Domain recommenders in Amazon Personalize - Amazon Personalize
Recommender statusesMinimum recommendation requests per second and auto-scaling

Domain recommenders in Amazon Personalize

If you created a Domain dataset group, after you import data you are ready to create recommenders for your domain use cases. A recommender is a Domain dataset group resource that generates recommendations. You use a recommender in your application to get real-time recommendations with the GetRecommendations operation.

When you create a recommender, you specify a use case and Amazon Personalize trains the models backing the recommender with the best configurations for the use case. Each use case has different API requirements for getting recommendations. For a list of recommender use cases by domain, see Choosing a use case. You can create at most 15 recommenders per region.

Amazon Personalize automatically retrains the models backing your recommenders every 7 days. This is a full retraining that creates entirely new models based on the entirety of the data in your datasets. With Top picks for you and Recommended for you use cases, Amazon Personalize updates the existing models every two hours to include new items in recommendations with exploration.

When you create a recommender, you can do the following:

After you create a recommender, you can do the following:

  • Evaluate the recommender – You can evaluate the performance of your recommender through offline and online metrics. For more information, see Evaluating an Amazon Personalize domain recommender.

  • Stop and restart the recommender – If you want to pause billing for an active recommender, you can stop the recommender and restart it later. For more information, see Stopping a recommender.

  • Update the recommender's configuration – You can update the columns the recommender uses in training and update the recommender's request capacity. For more information, see Updating a recommender.

  • Delete the recommender – You can delete recommenders with the DeleteRecommender operation. Or you can delete a recommender from the recommender details page in the Amazon Personalize console.

Topics

Recommender statuses

A recommender can be in one of the following states:

  • CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED

  • DELETE PENDING > DELETE IN_PROGRESS

To get the recommender status, navigate to the Recommenders page in the Amazon Personalize console or use the DescribeRecommender operation.

Minimum recommendation requests per second and auto-scaling

Important

A high minRecommendationRequestsPerSecond will increase your bill. We recommend starting with 1 for minRecommendationRequestsPerSecond (the default). Track your usage using Amazon CloudWatch metrics, and increase the minRecommendationRequestsPerSecond as necessary.

When you create a recommender, you can configure the recommender's minimum recommendation requests per second. The minimum recommendation requests per second (minRecommendationRequestsPerSecond) specifies the baseline recommendation request throughput provisioned by Amazon Personalize. The default minRecommendationRequestsPerSecond is 1. A recommendation request is a single GetRecommendations operation. Request throughput is measured in requests per second and Amazon Personalize uses your requests per second to derive your requests per hour and the price of your recommender usage.

If your requests per second increases beyond minRecommendationRequestsPerSecond, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minRecommendationRequestsPerSecond. There's a short time delay while the capacity is increased that might cause loss of requests.

Your bill is the greater of either the minimum requests per hour (based on minRecommendationRequestsPerSecond) or the actual number of requests. The actual request throughput used is calculated as the average requests/second within a one-hour window. We recommend starting with the default minRecommendationRequestsPerSecond, track your usage using Amazon CloudWatch metrics, and then increase the minRecommendationRequestsPerSecond as necessary.