Category: HAQM Personalize

This blog post was last reviewed or updated April, 2022 with database schema updates. We’re excited to announce dynamic filters in HAQM Personalize, which allow you to apply business rules to your recommendations on the fly, without any extra cost. Dynamic filters create better user experiences by allowing you to tailor your business rules for […]

Every day there is something new going on in the world of AWS Machine Learning—from launches to new to use cases to interactive trainings. We’re packaging some of the not-to-miss information from the ML Blog and beyond for easy perusing each month. Check back at the end of each month for the latest roundup. Launches […]

This blog post was last reviewed and updated April, 2022 with database schema updates. We’re excited to announce new efficiency improvements for HAQM Personalize. These improvements decrease the time required to train solutions (the machine learning models trained with your data) by up to 40% and reduce the latency for generating real-time recommendations by up […]

This blog post was last reviewed or updated April, 2022 with database schema updates. HAQM Personalize now makes it easier to manage your growing item and user catalogs with new APIs to incrementally add items and users in your datasets to create personalized recommendations. With the new putItems and putUsers APIs, you can simplify the […]

In this post, we show you how to select the right metadata for your use case when building a recommendation engine using HAQM Personalize. The aim is to help you optimize your models to generate more user-relevant recommendations. We look at which metadata is most relevant to include for different use cases, and where you […]

HAQM Personalize is a machine learning (ML) service that enables you to personalize your website, app, ads, emails, and more with private, custom ML models that you can create with no prior ML experience. We’re excited to announce the general availability of HAQM Personalize in the EU (Frankfurt) Region. You can use HAQM Personalize to […]

Machine learning (ML)-based recommender systems aren’t a new concept, but developing such a system can be a resource-intensive task—from data management during training and inference, to managing scalable real-time ML-based API endpoints. HAQM Personalize allows you to easily add sophisticated personalization capabilities to your applications by using the same ML technology used on HAQM.com for […]

This blog post was last reviewed or updated April, 2022 with database schema updates. HAQM Personalize now makes it easier to create personalized recommendations for fast-changing catalogs of books, movies, music, news articles, and more, improving recommendations by up to 50% (measured by click-through rate) with just a few clicks in the AWS console. Without […]

This is a guest blog post by David A. Smith at Thermo Fisher. In their own words, “Thermo Fisher Scientific is the world leader in serving science. Our Mission is to enable our customers to make the world healthier, cleaner, and safer. Whether our customers are accelerating life sciences research, solving complex analytical challenges, improving […]

Getting relevant recommendations in front of your users at the right time is a crucial step for the success of your personalization strategy. However, your customer’s decision-making process shifts depending on the context at the time when they’re interacting with your recommendations. In this post, I show you how to set up and query a […]