The Three Phases of Amazon Search

Amazon’s search engine is one of the most advanced in the world. To get a better understanding of Amazon search ranking, we need to understand how Amazon thinks about search internally. Let’s dive deeper into how it works behind the scenes across three core phases: Query understanding, Matching, and Ranking.

Phase 1 – Query Understanding

The query understanding phase is all about analyzing the user’s search query to discern intent. Amazon utilizes a variety of techniques here:

• Looking at past user queries and purchase history for contextual clues. If a user searched for “running shoes” in the past and bought Nike products, that provides useful signals.
• Leveraging extensive machine learning and natural language processing to handle misspellings, typos, and alternative phrasing. So a search for “runing shoes” would understand the intent and normalize to “running shoes.”
• Applying sophisticated spell check algorithms developed through deep research. These can identify and correct typos through probabilistic modeling of language.
• Refining and expanding the original query to include synonyms, related terms, and disambiguating context. This helps match relevant products even if the exact search keywords don’t appear in the product metadata.

The end result is a refined and expanded query that captures the underlying searcher intent as best as possible.

Phase 2 – Matching Products Phase

Next up is the matching phase where Amazon searches its vast catalog to identify potential products to display. A few key aspects of this phase:

• For a typical search query, Amazon’s algorithms will match around 1000 products. This provides sufficient options to find close matches without overwhelming users.
• Matching relies heavily on lexical semantics – how closely the product title, description, bullets, and other metadata match the original search keywords. Products with text that lexically aligns with the query get prioritized.
• Matching may also incorporate contextual signals like past purchases, user demographics, supply availability, and more to surface better results. But the textual relevance is the dominant factor.
• For very popular search terms, the matching pool may be expanded to provide more options. Obscure long-tail queries may have fewer than 1000 matches.

Phase 3 – Ranking Products Phase

The final phase is ranking, where Amazon determines the order to display the matched products. This ranking is customized based on different business goals:

• For general web searches, the top priority is “most likely to purchase”, placing products predicted to drive sales at the top.
• For new product launches, the goal may be “foster discovery” to give new items a chance to be seen and purchased.
• Other goals like “promote under-represented sellers” or “increase customer satisfaction” may factor in.

Amazon uses advanced machine learning algorithms called “learning to rank” to balance and blend hundreds of ranking signals:

• Query relevance factors like how closely the product metadata matches the search text.
• Behavioral signals like past purchases, browsing history, and shopping cart additions.
• Crowdsourced signals like product ratings, reviews, and answer questions.
• Business metadata like price, availability, and seller performance.
• Predictive metrics that estimate the likelihood a particular product will satisfy the user.

The sophistication of these ranking models allows Amazon to tune results for different objectives. Products can be promoted or demoted based on business needs.

The Future – Semantic Search

Looking ahead, Amazon is investing heavily in “semantic search” capabilities. Rather than just matching keywords, semantic search aims to understand the meaning and intent behind queries.

So a search for “comfortable running shoes for marathon training” would infer the need for well-cushioned shoes with pronation control, even if those exact terms are absent from the product description.

This level of “meaning matching” vs. “keyword matching” would enable Amazon to deliver dramatically better search results. The technology is still evolving but could be transformative once perfected.

In summary, Amazon search combines multiple phases – query analysis, massive product matching, and complex ranking – to deliver the most relevant products to each shopper. While already incredibly advanced, expect Amazon’s algorithms to keep improving as techniques like semantic search mature.