About Google's New Algorithm

So Rankbrain is part of Google's Hummingbird Algorithm?

This is our understanding. Hummingbird is the whole search algorithm, just like a car has a whole engine. The engine is composed of many parts, such as: oil filter, fuel pump, cooling system and so on. Likewise, Hummingbird contains many parts, and Rankbrain is the newest part.

We learned that Rankbrain is part of the whole Hummingbird algorithm, because the Bloomberg article already stated that Rankbrain is not responsible for handling all searches, because only the whole algorithm can handle it.

Hummingbird also includes other algorithm names that are familiar in the SEO world, such as: Panda, Penguin, Payday to fight spam, Pigeon to improve local search, Top Heavy to downgrade pages with too many ads, and search engine friendly to to improve mobile-friendly pages, and Pirate to combat copyright infringement.

I think Google's algorithm should be called "PageRank"

PageRank is part of the whole Hummingbird algorithm, and it has a special way of giving weight to links from other pages.

PageRank is special because in 1998, when the search engine first started, PageRank was the first time Google gave a name to some of its algorithms.

What are these "signals" that Google uses to rank?

Signals are used by Google to help determine page rankings. For example, he reads the text in the web page, so the text is a signal. If some words are in bold, that might be another sign. These calculations are usually part of PageRank and are used to give pages a PageRank score, which is then used as a signal. If a page is marked as mobile friendly, that's another sign of being registered.

All signals are processed and analyzed by various parts of the Hummingbird algorithm to determine which pages should ultimately be displayed in various searches.

How many signals are there?

Google talks about over 200 primary ranking signals, and possibly over 10,000 variables or secondary signals. He usually says there are "hundreds" of factors, as in yesterday's Bloomberg article.

If you want a more intuitive guide to ranking signals, check out our " Periodic Table of SEO Success Factors " article which

we think is a really good guide to help your pages get listed on search engines like Google Get a ranking.

And is Rankbrain the third most important signal?

That's right. Surprisingly, the new system has become what Google says is the third most important factor in ranking pages. According to the Bloomberg article:

Corrado said: Rankbrain is one of the "hundreds" of ranking signals in the algorithm that determines how it appears and ranks on Google's search results pages. He said: "It has already been launched in the past few months, and Rankbrain has become a third ranking factor and contributes to search.

What are the first and second most important signals?

Google doesn't tell us what the first and second most important signals are. We have already asked. And asked twice...

Google doesn't tell us the first two signals, which is really annoying and confusing. Google wants to use PR to break through the development of machine learning.

But if you really want to assess the breakthrough and the impact of Rankbrain after that, it helps to understand other important factors that Google is using now. That's why Google should explain.

By the way, my personal guess is that links will still be the most important signal, and Google will aggregate those links.

 This is also a very old system. 

As for the second most important signal, I'm guessing it will be "text", which includes the text on the page and how Google understands the text people type into the search box in the Rankbrain analysis.

What exactly can Rankbrain do?

From Google's letter, I speculate that Rankbrain is primarily used to parse people's submissions for searches that may not contain precise text.

Can't find a page without a precise query Google?

No, it's been a long time since Google could find pages without exact words. For example, many, many years ago, if you typed in something like "a shoe," Google might not be able to find a page about "a pair of shoes," because technically those were two different things. word. But "stemming" can make Google a little smarter, in order to understand that a pair of shoes is a deformation of a shoe, just as "running" is a deformation of "running".

Google also has the wisdom of synonyms, so if you search for "sneakers," Google may know you're looking for "jogging shoes." To understand the tech company "apple" vs the fruit "apple", even get conceptual wisdom.

What is a knowledge graph?

The Knowledge Graph, developed in 2012, is Google's way of making connections between words smarter. More importantly, he learned to search for "things, not strings," as Google once described it.

A string means a search for a string of letters, like a page with the spelling "Obama". Conversely, things mean that Google understands that when someone searches for "Obama," they may be referring to real-life people connected to other people, places, and things by US President Barack Obama.

A knowledge graph is a database of things in the world and the connections between those things. That's why when you search for "when was Obama's wife born" instead of his name, you get answers like Michelle Obama:

How does Rankbrain help optimize searches?

The methods Google uses to redefine search generally go back to the people who work somewhere, as are the stemming lists that have been created or the synonym lists or databases that make connections between things. Of course, some are automated. But most of them depend on manpower.

The problem is that Google processes 3 billion searches a day . In 2007, Google said that 20 to 25 percent of queries were seen before. In 2013, that number dropped to 15 percent , which again was used as a Bloomberg article and Google confirmed it again. But 15 percent of those 3 billion that haven't been searched yet is still a huge number -- 450 million a day.

Those can also be more complex, multi-group word queries, and can also do "long tail word" queries. Rankbrain is designed to help those queries and translations more efficiently, behind the method is to find the best pages for searchers.
As Google puts it, he can understand compound queries that seem unrelated on the surface, and their similarity to each other. And this study can learn more about compound queries, and whether they are related to other topics. Most importantly, Google tells us that it can connect query phrases with search results that searchers will love.

Google doesn't provide examples of query phrases or details on how Rankbrain guesses the best page. The latter may be because if he can translate ambiguous queries into explicit ones, he can bring better results.

How about an example?

While Google doesn't give examples of query phrases, and Bloomberg articles have a single example for searches, Rankbrain may help. Here's:

Who is the highest consumer in the food chain?
To a layman like me, "consumer" sounds like someone who buys something. However, he is also a scientific term for a species that consumes food. There are multiple levels of consumers in the food chain. What about the top consumer? He calls it "the carnivore."

Enter that word into Google, and Google provides a good answer, although the query itself is still very strange:

Now think about the similarity of results for a query like "top of the food chain", as follows:

Imagine that Rankbrain takes that original Long and complex searches link to the shorter one, which is probably more common practice. He was able to understand that they were very similar.

 The result is that by getting answers from the more common queries to improve the less common ones, Google can make everything it knows work. 

I want to stress that I didn't know Google joined the two queries. I only know that Google gave the first example. This is a brief description of how Rankbrain may be used to link uncommon and common queries to improve search results.