Personalize Recommender

Docs > DDSQL Reference > Data Directory > Personalize Recommender

Personalize Recommender in AWS is a managed resource that provides personalized item recommendations based on user behavior and item data. It uses machine learning models trained in Amazon Personalize to deliver real-time, tailored suggestions without requiring ML expertise. This service helps improve user engagement and conversions by offering relevant recommendations for applications such as e-commerce, media, and content platforms.

aws.personalize_recommender

Fields

ID	Type	Data Type	Description
_key	core	string
account_id	core	string
creation_date_time	core	timestamp	The date and time (in Unix format) that the recommender was created.
dataset_group_arn	core	string	The Amazon Resource Name (ARN) of the Domain dataset group that contains the recommender.
failure_reason	core	string	If a recommender fails, the reason behind the failure.
last_updated_date_time	core	timestamp	The date and time (in Unix format) that the recommender was last updated.
latest_recommender_update	core	json	Provides a summary of the latest updates to the recommender.
model_metrics	core	string	Provides evaluation metrics that help you determine the performance of a recommender. For more information, see Evaluating a recommender.
name	core	string	The name of the recommender.
recipe_arn	core	string	The Amazon Resource Name (ARN) of the recipe (Domain dataset group use case) that the recommender was created for.
recommender_arn	core	string	The Amazon Resource Name (ARN) of the recommender.
recommender_config	core	json	The configuration details of the recommender.
status	core	string	The status of the recommender. A recommender can be in one of the following states: CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE DELETE PENDING > DELETE IN_PROGRESS
tags	core	hstore