AWS Machine Learning Blog

Category: Amazon Personalize

Accelerate and improve recommender system training and predictions using Amazon SageMaker Feature Store

Many companies must tackle the difficult use case of building a highly optimized recommender system. The challenge comes from processing large volumes of data to train and tune the model daily with new data and then make predictions based on user behavior during an active engagement. In this post, we show you how to use […]

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Personalize cross-channel customer experiences with Amazon SageMaker, Amazon Personalize, and Twilio Segment

Today, customers interact with brands over an increasingly large digital and offline footprint, generating a wealth of interaction data known as behavioral data. As a result, marketers and customer experience teams must work with multiple overlapping tools to engage and target those customers across touchpoints. This increases complexity, creates multiple views of each customer, and […]

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Improve the return on your marketing investments with intelligent user segmentation in Amazon Personalize

Today, we’re excited to announce intelligent user segmentation powered by machine learning (ML) in Amazon Personalize, a new way to deliver personalized experiences to your users and run more effective campaigns through your marketing channels. Traditionally, user segmentation depends on demographic or psychographic information to sort users into predefined audiences. More advanced techniques look to […]

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Amazon Personalize announces recommenders optimized for Retail and Media & Entertainment

Today, we’re excited to announce the launch of personalized recommenders in Amazon Personalize that are optimized for retail and media and entertainment, making it even easier to personalize your websites, apps, and marketing campaigns. With this launch, we have drawn on Amazon’s rich experience creating unique personalized user experiences using machine learning (ML) to build […]

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Your guide to AI and ML at AWS re:Invent 2021

It’s almost here! Only 9 days until AWS re:Invent 2021, and we’re very excited to share some highlights you might enjoy this year. The AI/ML team has been working hard to serve up some amazing content and this year, we have more session types for you to enjoy. Back in person, we now have chalk […]

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Amazon Personalize can now unlock intrinsic signals in your catalog to recommend similar items

Today, we’re excited to announce a new similar items recommendation recipe (aws-similar-items) in Amazon Personalize that helps you leverage your users’ interaction histories and what you know about the items in your catalog to deliver relevant recommendations. Across Amazon, we provide personalized experiences for each of our users, and based on a user’s interests, we […]

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Personalizing wellness recommendations at Calm with Amazon Personalize

This is a guest post by Shae Selix (Staff Data Scientist at Calm) and Luis Lopez Soria (Sr. AI/ML Specialist SA at AWS). Today, content is proliferating. It’s being produced in many different forms by a host of content providers, both large and small. Whether it’s on-demand video, music, podcasts, or other forms of rich […]

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Optimize personalized recommendations for a business metric of your choice with Amazon Personalize

Amazon Personalize now enables you to optimize personalized recommendations for a business metric of your choice, in addition to improving relevance of recommendations for your users. You can define a business metric such as revenue, profit margin, video watch time, or any other numerical attribute of your item catalog to optimize your recommendations. Amazon Personalize […]

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Attendee matchmaking at virtual events with Amazon Personalize

Amazon Personalize enables developers to build applications with the same machine learning (ML) technology used by Amazon.com for real-time personalized recommendations—no ML expertise required. Amazon Personalize makes it easy for developers to build applications capable of delivering a wide array of personalization experiences, including specific product recommendations, personalized product re-ranking, and customized direct marketing. Besides […]

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Use contextual information and third party data to improve your recommendations

Have you noticed that your shopping preferences are influenced by the weather? For example, on hot days would you rather drink a lemonade vs. a hot coffee? Customers from consumer-packaged goods (CPG) and retail industries wanted to better understand how weather conditions like temperature and rain can be used to provide better purchase suggestions to […]

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