Table of Contents
A low-level client representing Amazon Personalize Runtime
import boto3
client = boto3.client('personalize-runtime')
These are the available methods:
Check if an operation can be paginated.
Create a paginator for an operation.
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.
Note
The solution backing the campaign must have been created using a recipe of type PERSONALIZED_RANKING.
See also: AWS API Documentation
Request Syntax
response = client.get_personalized_ranking(
campaignArn='string',
inputList=[
'string',
],
userId='string',
context={
'string': 'string'
},
filterArn='string',
filterValues={
'string': 'string'
}
)
[REQUIRED]
The Amazon Resource Name (ARN) of the campaign to use for generating the personalized ranking.
[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.
[REQUIRED]
The user for which you want the campaign to provide a personalized ranking.
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.
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 .
dict
Response Syntax
{
'personalizedRanking': [
{
'itemId': 'string',
'score': 123.0
},
],
'recommendationId': 'string'
}
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 .
recommendationId (string) --
The ID of the recommendation.
Exceptions
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:
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
response = client.get_recommendations(
campaignArn='string',
itemId='string',
userId='string',
numResults=123,
context={
'string': 'string'
},
filterArn='string',
filterValues={
'string': 'string'
},
recommenderArn='string'
)
The item ID to provide recommendations for.
Required for RELATED_ITEMS recipe type.
The user ID to provide recommendations for.
Required for USER_PERSONALIZATION recipe type.
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.
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 .
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 .
dict
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.
Exceptions
Returns an object that can wait for some condition.
The available paginators are: