Communications and Internet 

Do I need to update Yandex on Queen. Yandex algorithm - Korolev. Additional official sources

In 2015 on the market binary options The Korolev Algorithm project appeared, its website is algoritm-koroleva.com, which is an automatic trading system. As the authors state, to use it you need to have minimal knowledge about financial markets, since it is a full-fledged trading robot that concludes transactions in automatic mode. The main condition for the successful work of an advisor is the presence required capacities computer equipment.

The developer of the project, Denis Korolev and Maxim Nikitin, created a system that determines the power of the computer and, if the necessary requirements are met, the user becomes a participant in the project. After installing the program, computers are united into large segments that form a stable trend. This gives each participant in the system the opportunity to earn money. The choice of binary options is due to the availability of earning methods and significant profits.

The authors encourage project participants to become financially independent and work only for themselves. Denis Korolev and Maxim Nikitin are young specialists in the field information technologies, who, despite their youth, managed to create their own product and earn more than 2 million dollars in just 14 months.

The daily profit of a trader working with the project can reach more than $500 per day, and the owners powerful computers can count on an amount several times more. Suitable for advisor work not only Personal Computer, but also smartphones, tablets and other mobile devices.

How does the Korolev Algorithm work?

Each client who visits the site undergoes a check of his computer and receives results about possible daily profits. Next, you need to become a subscriber and register a trading account with one of the financial intermediaries. The authors include brokers WhiteOption and Ubinary as reliable companies.

The initial deposit amount may be minimal, but recommended investments start at $300. The total number of participants in the system is more than 1000 people. The success of the project is confirmed by numerous video reviews left by each participant.

The website contains a table with daily transaction statistics. Based on these data, it follows that the most profitable contracts are concluded on currency pairs, and the average profit of a participant reaches more than $200 per transaction. The program is provided free of charge, the authors earn the same as all participants. A large number of subscribers guarantees success for the entire team.

What are the reviews online?

The Korolev Algorithm project is listed as fraudulent on many Internet resources. In fact, the authors are just agents of brokers and work for referral fees from the deposits of attracted traders. Reviews from the project website are not true and are made on a paid basis. All trading statistics are “drawn” by the authors to attract gullible investors.

Yandex has launched a new ranking algorithm - “Korolev”. Now the search engine matches the meaning of the search query and the page. This is very convenient for users. However, what does the new algorithm mean for optimizers and website owners, how will promotion change and whether we should expect changes in traffic.

More than ever, the entire SEO world was waiting for the launch of a new ranking algorithm, announced on August 22, 2017. Of course, such announcements are absolutely atypical for Yandex; usually they prefer not to talk about their plans, and announce the next release of the ranking algorithm after the fact.

On August 22, 2017, Yandex launched a new version of search. It is based on search algorithm“Korolev” (since 2008, new ranking algorithms in Yandex are named after cities). Using a neural network, the algorithm compares the meaning of queries and web pages - this allows Yandex to more accurately respond to complex queries. For training new version search uses search statistics and ratings from millions of people. Thus, not only developers, but also all Yandex users contribute to the development of search.

The scope of the new algorithm practically does not affect traditional SEO areas of interest, which primarily include commercial search results. “Korolev” turned out to be a logical continuation of the “Palekh” algorithm and is designed to serve a long tail of micro-frequency requests, usually asked in natural language. The peculiarity of such queries is that the documents relevant to them may not contain many of the words included in the query. This confounds traditional ranking algorithms based on textual relevance.

A solution was found in the form of using neural networks, which are trained, among other things, on user behavior. Therefore, the new Yandex algorithm works based on a neural network. It learns from examples of user queries and selects answers based on the meaning of the text on the page. This means, in particular, that it will be much more effective at working with non-standard queries when users themselves are not sure what the name of what they want to find is called. A lot comes down to computing power here.

In general, such an approach to solving the problem of ranking the long microfrequency tail of queries is not new. Back in 2015, it became known about the technology used by the search engine. Google system to find answers to multi-word queries specified in natural language - RankBrain. This technology, also based on machine learning, allows you to recognize the most significant words in queries and analyze the context in which the search is carried out. This allows you to find relevant documents that do not contain all the query words.

In addition, the algorithm also works with pictures. It analyzes the content of the image and selects the necessary option based on it, and not just from the description in the tags or the text surrounding it.

However, the long tail of micro-frequency multi-word queries in natural language may well be of interest to “burners” of information semantics - the creators of so-called information sites “for all occasions”. In general, they already try to respond to as many requests as possible, known to them, which they manage to get with the help of various methods collecting semantics, organizing exact entry into your texts. In the same place where there will be no exact occurrences, i.e. for queries that were not sucked up by the “semantic vacuum cleaner” of the creators of information sites or for which they were unable to provide exact occurrences in the content, the domain of “Korolev” begins, which is designed to look for correspondence between queries and answers in the case when there are few intersections between them on key words. In such cases, Korolev will undoubtedly increase the requirements for the quality of content, and really interesting readable articles will benefit even more from collections of entries key phrases, diluted with water, because It is precisely such articles that may contain signals useful for the new algorithm. Well, all other SEOs can really relax - the next spanking is postponed. There are no casualties or destruction.

By launching Palekh, Yandex taught neural network convert search queries and web page titles into groups of numbers - semantic vectors.

An important property of such vectors is that they can be compared with each other: the stronger the similarity, the closer the query and header are to each other in meaning.

How is it different from Palekh?

The main difference of the new algorithm, in addition to improving the technical implementation, is the ability to recognize similar “meanings” throughout the document, and not just by the title (Title), which appears in the browser window.

How the Korolev algorithm works

Search algorithm "Korolev" compares semantic vectors search engines queries and entire web pages- and not just their headlines. This allows us to reach a new level of understanding of meaning.

As in the case of Palekh, the texts of web pages are converted into semantic vectors by a neural network. This operation requires a lot of computing resources. Therefore, Korolev calculates page vectors not in real time, but in advance, at the indexing stage.

When a person asks a query, the algorithm compares the query vector with the page vectors already known to it.

"Queen" effect

The ability to understand meaning is especially useful when processing rare and unusual queries - when people try to describe the properties of an object in their own words and expect that the search will prompt its name.


This scheme allows you to start selecting web pages that match your search query in the early stages of ranking. In "Palekh" semantic analysis- one of the final stages: only 150 documents go through it. At Korolev it is produced for 200,000 documents.

In addition, the new algorithm not only compares the text of a web page with search query, but also pays attention to other requests for which people come to this page.

This way you can establish additional semantic connections.

People teach machines

Usage machine learning, and especially neural networks, will sooner or later make it possible to teach search to operate with meanings at the human level. For a machine to understand how to solve a particular problem, you need to show it great amount examples: positive and negative. Such examples are given by Yandex users.

The neural network used by the Korolev algorithm is trained on anonymized search statistics. Statistics collection systems take into account which pages users go to for certain queries and how much time they spend there.

If a person opens a web page and hangs there for a long time, he probably found what he was looking for - that is, the page answers his request well. This is a positive example.

It is much easier to find negative examples: just take a request and any random web page. The statistics that are used to train the algorithm are anonymized

Matrixnet, which is building a ranking formula, also needs people’s help.

Cleanup

For search to grow, people must continually evaluate its performance. Once upon a time, only Yandex employees, the so-called assessors. But the more ratings, the better - so Yandex attracted everyone to this and launched the Yandex.Toloka service. Now more than a million users are registered there: they analyze the quality of search and participate in improving other Yandex services. Toloka tasks are paid - the amount that can be earned is indicated next to the task. Over the two-plus years of the service’s existence, talkers have given about two billion ratings.

Modern search is based on complex algorithms. Algorithms are invented by developers, and taught by millions of Yandex users. Any request is an anonymous signal that helps the machine understand people better. New search is a search that we do together.

Hello, dear readers of the blog site. I apologize that some posts are published over a long period of time, but I launched several more projects that suddenly rose to the TOP in 1.5 months, using my knowledge in the field of blogging (if anyone needs advice, write in a personal message). I have to be torn between projects and building a house for my family.

Today we will touch on the new Korolev algorithm from Yandex and try to compare it with its predecessors. Personally, it didn’t have much impact on my blog, except that useful and voluminous articles became even higher in the TOP. Well, let's take a closer look at everything in the article and draw the necessary conclusions after observing this algorithm.

Korolev Yandex algorithm - what it is and how it works

At the end of August 2017, a new Yandex Queen algorithm was released. The news about the update in the search engine immediately attracted interest from SEO specialists and the media.

main feature The Queen is to increase the speed of information processing and improve the quality of semantic analysis of the text.

The speed of data processing has increased several thousand times. Palekh used 150 documents to form the TOP. Now more than 200,000 articles are compared with each other. This result was achieved by optimizing the ranking protocol.

To understand the new algorithm, we need to go back a step to Palekh. His presentation was held on November 2, 2016. Statistics showed that the largest portion of search phrases were low-frequency phrases tailored to the only correct answer. This part falls on the bird's long tail.

To give the desired answer, the client must have associative thinking and self-learning skills, like a person. Neural networks are best suited for such tasks, which is why they became the basis of the new algorithm.

The main goal of "Korolev"

If a person wants to find a specific object, he begins to describe its properties; these are features of associative thinking. If we have forgotten the name of the video, then we begin to say what was contained inside: “a film about girls during the war” or “a film about a creature with a tail and wings.” In the first case, Yandex provides “And the dawns here are quiet”, in the second option we get “chimera”.

Yandex improves the quality of comparison of multi-word phrases. The program analyzes the connection between each word in a sentence and builds a unique association with multiple answer options. Just like the human brain does.

What's new?

Innovations:

  • semantic vector for all content, not just the title;
  • comparison of more than 200,000 articles when creating search results;
  • user behavior on the page is taken into account;
  • people help train the system.

Korolev analyzes not only the title, but the entire content (including photos, videos, tables, etc.) and composes a semantic vector based on it.

The main innovation was the multiple acceleration of search methods. In the past, the semantic vector was built at the moment the phrase was entered into the search bar. This method heavily loaded the servers and delayed the speed of response.

When you send a search phrase, its semantic vector is compared with the array already recorded in the database. Palekh compared about 150 options, but the new version analyzes more than 200,000 articles at a time. This increases the chance of finding the desired answer.

Yandex neural network: operating principle of the Korolev neural network + examples

The main feature of a neural network is the ability to self-learn. Work is carried out not only according to deliberate formulas, but also on the basis of previous experience and mistakes.

The human brain is a huge neural network with associative thinking, and computers try to emulate human behavior by recreating the architecture of neural networks.

Features of the neural network structure

A neural network is a set of single neurons, each of which stores or processes information. Each of the neurons is capable of receiving, processing and transmitting signals. The input data stream is gradually processed from one neuron to another and in the end the desired result is obtained.

Artificial neural networks transmit conditional weights—numbers from 0 to 1—to each other to determine how well a particular version of the incoming information corresponds necessary information. After the analysis is completed, the neuron with the highest weight is considered the most suitable to answer the question.

The diagram depicts a neural network. The first two layers do the processing. Each of the neurons contains a specific function that receives input data and, after processing, produces the necessary response. This is how semantic vectors are compared.

Semantic vectors

Computers cannot operate with words or pictures, so they use arrays of numbers to compare information with each other. Search engines must independently determine the main topic and idea of ​​the text in order to give the user what he needs.

How are vectors similar? the question asked and text, the higher the article is in search priority. Korolev uses analysis of all content:

  • tables;
  • text;
  • photo;
  • video;
  • headers;
  • quotes;
  • lists;
  • emphasis (italics, bold, etc.).

The quality of vector construction increases several times due to the conversion of more information.

To create vectors, a neural network is used, the text is passed through a sequence of neurons, and as a result, an output three-hundred-dimensional array of numbers is obtained. Subsequently, it is entered into a single database and used for comparison.

Education

The main feature of neural networks is learning ability. Unlike standard algorithms, neurons are able to remember their previous experience and self-learn from it. The computer is getting better and better at distinguishing information each time.

In the past, training was carried out by company employees, their task was to navigate through millions of requests and change issuance priorities at their discretion. Then the developers created the Yandex.Toloka application, it is a list of simple tasks. You need to go through queries and evaluate the quality of search results. For each task they pay about 0.1-1$

What content does the new search algorithm think is good?

The most suitable article for the TOP search results will be the one containing the maximum useful information for the user and corresponding to the request. Therefore, it should cover all sorts of client questions section by section.

In Korolev, user behavior on the page is taken into account as a priority. Therefore, the task of administrators is to try to retain the user and interest him. To do this, use structured headings, tables, lists, highlights, photos and videos.

New search priorities

SEO specialists, after the release, conducted a study to evaluate changes in ranking priorities. No significant changes were observed; priorities remain:

  • text structure;
  • completeness of the topic;
  • prostate reading content;
  • correspondence of headings to the semantic content of the text;
  • correct formation of the semantic core.

The main thing is to write for living people; this priority remains the most important.

Why Yandex launched a new search algorithm and how it threatens sites

Any company strives to make its products the best in the service market. In this case, Yandex's biggest rival is Google. Innovations were created for the following purposes:

  • improving the quality of search on non-standard issues;
  • attracting new investors;
  • increase in ranking productivity (more than 200,000 articles when generating results).

The main goal was to improve the quality of delivery. In addition, it was necessary to show investors that the company’s work was in full swing and their money was being used for its intended purpose. Innovations were subsequently used to create voice assistant"Alice".

Line of previous algorithms

To better understand new technologies, we need to go back to the past. In this case, we will consider the line of previous algorithms that were used by the search engine for ranking.

At first, the Internet contained only a couple of thousand sites; to find the desired article on them, it was enough to compare the keywords of the search phrase. Subsequently global network has grown exponentially, now on one topic you can find more than hundreds of thousands of similar sites with a million articles.

Therefore, it was necessary to complicate the ranking systems and began to take into account the following additional parameters:

  • number of referring materials;
  • uniqueness of content;
  • client behavior on the page.

Matrixnet

In 2009, Yandex faced a problem that articles increasingly did not answer user questions. To fix this error, it was necessary to teach the server to make decisions independently and learn on its own.

A complex mathematical formula with many parameters was invented to determine whether text matches a search phrase.

But the following problems remained:

  • search depends on words;
  • auxiliary materials (photos, videos, quotes, etc.) are not taken into account.

The main problem was that it was not always possible to fully describe the meaning of the article in one title. Quite often the article does not contain specific keywords, but at the same time it fully reveals the topic and gives a detailed answer to the user’s question.

Palekh algorithm

In 2016, the ranking system used computer model neural networks. The main feature of this approach is that the computer is now able to remember its mistakes and learn from its own experience.

In the same year, semantic vectors were introduced. The title of the article was passed through a neural network and decomposed into many vectors. Now computers compared not words from the search, but multidimensional arrays of numbers and vectors. We managed to move away from direct dependence on the number of certain words in a phrase, and give priority to the semantic content.

One of the shortcomings remains the problem of low speed. To create the search results, only 200 of the most relevant articles were compared. Therefore, it was difficult for the system to find multi-word semantic phrases like “a film about a girl, a spy who runs away and goes to school.”

Yandex Korolev algorithm

In the latest innovation, we primarily optimized the neural network and improved the productivity of text processing. Now the vectors are compared in advance in offline mode, thanks to this it has been possible to increase the effectiveness of the search.

Yandex independently collects statistics on user interest and uses them to create pre-prepared search results.

Thanks to optimization, a semantic vector is compiled not only for headings, but for the entire content. It is possible to find a maximum of semantic connections between words.

Threats to websites

In general, no dangers have been created for the sites and the conversion statistics do not change much. First of all, the innovations will affect information blogs, forums and sites with films.

Websites that do not meet the interests of the user may fall from their leading positions. For example, the title is “homemade apple juice,” but the article discusses methods of growing trees, pancakes with jam, and a completely different text.

Don't forget to repost and subscribe to the blog newsletter. All the best.

All the best, Galiuin Ruslan.

That it analyzes not only the page title, but also the entire content of the page before showing the user the results of the query.

Only a week has passed, it’s too early to draw conclusions, but, nevertheless, we asked representatives of the SEO community about their expectations from the new algorithm and changes in the work of SEO masters.

Kirill Nikolaev, technical director of the WEBLAB studio:

Immediately after Palekh’s release, the further vector of development of the new algorithm was clear. Just before the announcement, there were heated debates about what would happen (the most popular option: the first page - online direct), but deep down we all knew what to expect. Yandex has increased the numbers, and this is good news. If earlier there were 150 documents in the RAM for popular requests, now their number has exceeded 200,000, with the help of special agents from Toloka very diligently. To be in the top of these 200,000, you need to have good behavioral (which is logical) and similar semantics, which makes you think that the days of content theft are returning with renewed vigor. And the times of long sheets in online store catalogs also tell us “Hello, Andrey!”

However, as far as I know, the matrix for popular queries is stored in the database for the duration of the next update, while the matrix for low-frequency/low-frequency queries is generated on the fly.

You shouldn’t expect such colossal changes to occur in the search results as after the launch of Snezhinsk in 2009, so we’ll leave HF/MF queries alone and talk about more mundane things.

Personally, what interested me most was this statement:

"...the new algorithm not only compares the text of a web page with the search query, but also pays attention to other queries that bring people to that page." For the industry this could mean two things:

1. Good: an even more careful selection of semantics, even more diligent clustering will bear fruit. Text factors become leaders in importance and relevance; work becomes more difficult, but the result gets better.

2. Bad: the maximum amount of text for indexing one document is 32 thousand characters. So I can expect that now, under the catalog description in some store, you can read short stories about water delivery, in which there is a beginning, development, climax, denouement and epilogue. This makes sense because it is the simplest method to extend the semantics. Of course, I’m exaggerating, because it’s clear that the TOP is formed somewhat differently, but I very much suspect that our “content kings” will perceive it that way.

Well, in addition, I’ll just throw out a thought: what if you don’t rewrite texts and don’t waste time on complex things, but try to competently generate traffic for low-frequency/low-frequency requests? An interesting field for experimentation.

Yandex is developing and we are growing with it.

This is cool. I would like to imagine what it will be like in 5-10-15 years.

If you want to be a good SEO, learn hardware.

This is wonderful. I look forward to new courses from BM on the topic “Semantic vectors for business.” But seriously, the profession is becoming more difficult, which is good news. I hope that very soon the individuals who do link runs on databases compiled according to “the latest research by Dmitry Shakhov (a well-known SEO practitioner)” will disappear.

Even deeper dive into text factors

More than the courses from BM, I am only waiting for the launch of neural network selection of semantics and clustering from Chekushin. And a course from Devaki, of course.

Alaev Alexander, director of the web studio "Alaich and Co":

Yandex rolled out a new algorithm with great noise and excitement. I thought that my life as an optimizer would change irrevocably, but... I hasten to reassure everyone - nothing has changed!

The new Yandex algorithm is aimed at improving results for “long” information queries (such queries are typical for voice search). Yandex with its neural network began to understand and search by meaning, that is, not only by keywords, but also by what they mean. A continuation of “Palekh,” which searched for meanings only by document headings, “Korolyov” searches for meanings throughout the entire document. But you already know this if you watched the presentation or read publications based on it.

Let's talk about how this will affect the lives of webmasters and SEOs. I repeat - no way. The new algorithm will not affect commercial requests in any way. If a person wants to buy or order something, then he certainly knows what it is. And even if he doesn’t know, then anyway, meaning a laptop, he won’t ask “compact desktop computer consisting of two halves,” and first find out what this thing is called.

Korolev should respond positively to quality information pages. But generated, synonymized and similar texts should lose traffic, if they had any at all. I think that rewriting and copywriting written without immersion in the topic can also suffer, giving way to higher-quality texts, albeit without using the right keywords in the right quantities.

As you can see, I didn’t say anything new, but sites, as before, need to be made of high quality for people!

Alexander Ozhigbesov, project managerozhgibesov.net:

By introducing new algorithms into search, Yandex is taking small but confident steps towards understanding the meaning of the request and finding the same meaningful answer - this is how Palekh and Korolev present to us in the company. In fact, this is a copy of the Google algorithm - Hummingbird, which was launched in 13, however, it is worth realistically assessing the company’s available power. Yandex cannot provide answers to all unique requests tomorrow and rebuild the search results. The company’s mistake is that they presented the algorithm as something new, although Google did it earlier and without such “Russian” pathos, but this is certainly a strong achievement and I am sure that in the future neural networks will be able to show us the ideal search, if At that time, the domestic search engine will not make the entire first page of search results paid. But this is also not particularly scary, let’s redistribute priorities and scale the semantics for the context.

What changes await optimizers and will there be any at all?

Special changes after Palekh and Korolev this moment not in popular e-commerce. While Yandex is testing its algorithms on the MNC, one cannot expect drastic changes in the companies’ regulations and methods. Here SEOs don’t have much to worry about; long and unique queries are present only in information topics and complex commercial services. But Palekh and Korolev’s task is not to replace the current ranking parameters; they are trying to give meaningful answers to complex queries, so selecting queries like “A red dress with panties visible through” is not at all necessary. My personal opinion is that I have been and will continue to strive for high-quality writing and structuring of content, subsequent analytics and additional optimization, so the algorithm will not cause damage to serious commercial projects, as was the case, for example, with Minusinsk.

After the new Yandex algorithm “Korolev” was presented on August 22, many SEO specialists had concerns about a possible drop in site traffic. On the other hand, if search traffic for some sites drops, then others will see an increase.

But let's figure it out together whether everything is so scary.

By the way, based on Yandex Metrica data, we see that many users enter the query “How to enable Yandex Korolev?” and visit our article. In fact no need to turn anything on, This new system ranking already works for everyone automatically.

What is the Yandex Korolev algorithm?

In essence, “Korolyov” is a pumped-up version Palekh, whose work is based on recognizing meaning using a neural network. If Palekh could only recognize headings and processed up to 150 documents, then Korolev evaluates all the text on the page and can process over 200 thousand pages.

The official blog also states that the changes concern not only the application neural networks for search not in words, but in meaning, but also in the very architecture of the search results index.

How the Korolev algorithm works

According to the creators of the algorithm, it will allow us to move to a completely different level of understanding the meaning of user requests. Now the entire site page will be evaluated with semantic vector search queries.

When a user enters a query, the search engine needs to understand which page and which title matches it the most. To do this, the request and title are converted into a multiplication of vectors, and the larger the result, the greater the relevance of the page to the request. At the moment of generating a response to a request, the text of headers and requests is instantly converted into vectors and compared. This makes it possible to identify possible connections in meaning, but at the same time requires enormous computing power. This is how Palekh works.

What has been done to improve its performance? The Korolev algorithm performs a preliminary calculation of vectors, which allows you not to load the server during the request itself, but to take a ready-made result. In addition, as mentioned above, Korolev converts not only the page title, but also its entire content into a semantic vector.

But you should understand that “Korolev” is not a revolutionary website ranking algorithm that will turn Yandex search results upside down. This is a complex of already implemented solutions, improved with the help of neural networks and user experience.

What awaits the industry after the release of “Korolev”?

At the moment, there are no global changes in search results and they are unlikely to occur in the near future. For example, in the search there are still many pages that answer the synonymous queries “kitchen interior” and “kitchen design”, using different pages, where there is a direct occurrence of the key.

Real changes will come when there is no need to collect a database for one “big” request low frequency queries, write text under them of 10,000 characters.