Getting recommendations (Custom dataset group) - Amazon Personalize
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Getting recommendations (Custom dataset group)

With Amazon Personalize Custom dataset groups, you can get recommendations in real-time or you can get batch recommendations. For real-time recommendations, you must create a campaign before you get recommendations. For batch recommendations, you don't need to create a campaign. For information on campaigns see Creating a campaign.

The following topics explain how and when to use each recommendation type.

Recommendation scores

To make recommendations, Amazon Personalize generates scores for the items in your Items dataset based on a user's interaction data and metadata. These scores represent the relative certainty that Amazon Personalize has in which item the user will select next. Higher scores represent greater certainty.

The formulas that calculate scores depend on the recommendation use case and the recipe that was used to train the model. You can view item scores in the Amazon Personalize console or by using the Amazon Personalize Runtime APIs. For more information on how scores are calculated and what they mean, see Getting real-time recommendations and Getting batch recommendations and user segments.