Amazon Personalize Documentation
Automated machine learning
Once you have provided your data via Amazon S3 or via real-time integrations, Amazon Personalize can load and inspect the data, enable you to select the right algorithms, train a model, provide metrics, and generate personalized recommendations. As your data set grows over time from new metadata and the consumption of real-time user event data, your models can be retrained to provide relevant and personalized recommendations.
User Personalization
User personalization predicts the items that a user will interact with based on their historical interactions with your catalog items.
Personalized rankings
Personalized rankings enables you to deliver a list of recommended items that are ranked for a specific user.
Similar item recommendations
Personalize enables you to recommend similar items in your catalogue to the items users are viewing, exploring, or searching for.
Trending Now
Helps you recommend items gaining popularity at the fastest pace among your users.
Next best action
Next best action is designed to generate recommendations for actions that your users are likely to take based on their previous interactions with your catalogue.
Batch recommendations
Compute recommendations for large numbers of users or items, store them, and feed them to batch-oriented workflows such as email systems.
Contextual recommendations
Improve relevance of recommendations by generating them within a context.
Business rules and filters
You are enabled to apply business rules and filters to help you optimize customer experience.
Promotions
You are enabled to promote specific items or content based on rules that align with your business goals. Amazon Personalize is designed to find the most relevant items or content to be promoted for each user within the business rule provided and distributes it within the user’s recommendations.
Unstructured text support
Amazon Personalize is designed to enable you to unlock the information trapped in product descriptions, reviews, movie synopses or other unstructured text to help you generate highly relevant recommendations for users.
Generative AI capabilities
Content Generator
Amazon Personalize Content Generator uses generative AI to make recommendations with text generated by foundation models. It enables personalization by accompanying each recommendation with a tailored snippet that describes the thematic similarity between recommended items.
LangChain integration
Builders are enabled to use a custom chain on LangChain to integrate Amazon Personalize with generative AI solutions. With pre-configured LangChain code, you can invoke Amazon Personalize, retrieve recommendations for a campaign or a recommender, and feed it into your generative AI applications within the LangChain ecosystem.
Return metadata in inference response
Amazon Personalize enables the inclusion of return item metadata as part of the inference output.
Additional Information
For additional information about service controls, security features and functionalities, including, as applicable, information about storing, retrieving, modifying, restricting, and deleting data, please see https://docs.aws.amazon.com/index.html. This additional information does not form part of the Documentation for purposes of the AWS Customer Agreement available at http://aws.amazon.com/agreement, or other agreement between you and AWS governing your use of AWS’s services.