Table of Contents
PersonalizeRuntime.Client¶A low-level client representing Amazon Personalize Runtime
import boto3
client = boto3.client('personalize-runtime')
These are the available methods:
can_paginate()close()get_paginator()get_personalized_ranking()get_recommendations()get_waiter()can_paginate(operation_name)¶Check if an operation can be paginated.
create_foo, and you'd normally invoke the
operation as client.create_foo(**kwargs), if the
create_foo operation can be paginated, you can use the
call client.get_paginator("create_foo").True if the operation can be paginated,
False otherwise.close()¶Closes underlying endpoint connections.
get_paginator(operation_name)¶Create a paginator for an operation.
create_foo, and you'd normally invoke the
operation as client.create_foo(**kwargs), if the
create_foo operation can be paginated, you can use the
call client.get_paginator("create_foo").client.can_paginate method to
check if an operation is pageable.get_personalized_ranking(**kwargs)¶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,
'promotionName': 'string'
},
],
'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 .
promotionName (string) --
The name of the promotion that included the predicted item.
recommendationId (string) --
The ID of the recommendation.
Exceptions
PersonalizeRuntime.Client.exceptions.InvalidInputExceptionPersonalizeRuntime.Client.exceptions.ResourceNotFoundExceptionget_recommendations(**kwargs)¶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:
userId required, itemId not useditemId required, userId not usedNote
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',
promotions=[
{
'name': 'string',
'percentPromotedItems': 123,
'filterArn': 'string',
'filterValues': {
'string': '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 and user segments .
The promotions to apply to the recommendation request. A promotion defines additional business rules that apply to a configurable subset of recommended items.
Contains information on a promotion. A promotion defines additional business rules that apply to a configurable subset of recommended items.
The name of the promotion.
The percentage of recommended items to apply the promotion to.
The Amazon Resource Name (ARN) of the filter used by the promotion. This filter defines the criteria for promoted items. For more information, see Promotion filters .
The values to use when promoting items. For each placeholder parameter in your promotion's 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 on creating filters, see Filtering recommendations and user segments .
dict
Response Syntax
{
'itemList': [
{
'itemId': 'string',
'score': 123.0,
'promotionName': 'string'
},
],
'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 .
promotionName (string) --
The name of the promotion that included the predicted item.
recommendationId (string) --
The ID of the recommendation.
Exceptions
PersonalizeRuntime.Client.exceptions.InvalidInputExceptionPersonalizeRuntime.Client.exceptions.ResourceNotFoundExceptionget_waiter(waiter_name)¶Returns an object that can wait for some condition.
The available paginators are: