[ aws . personalize-runtime ]
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
See ‘aws help’ for descriptions of global parameters.
get-personalized-ranking
--campaign-arn <value>
--input-list <value>
--user-id <value>
[--context <value>]
[--filter-arn <value>]
[--filter-values <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
--campaign-arn
(string)
The Amazon Resource Name (ARN) of the campaign to use for generating the personalized ranking.
--input-list
(list)
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)
Syntax:
"string" "string" ...
--user-id
(string)
The user for which you want the campaign to provide a personalized ranking.
--context
(map)
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.
key -> (string)
value -> (string)
Shorthand Syntax:
KeyName1=string,KeyName2=string
JSON Syntax:
{"string": "string"
...}
--filter-arn
(string)
The Amazon Resource Name (ARN) of a filter you created to include items or exclude items from recommendations for a given user. For more information, see Filtering Recommendations .
--filter-values
(map)
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 anEXCLUDE
element to exclude items, you can omit thefilter-values
.In this case, Amazon Personalize doesn’t use that portion of the expression to filter recommendations.For more information, see Filtering Recommendations .
key -> (string)
value -> (string)
Shorthand Syntax:
KeyName1=string,KeyName2=string
JSON Syntax:
{"string": "string"
...}
--cli-input-json
| --cli-input-yaml
(string)
Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton
. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml
.
--generate-cli-skeleton
(string)
Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input
, prints a sample input JSON that can be used as an argument for --cli-input-json
. Similarly, if provided yaml-input
it will print a sample input YAML that can be used with --cli-input-yaml
. If provided with the value output
, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.
See ‘aws help’ for descriptions of global parameters.
personalizedRanking -> (list)
A list of items in order of most likely interest to the user. The maximum is 500.
(structure)
An object that identifies an item.
The and APIs return a list of
PredictedItem
s.itemId -> (string)
The recommended item ID.
score -> (double)
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.