RankBrain is a part of Google’s algorithm that is used in processing search queries and determining SERPs. However, RankBrain is not just any piece of the algorithm, but a subsystem that contains the first beginnings of artificial intelligence and can therefore learn from the search queries it receives. This helps the search engine giant to better answer unknown or complex queries.
RankBrain was introduced by Google in 2015 and after a short time it was already an important factor in the selection of search results. Given the fact that there are around 200 “signals” for Google to decide the ranking of a page, this is quite astonishing – but only at first glance.
This is because RankBrain can especially show its strengths when answering search queries that have not yet been asked. Of course, one might think that with several million queries per day, every possible variant must have landed in Google’s search box at some point. However, that would be a fallacy, because according to Google, around fifteen percent of all incoming search queries have never been made in this form. This in turn can lead to problems with “normal” search algorithms. How should the query be classified? Does it perhaps contain colloquial or ambiguous terms? How are these to be evaluated? These algorithms can hardly deal with these questions and, in the worst case, deliver completely inappropriate search results.
RankBrain, on the other hand, can draw conclusions and thus correctly classify even unknown words and phrases with a high probability. In addition, it is capable of learning, it stores new terms and can include them in later search queries – among other things, also to recognize the meaning of other, unknown terms. This machine learning makes RankBrain very special among Google’s algorithms, because it is not a static system that always evaluates data according to the same scheme. It changes and expands with each unknown query. This makes it very powerful – and quite problematic from an SEO point of view, as there is no longer a fixed set of rules that is used to rank pages.
How does RankBrain work in detail?
Similar to other Google updates, the search engine giant is of course mostly keeping quiet about how RankBrain works. By its standards, however, Google is unusually open about RankBrain: In an interview with Bloomberg, Senior Research Scientist Greg Corrado reveals how RankBrain works.
Apparently, the algorithm is able to break down the input in the search field into so-called “word vectors” using a mathematical system. In this way, words and the relationships between them become measurable and regularities can be derived. These in turn serve as the basis for evaluation when RankBrain encounters words or word combinations that it does not know. If a term is not found in the database, the algorithm can “guess” the meaning based on the relationships and regularities determined so far. To do this, Google relies on a conversational model for processing queries in RankBrain, which considers the entire query as a single unit instead of treating words individually. The model is supported by a so-called “sequence to sequence framework” that uses previous sequences (queries) to infer the next sequence.
So in RankBrain, machine learning and artificial intelligence combine to create a system that gets better and more reliable with use over time. Of course, this required considerable effort in advance, because Google first had to create a broad database for RankBrain. Without this, the system would have no basis on which to process new search queries. Apparently, however, this basis was already quite good at the start of RankBrain. During an initial test phase, RankBrain’s results were compared with those of normal users. The human users were able to correctly assign around seventy percent of all search queries. The machine, on the other hand, came up with eighty percent – and has since become even better as a result of the incoming search queries. It is therefore hardly surprising that Google gives RankBrain such high importance in the compilation of SERPs.
How “intelligent” is RankBrain really?
Although Google itself repeatedly talks about artificial intelligence in connection with RankBrain, the term is often equated with machine learning, which is not correct in terms of content. It is undisputed that RankBrain uses machine learning – but how much artificial intelligence is in the system?
It is difficult to find an answer to this question, because there is neither a universally valid definition of intelligence, nor can the term artificial intelligence be precisely delimited. However, intelligence is usually understood as the ability to solve complex problems or gain deeper insights through logical thinking.
Of course, RankBrain is still very far away from this, because it evaluates the incoming search queries, but cannot understand or interpret them.
Nevertheless, it is possible to speak of artificial intelligence in RankBrain – at least to a certain extent – because the system is able to evaluate input according to a certain classification and to classify it on the basis of previous “experience”. It will certainly be many years before this becomes real artificial intelligence, but it is a first step – and a very important one for Google. For some time now, the search engine giant has not only been concentrating on its core competence, but is increasingly focusing on the development of intelligent and self-learning systems.
RankBrain is therefore not only a way for Google to improve the performance of its search engine, but also a research project that the company can build on in the development of further systems.
RankBrain and the importance for search engine optimization
As already mentioned, RankBrain is a crucial factor in the compilation of search results – according to Google even the third most important. Of course, this makes SEO experts sit up and take notice.
However, the question is whether one can influence a system that is designed for complex and previously unknown search queries in such a way that one’s own page moves up in the search results. Classic SEO elements such as keywords, text structure and backlinks ultimately come to nothing here. Instead, RankBrain focuses on semantic connections and content. With simple “trickery” you will hardly achieve anything with RankBrain. Rather, the system provides an outlook on what will be increasingly in focus for websites in the future: structured content with high thematic relevance. Because only if the “word vectors” of the page match those of the query can the page make it to the top of the SERPs.
How exactly this can be achieved is still an open question, but the growing importance of content has already become apparent in previous Google updates, and this trend will certainly be reinforced by the successful use of RankBrain.
With RankBrain, Google has for the first time integrated a system into the search algorithm that can learn independently and thus improve with each use. The great success of this system apparently came as a bit of a surprise even to the company itself, but should ensure that Google continues to increase its efforts in the field of artificial intelligence.
RankBrain advanced to become the third most important factor in ranking shortly after its launch, however, the high complexity of the system makes it difficult to use it effectively for search engine optimization. However, a clear semantic structure and high thematic relevance should in all likelihood help to achieve a good ranking via RankBrain as well. Since content is becoming increasingly important for Google when evaluating pages anyway, this is a promising approach in any case.
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