This pattern shows you how to use Amazon Personalize to generate personalized recommendations—including re-ranked recommendations—for your users based on the ingestion of real-time user-interaction data from those users. The example scenario used in this pattern is based on a pet adoption website that generates recommendations for its users based on their interactions (for example, what pets a user visits). By following the example scenario, you learn to use Amazon Kinesis Data Streams to ingest interaction data, AWS Lambda to generate recommendations and re-rank the recommendations, and Amazon Data Firehose to store the data in an Amazon Simple Storage Service (Amazon S3) bucket. You also learn to use AWS Step Functions to build a state machine that manages the solution version (that is, a trained model) that generates your recommendations.
