Creating recommenders (console) - Amazon Personalize

Creating recommenders (console)

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 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 Create recommenders 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 recommender use cases.

  5. To make changes to your recommender configuration, optionally toggle Use default recommender configurations to modify the following:

    • Optionally make any changes to the recommender's Minimum recommendation requests per second. 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 is the process that Amazon Personalize uses to test different item recommendations and learn how users respond to new items with no or very little interaction data. Use the following fields to configure exploration:

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

      • Exploration item age cutoff: Enter the maximum item age (in days) since the latest interaction. This defines the scope of item exploration. To increase the items Amazon Personalize considers during exploration, enter a greater value.

        For example, if you enter 10, Amazon Personalize exploration includes only items with interactions data from the 10 days since the latest interaction in the dataset.


        Recommendations might include items without interactions data from outside this time frame. This is because these items are relevant to the user's interests. Exploration wasn't required to identify them.

  6. 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.