Amazon 搜索算法如何运作：A9 排序与搜索栏推荐机制解析

Amazon controls product visibility directly through its A9 algorithm. Sellers have spent years trying to figure out exactly how the Amazon search algorithm works from the inside out. 

There are no clear answers. There’s only a set of facts that when put together, can give you enough context to improve your listing optimization and start ranking higher.

This article covers everything we know about this complex topic and shares best practices you can apply today across your catalog.

All We Know About Amazon’s A9 or A10 Algorithm

Amazon has not publicly shared how their algorithm works. 

Amazon’s product search and ranking technology is officially named A9, still to this day. The A10 algorithm is an industry name and not official. What we do know about Amazon’s algorithm comes directly from Amazon’s papers and observed performance. 

In general, the system prioritizes products that generate the most sales. Conversion rate, of course, is the strongest signal. 

Sales velocity, competitive pricing, Amazon stock availability, and fulfillment speed directly impact where a product appears on the search page.

It doesn’t matter if your product sells well for a specific keyword if at the moment you won’t be able to deliver your product fast or you’re out of stock. 

That’s why stockouts are one of the biggest performance killers on the platform. Recovering from them is tough. It takes a lot of time to regain the momentum you had before the stockout.

How Does Amazon Decide Which Products to Show?

This is the question every seller wants a clear answer for. All we can say is that Amazon hasn’t released an official breakdown of the system. We do know a few facts, though.

We know the system runs in two stages. 

1. The first stage decides which products are eligible to appear for a customer search. 
2. The second stage gives an order to the eligible products.

First, the retrieval stage scans text. It looks at your copy listing as a whole. Amazon takes the query and finds products whose indexed text contains those terms. 

Second, the ranking stage gives order to the eligible products. This is where performance takes over. 

Amazon has said it considers factors including text relevance, price, availability, and sales history when ranking. 

And like we mentioned earlier, it also uses click-through rates and conversion metrics..

All We Know About How Amazon’s Search Bar Works

We now know that Amazon monitors what shoppers do between searches. This partially explains why the platform is so good at converting customers.

Patent 12,561,383 is officially titled “Systems for recommending search queries based on determined user intent.” The United States Patent and Trademark Office granted it to Amazon Technologies Inc. on February 24, 2026.

The patent describes a system that analyzes search queries as a sequence of related events

• The system records a string of search queries from a single user during a session.
• It analyzes how the user alters the text from one query to the next. For example, it tracks if a user adds a word, removes a word, or swaps a brand name.
• Based on those changes, the system assigns a specific intent to each step (like narrowing or broadening a search).
• The algorithm takes this sequence of intents and combines it with the search results the user clicked on, then predicts what the user intends to do next. 
• It then uses that prediction to recommend the next set of search queries or products.

When a buyer makes a search and the results don’t satisfy them, they change the words in the search bar. 

Amazon tracks that exact change and sorts it into one of six categories. This information is used to improve the recommendations.

Here are the six categories that make the hexagon:

1. Specification: The buyer adds words to narrow the results. 
2. Generalization: The buyer removes words because the previous search was too specific and showed limited results. 
3. Substitution: The buyer swaps one specific brand or feature for another. 
4. Equivalence: The buyer stops browsing and searches for an exact model number or highly specific product name. 
5. Complementary: The buyer searches for an accessory that matches an item they just bought or viewed. 
6. Irrelevant: The buyer abandons the previous topic entirely and starts looking for a completely unrelated item.

In conclusion, the patent reveals key features that come into play when Amazon recommends products in the search bar. These are:

1. Amazon predicts when a buyer is switching their query.
2. Amazon recommends accessories immediately after a customer buys a product (you can be one of them).
3. Every unrelated search starts a new cycle. Data doesn’t build up. All listings have to earn their relevance in those recommendations from scratch.

Updated Amazon Listing Optimization Strategies for 2026

We have to mention the role Amazon’s AI systems are playing right now in product recommendations. 

The truth is that Amazon customers are embracing Alexa for Shopping. According to Amazon, over 350 million customers used Alexa for Shopping over the past 12 months. And more importantly, US customers who use Alexa for Shopping spend 40% more per order on average than shoppers who rely only on traditional search.

That basically means you have to optimize your listings for Amazon’s algorithms AND Alexa for Shopping. Below are some practices you can start applying right now.

1. Answer Questions Directly in Your Bullet Points

Amazon’s AI systems scan your bullet points to answer user questions in real time. 

Lead every bullet point with a capitalized benefit and follow it with concrete data. 

State exact dimensions, materials, and compatibility limits. Do not use generic marketing text. Include as much relevant info as possible. And optimize it with high Search Volume keywords.

2. Structure Titles for the Intent Hexagon

Buyers often refine their searches by adding specific attributes. Place your highest-volume target keyword in the 75 characters title and include specific materials, sizes, and primary use cases in the Item Highlights. 

3. Complete All Backend Structured Attributes

Fill out every applicable attribute field. The algorithm uses these exact fields to filter results when a buyer narrows their search. Alexa for Shopping scans them when analyzing products too.

4. Update Images for Text Scanning

The AI systems now scan the text inside your images. Include comparison charts and specific metrics directly on your infographics. 

Include uses and features. The AI reads these exact phrases and uses them to match your product to specific shopper queries.

Conclusion

Mastering the Amazon search algorithm revolves around customer search intent. 

Sellers who want to know how to rank higher on Amazon must focus on proper Amazon listing optimization paired with consistent inventory management keeps your listing visible and converting.

Frequently Asked Questions about the Amazon Search Algorithm (FAQs)

What is the difference between A9 and A10 in the Amazon search algorithm?

Amazon does not use the names A9 or A10. The core amazon search algorithm relies on conversion rates and sales velocity to order products after text matching occurs.

How does the Amazon search algorithm work during product retrieval?

Learning how the Amazon search algorithm works during retrieval comes down to text matching. The system scans titles, bullets, and hidden attributes to filter eligible products for a query. Then orders the candidates by performance & fulfillment metrics.

What is the best approach for Amazon backend search terms optimization?

Proper amazon backend search terms optimization requires keeping backend keywords under 249 bytes and using spaces instead of commas. Focus on synonyms, ingredients, attributes and misspellings not present in public copy.

How do you rank higher on Amazon search?

Knowing how to rank higher on amazon search requires driving early sales velocity and maintaining stock. Strong conversion rates push indexed listings up the page.

How does the Amazon product recommendation algorithm select items?

The amazon product recommendation algorithm analyzes session search patterns to predict shopper intent. It suggests complementary items or alternatives based on how customers keep searching.

Stop Guessing the Algorithm & Build an Engine That Converts

Ranking on Amazon requires clean text indexing paired with strong conversion performance. Our team reviews your catalog to align your copy, backend search terms, and buyer intent signals with Amazon’s actual search framework.

Submit your details in the form below to schedule a session with our eCommerce team. We will evaluate your current indexing and provide clear steps to help your products rank higher.

Fill out the form below to book your strategy call.