Amazon Personalize Runtime

{'filterArn': 'string'}

Returns a list of recommended items. The required input depends on the recipe type used to create the solution backing the campaign, as follows:

• RELATED_ITEMS - itemId required, userId not used

• USER_PERSONALIZATION - itemId optional, userId required

See also: AWS API Documentation

Request Syntax

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

type campaignArn

string

param campaignArn

[REQUIRED]

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 Using Filters with Amazon Personalize.

rtype

dict

returns

Response Syntax

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

Response Structure

• (dict) --

  • itemList (list) --

    A list of recommendations sorted in ascending 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.