Skip to content

BatchInferenceJobConfig

Used by
Source code in docs_src/capo_personalize/types/batch_inference_job_config.py 
12
13
14
15
16
17
18
19
20
class BatchInferenceJobConfig(TypedDict, closed=True):
    item_exploration_config: NotRequired[
        "capo_personalize.types.hyper_parameters.HyperParameters"
    ]
    """<p>A string to string map specifying the exploration configuration hyperparameters, including <code>explorationWeight</code> and <code>explorationItemAgeCutOff</code>, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. See <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a>.</p>"""
    ranking_influence: NotRequired[
        "capo_personalize.types.ranking_influence.RankingInfluence"
    ]
    """<p>A map of ranking influence values for POPULARITY and FRESHNESS. For each key, specify a numerical value between 0.0 and 1.0 that determines how much influence that ranking factor has on the final recommendations. A value closer to 1.0 gives more weight to the factor, while a value closer to 0.0 reduces its influence.</p>"""

item_exploration_config instance-attribute 

item_exploration_config: NotRequired[HyperParameters]

A string to string map specifying the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. See User-Personalization.

ranking_influence instance-attribute 

ranking_influence: NotRequired[RankingInfluence]

A map of ranking influence values for POPULARITY and FRESHNESS. For each key, specify a numerical value between 0.0 and 1.0 that determines how much influence that ranking factor has on the final recommendations. A value closer to 1.0 gives more weight to the factor, while a value closer to 0.0 reduces its influence.