Technical SEOPublished02.05.2026

Search Algorithm

How search engines determine the order of sites in results. Evolution from keyword counting to machine learning and E-E-A-T.

In brief

A search algorithm is a set of mathematical rules and machine learning models that a search engine uses to rank web pages in response to a user query. The exact formula is kept secret, but its principles are studied through patent filings and official statements.

What Is a Search Algorithm

A search algorithm is a complex system that analyses hundreds of factors to determine which page best answers a user's query. These factors include content relevance, backlink quality and quantity, behavioural signals, technical health, and many others. Search engines constantly update algorithms to make them smarter and more resistant to manipulation.

Google makes thousands of algorithm changes every year — most are small, but some (core updates) can dramatically reshape search results.

Evolution of Algorithms

Search algorithms have come a long way. Their evolution can be divided into three eras:

  • Keyword era (1990s): Simply repeating the target query many times on a page was enough. This led to keyword stuffing.
  • Link era (2000s): Google introduces PageRank — a page's importance is determined by the number and quality of links pointing to it. A race for links begins.
  • Machine learning era (2015–present): Algorithms stop simply counting occurrences and start understanding meaning, user satisfaction, and query context.

Modern Algorithms: RankBrain, BERT, YATI

Modern Google search is not one algorithm but a family of machine learning models, each solving a specific task:

  • RankBrain (2015) — Google's first neural network that helps process rare and long-tail queries by matching them to pages even when exact words don't appear.
  • BERT (2019) — Natural language processing (NLP) technology that accounts for word context (prepositions, word order). Understands queries like 'how to treat a dog with aspirin'.
  • YATI (2023) — A more powerful neural network that improves understanding of author experience and content depth, especially in YMYL topics.
TXT
BERT example:
Query: "can you give medicine A to a dog"
Before BERT: matches pages with "medicine A" and "dog"
With BERT: understands the relationship "give to a dog", not just mentions.

The Role of E-E-A-T

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is not a direct ranking factor but a set of criteria algorithms use to evaluate page quality. Especially crucial for YMYL topics (health, finance, safety). Algorithms analyse:

  • Evidence of real experience from the author (Experience)
  • Deep expertise in the subject (Expertise)
  • Authority of the site and brand (Authoritativeness)
  • Reliability and transparency of information (Trustworthiness)
Even with perfect technical SEO, a page may not rank if algorithms deem it lacks E-E-A-T, especially in competitive or sensitive niches.
FAQ

Common questions

No. The exact formula is a trade secret, but the SEO community reconstructs principles through experiments, patents, and official statements from Google.
Google makes thousands of small changes per year and several major core updates — usually 4–6 times a year.
No need to chase each update. Following best practices (quality content, natural link profile, user experience) is more reliable than chasing ephemeral factors.
No. Yandex has its own algorithms (Arcadia, Korolev, Alice), but many basic principles (links, behaviour, E-E-A-T-like approach) are similar.
It refers to the lack of transparency: we see only inputs (a page) and output (ranking position) but not how the decision was made. This protects the search engine from manipulation.
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