Understanding how Amazon’s algorithms work can greatly benefit anyone who sells on the platform or analyzes how the world’s largest marketplace positions products. Contrary to what many people believe, Amazon is not driven solely by keywords or sales. Since 2023, the company has published official research revealing how its system uses knowledge graphs, hyperconnections, and advanced machine learning to organize millions of products in milliseconds.
In this article, we explain in a simple way—based on recent official sources from Amazon and Amazon Science—how Amazon decides what to show first, why some products rise while others fall, and how the technology that powers its marketplace is evolving.
Amazon doesn’t reward sellers — it rewards customer satisfaction
Amazon stated it clearly: the customer “chooses the winners.”
When users interact with a product (buy it, return it, rate it, or abandon it), these signals influence how the system rearranges product visibility.
This makes it clear that the algorithm is not static: it learns and adapts the order of products based on real buyer satisfaction. If a product delights customers, it rises. If it generates returns or complaints, it falls.
Amazon uses “product graphs”: an advanced way to understand what you’re looking for
One of the most important changes in Amazon’s technology became public in 2023: the company uses product graphs to understand what each product is, how it relates to others, and the context in which it fits.
Instead of simply reading the title or keywords, Amazon creates a map of relationships between products, attributes, and audiences. This graph helps the system understand that a pair of shoes could be “for walking,” “for light running,” “for long work shifts,” or even “for pregnancy,” depending on user behavior.
This approach completely transforms how the marketplace works: Amazon is no longer a literal search engine—it becomes a system that interprets intention and context.
Hypergraphs: the advanced engine that connects products based on real user behavior
In another 2023 study, Amazon explained that its search engine uses hypergraphs, a structure even richer than traditional graphs. While a graph connects two elements, a hypergraph connects entire groups of products based on what users view, compare, or buy in the same session.
This allows Amazon to show products that fit what similar users usually buy—even if the keyword typed isn’t perfect.
In simple terms, the algorithm becomes much smarter and more contextual: it understands “what you really meant,” even if you didn’t write it precisely.
Customer behavior outweighs everything else
User behavior is critical for Amazon’s rankings. The company confirms it: if a product generates dissatisfaction, it moves down; if it generates positive interactions, it moves up.
Amazon analyzes factors such as:
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Clicks and time on page
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Purchases and conversions
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Returns and reasons for return
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Ratings and review sentiment
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Behavioral patterns across millions of shoppers
If thousands of users click on a product but very few buy it, the algorithm interprets that “it doesn’t match what they were looking for.” But if many buy it and few return it, the product rises quickly.
Generative AI: the new brain that gives context to searches
Since 2023, Amazon has been using generative AI to improve search results and product descriptions. AI rewrites text, summarizes reviews, and adapts recommendations to each user.
This means two people can see different results for the same query. The algorithm adapts to your history, interests, previous purchases, and even the types of products you’ve recently compared.
What factors determine Amazon’s rankings today?
Based on all official sources since 2023, the factors that truly influence ranking can be summarized as:
Semantic relevance
Thanks to graphs and hypergraphs, Amazon understands which products “fit” the search intent—even if the keywords aren’t an exact match.
Customer experience
Amazon prioritizes products with strong real-world performance: purchases, satisfaction, and low return rates.
Quality and availability
Products with stock, fast delivery, and strong logistics are more competitive.
Seller performance
Amazon values compliance, reliability, and customer service.
Personalization
AI tailors results to each user, making rankings more dynamic than ever.
What Amazon doesn’t reveal—and never will
Despite increased scientific transparency, Amazon never publishes:
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The exact weight of each signal (user behavior, etc.)
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The formula used to rank results
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Internal seller quality metrics
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Buy Box criteria
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Anti-fraud systems
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Detailed workings of its AI models
This protects the marketplace from manipulation and maintains fair competition.
How Amazon’s algorithm works — in 30 seconds
The simplest way to understand it is to reduce the process to five real steps:
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It interprets your search using AI and semantic models.
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It analyzes the product graph to find items that match your intent.
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It ranks products based on satisfaction, conversion, ratings, and massive behavioral signals.
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It personalizes results according to your history, preferences, and context.
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It continuously learns and updates rankings based on how millions of users react each day.
This is the invisible engine that powers the marketplace.
Conclusion: Amazon no longer ranks products—it ranks intentions
In a short time, Amazon has completely transformed its algorithms.
What used to be a keyword-based search engine is now an intelligent system capable of understanding products as concepts, detecting patterns, and reacting in real time to user satisfaction and behavior.
For sellers, analysts, or creators of e-commerce content, understanding this shift is essential: Amazon doesn’t just classify products—it interprets buying intent at massive scale and delivers the best response to meet a need.
