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for that query. In addition to better understanding the concepts of a web page, Rank rain also helps to better understand the association between several queries, such as: "Where is the Eiffel tower?" Followed by: "How tall is he?" How does Rank rain learn? Examples of Rank rain in action Essentially, RankBrain can use sets of "training" data created by humans to help establish a baseline, and then can apply machine learning to determine the best search results based on various factors over time. Google has confirmed in the Bloomberg article and in this Search Engine Land article that they periodically update the system giving it new data to better reason with new concepts. At SMX West 2016, some presenters shared examples of RankBrain in action. A study showed how RankBrain better interpreted relationships between words. This could include the use of company employee list stop words in a search query (“the”, “without”, etc.) — words that were historically ignored by Google but are sometimes critically important to understanding the intent behind a request. For example, take the TV series “The Office”. This is an example of research that would be taken out of context without the all-important “the”. Here's another sample query from an interview with Googler Gary Illyes: "Can you score 100% on Super Mario without taking the step?" Ignoring "without" would potentially return search results about getting a 100 percent score on Super Mario with a tour...so the opposite of the results someone was trying to get. There are other theories about how RankBrain might use data to figure out what the best results are for a search query. It's possible that searchers' engagement with search results could be

queries. At the heart of Rank rain is a goal to better interpret the user intent behind search queries to surface the most relevant search results. This is a long-standing goal of Google search. Mobile: a main driver of Rank Brain's existence Mobile has taken the need for Rank rain even further. Mobile search behavior has been a game changer, especially when it comes to voice search, which many mobile users are taking advantage of. As you may know, queries tend to be much more conversational using voice search rather than typing. Rank rain handles long-tail queries that are common in voice search today well, although there are also many long-tail searches typed into a search bar. I believe Rank rain is preparing for a world where voice search will increasingly become the norm. Remember that company employee list voice search is already on the rise. In a presentation by Mary Meeker on the popular 2016 Internet Trends Report, she reported that voice search has grown 7x since 2010. search-voice-queries-kpcb-internet-trends And it's not just voice search from mobile devices. Now we have to consider things like voice assistants like Google Home, where it remains to be seen how device responses will pull web results. This is what RankBrain does Rank rain was designed to better analyze the language of websites in Google's index and then apply that analysis to a search query. By better understanding the search query, it can better match users with websites and pages. The goal is to better understand the content meaning and intent behind a search query. Once RankBrain understands the intent better, it can then likely apply the appropriate Google algorithm signals that deserve the most weight