Amazon Personalize improves quality for batch recommendations by using the latest streamed data
Amazon Personalize now uses the latest streamed data for batch recommendations, improving recommendation quality by capturing recent user interactions. Batch recommendations now use newly recorded events streamed via Personalize’s event tracker to generate recommendations without requiring retraining of the model. Previously, batch recommendations would only consider the interactions up to the point of the last model retraining. By considering more recent interactions, Personalize’s recommendations can now better respond to shifts in user behavior.
Amazon Personalize now uses the latest streamed data for batch recommendations, improving recommendation quality by capturing recent user interactions. Batch recommendations now use newly recorded events streamed via Personalize’s event tracker to generate recommendations without requiring retraining of the model. Previously, batch recommendations would only consider the interactions up to the point of the last model retraining. By considering more recent interactions, Personalize’s recommendations can now better respond to shifts in user behavior.
Using streamed data for batch recommendations is easy. Simply create an event tracker and run a batch inference job. The recommendations generated by the batch inference job will then consider the latest data streamed to the event tracker.
Amazon Personalize enables you to personalize your website, app, ads, emails, and more, using the same machine learning technology used by Amazon, without requiring any prior machine learning experience. To get started with Amazon Personalize, visit our documentation.