Amazon Personalize Runtime

{'filterArn': 'string'}

Re-ranks a list of recommended items for the given user. The first item in the list is deemed the most likely item to be of interest to the user.

See also: AWS API Documentation

Request Syntax

client.get_personalized_ranking(
    campaignArn='string',
    inputList=[
        'string',
    ],
    userId='string',
    context={
        'string': 'string'
    },
    filterArn='string'
)

type campaignArn

string

param campaignArn

[REQUIRED]

The Amazon Resource Name (ARN) of the campaign to use for generating the personalized ranking.

type inputList

list

param inputList

[REQUIRED]

A list of items (by itemId ) to rank. If an item was not included in the training dataset, the item is appended to the end of the reranked list. The maximum is 500.

• (string) --

type userId

string

param userId

[REQUIRED]

The user for which you want the campaign to provide a personalized ranking.

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 Amazon Resource Name (ARN) of a filter you created to include or exclude items from recommendations for a given user.

rtype

dict

returns

Response Syntax

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

Response Structure

• (dict) --

  • personalizedRanking (list) --

    A list of items in order of most likely interest to the user. The maximum is 500.

    • (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.