

A truncated version of the BatchInferenceJob. The ListBatchInferenceJobs operation returns a list of batch inference job summaries.

Describes the configuration properties for the solution.

If hyperparameter optimization (HPO) was performed, contains the hyperparameter values of the best performing model.

When the solution performs AutoML (performAutoML is true in CreateSolution), specifies the recipe that best optimized the specified metric.

Provides a summary of the properties of a solution update. For a complete listing, call the DescribeSolution API.

Provides a summary of the properties of a recommender update. For a complete listing, call the DescribeRecommender API.

The output configuration details for a metric attribution.

Describes an update to a dataset.

Describes the data source that contains the data to upload to a dataset, or the list of records to delete from Amazon Personalize.

The output configuration parameters of a dataset export job.

Provides a summary of the properties of a campaign update. For a complete listing, call the DescribeCampaign API.

The input configuration of a batch segment job.

The output configuration parameters of a batch segment job.

The configuration details of a batch inference job.

The input configuration of a batch inference job.

The output configuration parameters of a batch inference job.

The configuration details for generating themes with a batch inference job.

Describes an algorithm image.

Specifies the hyperparameters and their default ranges. Hyperparameters can be categorical, continuous, or integer-valued.

Provide a valid value for the field or parameter.

The limit on the number of requests per second has been exceeded.

The specified resource is in use.

Could not find the specified resource.

The specified resource already exists.

The request contains more tag keys than can be associated with a resource (50 tag keys per resource).

You have exceeded the maximum number of tags you can apply to this resource.

The token is not valid.

An object that provides information about a specific version of a Solution in a Custom dataset group.

By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can update the solution to turn off automatic training. For information about training costs, see Amazon Personalize pricing. An object that provides information about a solution. A solution includes the custom recipe, customized parameters, and trained models (Solution Versions) that Amazon Personalize uses to generate recommendations. After you create a solution, you can’t change its configuration. If you need to make changes, you can clone the solution with the Amazon Personalize console or create a new one.

Describes the schema for a dataset. For more information on schemas, see CreateSchema.

Describes a recommendation generator for a Domain dataset group. You create a recommender in a Domain dataset group for a specific domain use case (domain recipe), and specify the recommender in a GetRecommendations request.

Provides information about a recipe. Each recipe provides an algorithm that Amazon Personalize uses in model training when you use the CreateSolution operation.

Contains information on a metric attribution. A metric attribution creates reports on the data that you import into Amazon Personalize. Depending on how you import the data, you can view reports in Amazon CloudWatch or Amazon S3. For more information, see Measuring impact of recommendations.

Contains information on a recommendation filter, including its ARN, status, and filter expression.

Provides feature transformation information. Feature transformation is the process of modifying raw input data into a form more suitable for model training.

Provides information about an event tracker.

Provides metadata for a dataset.

Describes a job that imports training data from a data source (Amazon S3 bucket) to an Amazon Personalize dataset. For more information, see CreateDatasetImportJob. A dataset import job can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED

A dataset group is a collection of related datasets (Item interactions, Users, Items, Actions, Action interactions). You create a dataset group by calling CreateDatasetGroup. You then create a dataset and add it to a dataset group by calling CreateDataset. The dataset group is used to create and train a solution by calling CreateSolution. A dataset group can contain only one of each type of dataset. You can specify an Key Management Service (KMS) key to encrypt the datasets in the group.

Describes a job that exports a dataset to an Amazon S3 bucket. For more information, see CreateDatasetExportJob. A dataset export job can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED

Describes a job that deletes all references to specific users from an Amazon Personalize dataset group in batches. For information about creating a data deletion job, see Deleting users.

An object that describes the deployment of a solution version. For more information on campaigns, see CreateCampaign.

Contains information on a batch segment job.

Contains information on a batch inference job.

Describes a custom algorithm.

Updates an Amazon Personalize solution to use a different automatic training configuration. When you update a solution, you can change whether the solution uses automatic training, and you can change the training frequency. For more information about updating a solution, see Updating a solution. A solution update can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED To get the status of a solution update, call the DescribeSolution API operation and find the status in the latestSolutionUpdate.

Updates an Amazon Personalize solution to use a different automatic training configuration. When you update a solution, you can change whether the solution uses automatic training, and you can change the training frequency. For more information about updating a solution, see Updating a solution. A solution update can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED To get the status of a solution update, call the DescribeSolution API operation and find the status in the latestSolutionUpdate.

Updates the recommender to modify the recommender configuration. If you update the recommender to modify the columns used in training, Amazon Personalize automatically starts a full retraining of the models backing your recommender. While the update completes, you can still get recommendations from the recommender. The recommender uses the previous configuration until the update completes. To track the status of this update, use the latestRecommenderUpdate returned in the DescribeRecommender operation.

Updates the recommender to modify the recommender configuration. If you update the recommender to modify the columns used in training, Amazon Personalize automatically starts a full retraining of the models backing your recommender. While the update completes, you can still get recommendations from the recommender. The recommender uses the previous configuration until the update completes. To track the status of this update, use the latestRecommenderUpdate returned in the DescribeRecommender operation.

Updates a metric attribution.

Updates a metric attribution.

Update a dataset to replace its schema with a new or existing one. For more information, see Replacing a dataset's schema.

Update a dataset to replace its schema with a new or existing one. For more information, see Replacing a dataset's schema.

Updates a campaign to deploy a retrained solution version with an existing campaign, change your campaign's minProvisionedTPS, or modify your campaign's configuration. For example, you can set enableMetadataWithRecommendations to true for an existing campaign. To update a campaign to start automatically using the latest solution version, specify the following: For the SolutionVersionArn parameter, specify the Amazon Resource Name (ARN) of your solution in SolutionArn/$LATEST format. In the campaignConfig, set syncWithLatestSolutionVersion to true. To update a campaign, the campaign status must be ACTIVE or CREATE FAILED. Check the campaign status using the DescribeCampaign operation. You can still get recommendations from a campaign while an update is in progress. The campaign will use the previous solution version and campaign configuration to generate recommendations until the latest campaign update status is Active. For more information about updating a campaign, including code samples, see Updating a campaign. For more information about campaigns, see Creating a campaign.

Updates a campaign to deploy a retrained solution version with an existing campaign, change your campaign's minProvisionedTPS, or modify your campaign's configuration. For example, you can set enableMetadataWithRecommendations to true for an existing campaign. To update a campaign to start automatically using the latest solution version, specify the following: For the SolutionVersionArn parameter, specify the Amazon Resource Name (ARN) of your solution in SolutionArn/$LATEST format. In the campaignConfig, set syncWithLatestSolutionVersion to true. To update a campaign, the campaign status must be ACTIVE or CREATE FAILED. Check the campaign status using the DescribeCampaign operation. You can still get recommendations from a campaign while an update is in progress. The campaign will use the previous solution version and campaign configuration to generate recommendations until the latest campaign update status is Active. For more information about updating a campaign, including code samples, see Updating a campaign. For more information about campaigns, see Creating a campaign.

Removes the specified tags that are attached to a resource. For more information, see Removing tags from Amazon Personalize resources.

Removes the specified tags that are attached to a resource. For more information, see Removing tags from Amazon Personalize resources.

Add a list of tags to a resource.

Add a list of tags to a resource.

Stops creating a solution version that is in a state of CREATE_PENDING or CREATE IN_PROGRESS. Depending on the current state of the solution version, the solution version state changes as follows: CREATE_PENDING > CREATE_STOPPED or CREATE_IN_PROGRESS > CREATE_STOPPING > CREATE_STOPPED You are billed for all of the training completed up until you stop the solution version creation. You cannot resume creating a solution version once it has been stopped.

Stops a recommender that is ACTIVE. Stopping a recommender halts billing and automatic retraining for the recommender.

Stops a recommender that is ACTIVE. Stopping a recommender halts billing and automatic retraining for the recommender.

Starts a recommender that is INACTIVE. Starting a recommender does not create any new models, but resumes billing and automatic retraining for the recommender.

Starts a recommender that is INACTIVE. Starting a recommender does not create any new models, but resumes billing and automatic retraining for the recommender.

Get a list of tags attached to a resource.

Get a list of tags attached to a resource.

Returns a list of solutions in a given dataset group. When a dataset group is not specified, all the solutions associated with the account are listed. The response provides the properties for each solution, including the Amazon Resource Name (ARN). For more information on solutions, see CreateSolution.

Returns a list of solutions in a given dataset group. When a dataset group is not specified, all the solutions associated with the account are listed. The response provides the properties for each solution, including the Amazon Resource Name (ARN). For more information on solutions, see CreateSolution.

Returns a list of solution versions for the given solution. When a solution is not specified, all the solution versions associated with the account are listed. The response provides the properties for each solution version, including the Amazon Resource Name (ARN).

Returns a list of solution versions for the given solution. When a solution is not specified, all the solution versions associated with the account are listed. The response provides the properties for each solution version, including the Amazon Resource Name (ARN).

Returns the list of schemas associated with the account. The response provides the properties for each schema, including the Amazon Resource Name (ARN). For more information on schemas, see CreateSchema.

Returns the list of schemas associated with the account. The response provides the properties for each schema, including the Amazon Resource Name (ARN). For more information on schemas, see CreateSchema.

Returns a list of recommenders in a given Domain dataset group. When a Domain dataset group is not specified, all the recommenders associated with the account are listed. The response provides the properties for each recommender, including the Amazon Resource Name (ARN). For more information on recommenders, see CreateRecommender.

Returns a list of recommenders in a given Domain dataset group. When a Domain dataset group is not specified, all the recommenders associated with the account are listed. The response provides the properties for each recommender, including the Amazon Resource Name (ARN). For more information on recommenders, see CreateRecommender.

Returns a list of available recipes. The response provides the properties for each recipe, including the recipe's Amazon Resource Name (ARN).

Returns a list of available recipes. The response provides the properties for each recipe, including the recipe's Amazon Resource Name (ARN).

Lists metric attributions.

Lists metric attributions.

Lists the metrics for the metric attribution.

Lists the metrics for the metric attribution.

Lists all filters that belong to a given dataset group.

Lists all filters that belong to a given dataset group.

Returns the list of event trackers associated with the account. The response provides the properties for each event tracker, including the Amazon Resource Name (ARN) and tracking ID. For more information on event trackers, see CreateEventTracker.

Returns the list of event trackers associated with the account. The response provides the properties for each event tracker, including the Amazon Resource Name (ARN) and tracking ID. For more information on event trackers, see CreateEventTracker.

Returns the list of datasets contained in the given dataset group. The response provides the properties for each dataset, including the Amazon Resource Name (ARN). For more information on datasets, see CreateDataset.

Returns the list of datasets contained in the given dataset group. The response provides the properties for each dataset, including the Amazon Resource Name (ARN). For more information on datasets, see CreateDataset.

Returns a list of dataset import jobs that use the given dataset. When a dataset is not specified, all the dataset import jobs associated with the account are listed. The response provides the properties for each dataset import job, including the Amazon Resource Name (ARN). For more information on dataset import jobs, see CreateDatasetImportJob. For more information on datasets, see CreateDataset.

Returns a list of dataset import jobs that use the given dataset. When a dataset is not specified, all the dataset import jobs associated with the account are listed. The response provides the properties for each dataset import job, including the Amazon Resource Name (ARN). For more information on dataset import jobs, see CreateDatasetImportJob. For more information on datasets, see CreateDataset.

Returns a list of dataset groups. The response provides the properties for each dataset group, including the Amazon Resource Name (ARN). For more information on dataset groups, see CreateDatasetGroup.

Returns a list of dataset groups. The response provides the properties for each dataset group, including the Amazon Resource Name (ARN). For more information on dataset groups, see CreateDatasetGroup.

Returns a list of dataset export jobs that use the given dataset. When a dataset is not specified, all the dataset export jobs associated with the account are listed. The response provides the properties for each dataset export job, including the Amazon Resource Name (ARN). For more information on dataset export jobs, see CreateDatasetExportJob. For more information on datasets, see CreateDataset.

Returns a list of dataset export jobs that use the given dataset. When a dataset is not specified, all the dataset export jobs associated with the account are listed. The response provides the properties for each dataset export job, including the Amazon Resource Name (ARN). For more information on dataset export jobs, see CreateDatasetExportJob. For more information on datasets, see CreateDataset.

Returns a list of data deletion jobs for a dataset group ordered by creation time, with the most recent first. When a dataset group is not specified, all the data deletion jobs associated with the account are listed. The response provides the properties for each job, including the Amazon Resource Name (ARN). For more information on data deletion jobs, see Deleting users.

Returns a list of data deletion jobs for a dataset group ordered by creation time, with the most recent first. When a dataset group is not specified, all the data deletion jobs associated with the account are listed. The response provides the properties for each job, including the Amazon Resource Name (ARN). For more information on data deletion jobs, see Deleting users.

Returns a list of campaigns that use the given solution. When a solution is not specified, all the campaigns associated with the account are listed. The response provides the properties for each campaign, including the Amazon Resource Name (ARN). For more information on campaigns, see CreateCampaign.

Returns a list of campaigns that use the given solution. When a solution is not specified, all the campaigns associated with the account are listed. The response provides the properties for each campaign, including the Amazon Resource Name (ARN). For more information on campaigns, see CreateCampaign.

Gets a list of the batch segment jobs that have been performed off of a solution version that you specify.

Gets a list of the batch segment jobs that have been performed off of a solution version that you specify.

Gets a list of the batch inference jobs that have been performed off of a solution version.

Gets a list of the batch inference jobs that have been performed off of a solution version.

Gets the metrics for the specified solution version.

Gets the metrics for the specified solution version.

Describes a specific version of a solution. For more information on solutions, see CreateSolution

Describes a specific version of a solution. For more information on solutions, see CreateSolution

Describes a solution. For more information on solutions, see CreateSolution.

Describes a solution. For more information on solutions, see CreateSolution.

Describes a schema. For more information on schemas, see CreateSchema.

Describes a schema. For more information on schemas, see CreateSchema.

Describes the given recommender, including its status. A recommender can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE DELETE PENDING > DELETE IN_PROGRESS When the status is CREATE FAILED, the response includes the failureReason key, which describes why. The modelMetrics key is null when the recommender is being created or deleted. For more information on recommenders, see CreateRecommender.

Describes the given recommender, including its status. A recommender can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE DELETE PENDING > DELETE IN_PROGRESS When the status is CREATE FAILED, the response includes the failureReason key, which describes why. The modelMetrics key is null when the recommender is being created or deleted. For more information on recommenders, see CreateRecommender.

Describes a recipe. A recipe contains three items: An algorithm that trains a model. Hyperparameters that govern the training. Feature transformation information for modifying the input data before training. Amazon Personalize provides a set of predefined recipes. You specify a recipe when you create a solution with the CreateSolution API. CreateSolution trains a model by using the algorithm in the specified recipe and a training dataset. The solution, when deployed as a campaign, can provide recommendations using the GetRecommendations API.

Describes a recipe. A recipe contains three items: An algorithm that trains a model. Hyperparameters that govern the training. Feature transformation information for modifying the input data before training. Amazon Personalize provides a set of predefined recipes. You specify a recipe when you create a solution with the CreateSolution API. CreateSolution trains a model by using the algorithm in the specified recipe and a training dataset. The solution, when deployed as a campaign, can provide recommendations using the GetRecommendations API.

Describes a metric attribution.

Describes a metric attribution.

Describes a filter's properties.

Describes a filter's properties.

Describes the given feature transformation.

Describes the given feature transformation.

Describes an event tracker. The response includes the trackingId and status of the event tracker. For more information on event trackers, see CreateEventTracker.

Describes an event tracker. The response includes the trackingId and status of the event tracker. For more information on event trackers, see CreateEventTracker.

Describes the given dataset. For more information on datasets, see CreateDataset.

Describes the given dataset. For more information on datasets, see CreateDataset.

Describes the dataset import job created by CreateDatasetImportJob, including the import job status.

Describes the dataset import job created by CreateDatasetImportJob, including the import job status.

Describes the given dataset group. For more information on dataset groups, see CreateDatasetGroup.

Describes the given dataset group. For more information on dataset groups, see CreateDatasetGroup.

Describes the dataset export job created by CreateDatasetExportJob, including the export job status.

Describes the dataset export job created by CreateDatasetExportJob, including the export job status.

Describes the data deletion job created by CreateDataDeletionJob, including the job status.

Describes the data deletion job created by CreateDataDeletionJob, including the job status.

Describes the given campaign, including its status. A campaign can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS When the status is CREATE FAILED, the response includes the failureReason key, which describes why. For more information on campaigns, see CreateCampaign.

Describes the given campaign, including its status. A campaign can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS When the status is CREATE FAILED, the response includes the failureReason key, which describes why. For more information on campaigns, see CreateCampaign.

Gets the properties of a batch segment job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate segments.

Gets the properties of a batch segment job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate segments.

Gets the properties of a batch inference job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate the recommendations.

Gets the properties of a batch inference job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate the recommendations.

Describes the given algorithm.

Describes the given algorithm.

Deletes all versions of a solution and the Solution object itself. Before deleting a solution, you must delete all campaigns based on the solution. To determine what campaigns are using the solution, call ListCampaigns and supply the Amazon Resource Name (ARN) of the solution. You can't delete a solution if an associated SolutionVersion is in the CREATE PENDING or IN PROGRESS state. For more information on solutions, see CreateSolution.

Deletes a schema. Before deleting a schema, you must delete all datasets referencing the schema. For more information on schemas, see CreateSchema.

Deactivates and removes a recommender. A deleted recommender can no longer be specified in a GetRecommendations request.

Deletes a metric attribution.

Deletes a filter.

Deletes the event tracker. Does not delete the dataset from the dataset group. For more information on event trackers, see CreateEventTracker.

Deletes a dataset. You can't delete a dataset if an associated DatasetImportJob or SolutionVersion is in the CREATE PENDING or IN PROGRESS state. For more information about deleting datasets, see Deleting a dataset.

Deletes a dataset group. Before you delete a dataset group, you must delete the following: All associated event trackers. All associated solutions. All datasets in the dataset group.

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 request. For information on creating campaigns, see CreateCampaign.

Trains or retrains an active solution in a Custom dataset group. A solution is created using the CreateSolution operation and must be in the ACTIVE state before calling CreateSolutionVersion. A new version of the solution is created every time you call this operation. Status A solution version can be in one of the following states: CREATE PENDING CREATE IN_PROGRESS ACTIVE CREATE FAILED CREATE STOPPING CREATE STOPPED To get the status of the version, call DescribeSolutionVersion. Wait until the status shows as ACTIVE before calling CreateCampaign. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed. Related APIs ListSolutionVersions DescribeSolutionVersion ListSolutions CreateSolution DescribeSolution DeleteSolution

Trains or retrains an active solution in a Custom dataset group. A solution is created using the CreateSolution operation and must be in the ACTIVE state before calling CreateSolutionVersion. A new version of the solution is created every time you call this operation. Status A solution version can be in one of the following states: CREATE PENDING CREATE IN_PROGRESS ACTIVE CREATE FAILED CREATE STOPPING CREATE STOPPED To get the status of the version, call DescribeSolutionVersion. Wait until the status shows as ACTIVE before calling CreateCampaign. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed. Related APIs ListSolutionVersions DescribeSolutionVersion ListSolutions CreateSolution DescribeSolution DeleteSolution

By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can update the solution to turn off automatic training. For information about training costs, see Amazon Personalize pricing. Creates the configuration for training a model (creating a solution version). This configuration includes the recipe to use for model training and optional training configuration, such as columns to use in training and feature transformation parameters. For more information about configuring a solution, see Creating and configuring a solution. By default, new solutions use automatic training to create solution versions every 7 days. You can change the training frequency. Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information, see Configuring automatic training. To turn off automatic training, set performAutoTraining to false. If you turn off automatic training, you must manually create a solution version by calling the CreateSolutionVersion operation. After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion. After training completes you can evaluate model accuracy by calling GetSolutionMetrics. When you are satisfied with the solution version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API. Amazon Personalize doesn't support configuring the hpoObjective for solution hyperparameter optimization at this time. Status A solution can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS To get the status of the solution, call DescribeSolution. If you use manual training, the status must be ACTIVE before you call CreateSolutionVersion. Related APIs UpdateSolution ListSolutions CreateSolutionVersion DescribeSolution DeleteSolution ListSolutionVersions DescribeSolutionVersion

By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can update the solution to turn off automatic training. For information about training costs, see Amazon Personalize pricing. Creates the configuration for training a model (creating a solution version). This configuration includes the recipe to use for model training and optional training configuration, such as columns to use in training and feature transformation parameters. For more information about configuring a solution, see Creating and configuring a solution. By default, new solutions use automatic training to create solution versions every 7 days. You can change the training frequency. Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information, see Configuring automatic training. To turn off automatic training, set performAutoTraining to false. If you turn off automatic training, you must manually create a solution version by calling the CreateSolutionVersion operation. After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion. After training completes you can evaluate model accuracy by calling GetSolutionMetrics. When you are satisfied with the solution version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API. Amazon Personalize doesn't support configuring the hpoObjective for solution hyperparameter optimization at this time. Status A solution can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS To get the status of the solution, call DescribeSolution. If you use manual training, the status must be ACTIVE before you call CreateSolutionVersion. Related APIs UpdateSolution ListSolutions CreateSolutionVersion DescribeSolution DeleteSolution ListSolutionVersions DescribeSolutionVersion

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. If you are creating a schema for a dataset in a Domain dataset group, you provide the domain of the Domain dataset group. You specify a schema when you call CreateDataset. Related APIs ListSchemas DescribeSchema DeleteSchema

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. If you are creating a schema for a dataset in a Domain dataset group, you provide the domain of the Domain dataset group. You specify a schema when you call CreateDataset. Related APIs ListSchemas DescribeSchema DeleteSchema

Creates a recommender with the recipe (a Domain dataset group use case) you specify. You create recommenders for a Domain dataset group and specify the recommender's Amazon Resource Name (ARN) when you make a GetRecommendations request. Minimum recommendation requests per second A high minRecommendationRequestsPerSecond will increase your bill. We recommend starting with 1 for minRecommendationRequestsPerSecond (the default). Track your usage using Amazon CloudWatch metrics, and increase the minRecommendationRequestsPerSecond as necessary. When you create a recommender, you can configure the recommender's minimum recommendation requests per second. The minimum recommendation requests per second (minRecommendationRequestsPerSecond) specifies the baseline recommendation request throughput provisioned by Amazon Personalize. The default minRecommendationRequestsPerSecond is 1. A recommendation request is a single GetRecommendations operation. Request throughput is measured in requests per second and Amazon Personalize uses your requests per second to derive your requests per hour and the price of your recommender usage. If your requests per second increases beyond minRecommendationRequestsPerSecond, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minRecommendationRequestsPerSecond. There's a short time delay while the capacity is increased that might cause loss of requests. Your bill is the greater of either the minimum requests per hour (based on minRecommendationRequestsPerSecond) or the actual number of requests. The actual request throughput used is calculated as the average requests/second within a one-hour window. We recommend starting with the default minRecommendationRequestsPerSecond, track your usage using Amazon CloudWatch metrics, and then increase the minRecommendationRequestsPerSecond as necessary. Status A recommender can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE DELETE PENDING > DELETE IN_PROGRESS To get the recommender status, call DescribeRecommender. Wait until the status of the recommender is ACTIVE before asking the recommender for recommendations. Related APIs ListRecommenders DescribeRecommender UpdateRecommender DeleteRecommender

Creates a recommender with the recipe (a Domain dataset group use case) you specify. You create recommenders for a Domain dataset group and specify the recommender's Amazon Resource Name (ARN) when you make a GetRecommendations request. Minimum recommendation requests per second A high minRecommendationRequestsPerSecond will increase your bill. We recommend starting with 1 for minRecommendationRequestsPerSecond (the default). Track your usage using Amazon CloudWatch metrics, and increase the minRecommendationRequestsPerSecond as necessary. When you create a recommender, you can configure the recommender's minimum recommendation requests per second. The minimum recommendation requests per second (minRecommendationRequestsPerSecond) specifies the baseline recommendation request throughput provisioned by Amazon Personalize. The default minRecommendationRequestsPerSecond is 1. A recommendation request is a single GetRecommendations operation. Request throughput is measured in requests per second and Amazon Personalize uses your requests per second to derive your requests per hour and the price of your recommender usage. If your requests per second increases beyond minRecommendationRequestsPerSecond, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minRecommendationRequestsPerSecond. There's a short time delay while the capacity is increased that might cause loss of requests. Your bill is the greater of either the minimum requests per hour (based on minRecommendationRequestsPerSecond) or the actual number of requests. The actual request throughput used is calculated as the average requests/second within a one-hour window. We recommend starting with the default minRecommendationRequestsPerSecond, track your usage using Amazon CloudWatch metrics, and then increase the minRecommendationRequestsPerSecond as necessary. Status A recommender can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE DELETE PENDING > DELETE IN_PROGRESS To get the recommender status, call DescribeRecommender. Wait until the status of the recommender is ACTIVE before asking the recommender for recommendations. Related APIs ListRecommenders DescribeRecommender UpdateRecommender DeleteRecommender

Creates a metric attribution. A metric attribution creates reports on the data that you import into Amazon Personalize. Depending on how you imported the data, you can view reports in Amazon CloudWatch or Amazon S3. For more information, see Measuring impact of recommendations.

Creates a metric attribution. A metric attribution creates reports on the data that you import into Amazon Personalize. Depending on how you imported the data, you can view reports in Amazon CloudWatch or Amazon S3. For more information, see Measuring impact of recommendations.

Creates a recommendation filter. For more information, see Filtering recommendations and user segments.

Creates a recommendation filter. For more information, see Filtering recommendations and user segments.

Creates an event tracker that you use when adding event data to a specified dataset group using the PutEvents API. Only one event tracker can be associated with a dataset group. You will get an error if you call CreateEventTracker using the same dataset group as an existing event tracker. When you create an event tracker, the response includes a tracking ID, which you pass as a parameter when you use the PutEvents operation. Amazon Personalize then appends the event data to the Item interactions dataset of the dataset group you specify in your event tracker. The event tracker can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS To get the status of the event tracker, call DescribeEventTracker. The event tracker must be in the ACTIVE state before using the tracking ID. Related APIs ListEventTrackers DescribeEventTracker DeleteEventTracker

Creates an event tracker that you use when adding event data to a specified dataset group using the PutEvents API. Only one event tracker can be associated with a dataset group. You will get an error if you call CreateEventTracker using the same dataset group as an existing event tracker. When you create an event tracker, the response includes a tracking ID, which you pass as a parameter when you use the PutEvents operation. Amazon Personalize then appends the event data to the Item interactions dataset of the dataset group you specify in your event tracker. The event tracker can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS To get the status of the event tracker, call DescribeEventTracker. The event tracker must be in the ACTIVE state before using the tracking ID. Related APIs ListEventTrackers DescribeEventTracker DeleteEventTracker

Creates an empty dataset and adds it to the specified dataset group. Use CreateDatasetImportJob to import your training data to a dataset. There are 5 types of datasets: Item interactions Items Users Action interactions Actions Each dataset type has an associated schema with required field types. Only the Item interactions dataset is required in order to train a model (also referred to as creating a solution). A dataset can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS To get the status of the dataset, call DescribeDataset. Related APIs CreateDatasetGroup ListDatasets DescribeDataset DeleteDataset

Creates an empty dataset and adds it to the specified dataset group. Use CreateDatasetImportJob to import your training data to a dataset. There are 5 types of datasets: Item interactions Items Users Action interactions Actions Each dataset type has an associated schema with required field types. Only the Item interactions dataset is required in order to train a model (also referred to as creating a solution). A dataset can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS To get the status of the dataset, call DescribeDataset. Related APIs CreateDatasetGroup ListDatasets DescribeDataset DeleteDataset

Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset. To allow Amazon Personalize to import the training data, you must specify an IAM service role that has permission to read from the data source, as Amazon Personalize makes a copy of your data and processes it internally. For information on granting access to your Amazon S3 bucket, see Giving Amazon Personalize Access to Amazon S3 Resources. If you already created a recommender or deployed a custom solution version with a campaign, how new bulk records influence recommendations depends on the domain use case or recipe that you use. For more information, see How new data influences real-time recommendations. By default, a dataset import job replaces any existing data in the dataset that you imported in bulk. To add new records without replacing existing data, specify INCREMENTAL for the import mode in the CreateDatasetImportJob operation. Status A dataset import job can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED To get the status of the import job, call DescribeDatasetImportJob, providing the Amazon Resource Name (ARN) of the dataset import job. The dataset import is complete when the status shows as ACTIVE. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed. Importing takes time. You must wait until the status shows as ACTIVE before training a model using the dataset. Related APIs ListDatasetImportJobs DescribeDatasetImportJob

Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset. To allow Amazon Personalize to import the training data, you must specify an IAM service role that has permission to read from the data source, as Amazon Personalize makes a copy of your data and processes it internally. For information on granting access to your Amazon S3 bucket, see Giving Amazon Personalize Access to Amazon S3 Resources. If you already created a recommender or deployed a custom solution version with a campaign, how new bulk records influence recommendations depends on the domain use case or recipe that you use. For more information, see How new data influences real-time recommendations. By default, a dataset import job replaces any existing data in the dataset that you imported in bulk. To add new records without replacing existing data, specify INCREMENTAL for the import mode in the CreateDatasetImportJob operation. Status A dataset import job can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED To get the status of the import job, call DescribeDatasetImportJob, providing the Amazon Resource Name (ARN) of the dataset import job. The dataset import is complete when the status shows as ACTIVE. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed. Importing takes time. You must wait until the status shows as ACTIVE before training a model using the dataset. Related APIs ListDatasetImportJobs DescribeDatasetImportJob

Creates an empty dataset group. A dataset group is a container for Amazon Personalize resources. A dataset group can contain at most three datasets, one for each type of dataset: Item interactions Items Users Actions Action interactions A dataset group can be a Domain dataset group, where you specify a domain and use pre-configured resources like recommenders, or a Custom dataset group, where you use custom resources, such as a solution with a solution version, that you deploy with a campaign. If you start with a Domain dataset group, you can still add custom resources such as solutions and solution versions trained with recipes for custom use cases and deployed with campaigns. A dataset group can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING To get the status of the dataset group, call DescribeDatasetGroup. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the creation failed. You must wait until the status of the dataset group is ACTIVE before adding a dataset to the group. You can specify an Key Management Service (KMS) key to encrypt the datasets in the group. If you specify a KMS key, you must also include an Identity and Access Management (IAM) role that has permission to access the key. APIs that require a dataset group ARN in the request CreateDataset CreateEventTracker CreateSolution Related APIs ListDatasetGroups DescribeDatasetGroup DeleteDatasetGroup

Creates an empty dataset group. A dataset group is a container for Amazon Personalize resources. A dataset group can contain at most three datasets, one for each type of dataset: Item interactions Items Users Actions Action interactions A dataset group can be a Domain dataset group, where you specify a domain and use pre-configured resources like recommenders, or a Custom dataset group, where you use custom resources, such as a solution with a solution version, that you deploy with a campaign. If you start with a Domain dataset group, you can still add custom resources such as solutions and solution versions trained with recipes for custom use cases and deployed with campaigns. A dataset group can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING To get the status of the dataset group, call DescribeDatasetGroup. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the creation failed. You must wait until the status of the dataset group is ACTIVE before adding a dataset to the group. You can specify an Key Management Service (KMS) key to encrypt the datasets in the group. If you specify a KMS key, you must also include an Identity and Access Management (IAM) role that has permission to access the key. APIs that require a dataset group ARN in the request CreateDataset CreateEventTracker CreateSolution Related APIs ListDatasetGroups DescribeDatasetGroup DeleteDatasetGroup

Creates a job that exports data from your dataset to an Amazon S3 bucket. To allow Amazon Personalize to export the training data, you must specify an service-linked IAM role that gives Amazon Personalize PutObject permissions for your Amazon S3 bucket. For information, see Exporting a dataset in the Amazon Personalize developer guide. Status A dataset export job can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED To get the status of the export job, call DescribeDatasetExportJob, and specify the Amazon Resource Name (ARN) of the dataset export job. The dataset export is complete when the status shows as ACTIVE. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed.

Creates a job that exports data from your dataset to an Amazon S3 bucket. To allow Amazon Personalize to export the training data, you must specify an service-linked IAM role that gives Amazon Personalize PutObject permissions for your Amazon S3 bucket. For information, see Exporting a dataset in the Amazon Personalize developer guide. Status A dataset export job can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED To get the status of the export job, call DescribeDatasetExportJob, and specify the Amazon Resource Name (ARN) of the dataset export job. The dataset export is complete when the status shows as ACTIVE. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed.

Creates a batch job that deletes all references to specific users from an Amazon Personalize dataset group in batches. You specify the users to delete in a CSV file of userIds in an Amazon S3 bucket. After a job completes, Amazon Personalize no longer trains on the users’ data and no longer considers the users when generating user segments. For more information about creating a data deletion job, see Deleting users. Your input file must be a CSV file with a single USER_ID column that lists the users IDs. For more information about preparing the CSV file, see Preparing your data deletion file and uploading it to Amazon S3. To give Amazon Personalize permission to access your input CSV file of userIds, you must specify an IAM service role that has permission to read from the data source. This role needs GetObject and ListBucket permissions for the bucket and its content. These permissions are the same as importing data. For information on granting access to your Amazon S3 bucket, see Giving Amazon Personalize Access to Amazon S3 Resources. After you create a job, it can take up to a day to delete all references to the users from datasets and models. Until the job completes, Amazon Personalize continues to use the data when training. And if you use a User Segmentation recipe, the users might appear in user segments. Status A data deletion job can have one of the following statuses: PENDING > IN_PROGRESS > COMPLETED -or- FAILED To get the status of the data deletion job, call DescribeDataDeletionJob API operation and specify the Amazon Resource Name (ARN) of the job. If the status is FAILED, the response includes a failureReason key, which describes why the job failed. Related APIs ListDataDeletionJobs DescribeDataDeletionJob

Creates a batch job that deletes all references to specific users from an Amazon Personalize dataset group in batches. You specify the users to delete in a CSV file of userIds in an Amazon S3 bucket. After a job completes, Amazon Personalize no longer trains on the users’ data and no longer considers the users when generating user segments. For more information about creating a data deletion job, see Deleting users. Your input file must be a CSV file with a single USER_ID column that lists the users IDs. For more information about preparing the CSV file, see Preparing your data deletion file and uploading it to Amazon S3. To give Amazon Personalize permission to access your input CSV file of userIds, you must specify an IAM service role that has permission to read from the data source. This role needs GetObject and ListBucket permissions for the bucket and its content. These permissions are the same as importing data. For information on granting access to your Amazon S3 bucket, see Giving Amazon Personalize Access to Amazon S3 Resources. After you create a job, it can take up to a day to delete all references to the users from datasets and models. Until the job completes, Amazon Personalize continues to use the data when training. And if you use a User Segmentation recipe, the users might appear in user segments. Status A data deletion job can have one of the following statuses: PENDING > IN_PROGRESS > COMPLETED -or- FAILED To get the status of the data deletion job, call DescribeDataDeletionJob API operation and specify the Amazon Resource Name (ARN) of the job. If the status is FAILED, the response includes a failureReason key, which describes why the job failed. Related APIs ListDataDeletionJobs DescribeDataDeletionJob

You incur campaign costs while it is active. To avoid unnecessary costs, make sure to delete the campaign when you are finished. For information about campaign costs, see Amazon Personalize pricing. Creates a campaign that deploys a solution version. When a client calls the GetRecommendations and GetPersonalizedRanking APIs, a campaign is specified in the request. Minimum Provisioned TPS and Auto-Scaling A high minProvisionedTPS will increase your cost. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary. When you create an Amazon Personalize campaign, you can specify the minimum provisioned transactions per second (minProvisionedTPS) for the campaign. This is the baseline transaction throughput for the campaign provisioned by Amazon Personalize. It sets the minimum billing charge for the campaign while it is active. A transaction is a single GetRecommendations or GetPersonalizedRanking request. The default minProvisionedTPS is 1. If your TPS increases beyond the minProvisionedTPS, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minProvisionedTPS. There's a short time delay while the capacity is increased that might cause loss of transactions. When your traffic reduces, capacity returns to the minProvisionedTPS. You are charged for the the minimum provisioned TPS or, if your requests exceed the minProvisionedTPS, the actual TPS. The actual TPS is the total number of recommendation requests you make. We recommend starting with a low minProvisionedTPS, track your usage using Amazon CloudWatch metrics, and then increase the minProvisionedTPS as necessary. For more information about campaign costs, see Amazon Personalize pricing. Status A campaign can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS To get the campaign status, call DescribeCampaign. Wait until the status of the campaign is ACTIVE before asking the campaign for recommendations. Related APIs ListCampaigns DescribeCampaign UpdateCampaign DeleteCampaign

You incur campaign costs while it is active. To avoid unnecessary costs, make sure to delete the campaign when you are finished. For information about campaign costs, see Amazon Personalize pricing. Creates a campaign that deploys a solution version. When a client calls the GetRecommendations and GetPersonalizedRanking APIs, a campaign is specified in the request. Minimum Provisioned TPS and Auto-Scaling A high minProvisionedTPS will increase your cost. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary. When you create an Amazon Personalize campaign, you can specify the minimum provisioned transactions per second (minProvisionedTPS) for the campaign. This is the baseline transaction throughput for the campaign provisioned by Amazon Personalize. It sets the minimum billing charge for the campaign while it is active. A transaction is a single GetRecommendations or GetPersonalizedRanking request. The default minProvisionedTPS is 1. If your TPS increases beyond the minProvisionedTPS, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minProvisionedTPS. There's a short time delay while the capacity is increased that might cause loss of transactions. When your traffic reduces, capacity returns to the minProvisionedTPS. You are charged for the the minimum provisioned TPS or, if your requests exceed the minProvisionedTPS, the actual TPS. The actual TPS is the total number of recommendation requests you make. We recommend starting with a low minProvisionedTPS, track your usage using Amazon CloudWatch metrics, and then increase the minProvisionedTPS as necessary. For more information about campaign costs, see Amazon Personalize pricing. Status A campaign can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED DELETE PENDING > DELETE IN_PROGRESS To get the campaign status, call DescribeCampaign. Wait until the status of the campaign is ACTIVE before asking the campaign for recommendations. Related APIs ListCampaigns DescribeCampaign UpdateCampaign DeleteCampaign

Creates a batch segment 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 and user segments.

Creates a batch segment 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 and user segments.

Generates batch recommendations based on a list of items or users stored in Amazon S3 and exports the recommendations to an Amazon S3 bucket. To generate batch recommendations, specify the ARN of a solution version and an Amazon S3 URI for the input and output data. For user personalization, popular items, and personalized ranking solutions, the batch inference job generates a list of recommended items for each user ID in the input file. For related items solutions, the job generates a list of recommended items for each item ID in the input file. For more information, see Creating a batch inference job . If you use the Similar-Items recipe, Amazon Personalize can add descriptive themes to batch recommendations. To generate themes, set the job's mode to THEME_GENERATION and specify the name of the field that contains item names in the input data. For more information about generating themes, see Batch recommendations with themes from Content Generator . You can't get batch recommendations with the Trending-Now or Next-Best-Action recipes.

Generates batch recommendations based on a list of items or users stored in Amazon S3 and exports the recommendations to an Amazon S3 bucket. To generate batch recommendations, specify the ARN of a solution version and an Amazon S3 URI for the input and output data. For user personalization, popular items, and personalized ranking solutions, the batch inference job generates a list of recommended items for each user ID in the input file. For related items solutions, the job generates a list of recommended items for each item ID in the input file. For more information, see Creating a batch inference job . If you use the Similar-Items recipe, Amazon Personalize can add descriptive themes to batch recommendations. To generate themes, set the job's mode to THEME_GENERATION and specify the name of the field that contains item names in the input data. For more information about generating themes, see Batch recommendations with themes from Content Generator . You can't get batch recommendations with the Trending-Now or Next-Best-Action recipes.