AWS Personalize

Amazon Personalize is a fully managed machine learning service that uses your data to generate item recommendations for your users. It can also generate user segments based on a user's affinity for certain items or item metadata.

Common use case examples include the following:

Personalizing a video streaming app: You can use preconfigured or customizable Amazon Personalize resources to add multiple types of personalized video recommendations to your streaming app.

Adding product recommendations to an online shopping app: You can use preconfigured or customizable Amazon Personalize resources to add multiple types of personalized product recommendations to your retail app. For example, Recommended for you, Frequently bought together and Customers who viewed X also viewed product recommendations.

Amazon Personalize includes API operations for real-time personalization and batch operations for bulk recommendations and user segments. You can get started quickly with use-case optimized recommenders for your business domain, or you can create configurable custom resources.

With Amazon Personalize, your data can come from both your historical bulk interaction records in a CSV file and real-time events from your users as they interact with your catalog. Before Amazon Personalize can generate recommendations, your interactions data must have a minimum of 1000 interactions records from users interacting with items in your catalog. These interactions can be from bulk imports, streamed events, or both.

Different use cases may have additional data requirements. If you don't have enough data, you can use Amazon Personalize to first collect real-time event data. After you have recorded enough events, Amazon Personalize can generate recommendations.