How Amazon is Personalizing Your Shopping Experience with Generative AI

Amazon has introduced a new layer of personalization to its online shopping platform, leveraging generative AI to tailor product recommendations and descriptions to individual customers. This advanced system aims to streamline the shopping process and help users find relevant products more efficiently.

Traditionally, Amazon has employed machine learning algorithms to provide generic product suggestions like “More like this” on its homepage and throughout the shopping journey. However, the integration of generative AI takes personalization a step further. Now, customers encounter specific recommendation categories such as “Gift boxes in time for Mother’s Day” or “Cool deals to improve your curling game,” directly reflecting their individual shopping behaviors and interests.

This personalization extends to product descriptions as well. For example, if a shopper frequently searches for gluten-free items, the AI may incorporate the term “gluten-free” in relevant product descriptions.

To be clear, Amazon is not just arbitraily adding the term to a product to drive sales. The AI system analyzes a combination of product attributes and comprehensive customer data, including search queries, browsing history, and purchase records.

It then modifies qualified product descriptions to ensure that the important attributes to each user is prominently displayed. Such targeted personalization is particularly beneficial for mobile users, who navigate limited screen space and can now locate suitable products more efficiently.

To ensure accuracy and relevance, Amazon employs a two-step AI process. After the initial personalization, a second AI model, called an “evaluator LLM,” reviews and refines the recommendations. This additional layer helps maintain the quality and precision of the personalized content.

By intelligently reordering and enhancing product information, the generative AI ensures that the most important details are emphasized, aligning with what each customer values most.