Posted On: May 24, 2022

Amazon Personalize now provides offline model metrics for recommenders enabling you to evaluate the quality of recommendations. A recommender is a resource that provides recommendations optimized for specific use cases, such as “Frequently bought together” for Retail and “Top picks for you” for Media and Entertainment. Offline metrics are metrics that Amazon Personalize generates when you create a recommender. You can use offline metrics to analyze the performance of the recommender's underlying model. Offline metrics allow you to compare the model with other models trained on the same data. The metrics provided include coverage, mean reciprocal rank, normalized discounted cumulative gain (NDCG) and precision.

You can view model metrics for a recommender on the recommender details page on the Amazon Personalize console, or by using the DescribeRecommender API as part of the AWS Command Line Interface (AWS CLI) / AWS SDKs. For more information on how to retrieve model metrics for a recommender, see the Amazon Personalize Developer Guide.

Amazon Personalize enables you to personalize your website, app, ads, emails, and more, using the same machine learning technology as used by Amazon, without requiring any prior machine learning experience. To get started with Amazon Personalize, visit our documentation.