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Amazon Payments uses contextual bandits on SageMaker for AI personalization

By PulseAugur Editorial · [1 sources] ·

Amazon Payments has implemented a multi-objective contextual multi-armed bandit model on Amazon SageMaker to personalize content for customers. This approach aims to optimize the selection of personalized content generated by generative AI, addressing the challenge of choosing the best option for each user. In a seven-week test, the system showed a high single-digit percentage lift in conversion for one customer group, though another group saw no improvement, indicating the content itself was the limiting factor. AI

IMPACT Enhances AI-driven personalization by optimizing content selection, potentially improving customer conversion rates.

RANK_REASON Article describes the application of an AI method (contextual bandits) to a specific product/service (personalization on AWS) by a company (Amazon Payments).

Read on AWS Machine Learning Blog →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Amazon Payments uses contextual bandits on SageMaker for AI personalization

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Article describes the application of an AI method (contextual bandits) to a specific product/service (personalization on AWS) by a company (Amazon Payments).
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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product, infra
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High
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COVERAGE [1]

  1. AWS Machine Learning Blog TIER_1 English(EN) · Chidi Prince John ·

    Uplifting conversion across the acquisition funnel with personalization using contextual bandits on AWS

    Generative AI makes it cheap to produce personalized content at scale, but which variation do you show each customer? Amazon Payments used a multi-objective contextual bandit on Amazon SageMaker AI to personalize an acquisition funnel, achieving a high single-digit conversion lif…

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