Amazon Personalize Runtime

2021/11/29 - Amazon Personalize Runtime - 1 updated api methods

Changes  This release adds API support for Recommenders and BatchSegmentJobs.

GetRecommendations (updated) Link ΒΆ
Changes (request)
{'recommenderArn': 'string'}

Returns a list of recommended items. For campaigns, the campaign's Amazon Resource Name (ARN) is required and the required user and item input depends on the recipe type used to create the solution backing the campaign as follows:

  • USER_PERSONALIZATION - userId required, itemId not used

  • RELATED_ITEMS - itemId required, userId not used

Note

Campaigns that are backed by a solution created using a recipe of type PERSONALIZED_RANKING use the API.

For recommenders, the recommender's ARN is required and the required item and user input depends on the use case (domain-based recipe) backing the recommender. For information on use case requirements see Choosing recommender use cases .

See also: AWS API Documentation

Request Syntax

client.get_recommendations(
    campaignArn='string',
    itemId='string',
    userId='string',
    numResults=123,
    context={
        'string': 'string'
    },
    filterArn='string',
    filterValues={
        'string': 'string'
    },
    recommenderArn='string'
)
type campaignArn

string

param campaignArn

The Amazon Resource Name (ARN) of the campaign to use for getting recommendations.

type itemId

string

param itemId

The item ID to provide recommendations for.

Required for RELATED_ITEMS recipe type.

type userId

string

param userId

The user ID to provide recommendations for.

Required for USER_PERSONALIZATION recipe type.

type numResults

integer

param numResults

The number of results to return. The default is 25. The maximum is 500.

type context

dict

param context

The contextual metadata to use when getting recommendations. Contextual metadata includes any interaction information that might be relevant when getting a user's recommendations, such as the user's current location or device type.

  • (string) --

    • (string) --

type filterArn

string

param filterArn

The ARN of the filter to apply to the returned recommendations. For more information, see Filtering Recommendations .

When using this parameter, be sure the filter resource is ACTIVE .

type filterValues

dict

param filterValues

The values to use when filtering recommendations. For each placeholder parameter in your filter expression, provide the parameter name (in matching case) as a key and the filter value(s) as the corresponding value. Separate multiple values for one parameter with a comma.

For filter expressions that use an INCLUDE element to include items, you must provide values for all parameters that are defined in the expression. For filters with expressions that use an EXCLUDE element to exclude items, you can omit the filter-values .In this case, Amazon Personalize doesn't use that portion of the expression to filter recommendations.

For more information, see Filtering Recommendations .

  • (string) --

    • (string) --

type recommenderArn

string

param recommenderArn

The Amazon Resource Name (ARN) of the recommender to use to get recommendations. Provide a recommender ARN if you created a Domain dataset group with a recommender for a domain use case.

rtype

dict

returns

Response Syntax

{
    'itemList': [
        {
            'itemId': 'string',
            'score': 123.0
        },
    ],
    'recommendationId': 'string'
}

Response Structure

  • (dict) --

    • itemList (list) --

      A list of recommendations sorted in descending order by prediction score. There can be a maximum of 500 items in the list.

      • (dict) --

        An object that identifies an item.

        The and APIs return a list of PredictedItem s.

        • itemId (string) --

          The recommended item ID.

        • score (float) --

          A numeric representation of the model's certainty that the item will be the next user selection. For more information on scoring logic, see how-scores-work .

    • recommendationId (string) --

      The ID of the recommendation.