Creating recommenders (console)

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. For more information see Minimum recommendation requests per second and auto-scaling.

Create recommenders for each of your use cases with the Amazon Personalize console as follows. If you just created your Domain dataset group and you are already on the Overview page, skip to step 3.

To create recommenders

1. Open the Amazon Personalize console at https://console.aws.amazon.com/personalize/home and sign in to your account.

2. On the Dataset groups page, choose your Domain dataset group.

3. On the Use <domain name> recommenders tab of the middle card choose Create recommenders.

4. On the Choose use cases page, choose the use cases you want to create recommenders and give each a Recommender name. Amazon Personalize creates a recommender for each use case that you choose. The available use cases depend on your domain. For information on choosing a use case see Choosing a domain use case.

5. Choose Next.

6. On the Advanced configuration page, configure each recommender depending on your business needs:

   • You can modify the Columns for training to choose the columns Amazon Personalize considers when training the models backing your recommender. By default, Amazon Personalize uses all columns that can be used when training. Columns with the boolean data type and custom non-categorical string fields aren't used. You can't exclude EVENT_TYPE columns.

     You can change the columns used when training to control what data Amazon Personalize uses when creating your recommender. You might do this to experiment with different combinations of training data. Or you might exclude columns without meaningful data. For example, might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.

   • You can modify Minimum recommendation requests per second to specify a new minimum request capacity for your recommender. A high minRecommendationRequestsPerSecond will increase your bill. We recommend starting with 1 (the default). Track your usage using Amazon CloudWatch metrics, and increase the minRecommendationRequestsPerSecond as necessary. For more information see Minimum recommendation requests per second and auto-scaling.

   • For Top picks for your or Recommended for you use cases, optionally make changes to exploration configuration. Exploration involves testing different item recommendations to learn how users respond to items with very little interaction data. Use the following fields to configure exploration:

     • Emphasis on exploring less relevant items (exploration weight) – Configure how much to explore. Specify a decimal value between 0 to 1. The default is 0.3. The closer the value is to 1, the more exploration. With more exploration, recommendations include more items with less interactions data or relevance. At zero, no exploration occurs and recommendations are based on current data (relevance).

     • Exploration item age cutoff – Specify the maximum item age in days since the latest interaction across all items in the Interactions dataset. This defines the scope of item exploration based on item age. Amazon Personalize determines item age based on its creation timestamp or, if creation timestamp data is missing, interactions data. For more information how Amazon Personalize determines item age, see Creation timestamp data.

       To increase the items Amazon Personalize considers during exploration, enter a greater value. The minimum is 1 day and the default is 30 days. Recommendations might include items that are older than the item age cut off you specify. This is because these items are relevant to the user and exploration didn't identify them.

   • For Tags, optionally add any tags. For more information about tagging Amazon Personalize resources, see Tagging Amazon Personalize resources.

7. Choose Create recommenders to create recommenders for each of your use cases.

   You can monitor the status of each recommender on the Recommenders page. When your recommender status is Active, you can use it in your application to get recommendations.