RankBrain
RankBrain is a machine learning-based search ranking system that Google introduced in 2015. It learns the relationships between words and concepts to interpret the meaning of unfamiliar queries and connect them to the most relevant results. It was Google's first application of machine learning to search ranking.
- RankBrain is a machine learning-based search ranking system Google introduced in 2015, marking the company's first use of machine learning in search ranking.
- Its core function is interpreting the meaning of never-before-seen queries, converting words into vectors (embeddings) and matching them to known queries and concepts with similar meaning.
- In 2015, Google named RankBrain its third most important ranking factor, behind content and links.
- The SEO takeaway is that search intent and conceptual relevance matter more than exact keyword matching.
- Alongside BERT and Hummingbird, it belongs to the family of meaning-understanding systems, but RankBrain is distinct as a machine learning ranking system.
Overview
RankBrain is a machine learning-based search ranking system that Google introduced in 2015. Google officially confirmed it on October 26, 2015, the same day Bloomberg first reported on it. The rollout itself happened in the spring of 2015, with the announcement following later. Google's official ranking systems guide describes RankBrain as an AI system that helps the search engine understand how words relate to concepts. In other words, even when the exact words used in a query do not appear verbatim in a piece of content, RankBrain can recognize that the content is associated with related words and concepts and return it as a relevant result.
RankBrain's most important purpose is handling queries Google has never seen before. A substantial share of the queries that come in each day use novel phrasing, and RankBrain connects these unfamiliar queries to known ones with similar meaning to predict the most suitable results. This is widely regarded as the first application of machine learning to search ranking.
How It Works
RankBrain processes queries by converting words into word vectors (distributed representations, or embeddings). Linguistically similar queries sit close to one another in this vector space, and when RankBrain encounters an unfamiliar word or phrase, it infers words and phrases with similar meaning to filter the results. This meaning-based matching lets it group queries that carry the same intent under the same concept, even when their surface-level keywords differ.
SEO Implications
The arrival of RankBrain exposed the limits of an SEO strategy centered on exact keyword matching. Because Google groups and understands the many different ways of expressing the same intent at a conceptual level, content that thoroughly addresses search intent and topical relevance fares better than content that repeatedly inserts a single keyword. In 2015, Google described RankBrain as the third most important factor among roughly 200 ranking signals, behind content and links.
Evidence and Distinctions
RankBrain belongs to the same family as BERT and Hummingbird for understanding query meaning, but their roles are not identical. Per Google's guide, RankBrain is a machine learning ranking system that understands the relationship between words and concepts; BERT (introduced in 2019) is a system that interprets how the combination and order of words shift meaning and intent; and neural matching is a separate system that connects the conceptual representations of queries and pages. The three work in a complementary way.