User-Personalization-v2 recipe
The User-Personalization-v2 (aws-user-personalization-v2) recipe recommends items a user will interact with based on their preferences. For example, you might use User-Personalization-v2 to generate personalized movie recommendations for a streaming app, or personalized product recommendations for a retail app. Other use cases include generating real-time recommendations for a news site or batch recommendations for a personalized marketing campaign.
User-Personalization-v2 can train on up to 5 million items from Item interactions and Items datasets. And it generates more relevant recommendations with lower latency than User-Personalization.
Because User-Personalization-v2 recommends the most relevant items to users based on your data, it more frequently recommends existing items with interactions data. To make sure recommendations include new items, you can use a promotion that includes some items based on creation timestamp. For more information about promotions, see Promoting items in real-time recommendations.
This recipe uses a transformer-based architecture to train a model that learns context
and tracks relationships and patterns in your data. Transformers are a type of neural network architecture that transforms or changes an
input sequence into an output sequence.
For Amazon Personalize, the input sequence is a user's item interaction history in your data. The output sequence is their personalized recommendations. For more information about
transformers, see What Are Transformers In Artificial Intelligence?
User-Personalization-v2 uses a different pricing model than other recipes. For more information about pricing, see Amazon Personalize pricing
Recipe features
User-Personalization-v2 uses the following Amazon Personalize recipe features when generating item recommendations:
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Real-time personalization – With real-time personalization, Amazon Personalize updates and adapts item recommendations according to a user's evolving interest. For more information, see Real-time personalization.
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Exploration – With exploration, recommendations include items with less interactions data or relevance to the user. With User-Personalization-v2, Amazon Personalize handles exploration configuration for you. To make sure recommendations include new items, you can use promotions to include new items based on their creation timestamp. For more information about promotions, see Promoting items in real-time recommendations.
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Automatic updates – With automatic updates, Amazon Personalize automatically updates the latest model (solution version) every two hours to consider new items for recommendations. For more information, see Automatic updates.
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Metadata with recommendations – With the User-Personalization-v2 recipe, if you have an Items dataset with at minimum one column of metadata, campaigns automatically have the option to include item metadata with recommendation results. You don't have manually enable metadata for your campaign. You might use metadata to enrich recommendations in your user interface, such as adding the genres for movies to carousels. For more information, see Item metadata in recommendations.
Required and optional datasets
To use the User-Personalization-v2, you must create an Item interactions dataset and import at minimum 1000 item interactions. Amazon Personalize generates recommendations primarily based on item interaction data. For more information, see Item interaction data. User-Personalization-v2 can train on up to 5 million items across Item interactions and Items datasets.
With User-Personalization-v2, Amazon Personalize can use Item interactions data that includes the following:
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Event type and event value data – Amazon Personalize uses event type data, such as click or watch event types, to identify user intent and interest through any patterns in their behavior. Also, you can use event type and event value data to filter records before training. For more information, see Event type and event value data.
Note
With User-Personalization-v2, your training cost is based on your interactions data before filtering by event type or value. For more information about pricing, see Amazon Personalize pricing
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Contextual metadata – Contextual metadata is interactions data you collect on the user's environment at the time of an event, such as their location or device type. For more information, see Contextual metadata.
The following datasets are optional and can improve recommendations:
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Users dataset – Amazon Personalize can use data in your Users dataset to better understand your users and their interests. You can also use data in a Users dataset to filter recommendations. For information about the user data you can import, see User metadata.
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Items dataset – Amazon Personalize can use data in your Items dataset to identify connections and patterns in their behavior. This helps Amazon Personalize understand your users and their interests. You can also use data in a Items dataset to filter recommendations. For information about the item data you can import, see Item metadata.
Properties and hyperparameters
The User-Personalization-v2 recipe has the following properties:
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Name –
aws-user-personalization-v2 -
Recipe Amazon Resource Name (ARN) –
arn:aws:personalize:::recipe/aws-user-personalization-v2 -
Algorithm ARN –
arn:aws:personalize:::algorithm/aws-user-personalization-v2
For more information, see Choosing a recipe.
The following table describes the hyperparameters for the User-Personalization-v2 recipe. A hyperparameter is an algorithm parameter that you can adjust to improve model performance. Algorithm hyperparameters control how the model performs. The process of choosing the best value for a hyperparameter is called hyperparameter optimization (HPO). With User-Personalization-v2, if you turn on automatic training, Amazon Personalize automatically performs HPO every 90 days. Without automatic training, no HPO occurs.
The table provides the following information for each hyperparameter:
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Range: [lower bound, upper bound]
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Value type: Integer, Continuous (float), Categorical (Boolean, list, string)
| Name | Description |
|---|---|
| Algorithm hyperparameters | |
apply_recency_bias |
Determines whether the model should give more weight to the most recent item interactions data in your Item interactions dataset. The most recent interactions data might include sudden changes in the underlying patterns of interaction events.
To train a model that places more weight on recent events,
set Default value: Range: Value type: Boolean HPO tunable: No |
