Filtering batch recommendations and user segments (custom resources)

Filtering batch recommendations and user segments works nearly the same as filtering real-time recommendations. It follows the same workflow described in Getting batch item recommendations or Getting batch user segments. To filter batch recommendations or user segments, you do the following:

Providing filter values in your input JSON

For filters with placeholder parameters, such as $GENRE, you must provide the values for the parameters in a filterValues object in your input JSON. For a filterValues object, each key is a parameter name. Each value is the criteria that you are passing as a parameter. Surround each value with escaped quotes: "filterValues":{"GENRES":"\"drama\""}. For multiple values, separate each value with a comma: "filterValues":{"GENRES":"\"horror\",\"comedy\",\"drama\""}

Batch inference job input JSON example

The following is an example of the first few lines of a JSON input file for a batch inference job. The example includes the filterValues object. The GENRES key corresponds to a $GENRES placeholder in the filter expression. The job in this example uses the User-Personalization recipe. For RELATED_ITEMS recipes, provide an itemId instead of the userId. For PERSONALIZED_RANKING recipes provide the userID and an itemList.

{"userId": "5","filterValues":{"GENRES":"\"horror\",\"comedy\",\"drama\""}}
{"userId": "3","filterValues":{"GENRES":"\"horror\",\"comedy\""}}
{"userId": "34","filterValues":{"GENRES":"\"drama\""}}
    

For more examples of batch inference job input data by recipe see Batch inference job input and output JSON examples. You can use these examples as a starting point and add the filterValues object from the above example.

Batch segment job input JSON example

The following is an example of the first few lines of a JSON input file with filter values for a batch segment job. The GENRES key corresponds to a $GENRES placeholder in the filter expression.

{"itemAttributes": "ITEMS.genres = \"Comedy\" AND ITEMS.genres = \"Action\"","filterValues":{"COUNTRY":"\"Japan\""}}
{"itemAttributes": "ITEMS.genres = \"Horror\"","filterValues":{"COUNTRY":"\"United States\"\""}}
{"itemAttributes": "ITEMS.genres = \"Action\" AND ITEMS.genres = \"Adventure\"","filterValues":{"COUNTRY":"\"England\""}}
    

For more examples of batch inference job input data by recipe see Batch segment job input and output JSON examples. You can use these examples as a starting point and add the filterValues object from the above example.

Filtering batch workflows (console)

To filter batch workflows with the Amazon Personalize console, you create a filter and then you create a batch inference job or batch segment job and choose the filter. For complete step by step instructions, see Creating a batch inference job (console) and Creating a batch segment job (console).

Filtering batch workflows (AWS SDKs)

To filter batch recommendations with the AWS SDKs, create a filter and include the FilterArn parameter in the CreateBatchInferenceJob or CreateBatchSegmentJob request.

The following code shows how to create a batch inference job with a filter using the AWS SDK for Python (Boto3). We recommend using a different location for your output data (either a folder or a different Amazon S3 bucket). For complete explanation of all fields, see see Creating a batch inference job (AWS SDKs).

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"}}
)