What is the difference between "Use Case Optimized Recommenders" and "Custom Recommender Solutions" in Amazon Personalize?

Training and retraining is managed by Personalize for Use Case Optimized Recommenders. They are designed specifically for the most common use cases in Media (VOD) and Retail and are intended to make it easier to launch and operate recommendation engines for these industries. They must be created within a Domain Dataset Group.

Domain dataset group: A dataset group containing preconfigured resources for different business domains and use cases. Amazon Personalize manages the life cycle of training models and deployment. When you create a Domain dataset group, you choose your business domain, import your data, and create recommenders for each of your use cases. You use your recommender in your application to get recommendations with the GetRecommendations operation.

Therefore, the cost for retraining Use Case Optimized Recommenders is built-into their pricing. There is still a cost for real-time recommenders when you exceed the free number of recommendations per hour.

Custom Recommenders do not support automatic training/retraining so you are responsible for initiating training by creating Solution Versions. Note that you can add custom recommenders to a domain dataset group but you cannot add use case optimized recommenders to a custom dataset group.

If you start with a Domain dataset group, you can still add custom resources such as solutions and solution versions trained with recipes for custom use cases.

Regardless of the dataset group type you create, you still want to keep your datasets updated with the latest interactions and item/user data.

User Segmentation is designed for building segments of users based on their affinity for items or item attributes. They are considered custom recommenders from a training/retraining perspective.

The AWS pricing calculator for Personalize was recently updated to support Use Case Optimized Recommenders and User Segmentation.