What is Rankbrain?
Introduction:
Today we are talking about Rankbrain, what it is, how does it work. We have compiled the following article for your review.
Rankbrain, What Is It?
[1]DATE GOOGLE CONFIRMED EXISTENCE OF RANKBRAIN: OCTOBER 26TH, 2015
RankBrain is a component of Google’s core algorithm which 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 question. 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 helpful search results, Google first “feeds” its data from various sources. The algorithm then takes it from there, calculating and teaching itself over time to match a variety of signals to multiple results and to order search engine rankings based on these calculations.
Understanding RankBrain
To 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 historical 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. For example, imagine that the Winter Games in Sochi, Russia, just concluded last month. The official Sochi Olympics website had made 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.
It’s within this complicated but common situation that the capacity of RankBrain emerges as essential. It’s only by being able to calculate results based on patterns the machine learning algorithm mathematically. In searcher behaviour, Google can determine, for example, the majority of people looking up “Olympics location” want to know where the 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 the majority of 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 investigation 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 taken into account to interpret intent and deliver the results most likely to satisfy searchers.
[2]Why Did Google Introduce RankBrain?
RankBrain was initially rolled out to satisfy one simple but significant problem.
Google had not seen 15% of queries used, and as such, had no context for them, nor past analytics to determine if their results were good or not at satisfying the user’s intent.
Enter RankBrain.
This system would look at the things instead of the strings.
RankBrain would also consider environmental contexts (e.g., searcher location) and extrapolate meaning where there had been done.
This could be a simple understanding that word order may function in the search process and not the intent.
How Does RankBrain Work?
Unsurprisingly, Google has never outlined how RankBrain functions specifically.
Nonetheless, we can make some educated guesses about what’s going on behind the scenes.
Common Entities
As outlined above, one of the core mechanisms they will use is entity recognition.
If they understand that a query contains the same entities as another, that would indicate that the result sets may be identical, highly similar, or at least drawn from the same shortlist of URLs.
[3]How RankBrain Understands Any Keyword That You Search For
A few years ago, Google had a problem:
- 15% of the keywords that people typed into Google were never seen before.
- 15% may not seem like a lot. But when you process billions of searches per day, that amounted to 450 million keywords that stumped Google every day.
- Before RankBrain, Google would scan pages to see if they contained the exact keyword someone searched for.
But because these keywords were brand new, Google had no clue what the searcher wanted. So they guessed.
For example, let’s say you searched for “the grey console developed by Sony.” Google would look for pages that contained the terms “grey,” “console,” “developed,” and “Sony.”
Today, RankBrain understands what you’re asking. And it provides a 100% accurate set of results:
What changed?
Before, Google would try to match the words in your search query to terms on a page.
Today, RankBrain tries to figure out what you mean. You know, like a human would.
How? By matching never-before-seen keywords to keywords that Google HAS seen before.
For example, Google RankBrain may have noticed that many people search for “grey console developed by Nintendo.”
And they’ve learned that people who search for “grey console developed by Nintendo” want to see a set of results about gaming consoles.
So when someone searches for “the grey console developed by Sony,” RankBrain brings up similar results to the keyword it already knows (“grey console developed by Nintendo”).
Conclusion:
We hope you enjoyed learning about Rankbrain and how Google uses it to prepare your search results.
Article compiled by hughesagency.ca
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