Posted On: Nov 29, 2021

Today, Amazon Personalize is excited to announce recommenders which are optimized to deliver personalized experiences for common use cases in Media & Entertainment and Retail. It is now faster and easier to deliver high performing personalized user experiences in your applications without any ML expertise required. Recommenders reduce the time needed to build and deliver personalized experiences and fully manage the lifecycle of the experience to help ensure you recommend what is most relevant to your users.

Tailoring experiences to users requires different types of recommendations at different points in a user’s journey. Media & Entertainment applications drive greater engagement and retention with personalized recommendations like “Top Picks” for users on the welcome screen and “More Like X” on video detail pages where the context of what a user has watched is critical to discover what to watch next. Retail businesses need recommendations to highlight “Best Sellers” and the items “Frequently Bought Together” to enable customers to more easily build their baskets at check-out. Amazon Personalize’s recommenders simplify the creation and maintenance of these personalized user experiences. Personalize considers the business-specific context and selects the optimal settings for our underlying machine learning models used to serve the recommendations. By fully managing the lifecycle of maintaining and hosting these models, Amazon Personalize makes it easier and faster to deliver these experiences in your application.

Media & Entertainment customers can choose from use cases such as:

  • “Most Popular”
  • “Because You Watched X”
  • “More Like X”
  • “Top Picks For You”

Retail customers can choose from use cases such as:

  • “Best Sellers”
  • “Most Viewed”
  • “Frequently Bought Together”
  • “Customers Who Viewed This Also View”
  • “Recommended For You”

Recommenders in Amazon Personalize enable you to personalize your website, app, ads, emails, and more, using the same machine learning technology as used by, without requiring any prior machine learning experience. To get started with Amazon Personalize, visit our documentation.