Creating recommenders (Amazon CLI)
After you create a Domain dataset group and import data, you can create recommenders for your domain use cases. A recommender is a Domain dataset group resource that generates recommendations.
For Top picks for your
or Recommended for you
use cases, Amazon Personalize
uses exploration when recommending items. For more information, see Configuring exploration.
Topics
Creating a recommender
Use the following Amazon CLI code to create a recommender for a domain use case. Run this code for each of your domain use
cases. For recipeArn
, provide the Amazon Resource Name (ARN) for your use case. The available use cases
depend on your domain. For a list of use cases and their ARNs see Choosing a use case.
aws personalize create-recommender \ --name
recommender name
\ --dataset-group-arndataset group ARN
\ --recipe-arnrecipe ARN
Configuring columns used when training
To exclude columns from training, provide the excludedDatasetColumns
object in the
trainingDataConfig
as part of the recommender configuration. For each key in the object, provide the
dataset type. For each value, provide the list of columns to exclude. For more information, see Configuring columns used when training.
aws personalize create-recommender \ --name
recommender name
\ --dataset-group-arndataset group ARN
\ --recipe-arnrecipe ARN
\ --recommender-config "{\"trainingDataConfig\": {\"excludedDatasetColumns\": { \"datasetType
\" : [ \"column1Name
\", \"column2Name
\"]}}}"
Configuring exploration
For Top picks for your
or Recommended for you
use cases,
Amazon Personalize uses exploration when recommending items. Exploration involves testing different item
recommendations to learn how users respond to items with very little interaction data. You
can configure exploration with the following:
-
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 item interactions data or relevance based on previous behavior. 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 Item 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, item 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.
The following code shows how to configure exploration when you create a recommender for
the Top picks for you
use case. The example uses the default values.
If you have an Items dataset and want the option to include metadata when you get recommendations, update the recommender-config
to add a enableMetadataWithRecommendations
field and set it to true
.
aws personalize create-recommender \ --name
recommender name
\ --dataset-group-arndataset group ARN
\ --recipe-arn arn:aws:personalize:::recipe/aws-vod-top-picks \ --recommender-config "{\"itemExplorationConfig\":{\"explorationWeight\":\"0.3\",\"explorationItemAgeCutOff\":\"30\"}}"
Enabling metadata in recommendations
If you have an Items dataset and want the option to include metadata when you get recommendations, set enableMetadataWithRecommendations
to true
in the recommender-config
.
aws personalize create-recommender \ --name
recommender name
\ --dataset-group-arndataset group
\ --recipe-arnrecipe ARN
\ --recommender-config "{\"enableMetadataWithRecommendations\": "true"}"