Choosing the item interaction data used for training - Amazon Personalize
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Choosing the item interaction data used for training

Important

After you create a solution, you can’t change its configuration. By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, make sure to delete the solution when you are finished. For information about training costs, see Amazon Personalize pricing.

You can choose the events in an Item interactions dataset that Amazon Personalize uses when creating a solution version (training a model). Choosing item interaction data before training allows you to use only a relevant subset of your data for training or remove noise to train a more optimized model. For more information about Item interactions datasets, see Schemas and Item interactions dataset.

Note

If you use User-Personalization-v2 or Personalized-Ranking-v2, your training cost is based on your item interactions data before filtering by event type or value. For more information about pricing, see Amazon Personalize pricing.

You can choose item interaction data as follows:

  • Choose records based on type – When you configure a solution, if your Item interactions dataset includes event types in an EVENT_TYPE column, you can optionally specify an event type to use in training. For example, if your Item interactions dataset includes purchase, click, and watch event types, and you want Amazon Personalize to train the model with only watch events, when you configure your solution, you would provide watch as the event type that Amazon Personalize uses in training.

    If your Item interactions dataset has multiple event types in an EVENT_TYPE column, and you do not provide an event type when you configure your solution, Amazon Personalize uses all item interaction data for training with equal weight regardless of type.

  • Choose records based on type and value – When you configure a solution, if your Item interactions dataset includes EVENT_TYPE and EVENT_VALUE fields, you can set a specific value as a threshold to exclude records from training. For example, if your EVENT_VALUE data for events with an EVENT_TYPE of watch is the percentage of a video that a user watched, if you set the event value threshold to 0.5, and the event type to watch, Amazon Personalize trains the model using only watch interaction events with an EVENT_VALUE greater than or equal to 0.5.

The following code shows how to use the SDK for Python (Boto3) to create a solution that uses only watch events where the use watched more than half of the video.

import boto3

personalize = boto3.client('personalize')

create_solution_response = personalize.create_solution(
    name = 'solution name',
    datasetGroupArn = 'arn:aws:personalize:region:accountId:dataset-group/datasetGroupName',
    recipeArn = 'arn:aws:personalize:::recipe/aws-user-personalization-v2',
    eventType = 'watch',
    solutionConfig = {
        "eventValueThreshold": "0.5"
    }
)

# Store the solution ARN
solution_arn = create_solution_response['solutionArn']

# Use the solution ARN to get the solution status
solution_description = personalize.describe_solution(solutionArn = solution_arn)['solution']
print('Solution status: ' + solution_description['status'])