Getting batch item recommendations with custom resources - Amazon Personalize
Batch workflowGuidelines and requirementsBatch workflow scoring

Getting batch item recommendations with custom resources

With custom resources, you can get item recommendations with an asynchronous batch flow. For example, you might get product recommendations for all users on an email list or item-to-item similarities across an inventory.

To get batch recommendations for items, you use a batch inference job. A batch inference job is a tool that imports your batch input data from an Amazon S3 bucket, uses your custom solution version to generate item recommendations, and then exports the item recommendations to an Amazon S3 bucket. Depending on the recipe, your input data is a list of users, or items, or a list of users each with a collection of items.

If your solution uses the Similar Items recipe and you have an Items dataset with textual data and item title data, you can generate batch recommendations with themes for each group of items. For more information, see Batch recommendations with themes from Content Generator.

After you create a custom solution version, how new data influences batch item recommendations depends on its type, the method of import, and the custom recipe you use. For information about how new data influences batch recommendations, see How new data influences batch recommendations (custom resources).

Topics

Batch workflow

The batch workflow is as follows:

  1. Prepare and upload your input data in JSON format to an Amazon S3 bucket. The format of your input data depends on the recipe you use. See Preparing input data for batch recommendations.

  2. Create a separate location for your output data, either a folder or a different Amazon S3 bucket.

  3. Create a batch inference job. See Creating a batch inference job.

  4. When the batch inference is complete, retrieve the item recommendations from your output location in Amazon S3.

Guidelines and requirements

The following are guidelines and requirements for getting batch recommendations:

  • Your Amazon Personalize IAM service role must have permission to read and add files to your Amazon S3 buckets. For information on granting permissions, see Service role policy for batch workflows. For more information on bucket permissions, see User policy examples in the Amazon Simple Storage Service Developer Guide. If you use AWS Key Management Service (AWS KMS) for encryption, you must grant Amazon Personalize and your Amazon Personalize IAM service role permission to use your key. For more information, see Giving Amazon Personalize permission to use your AWS KMS key.

  • You must create a custom solution and solution version before you create a batch inference job. However, you don't need to create an Amazon Personalize campaign. If you created a Domain dataset group, you can still create custom resources.

  • To generate themes with recommendations, you must use the Similar-Items recipe. And you must have an Items dataset with textual data and item title data. For more information about themed recommendations, see Batch recommendations with themes from Content Generator.

  • Your input data must be formatted as described in Preparing input data for user segments.

  • You can't get batch recommendations with the Trending-Now or Next-Best-Action recipes.

  • If you use a filter with placeholder parameters, you must include the values for the parameters in your input data in a filterValues object. For more information, see Providing filter values in your input JSON.

  • We recommend that you use a different location for your output data (either a folder or a different Amazon S3 bucket) than your input data.

  • Batch recommendations might not be exactly the same as real-time recommendations. This is because batch inference jobs take longer to complete and only consider data available 15 minutes before the start of the job.

Batch workflow scoring

Batch recommendations include scores as follows: