What is Amazon Personalize?
Amazon Personalize is a fully managed machine learning service that uses your data to generate item recommendations for your users. It can also generate user segments based on the users' affinity for certain items or item metadata.
Common use case include the following:
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Personalizing a video streaming app – You can use preconfigured or customizable Amazon Personalize resources to add multiple types of personalized video recommendations to your streaming app. For example, Top picks for you, More like X and Most popular video recommendations.
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Adding product recommendations to an ecommerce app – You can use preconfigured or customizable Amazon Personalize resources to add multiple types of personalized product recommendations to your retail app. For example, Recommended for you, Frequently bought together and Customers who viewed X also viewed product recommendations.
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Adding real-time next best action recommendations to your app – You can use customizable Amazon Personalize resources to recommend the actions that your users will most likely take based on their behavior. For example, you can add real-time recommendations for enrolling in your loyalty program, downloading your mobile app, or signing up for promotional emails.
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Creating personalized emails – You can use customizable Amazon Personalize resources to generate batch recommendations for all users on an email list. Then you can use an Amazon service or third party service to send users personalized emails recommending items in your catalog.
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Creating a targeted marketing campaign – You can use Amazon Personalize to generate segments of users who will most likely interact with items in your catalog. Then you can use an Amazon service or third party service to create a targeted marketing campaign that promotes different items to different user segments.
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Personalizing search results – You can use customizable Amazon Personalize resources to personalize search results for your users. For example, Amazon Personalize can re-rank search results that you generate with OpenSearch.
For most use cases, Amazon Personalize generates recommendations primarily based on item interaction data. Item interaction data comes from your users interacting with items in your catalog. For example, users clicking different items. Your item interaction data can come from both your historical bulk interaction records in a CSV file, and real-time events from your users as they interact with your catalog. In some cases, Amazon Personalize also uses data from items and users such as genre, price, or gender. And for next best action scenarios, it uses actions and action interaction data.
When you import bulk data, you can use Amazon SageMaker Data Wrangler to import data from 40+ sources and prepare it for Amazon Personalize. For more information, see Preparing and importing bulk data using Amazon SageMaker Data Wrangler.
Amazon Personalize includes API operations for real-time personalization, and batch operations for bulk recommendations and user segments. You can get started quickly with use-case optimized recommenders for your business domain, or you can create your own configurable custom resources.
Topics
Pricing for Amazon Personalize
With Amazon Personalize, there are no minimum fees and no upfront
commitments. The Amazon Free Tier
For a complete list of charges and prices, see Amazon Personalize pricing
Related Amazon services and solutions
Amazon Personalize integrates seamlessly with other Amazon services and solutions. For example, you can:
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Use Amazon SageMaker Data Wrangler (Data Wrangler) to import data from 40+ sources into an Amazon Personalize dataset. Data Wrangler is a feature of Amazon SageMaker Studio that provides an end-to-end solution to import, prepare, transform, and analyze data. For more information, see Preparing and importing bulk data using Amazon SageMaker Data Wrangler.
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Use Amazon Amplify to record item interaction events. Amplify includes a JavaScript library for recording events from web client applications. And it includes a library for recording events in server code. For more information, see Amplify Documentation
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Automate and schedule Amazon Personalize tasks with Maintaining Personalized Experiences with Machine Learning
. This Amazon Solutions Implementation automates the Amazon Personalize workflow, including data import, solution version training, and batch workflows. -
Use Amazon CloudWatch Evidently to perform A/B testing with Amazon Personalize recommendations. For more information, see A/B testing with CloudWatch Evidently.
Third-party services
Amazon Personalize works well with various third-party services.
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Amplitude – You can use Amplitude to track user actions to help you understand your users' behavior. For information on using Amplitude and Amazon Personalize, see the following Amazon Partner Network (APN) blog post: Measuring the Effectiveness of Personalization with Amplitude and Amazon Personalize
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Braze – You can use Braze to send users personalized emails recommending items in your catalog. Braze is a market leading messaging platform (email, push, SMS). For a workshop that shows how to integrate Amazon Personalize and Braze, see Amazon Personalize workshop
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mParticle – You can use mParticle to collect event data from your app. For an example that shows how to use mParticle and Amazon Personalize to implement personalized product recommendations, see How to harness the power of a CDP for machine learning: Part 2
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Optimizely – You can use Optimizely to perform A/B testing with Amazon Personalize recommendations. For information on using Optimizely and Amazon Personalize, see Optimizely integrates with Amazon Personalize to combine powerful machine learning with experimentation
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Segment – You can use Segment to send your data to Amazon Personalize. For more information on integrating Segment with Amazon Personalize, see Amazon Personalize Destination
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For a complete list of partners, see Amazon Personalize
Partners
Learn more
The following resources provide additional information about Amazon Personalize:
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For a quick reference to help you determine if Amazon Personalize fits your use case, see the Amazon Personalize Cheat Sheet
in the Amazon Personalize samples repository. -
For a series of videos on how to use Amazon Personalize, see the Amazon Personalize Deep Dive Video Series
found on YouTube. -
For in-depth tutorials and code samples, see the amazon-personalize-samples GitHub repository
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