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Amazon says it personalizes shopping using signals such as purchase history, searches, product reviews, Lists and conversations with Alexa for Shopping. Its disclosures also describe orders, viewed pages, returns, content usage and device interactions. These signals can help Amazon surface products it thinks may be relevant, but the company does not publish a complete formula or explain how much weight each signal receives. You can review and edit some personalization details through About You, where available.
What Amazon says it uses to personalize shopping
Amazon’s May 13, 2026 About You announcement names five types of information used to personalize the shopping experience: purchase history, conversations with Alexa for Shopping, product reviews, Lists and searches. Amazon describes these as information that can help shape the experience a customer sees. It does not say that each item affects every recommendation, or that the list is a complete inventory of all signals used by every feature or marketplace.
A separate Amazon profiling report, identified as a 2025 annex, describes a wider set of information associated with its Store profiling. It distinguishes information customers provide from activity and device information observed through use of Amazon services. The report says Store profiling uses customer engagement to show content related to shopping interests.
Information customers provide
Some information is deliberately supplied by the customer. The profiling report gives account information and wishlist items as examples. Reviews and Lists, which Amazon also names in its About You announcement, are other ways a customer can express interests or preferences. These signals have a different character from inferred activity: a wishlist or review is an intentional contribution, while a viewed page is evidence of an interaction, not necessarily a statement of intent.
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Activity Amazon describes
The profiling report lists orders, pages viewed, search history and returns history among the activity categories it describes. It also refers to content usage, including Prime Video or Kindle use, and device configuration, permissions and identifiers. The report’s broader account of data should not be read as proof that all of these categories shape product recommendations. Amazon’s public descriptions establish that they are among the data categories discussed in profiling, not that each category is used by every recommendation feature.
How shopping and browsing activity can affect suggestions
At a practical level, shopping and browsing activity can give Amazon clues about what a customer may want to find. A purchase may suggest a need for related items; a search or product-page visit may indicate an interest that has not yet led to a purchase. Amazon’s recommendation surfaces can use such clues to show products that seem related or potentially useful. This is a general explanation of the disclosed purpose, not a claim about a specific internal ranking rule.
Amazon’s example: a comforter and related items
In an article about its redesigned homepage, Amazon gives the example of a customer who recently bought a comforter being shown matching sheets and pillows. The example illustrates how a purchase can connect to complementary products. It does not guarantee that buying a comforter will always produce those recommendations, or that every suggested item is selected solely because of that purchase.
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Amazon also says the homepage may surface trends, new releases, best sellers and deals according to interests and preferences. Those are different kinds of product discovery: a suggestion may be connected to a customer’s prior activity, or it may reflect broader shopping content. Seeing an item therefore does not, by itself, reveal which signal led to its placement.
Why a signal is not the same as a conclusion
A page view can reflect comparison shopping, a brief look, or an accidental visit; a purchase is stronger evidence of a completed transaction but does not prove that the customer will want the same category again. A return likewise records an interaction, but the public descriptions do not explain whether or how a return changes a particular recommendation. It is more accurate to treat activity as a collection of possible signals than as a definitive statement about a person’s tastes.
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Recommendations may change as a customer’s activity changes, but Amazon’s public examples do not establish a fixed time window, a guaranteed response to a single action, or a precise rule such as “viewing a product once makes it appear on the homepage.” They also do not reveal the relative importance of purchase history, searches, page views, reviews or other categories.
What Amazon has not disclosed about the recommendation formula
The public descriptions identify broad information categories and examples of personalized shopping. They do not disclose a complete production ranking formula, the weight assigned to each signal, or a full account of how an individual product is selected for a particular customer. As a result, shoppers cannot use these statements to predict exactly why a product appeared or to infer a complete personal profile from a recommendation.
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Amazon’s announcement says that “nearly every shopping experience across Amazon is personalized” to help “hundreds of millions of customers” find products. That is Amazon’s own broad description and rounded customer figure, not an independently verified user count or a precise measure of how many people receive a given recommendation. The statement does not mean that every page element is individualized in the same way.
Amazon Personalize is not proof of Amazon retail’s internal system
AWS documentation for Amazon Personalize describes a cloud service that businesses can use to build recommendation systems. It discusses concepts such as scoring items from interaction data and metadata, adapting recommendations as new events are recorded in some supported use cases, and e-commerce patterns including “Recommended for you,” “Frequently bought together” and “Customers who viewed X also viewed.” These examples can help explain recommendation systems in general.
Amazon Personalize is a developer service, however. Its documentation does not establish that Amazon uses that service, those exact patterns, or any particular implementation for its own retail recommendations. Treating it as a description of Amazon’s undisclosed internal model would go beyond what the documentation supports.
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How to review and edit personalization details with About You
Amazon’s May 2026 announcement says About You lets customers view and edit details used for personalization, including removing information they do not want used for that purpose. Amazon lists the Shopping app, mobile web and desktop as ways to access it. The company says customers can provide preferences or correct details as well as remove information. Navigation can change when Amazon updates its interface, and the available controls may vary by account or marketplace.
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In the Amazon Shopping app
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Open the Amazon Shopping app and go to Me.
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Select your name.
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Open About You to review the personalization details Amazon presents.
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Use the available options to provide preferences, correct details or remove information you do not want used for personalization.
On desktop
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On Amazon’s desktop site, select your customer name above Account & Lists.
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Select About You.
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Review the information and make changes using the controls shown for your account.
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Amazon also identifies mobile web as an access point, but its announcement does not give a separate, more detailed mobile-web click path. If labels or locations differ from these instructions, use the About You option displayed in your current account rather than assuming an older route still applies. The announcement describes About You as a way to manage personalization details; it does not establish that editing this page erases all activity records or changes every form of Amazon data use.
Recommendations, advertising and cookies are separate controls
Product recommendations are not the same thing as interest-based advertising. Amazon’s EU Digital Services Act risk assessment report for 2024 discusses product recommendations, interest-based advertising, cookie preferences and privacy disclosures as separate contexts. A setting that changes one category should not be assumed to control the others.
For example, About You is described in Amazon’s 2026 announcement as a way to review and edit details used for shopping personalization. Cookie preferences concern cookies and related browser or device storage; advertising controls concern interest-based advertising. The existence of one control does not, by itself, show that it changes recommendation ranking, advertising selection and cookie use together.
What the EU disclosure says
The 2024 report is specific to Amazon’s EU services. It says EU Store customers can learn about data used for advertising and available controls, and it references an option in Your Account to opt out of product recommendations. That regional disclosure should not be treated as proof that the same opt-out, wording or navigation exists in every country. Amazon’s newer About You announcement and the EU report describe different information and control contexts; use the options shown for your own marketplace and account.
How to interpret a recommendation you see
When a product appears in a recommendation area, the public information supports only a limited set of conclusions: Amazon may use shopping-related information to personalize the experience, and prior shopping or browsing activity can be relevant. The appearance of an item does not prove which particular signal triggered it, how much that signal mattered, or that Amazon has reached a definitive conclusion about the customer.
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A related item may follow a purchase, as in Amazon’s comforter-and-bedding example, but the example is illustrative rather than a guaranteed rule.
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A product may also appear as part of broader homepage content such as trends, best sellers, new releases or deals.
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Amazon names several input categories, but does not publicly assign each one a weight or explain how they combine for a specific placement.
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About You provides a stated way to inspect and edit some personalization details; its controls should not be mistaken for a complete account of every data use.
This distinction matters if a recommendation seems unexpected or overly specific. A single suggestion is not enough to establish that Amazon is using a particular private detail, and removing or editing a detail through About You should be understood within the personalization controls Amazon describes—not as evidence that every record or unrelated processing purpose has been changed.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
