What is RankBrain?
RankBrain – A core component of Google’s algorithm that utilizes machine learning to evaluate search results and related queries. It is believed that RankBrain uses an interpretation model that can test a variety of potential factors and determine the intent of the search.
RankBrain is a component of Google’s core algorithm that uses machine learning (the ability of machines to teach themselves from data inputs) to determine the most relevant results to search engine queries. Pre-RankBrain, Google utilized its basic algorithm to determine which results to show for a given query. Post-RankBrain, it is believed that the query now goes through an interpretation model that can apply possible factors like the location of the searcher, personalization, and the words of the query to determine the searcher’s true intent. By discerning this true intent, Google can deliver more relevant results.
The machine learning aspect of RankBrain is what sets it apart from other updates. To “teach” the RankBrain algorithm to produce useful search results, Google first “feeds” its data from a variety of sources. The algorithm then takes it from there, calculating and teaching itself over time to match a variety of signals to a variety of results and to order search engine rankings based on these calculations.
To clearly conceptualize RankBrain, it can help to put yourself in Google’s shoes, trying to understand the intent of a search engine query like “Olympics location.”
What is the true intent of this search? Does the searcher want to know about the Summer or Winter Olympic Games? Are they referring to an Olympics that just concluded or one that will take place four years from now? Is the searcher attending the Olympics right now, sitting in a hotel and looking for directions to the venue for the opening ceremonies? Could they even be looking for historic information about the location of the very first Olympics in ancient Greece?
Now, imagine that in trying to answer this query, all you have is simplistic algorithm signals like the quality of content or the number of links a piece of content has earned to rank results for this searcher. Imagine that the Winter Games in Sochi, Russia just concluded last month, and the official Sochi Olympics website has earned millions of links for its content about this past event. If your algorithm is simplistic, it may only show results about the Sochi Games, because they have earned the most links… even if the searcher was hoping to learn the location of the next Winter Olympics in Pyeongchang, South Korea.
How It Works
It’s within this complicated but common situation that the capacity of RankBrain emerges as essential. It’s only by being able to mathematically calculate results based on patterns the machine learning algorithm has “noticed” in searcher behavior that Google can determine that, for example, the majority of people looking up “Olympics location” want to know where the very next Games (be they Summer or Winter) will be held. So, in this case, a Google answer box with the upcoming Games’ location in it will serve most searchers’ needs.
While that answer box may address the intent behind most “Olympics location” searches, there are notable exceptions Google must address. For instance, if the search is being performed by a user within an Olympic city (like Pyeongchang) the week of the games, Google might instead provide driving directions to the pavilion where the opening ceremonies will be held. In other words, signals like user location and content freshness must be considered to interpret intent and deliver the results most likely to satisfy searchers. Refer: Moz
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