2026/07/02 - Amazon Connect Customer Profiles - 5 updated api methods
Changes Amazon Connect Customer Profiles adds support for diversityConfig to recommenderConfig which can be used for diversifying the recommendations. This release also includes model versioning support which helps customer to rollback trained models.
{'RecommenderConfig': {'DiversityConfig': {'DiversityColumns': [{'CapType': 'PERCENTAGE '
'| '
'VALUE',
'Name': 'string',
'Target': 'string'}]}}}
Creates a recommender
See also: AWS API Documentation
Request Syntax
client.create_recommender(
DomainName='string',
RecommenderName='string',
RecommenderRecipeName='recommended-for-you'|'similar-items'|'frequently-paired-items'|'popular-items'|'trending-now'|'personalized-ranking',
RecommenderConfig={
'EventsConfig': {
'EventParametersList': [
{
'EventType': 'string',
'EventValueThreshold': 123.0,
'EventWeight': 123.0
},
]
},
'TrainingFrequency': 123,
'InferenceConfig': {
'MinProvisionedTPS': 123
},
'IncludedColumns': {
'string': [
'string',
]
},
'ExcludedColumns': {
'string': [
'string',
]
},
'DiversityConfig': {
'DiversityColumns': [
{
'Name': 'string',
'CapType': 'PERCENTAGE'|'VALUE',
'Target': 'string'
},
]
}
},
Description='string',
RecommenderSchemaName='string',
Tags={
'string': 'string'
}
)
string
[REQUIRED]
The unique name of the domain.
string
[REQUIRED]
The name of the recommender.
string
[REQUIRED]
The name of the recommeder recipe.
dict
The recommender configuration.
EventsConfig (dict) --
Configuration settings for how the recommender processes and uses events.
EventParametersList (list) -- [REQUIRED]
A list of event parameters configurations that specify how different event types should be handled.
(dict) --
Configuration parameters for events in the personalization system.
EventType (string) -- [REQUIRED]
The type of event being tracked (e.g., 'click', 'purchase', 'view').
EventValueThreshold (float) --
The minimum value threshold that an event must meet to be considered valid.
EventWeight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
TrainingFrequency (integer) --
How often the recommender should retrain its model with new data. If set to 0, automatic retraining will not be enabled.
InferenceConfig (dict) --
Configuration settings for how the recommender handles inference requests.
MinProvisionedTPS (integer) --
The minimum provisioned transactions per second (TPS) that the recommender supports. The default value is 1. A high MinProvisionedTPS will increase your cost.
IncludedColumns (dict) --
A map of dataset type to a list of column names to train on. The _webAnalytics and _catalogItem keys are supported. The column names must be a subset of the columns defined in the recommender schema. If not specified, all columns in the schema are used for training. The following columns are always included in training and do not need to be specified: Item.Id, EventTimestamp, and EventType for _webAnalytics; Id for _catalogItem. Mutually exclusive with ExcludedColumns — both cannot be specified in the same request.
(string) --
(list) --
(string) --
ExcludedColumns (dict) --
A map of dataset type to a list of column names to exclude from training. The _webAnalytics and _catalogItem keys are supported. The column names must be valid columns defined in the recommender schema. All columns in the schema except the listed columns will be used for training. The following columns are mandatory and cannot be excluded: Item.Id, EventTimestamp, and EventType for _webAnalytics; Id for _catalogItem. Mutually exclusive with IncludedColumns — both cannot be specified in the same request.
(string) --
(list) --
(string) --
DiversityConfig (dict) --
Configuration for diversity-aware recommendations. When set, the recommender applies diversity constraints defined per item column to reduce over-concentration of similar items in the results.
DiversityColumns (list) --
A list of up to two diversity columns. Each column defines a cap on the number or percentage of recommended items that share the same value for that column.
(dict) --
Defines a diversity constraint for a single item column, specifying a cap type and a target value or placeholder that controls how many recommended items may share the same column value.
Name (string) -- [REQUIRED]
The name of the item catalog column on which to apply the diversity cap. The column must be defined in the recommender schema.
CapType (string) -- [REQUIRED]
The type of diversity cap to apply. Valid values are PERCENTAGE (interpret Target as a percentage of returned items) and VALUE (interpret Target as an absolute count).
Target (string) -- [REQUIRED]
The diversity cap target. Either an integer literal (for example, "25") or a placeholder expression of the form $name whose value is supplied at inference time through GetProfileRecommendations.
string
The description of the domain object type.
string
The name of the recommender schema to use for this recommender. If not specified, the default schema is used.
dict
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
dict
Response Syntax
{
'RecommenderArn': 'string',
'Tags': {
'string': 'string'
}
}
Response Structure
(dict) --
RecommenderArn (string) --
The ARN of the recommender
Tags (dict) --
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
{'DiversityConfig': {'Enabled': 'boolean', 'Values': {'string': 'integer'}}}
Fetches the recommendations for a profile in the input Customer Profiles domain. Fetches all the profile recommendations
See also: AWS API Documentation
Request Syntax
client.get_profile_recommendations(
DomainName='string',
ProfileId='string',
RecommenderName='string',
Context={
'string': 'string'
},
RecommenderFilters=[
{
'Name': 'string',
'Values': {
'string': 'string'
}
},
],
RecommenderPromotionalFilters=[
{
'Name': 'string',
'Values': {
'string': 'string'
},
'PromotionName': 'string',
'PercentPromotedItems': 123
},
],
CandidateIds=[
'string',
],
MaxResults=123,
MetadataConfig={
'MetadataColumns': [
'string',
]
},
DiversityConfig={
'Enabled': True|False,
'Values': {
'string': 123
}
}
)
string
[REQUIRED]
The unique name of the domain.
string
[REQUIRED]
The unique identifier of the profile for which to retrieve recommendations.
string
[REQUIRED]
The unique name of the recommender.
dict
The contextual metadata used to provide dynamic runtime information to tailor recommendations.
(string) --
(string) --
list
A list of filters to apply to the returned recommendations. Filters define criteria for including or excluding items from the recommendation results.
(dict) --
A filter that specifies criteria for including or excluding items from recommendations.
Name (string) --
The name of the recommender filter to apply.
Values (dict) --
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.
(string) --
(string) --
list
A list of promotional filters to apply to the recommendations. Promotional filters allow you to promote specific items within a configurable subset of recommendation results.
(dict) --
Contains information on a promotion. A promotion defines additional business rules that apply to a configurable subset of recommended items.
Name (string) --
The name of the recommender filter to use for the promotion.
Values (dict) --
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.
(string) --
(string) --
PromotionName (string) --
The name of the promotion.
PercentPromotedItems (integer) --
The percentage of recommended items to apply the promotion to.
list
A list of item IDs to rank for the user. Use this when you want to re-rank a specific set of items rather than getting recommendations from the full item catalog. Required for personalized-ranking use cases.
(string) --
integer
The maximum number of recommendations to return. The default value is 10.
dict
Configuration for including item metadata in the recommendation response. Use this to specify which metadata columns to return alongside recommended items.
MetadataColumns (list) --
A list of metadata column names from your Items dataset to include in the recommendation response.
(string) --
dict
Runtime diversity configuration for this request. Enables diversity-aware recommendations and optionally supplies values for placeholder-based diversity caps configured on the recommender.
Enabled (boolean) -- [REQUIRED]
Whether diversity-aware recommendations are enabled for this request.
Values (dict) --
An optional map of placeholder name to integer cap value used to resolve $name placeholders defined in the recommender's DiversityConfig at inference time. Up to 2 entries are supported.
(string) --
(integer) --
dict
Response Syntax
{
'Recommendations': [
{
'CatalogItem': {
'Id': 'string',
'Name': 'string',
'Code': 'string',
'Type': 'string',
'Category': 'string',
'Description': 'string',
'AdditionalInformation': 'string',
'ImageLink': 'string',
'Link': 'string',
'CreatedAt': datetime(2015, 1, 1),
'UpdatedAt': datetime(2015, 1, 1),
'Price': 'string',
'Attributes': {
'string': 'string'
}
},
'Score': 123.0
},
]
}
Response Structure
(dict) --
Recommendations (list) --
List of recommendations generated by the recommender.
(dict) --
Represents a single recommendation generated by the recommender system.
CatalogItem (dict) --
The catalog item being recommended, including its complete details and attributes.
Id (string) --
The unique identifier for the catalog item.
Name (string) --
The display name of the catalog item.
Code (string) --
The product code or SKU of the catalog item.
Type (string) --
The type classification of the catalog item.
Category (string) --
The category to which the catalog item belongs.
Description (string) --
A detailed description of the catalog item.
AdditionalInformation (string) --
Supplementary information about the catalog item beyond the basic description.
ImageLink (string) --
The URL link to the item's image.
Link (string) --
The URL link to the item's detailed page or external resource.
CreatedAt (datetime) --
The timestamp when the catalog item was created.
UpdatedAt (datetime) --
The timestamp when the catalog item was last updated.
Price (string) --
The price of the catalog item.
Attributes (dict) --
Additional attributes or properties associated with the catalog item stored as key-value pairs.
(string) --
(string) --
Score (float) --
Recommendation Score between 0 and 1.
{'ActiveRecommenderVersionName': 'string',
'LatestRecommenderUpdate': {'RecommenderConfig': {'DiversityConfig': {'DiversityColumns': [{'CapType': 'PERCENTAGE '
'| '
'VALUE',
'Name': 'string',
'Target': 'string'}]}},
'RecommenderVersionName': 'string'},
'RecommenderConfig': {'DiversityConfig': {'DiversityColumns': [{'CapType': 'PERCENTAGE '
'| '
'VALUE',
'Name': 'string',
'Target': 'string'}]}},
'TrainingMetrics': {'RecommenderVersionName': 'string'}}
Retrieves a recommender.
See also: AWS API Documentation
Request Syntax
client.get_recommender(
DomainName='string',
RecommenderName='string',
TrainingMetricsCount=123
)
string
[REQUIRED]
The unique name of the domain.
string
[REQUIRED]
The name of the recommender.
integer
The number of training metrics to retrieve for the recommender.
dict
Response Syntax
{
'RecommenderName': 'string',
'RecommenderRecipeName': 'recommended-for-you'|'similar-items'|'frequently-paired-items'|'popular-items'|'trending-now'|'personalized-ranking',
'RecommenderSchemaName': 'string',
'RecommenderConfig': {
'EventsConfig': {
'EventParametersList': [
{
'EventType': 'string',
'EventValueThreshold': 123.0,
'EventWeight': 123.0
},
]
},
'TrainingFrequency': 123,
'InferenceConfig': {
'MinProvisionedTPS': 123
},
'IncludedColumns': {
'string': [
'string',
]
},
'ExcludedColumns': {
'string': [
'string',
]
},
'DiversityConfig': {
'DiversityColumns': [
{
'Name': 'string',
'CapType': 'PERCENTAGE'|'VALUE',
'Target': 'string'
},
]
}
},
'Description': 'string',
'Status': 'PENDING'|'IN_PROGRESS'|'ACTIVE'|'FAILED'|'STOPPING'|'INACTIVE'|'STARTING'|'DELETING',
'LastUpdatedAt': datetime(2015, 1, 1),
'CreatedAt': datetime(2015, 1, 1),
'FailureReason': 'string',
'LatestRecommenderUpdate': {
'RecommenderConfig': {
'EventsConfig': {
'EventParametersList': [
{
'EventType': 'string',
'EventValueThreshold': 123.0,
'EventWeight': 123.0
},
]
},
'TrainingFrequency': 123,
'InferenceConfig': {
'MinProvisionedTPS': 123
},
'IncludedColumns': {
'string': [
'string',
]
},
'ExcludedColumns': {
'string': [
'string',
]
},
'DiversityConfig': {
'DiversityColumns': [
{
'Name': 'string',
'CapType': 'PERCENTAGE'|'VALUE',
'Target': 'string'
},
]
}
},
'Status': 'PENDING'|'IN_PROGRESS'|'ACTIVE'|'FAILED'|'STOPPING'|'INACTIVE'|'STARTING'|'DELETING',
'CreatedAt': datetime(2015, 1, 1),
'LastUpdatedAt': datetime(2015, 1, 1),
'FailureReason': 'string',
'RecommenderVersionName': 'string'
},
'ActiveRecommenderVersionName': 'string',
'TrainingMetrics': [
{
'Time': datetime(2015, 1, 1),
'Metrics': {
'string': 123.0
},
'RecommenderVersionName': 'string'
},
],
'Tags': {
'string': 'string'
}
}
Response Structure
(dict) --
RecommenderName (string) --
The name of the recommender.
RecommenderRecipeName (string) --
The name of the recipe used by the recommender to generate recommendations.
RecommenderSchemaName (string) --
The name of the recommender schema associated with this recommender.
RecommenderConfig (dict) --
The configuration settings for the recommender, including parameters and settings that define its behavior.
EventsConfig (dict) --
Configuration settings for how the recommender processes and uses events.
EventParametersList (list) --
A list of event parameters configurations that specify how different event types should be handled.
(dict) --
Configuration parameters for events in the personalization system.
EventType (string) --
The type of event being tracked (e.g., 'click', 'purchase', 'view').
EventValueThreshold (float) --
The minimum value threshold that an event must meet to be considered valid.
EventWeight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
TrainingFrequency (integer) --
How often the recommender should retrain its model with new data. If set to 0, automatic retraining will not be enabled.
InferenceConfig (dict) --
Configuration settings for how the recommender handles inference requests.
MinProvisionedTPS (integer) --
The minimum provisioned transactions per second (TPS) that the recommender supports. The default value is 1. A high MinProvisionedTPS will increase your cost.
IncludedColumns (dict) --
A map of dataset type to a list of column names to train on. The _webAnalytics and _catalogItem keys are supported. The column names must be a subset of the columns defined in the recommender schema. If not specified, all columns in the schema are used for training. The following columns are always included in training and do not need to be specified: Item.Id, EventTimestamp, and EventType for _webAnalytics; Id for _catalogItem. Mutually exclusive with ExcludedColumns — both cannot be specified in the same request.
(string) --
(list) --
(string) --
ExcludedColumns (dict) --
A map of dataset type to a list of column names to exclude from training. The _webAnalytics and _catalogItem keys are supported. The column names must be valid columns defined in the recommender schema. All columns in the schema except the listed columns will be used for training. The following columns are mandatory and cannot be excluded: Item.Id, EventTimestamp, and EventType for _webAnalytics; Id for _catalogItem. Mutually exclusive with IncludedColumns — both cannot be specified in the same request.
(string) --
(list) --
(string) --
DiversityConfig (dict) --
Configuration for diversity-aware recommendations. When set, the recommender applies diversity constraints defined per item column to reduce over-concentration of similar items in the results.
DiversityColumns (list) --
A list of up to two diversity columns. Each column defines a cap on the number or percentage of recommended items that share the same value for that column.
(dict) --
Defines a diversity constraint for a single item column, specifying a cap type and a target value or placeholder that controls how many recommended items may share the same column value.
Name (string) --
The name of the item catalog column on which to apply the diversity cap. The column must be defined in the recommender schema.
CapType (string) --
The type of diversity cap to apply. Valid values are PERCENTAGE (interpret Target as a percentage of returned items) and VALUE (interpret Target as an absolute count).
Target (string) --
The diversity cap target. Either an integer literal (for example, "25") or a placeholder expression of the form $name whose value is supplied at inference time through GetProfileRecommendations.
Description (string) --
A detailed description of the recommender providing information about its purpose and functionality.
Status (string) --
The current status of the recommender, indicating whether it is active, creating, updating, or in another state.
LastUpdatedAt (datetime) --
The timestamp of when the recommender was edited.
CreatedAt (datetime) --
The timestamp of when the recommender was created.
FailureReason (string) --
If the recommender fails, provides the reason for the failure.
LatestRecommenderUpdate (dict) --
Information about the most recent update performed on the recommender, including status and timestamp.
RecommenderConfig (dict) --
The updated configuration settings applied to the recommender during this update.
EventsConfig (dict) --
Configuration settings for how the recommender processes and uses events.
EventParametersList (list) --
A list of event parameters configurations that specify how different event types should be handled.
(dict) --
Configuration parameters for events in the personalization system.
EventType (string) --
The type of event being tracked (e.g., 'click', 'purchase', 'view').
EventValueThreshold (float) --
The minimum value threshold that an event must meet to be considered valid.
EventWeight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
TrainingFrequency (integer) --
How often the recommender should retrain its model with new data. If set to 0, automatic retraining will not be enabled.
InferenceConfig (dict) --
Configuration settings for how the recommender handles inference requests.
MinProvisionedTPS (integer) --
The minimum provisioned transactions per second (TPS) that the recommender supports. The default value is 1. A high MinProvisionedTPS will increase your cost.
IncludedColumns (dict) --
A map of dataset type to a list of column names to train on. The _webAnalytics and _catalogItem keys are supported. The column names must be a subset of the columns defined in the recommender schema. If not specified, all columns in the schema are used for training. The following columns are always included in training and do not need to be specified: Item.Id, EventTimestamp, and EventType for _webAnalytics; Id for _catalogItem. Mutually exclusive with ExcludedColumns — both cannot be specified in the same request.
(string) --
(list) --
(string) --
ExcludedColumns (dict) --
A map of dataset type to a list of column names to exclude from training. The _webAnalytics and _catalogItem keys are supported. The column names must be valid columns defined in the recommender schema. All columns in the schema except the listed columns will be used for training. The following columns are mandatory and cannot be excluded: Item.Id, EventTimestamp, and EventType for _webAnalytics; Id for _catalogItem. Mutually exclusive with IncludedColumns — both cannot be specified in the same request.
(string) --
(list) --
(string) --
DiversityConfig (dict) --
Configuration for diversity-aware recommendations. When set, the recommender applies diversity constraints defined per item column to reduce over-concentration of similar items in the results.
DiversityColumns (list) --
A list of up to two diversity columns. Each column defines a cap on the number or percentage of recommended items that share the same value for that column.
(dict) --
Defines a diversity constraint for a single item column, specifying a cap type and a target value or placeholder that controls how many recommended items may share the same column value.
Name (string) --
The name of the item catalog column on which to apply the diversity cap. The column must be defined in the recommender schema.
CapType (string) --
The type of diversity cap to apply. Valid values are PERCENTAGE (interpret Target as a percentage of returned items) and VALUE (interpret Target as an absolute count).
Target (string) --
The diversity cap target. Either an integer literal (for example, "25") or a placeholder expression of the form $name whose value is supplied at inference time through GetProfileRecommendations.
Status (string) --
The current status of the recommender update operation.
CreatedAt (datetime) --
The timestamp when this recommender update was initiated.
LastUpdatedAt (datetime) --
The timestamp of when the recommender was edited.
FailureReason (string) --
If the update operation failed, provides the reason for the failure.
RecommenderVersionName (string) --
The name of the recommender version associated with this update operation.
ActiveRecommenderVersionName (string) --
The name of the recommender version currently serving recommendations. Omitted when no active recommender version is set.
TrainingMetrics (list) --
A set of metrics that provide information about the recommender's training performance and accuracy.
(dict) --
Contains metrics and performance indicators from the training of a recommender model.
Time (datetime) --
The timestamp when these training metrics were recorded.
Metrics (dict) --
A collection of performance metrics and statistics from the training process.
(string) --
(float) --
RecommenderVersionName (string) --
The name of the recommender version that produced these training metrics.
Tags (dict) --
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
{'Recommenders': {'LatestRecommenderUpdate': {'RecommenderConfig': {'DiversityConfig': {'DiversityColumns': [{'CapType': 'PERCENTAGE '
'| '
'VALUE',
'Name': 'string',
'Target': 'string'}]}},
'RecommenderVersionName': 'string'},
'RecommenderConfig': {'DiversityConfig': {'DiversityColumns': [{'CapType': 'PERCENTAGE '
'| '
'VALUE',
'Name': 'string',
'Target': 'string'}]}}}}
Returns a list of recommenders in the specified domain.
See also: AWS API Documentation
Request Syntax
client.list_recommenders(
DomainName='string',
MaxResults=123,
NextToken='string'
)
string
[REQUIRED]
The unique name of the domain.
integer
The maximum number of recommenders to return in the response. The default value is 100.
string
A token received from a previous ListRecommenders call to retrieve the next page of results.
dict
Response Syntax
{
'NextToken': 'string',
'Recommenders': [
{
'RecommenderName': 'string',
'RecipeName': 'recommended-for-you'|'similar-items'|'frequently-paired-items'|'popular-items'|'trending-now'|'personalized-ranking',
'RecommenderSchemaName': 'string',
'RecommenderConfig': {
'EventsConfig': {
'EventParametersList': [
{
'EventType': 'string',
'EventValueThreshold': 123.0,
'EventWeight': 123.0
},
]
},
'TrainingFrequency': 123,
'InferenceConfig': {
'MinProvisionedTPS': 123
},
'IncludedColumns': {
'string': [
'string',
]
},
'ExcludedColumns': {
'string': [
'string',
]
},
'DiversityConfig': {
'DiversityColumns': [
{
'Name': 'string',
'CapType': 'PERCENTAGE'|'VALUE',
'Target': 'string'
},
]
}
},
'CreatedAt': datetime(2015, 1, 1),
'Description': 'string',
'Status': 'PENDING'|'IN_PROGRESS'|'ACTIVE'|'FAILED'|'STOPPING'|'INACTIVE'|'STARTING'|'DELETING',
'LastUpdatedAt': datetime(2015, 1, 1),
'Tags': {
'string': 'string'
},
'FailureReason': 'string',
'LatestRecommenderUpdate': {
'RecommenderConfig': {
'EventsConfig': {
'EventParametersList': [
{
'EventType': 'string',
'EventValueThreshold': 123.0,
'EventWeight': 123.0
},
]
},
'TrainingFrequency': 123,
'InferenceConfig': {
'MinProvisionedTPS': 123
},
'IncludedColumns': {
'string': [
'string',
]
},
'ExcludedColumns': {
'string': [
'string',
]
},
'DiversityConfig': {
'DiversityColumns': [
{
'Name': 'string',
'CapType': 'PERCENTAGE'|'VALUE',
'Target': 'string'
},
]
}
},
'Status': 'PENDING'|'IN_PROGRESS'|'ACTIVE'|'FAILED'|'STOPPING'|'INACTIVE'|'STARTING'|'DELETING',
'CreatedAt': datetime(2015, 1, 1),
'LastUpdatedAt': datetime(2015, 1, 1),
'FailureReason': 'string',
'RecommenderVersionName': 'string'
}
},
]
}
Response Structure
(dict) --
NextToken (string) --
A token to retrieve the next page of results. Null if there are no more results to retrieve.
Recommenders (list) --
A list of recommenders and their properties in the specified domain.
(dict) --
Provides a summary of a recommender's configuration and current state.
RecommenderName (string) --
The name of the recommender.
RecipeName (string) --
The name of the recipe used by this recommender.
RecommenderSchemaName (string) --
The name of the recommender schema associated with this recommender.
RecommenderConfig (dict) --
The configuration settings applied to this recommender.
EventsConfig (dict) --
Configuration settings for how the recommender processes and uses events.
EventParametersList (list) --
A list of event parameters configurations that specify how different event types should be handled.
(dict) --
Configuration parameters for events in the personalization system.
EventType (string) --
The type of event being tracked (e.g., 'click', 'purchase', 'view').
EventValueThreshold (float) --
The minimum value threshold that an event must meet to be considered valid.
EventWeight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
TrainingFrequency (integer) --
How often the recommender should retrain its model with new data. If set to 0, automatic retraining will not be enabled.
InferenceConfig (dict) --
Configuration settings for how the recommender handles inference requests.
MinProvisionedTPS (integer) --
The minimum provisioned transactions per second (TPS) that the recommender supports. The default value is 1. A high MinProvisionedTPS will increase your cost.
IncludedColumns (dict) --
A map of dataset type to a list of column names to train on. The _webAnalytics and _catalogItem keys are supported. The column names must be a subset of the columns defined in the recommender schema. If not specified, all columns in the schema are used for training. The following columns are always included in training and do not need to be specified: Item.Id, EventTimestamp, and EventType for _webAnalytics; Id for _catalogItem. Mutually exclusive with ExcludedColumns — both cannot be specified in the same request.
(string) --
(list) --
(string) --
ExcludedColumns (dict) --
A map of dataset type to a list of column names to exclude from training. The _webAnalytics and _catalogItem keys are supported. The column names must be valid columns defined in the recommender schema. All columns in the schema except the listed columns will be used for training. The following columns are mandatory and cannot be excluded: Item.Id, EventTimestamp, and EventType for _webAnalytics; Id for _catalogItem. Mutually exclusive with IncludedColumns — both cannot be specified in the same request.
(string) --
(list) --
(string) --
DiversityConfig (dict) --
Configuration for diversity-aware recommendations. When set, the recommender applies diversity constraints defined per item column to reduce over-concentration of similar items in the results.
DiversityColumns (list) --
A list of up to two diversity columns. Each column defines a cap on the number or percentage of recommended items that share the same value for that column.
(dict) --
Defines a diversity constraint for a single item column, specifying a cap type and a target value or placeholder that controls how many recommended items may share the same column value.
Name (string) --
The name of the item catalog column on which to apply the diversity cap. The column must be defined in the recommender schema.
CapType (string) --
The type of diversity cap to apply. Valid values are PERCENTAGE (interpret Target as a percentage of returned items) and VALUE (interpret Target as an absolute count).
Target (string) --
The diversity cap target. Either an integer literal (for example, "25") or a placeholder expression of the form $name whose value is supplied at inference time through GetProfileRecommendations.
CreatedAt (datetime) --
The timestamp when the recommender was created.
Description (string) --
A description of the recommender's purpose and characteristics.
Status (string) --
The current operational status of the recommender.
LastUpdatedAt (datetime) --
The timestamp of when the recommender was edited.
Tags (dict) --
The tags used to organize, track, or control access for this resource.
(string) --
(string) --
FailureReason (string) --
If the recommender is in a failed state, provides the reason for the failure.
LatestRecommenderUpdate (dict) --
Information about the most recent update performed on the recommender, including its status and timing.
RecommenderConfig (dict) --
The updated configuration settings applied to the recommender during this update.
EventsConfig (dict) --
Configuration settings for how the recommender processes and uses events.
EventParametersList (list) --
A list of event parameters configurations that specify how different event types should be handled.
(dict) --
Configuration parameters for events in the personalization system.
EventType (string) --
The type of event being tracked (e.g., 'click', 'purchase', 'view').
EventValueThreshold (float) --
The minimum value threshold that an event must meet to be considered valid.
EventWeight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
TrainingFrequency (integer) --
How often the recommender should retrain its model with new data. If set to 0, automatic retraining will not be enabled.
InferenceConfig (dict) --
Configuration settings for how the recommender handles inference requests.
MinProvisionedTPS (integer) --
The minimum provisioned transactions per second (TPS) that the recommender supports. The default value is 1. A high MinProvisionedTPS will increase your cost.
IncludedColumns (dict) --
A map of dataset type to a list of column names to train on. The _webAnalytics and _catalogItem keys are supported. The column names must be a subset of the columns defined in the recommender schema. If not specified, all columns in the schema are used for training. The following columns are always included in training and do not need to be specified: Item.Id, EventTimestamp, and EventType for _webAnalytics; Id for _catalogItem. Mutually exclusive with ExcludedColumns — both cannot be specified in the same request.
(string) --
(list) --
(string) --
ExcludedColumns (dict) --
A map of dataset type to a list of column names to exclude from training. The _webAnalytics and _catalogItem keys are supported. The column names must be valid columns defined in the recommender schema. All columns in the schema except the listed columns will be used for training. The following columns are mandatory and cannot be excluded: Item.Id, EventTimestamp, and EventType for _webAnalytics; Id for _catalogItem. Mutually exclusive with IncludedColumns — both cannot be specified in the same request.
(string) --
(list) --
(string) --
DiversityConfig (dict) --
Configuration for diversity-aware recommendations. When set, the recommender applies diversity constraints defined per item column to reduce over-concentration of similar items in the results.
DiversityColumns (list) --
A list of up to two diversity columns. Each column defines a cap on the number or percentage of recommended items that share the same value for that column.
(dict) --
Defines a diversity constraint for a single item column, specifying a cap type and a target value or placeholder that controls how many recommended items may share the same column value.
Name (string) --
The name of the item catalog column on which to apply the diversity cap. The column must be defined in the recommender schema.
CapType (string) --
The type of diversity cap to apply. Valid values are PERCENTAGE (interpret Target as a percentage of returned items) and VALUE (interpret Target as an absolute count).
Target (string) --
The diversity cap target. Either an integer literal (for example, "25") or a placeholder expression of the form $name whose value is supplied at inference time through GetProfileRecommendations.
Status (string) --
The current status of the recommender update operation.
CreatedAt (datetime) --
The timestamp when this recommender update was initiated.
LastUpdatedAt (datetime) --
The timestamp of when the recommender was edited.
FailureReason (string) --
If the update operation failed, provides the reason for the failure.
RecommenderVersionName (string) --
The name of the recommender version associated with this update operation.
{'RecommenderConfig': {'DiversityConfig': {'DiversityColumns': [{'CapType': 'PERCENTAGE '
'| '
'VALUE',
'Name': 'string',
'Target': 'string'}]}},
'RecommenderVersionName': 'string'}
Updates the properties of an existing recommender, allowing you to modify its configuration and description.
See also: AWS API Documentation
Request Syntax
client.update_recommender(
DomainName='string',
RecommenderName='string',
Description='string',
RecommenderConfig={
'EventsConfig': {
'EventParametersList': [
{
'EventType': 'string',
'EventValueThreshold': 123.0,
'EventWeight': 123.0
},
]
},
'TrainingFrequency': 123,
'InferenceConfig': {
'MinProvisionedTPS': 123
},
'IncludedColumns': {
'string': [
'string',
]
},
'ExcludedColumns': {
'string': [
'string',
]
},
'DiversityConfig': {
'DiversityColumns': [
{
'Name': 'string',
'CapType': 'PERCENTAGE'|'VALUE',
'Target': 'string'
},
]
}
},
RecommenderVersionName='string'
)
string
[REQUIRED]
The unique name of the domain.
string
[REQUIRED]
The name of the recommender to update.
string
The new description to assign to the recommender.
dict
The new configuration settings to apply to the recommender, including updated parameters and settings that define its behavior.
EventsConfig (dict) --
Configuration settings for how the recommender processes and uses events.
EventParametersList (list) -- [REQUIRED]
A list of event parameters configurations that specify how different event types should be handled.
(dict) --
Configuration parameters for events in the personalization system.
EventType (string) -- [REQUIRED]
The type of event being tracked (e.g., 'click', 'purchase', 'view').
EventValueThreshold (float) --
The minimum value threshold that an event must meet to be considered valid.
EventWeight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
TrainingFrequency (integer) --
How often the recommender should retrain its model with new data. If set to 0, automatic retraining will not be enabled.
InferenceConfig (dict) --
Configuration settings for how the recommender handles inference requests.
MinProvisionedTPS (integer) --
The minimum provisioned transactions per second (TPS) that the recommender supports. The default value is 1. A high MinProvisionedTPS will increase your cost.
IncludedColumns (dict) --
A map of dataset type to a list of column names to train on. The _webAnalytics and _catalogItem keys are supported. The column names must be a subset of the columns defined in the recommender schema. If not specified, all columns in the schema are used for training. The following columns are always included in training and do not need to be specified: Item.Id, EventTimestamp, and EventType for _webAnalytics; Id for _catalogItem. Mutually exclusive with ExcludedColumns — both cannot be specified in the same request.
(string) --
(list) --
(string) --
ExcludedColumns (dict) --
A map of dataset type to a list of column names to exclude from training. The _webAnalytics and _catalogItem keys are supported. The column names must be valid columns defined in the recommender schema. All columns in the schema except the listed columns will be used for training. The following columns are mandatory and cannot be excluded: Item.Id, EventTimestamp, and EventType for _webAnalytics; Id for _catalogItem. Mutually exclusive with IncludedColumns — both cannot be specified in the same request.
(string) --
(list) --
(string) --
DiversityConfig (dict) --
Configuration for diversity-aware recommendations. When set, the recommender applies diversity constraints defined per item column to reduce over-concentration of similar items in the results.
DiversityColumns (list) --
A list of up to two diversity columns. Each column defines a cap on the number or percentage of recommended items that share the same value for that column.
(dict) --
Defines a diversity constraint for a single item column, specifying a cap type and a target value or placeholder that controls how many recommended items may share the same column value.
Name (string) -- [REQUIRED]
The name of the item catalog column on which to apply the diversity cap. The column must be defined in the recommender schema.
CapType (string) -- [REQUIRED]
The type of diversity cap to apply. Valid values are PERCENTAGE (interpret Target as a percentage of returned items) and VALUE (interpret Target as an absolute count).
Target (string) -- [REQUIRED]
The diversity cap target. Either an integer literal (for example, "25") or a placeholder expression of the form $name whose value is supplied at inference time through GetProfileRecommendations.
string
The name of a specific recommender version to activate as part of this update (for example, to roll back to a previously trained version).
dict
Response Syntax
{
'RecommenderName': 'string'
}
Response Structure
(dict) --
RecommenderName (string) --
The name of the recommender that was updated.