To use a recommender model with Amazon Pinpoint, you start by creating an Amazon Personalize solution and deploying that solution as an Amazon Personalize campaign. Then, you create a configuration for the recommender model in Amazon Pinpoint. In the configuration, you specify settings that determine how to retrieve and process recommendation data from the Amazon Personalize campaign. This includes whether to invoke an AWS Lambda function to perform additional processing of the data that's retrieved.
Amazon Personalize is an AWS service that's designed to help you create ML models that provide real-time, personalized recommendations for customers who use your applications. Amazon Personalize guides you through the process of creating and training an ML model, and then preparing and deploying the model as an Amazon Personalize campaign. You can then retrieve real-time, personalized recommendations from the campaign. To learn more about Amazon Personalize, see the Amazon Personalize Developer Guide.
AWS Lambda is a compute service that you can use to run code without provisioning or managing servers. You package your code and upload it to AWS Lambda as a Lambda function. AWS Lambda then runs the function when the function is invoked. A function can be invoked manually by you, automatically in response to events, or in response to requests from applications or services, including Amazon Pinpoint. For information about creating and invoking Lambda functions, see the AWS Lambda Developer Guide.
After you create an Amazon Pinpoint configuration for a recommender model, you can add recommendations from the model to messages that you send from campaigns and journeys. You do this by using message templates that contain message variables for recommended attributes. A recommended attribute is a dynamic endpoint or user attribute that's designed to store recommendation data. You define these attributes when you create the configuration for a recommender model.
You can use variables for recommended attributes in the following types of message templates:
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Email templates, for email messages that you send from campaigns or journeys.
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Push notification templates, for push notifications that you send from campaigns.
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SMS templates, for SMS text messages that you send from campaigns.
For more information about using recommender models with Amazon Pinpoint, see Machine Learning Models in the Amazon Pinpoint User Guide.
If you configure Amazon Pinpoint to invoke a Lambda function that processes recommendation data, Amazon Pinpoint performs the following general tasks each time it sends personalized recommendations in a message for a campaign or journey:
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Evaluates and processes the configuration settings and contents of the message and message template.
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Determines that the message template is connected to a recommender model.
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Evaluates the configuration settings for connecting to and using the model. These are defined by the Recommender Model resource for the model.
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Detects one or more message variables for recommended attributes that are defined by the configuration settings for the model.
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Retrieves recommendation data from the Amazon Personalize campaign that's specified in the configuration settings for the model. It uses the GetRecommendations operation of the Amazon Personalize Runtime API to perform this task.
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Adds the appropriate recommendation data to a dynamic recommended attribute (
RecommendationItems) for each message recipient. -
Invokes your Lambda function and sends the recommendation data for each recipient to that function for processing.
The data is sent as a JSON object that contains the endpoint definition for each recipient. Each endpoint definition includes a
RecommendationItemsfield that contains an ordered array of 1–5 values. The number of values in the array depends on the configuration settings for the model. -
Waits for your Lambda function to process the data and return the results.
The results are a JSON object that contains an updated endpoint definition for each recipient. Each updated endpoint definition contains a new
Recommendationsobject. This object contains 1–10 fields, one for each custom recommended attribute that you defined in the configuration settings for the model. Each of these fields stores enhanced recommendation data for the endpoint. -
Uses the updated endpoint definition for each recipient to replace each message variable with the appropriate value for that recipient.
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Sends a version of the message that contains the personalized recommendations for each message recipient.
To customize and enhance recommendations in this way, start by creating a Lambda function that processes the endpoint definitions sent by Amazon Pinpoint, and returns updated endpoint definitions. Next, assign a Lambda function policy to the function and authorize Amazon Pinpoint to invoke the function. Then, configure the recommender model in Amazon Pinpoint. When you configure the model, specify the function to invoke and define the recommended attributes to use.
