RecommenderConfig
¶
Used by
Source code in docs_src/capo_customer_profiles/types/recommender_config.py
diversity_config
instance-attribute
¶
diversity_config: NotRequired[DiversityConfig]
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.
events_config
instance-attribute
¶
events_config: NotRequired[EventsConfig]
Configuration settings for how the recommender processes and uses events.
excluded_columns
instance-attribute
¶
excluded_columns: NotRequired[IncludedColumns]
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.
included_columns
instance-attribute
¶
included_columns: NotRequired[IncludedColumns]
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.
inference_config
instance-attribute
¶
inference_config: NotRequired[InferenceConfig]
Configuration settings for how the recommender handles inference requests.
training_frequency
instance-attribute
¶
training_frequency: NotRequired[RecommenderConfigTrainingFrequencyInteger]
How often the recommender should retrain its model with new data. If set to 0, automatic retraining will not be enabled.