Enabling metadata in recommendations for a domain recommender in Amazon Personalize

When you create a recommender, you can enable the option to include item metadata from your Items dataset with recommendation results. If enabled, you can specify the columns from your Items dataset in your request for recommendations. Amazon Personalize returns this data for each item in the recommendation response.

You might use metadata to enrich recommendations in your user interface, such as adding the genres for movies to carousels. Or you might use it to visually assess recommendation quality. If you use generative AI in your app, you can plug the metadata into AI prompts to generate more relevant content. For more information about using Amazon Personalize with generative AI, see Amazon Personalize and generative AI.

To add metadata to recommendations, you must have an Items dataset with a column of metadata. You don't have to use the metadata in training. For information about creating a dataset, see Creating a schema and a dataset. For information updating data in a dataset, see Updating data in datasets after training.

The following code samples show how to enable the option to include item metadata with the AWS CLI or the AWS SDKs. To do this with the Amazon Personalize console, you enable metadata on the Advanced configuration page when you create the recommender. For more information, see Creating recommenders (console).

aws personalize create-recommender \
--name recommender name \
--dataset-group-arn dataset group \
--recipe-arn recipe ARN \
--recommender-config "{\"enableMetadataWithRecommendations\": "true"}"

import boto3

personalize = boto3.client('personalize')

create_recommender_response = personalize.create_recommender(
  name = 'recommender name',
  recipeArn = 'recipe name',
  datasetGroupArn = 'dataset group ARN',
  recommenderConfig = {"enableMetadataWithRecommendations": True}
)

recommender_arn = create_recommender_response['recommenderArn']

print('Recommender ARN:' + recommender_arn)