Introducing Recommendation Filters in Amazon Personalize

Amazon Personalize uses machine learning technology perfected from over 20 years of recommender systems development at Amazon.com. With Amazon Personalize you are can personalize recommendations for products, videos, music, ebooks, ads, marketing emails, and more, for your users, without any prior machine learning experience.

Today, we are pleased to announce the addition of Recommendation Filters in Amazon Personalize, which improve the relevance of personalized recommendations by filtering out recommendations for products that users have already purchased, videos they have already watched, or other digital content they have already consumed. Receiving such recommendations can be a frustrating experience for users, which can lead to lower engagement, and consequently lost revenue opportunities. Customers typically address this today by writing custom code, which compares the recommendations for each user with the conversion data stored in their database, and removes recommendations for products that the user has already purchased. This can be a time consuming and error prone process for customers. Recommendation Filters in Amazon Personalize eliminate the need to write custom code, and automatically filter out recommendations for products that users have already purchased. Setting up and using Recommendation Filters is simple. First, you use the Amazon Personalize console or API to create a filter using an Amazon Personalize-specific DSL (Domain Specific Language). Next, you apply this filter while querying for real time recommendations using the GetRecommendations or GetPersonalizedRanking API; or while generating recommendations in batch mode through a batch inference job. To learn more about this feature, visit our blog.

Recommendation Filters in Amazon Personalize are now available in US East (N. Virginia, Ohio), US West (Oregon), Canada (Central), Europe (Ireland),and Asia Pacific (Sydney, Tokyo, Mumbai, Singapore, Seoul).