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Not so long ago, if you wanted to find a place to eat, you need to search for a term like “Boston restaurants”. But, today, you can instantly find a good restaurant that is nearby if you are just looking for the term “, where should I go for dinner?”

This is because Google is sufficiently sophisticated to recognize your intention or the implications of your query. However, before 2015, you have to tap the simplest queries in the search engine to find the answers you are looking for.

So, how has Google evolved to understand the intention of their researchers and the implications so quickly? Well, on October 26, 2015, they confirmed that they have updated their algorithm with an artificial intelligence system for automatic learning called Groundbrain.

What is Google’s Rankbrain Algorithm?

Rankbrain is a central part of Google’s search algorithm. By exploiting the learning of the machine, it helps Google to understand how specific web pages concern certain concepts and serve, in turn, relevant web pages for the request of a researcher, but do not include the exact words or The expressions of the query.

In other words, Rankbrain helps Google to understand the intention of a researcher and serve them the most relevant content.

Google's RankBrain Algorithm

How Does Google’s RankBrain Work?

As explained above, Rankbrain was designed for the experience of worry, especially when it comes to understanding the intention and relationship behind seemingly complex research.

In order to better explain this feature, we decompose the way Rankbrain works jointly with Google’s algorithm as a whole:

RankBrain and Other Ranking Signals

Before RankBrain, Google uses a number of ranking signals to determine:

Relevance with search requests

Website Authority and certain pages to provide trustworthy answers

User experience so seekers will meet their needs in a fun and without friction

Some of these signals (or ranking factors) include: 

  • Crawlability
  • Quality content
  • Backlinks
  • Page speed
  • Mobile experience

While this ranking factor is still relevant, they don’t tell the whole story again. For one, they are mostly static and do not take into account semantic searches, and that’s where the seconds are different from the components of Google’s algorithms.

RankBrain and Machine Learning

Automatic learning is a form of artificial intelligence that “learns” data and improves according to experience. The advantage of machine learning is that it can analyze and connect multitudes of variables to “understand” beyond what a human analyst would be able to – if it has a sufficient amount of data.

Why is this relevant in the context of Rankbrain? Because Groundbrain is an example of learning the machine as implemented by Google.

To accurately determine the intention of a researcher, Google feeds GankBrain a massive amount of data. Then, Rankbrain analyzes and relies on how to serve the most relevant results based on some search signals, such as search history, device and location.

For example, if you type the query “Where should I go for dinner?” In Google, the search engine must first identify your location and will detect the device you are using. Then it will use these factors to interpret the intent of your request, which Google will translate into “which restaurants are currently open for dinner within walking distance of my current location?”, Help Help you serve the results the most relevant.

RankBrain and Hummingbird

Hummingbird is a version of Google’s search algorithm that extracts the meaning of the entire query rather than particular words. This component is the reason why Google can determine semantic meanings from specific queries to produce the best result.

Rankbrain feeds user signals in this aspect of the algorithm, improving Google’s ability to deduct meaning. To be more precise on the relationship between the two, Connectica makes the next analogy that: “Groundbrain is thought and hummingbird is memory.”

An example of rankbrain capabilities in this way is how the search results are similar for similar but different keywords. For example, “Bang Hairstyles” and “hairstyles with a fringe” have similar results, including keywords that are not optimized word word for the specific query.

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