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

What is the Yandex Korolev algorithm?

In essence, “Korolyov” is a pumped-up version Palekha, 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 allows us to identify possible connections meaningful, 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.

To order Yandex.Taxi in Korolev, leave a request on the official website, call the dispatcher number or use the phone application.

When ordering online via the Internet, fill in the “From” and “Where” fields, select the appropriate fare, the system will automatically calculate the cost of the trip. Within 3 minutes you will receive an SMS notification with information about the car and driver contacts.

If you plan to call a taxi by phone, then when you call, tell the dispatcher your route.

Phone number for ordering

Tariffs

In the city of Korolev all tariffs are “Economy”, “Comfort”, “Comfort+”, “Business”, “Minivan”, “Children’s”.

Economy

Comfort

Comfort+

Drivers with high ratings. Cars with a spacious and quiet interior.

Business

Luxury cars are manually checked and drivers are strictly selected.

Minivan

For traveling with six people or transporting a snowboard, skis, or bicycle.

Children's

Travel with children in a comfortable car with child seats.

• Reliable chairs CYBEX Aura-Fix and analogues
• Two child seats at once: a seat and a booster or two boosters
• Drivers prepared for traveling with children

Transfers

From the airport

To the airport

Additional services

Child seat - no more than 100 RUR

Transportation of animals - no more than 100 RUR

Air conditioning - no more than 0 R

A car with a yellow number - no more than 0 R

Non-smoking salon - no more than 0 RUR

Receipt - no more than 0 R

Booster - no more than 100 R

Surcharge for ordering a taxi by phone - no more than 1%

Promo code for discount

Install the official Yandex taxi app and save. Discount on your first trip when paying by card.

RUB 100 discount when paying with Google Pay

Work at Yandex Taxi Korolev

Learn more about how to get a job in Yandex taxi using your personal car or a company car (requirements, working conditions and connections, driver reviews).

Official partners in the city of Korolev

• IT'S ME Taxi TransInform LLC, 127106, Moscow, Altufevskoe highway 11, bldg. 2, apt. 137, OGRN: 1177746022904
• CBZT taxi LLC "BIOS" 129128, Moscow, st. Malakhitovaya, 27 B, room. 1A, room 5, OGRN: 1187746029580
• Mobidik Taxi LLC "EVO" 141075, Moscow region, Korolev, Kosmonavtov Avenue, building 14, apt. 279
• BOUQUET495 LLC "CBZT" 129128, Moscow, st. Malakhitovaya, 27B, fl. 2, room IA, com. 28, OGRN: 5177746111615
• MOSTAXILLC "Dispatch center "Taxi", 115172, Moscow, Goncharnaya embankment, 9/16, building 1, office 3, OGRN: 5147746337349
• Taxi 2412 LLC "Service 2412", 121059, Moscow, st. Kyiv, 14, OGRN: 5147746278169
• iCar Taxi LLC "AGERA" 117420, Moscow, st. Nametkina, 10B, building 2B/N, floor 1, room. 3, OGRN: 1167746059436
• RusTaxi LLC "RusTaxi" 109388, Moscow, st. Guryanova, 31, apt. 59, OGRN: 5147746255432
• LoyalTaxi SOFKAR LLC, 117545, Moscow, 1st Dorozhny passage, 5A, building 2, OGRN: 1127746359124
• Center Motor Service LLC "Center Motor Service", 109052, Moscow, st. Nizhegorodskaya, 104/3
• Victory Pobeda LLC, 129226, Moscow, st. Dokukina, 7, bldg. 1, room 3, OGRN: 1157746540621
• Taxi TK Gross TK Gross LLC, 115477, Moscow, Proletarsky Prospekt, 14/49, bldg. 1, room 16 N, OGRN: 1157746760192

A complete list of Yandex partner taxi companies can be found.

Don't forget to leave your feedback about your trip and the service. Thank you!

On August 22, 2017, Yandex officially announced the launch of a new search algorithm “Korolev” (named after the city, like most previous search algorithms). It is based on a mechanism for recognizing complex queries, which operates on the principle of a self-learning neural network. This means that Yandex must identify documents that are relevant in meaning, even if they do not contain the words from the request.

How is it different from Palekh?

Back in November 2016, Yandex launched the predecessor of “Korolev” - the “Palekh” search algorithm. The main difference of the new algorithm, in addition to improving the technical implementation, is the ability to recognize similar “meanings” throughout the entire document, and not just the title that appears in the browser window.

Why was the Korolev algorithm implemented?

Yandex has long been thinking about the problem of identifying relevant documents from a large pool of low-frequency queries that are asked in not entirely natural language. This is a large list of queries like:
— [in which picture the clock melts]
- [where cologne was invented]
— [in which movie does the writer go crazy in a hotel]

The main problem is that the matching documents might not contain the words from the request. In order to solve it and show more suitable results, it was conceived to create the “Korolev” algorithm - a self-learning neural network. As Yandex itself assures, a neural network based on machine learning will improve in understanding the “meanings” that a person implies when entering queries.

How does this algorithm work in practice?

The approach described by Yandex sounds, of course, good, but it is much more interesting to look at the specific results in the search results.

First, let's take a request that Yandex itself advertised:
[picture of the sky swirling]

In the object responses on the right, Yandex correctly determined what we meant by our request. He also indicated the correct answers in Yandex.Images. The rest of the output consists of news about the new algorithm. It becomes obvious: in this situation, Yandex uses traditional methods for determining relevance and the Korolev algorithm does not work for issuing results.

Let's try it differently and ask the following query:
[where the first parliament appeared]

In this case, you can see an interesting result. The value “England” appeared in the object responses. In the search results itself there are different sites that contain words from the query.

The algorithm in object answers works if we want to know:
- Where did the word “parliament” appear?
— where the first representative and legislative body, called “parliament,” appeared.

The algorithm doesn't work:
- if we want to know where the first legislative body appeared.

It is generally accepted that the first parliament appeared in Iceland, but it was called not “parliament”, but “althing”. In the search results (in the screenshot above) you can see the correct answer to our request. It appeared only because the title of the article contains words from the request.

It's important to understand:
A search engine can only understand a query if each word has one clear meaning.

If a word has multiple meanings, as in our case "parliament", problems can arise.

Let's do another experiment:
[song about the Warrington attack]

The request is as specific as possible and there can only be one specific answer to it - the song “Zombie” by The Cranberries.

If you change the query a little and specify [song about the terrorist attack in 1993], you can see that the search engine separates the results: some of the answers are about the song, and some are about the terrorist attack. Yandex does not quite understand what exactly we want to receive information about.

If we make the query even more general, then there will be no correct answer at all:
[song about the terrorist attack in England]

The output consists entirely of news about the terrorist attack, and there is no talk about the stated meaning.

Now let's type the query:
[film in which a writer goes crazy in a hotel]

In this case, you can see that the algorithm works. Yandex understands what we want to find and, at the same time, indicates that this request has two meanings (two intentions): the film “The Shining” and the film “1408”. It is also important here that the words from the query do not appear on the pages. In this case the algorithm works.

Now let's try to type a request:
[film in which Travolta dances]

Options for the most popular films are in the object responses, but not in the search results.

The answers become more specific if you modify the query:
[film in which Travolta dances young]

The correct option can only be seen in the form of an object answer and a Wikipedia page. The rest of the output is far from the desired result.

Let's change the query again and type:
[film in which Travolta dances in a bar]

As we can see, the algorithm fails. This happens because it is extremely difficult to give a definite answer to this request. For example, in the film "Pulp Fiction" the dancing takes place in a restaurant, in the film "Saturday Night Fever" - in a club. But there is a film “Michael” in which Travolta is dancing in a bar. If you test the search results several times to find the movie you want, relevant results will begin to appear.

What conclusions can be drawn from this?

• The algorithm shows its work in search results only on the pages of large information sites (such as Wikipedia or Kinopoisk) and in object responses.
• The algorithm understands only simple queries that contain one meaning.
• “Korolev” works better when searching for popular information (for example, for the query “movie” it will show the most popular, the most famous - the one about which there is the most information in the index).
• The algorithm only works with information requests.
• The algorithm is truly self-learning and with repeated calls the results get better.

For SEO, the algorithm now gives little. For most requests great value has a text factor. Where the new algorithm works, Yandex gives preference to more well-known sites, for example, Wikipedia. It will be difficult for small projects to compete with them. The ability to rank highly for such queries will only appear when the algorithm has a more complete knowledge base about the desires and preferences of users. But for this you now need:
— create text content that contains as many words as possible that define the topic of the page;
— improve behavioral factors so that the search engine knows for sure that the page will be useful to the user.

1. Users

From the point of view of users, it can be strange that queries that are identical in meaning, but differ in spelling, produce different results. Many users submit queries to a search engine as if they were asking a friend. The new algorithm will make it easier to answer these requests.

2. Webmasters

In an ideal world, webmasters should do good products, create quality content and don’t think about specifically promoting your site in search engines. In reality, they often have to adjust texts and the site itself for search engines.

3. SEO specialists

Some of the methods that helped promote websites before (for example, SEO copywriting) will no longer give such an effect. Of course, there will be attempts to outwit the new algorithm, but part of the effort will be aimed at creating high-quality content.

It’s too early to judge the quality of the new algorithm, but the more answers it gives, the better it will become. Therefore, in the long term, users should feel the difference.

What kind of machine is this?

With the introduction and growth of the IQ level of neural networks in search engines, the quality and relevance of the content returned will increase exponentially. The machine can analyze visual content, understands the meaning of words and expressions.

An attempt to weave into pages any popular news items that do not have a direct semantic relationship to the topic of the resource’s niche will lead to exclusion from the search results.

Advantages

The key advantage of a neural network is not that it can analyze, but that it can learn and remember. That is, resources that, at the choice of users, do not meet the expectations of the search results will also gradually drop out of the search results.

That is, the machine records that for request A, the relevant number of users always click on resource B and never click on resource D. Resource D will be excluded from the niche of matching request A.

Let's wait a few weeks and we'll see

On the one hand, the name is not as fucking as “Palekh”. And that's already good. On the other hand, everyone has not yet had time to perfectly adapt to “Palekh”, when here comes a new, more twisted algorithm that focuses more and more on content.

Content is a king – confirmed after each update

Of the advantages, it is obvious that this provides an increasing opportunity for progressive, savvy and new sites to compete in saturated niches with long-established search results leaders, as well as sending everything into the more distant astral plane of thoughtless SEO copywriters who made a garbage dump on sites by listing anchors in texts.

The algorithm allows for new professional growth copywriters with a head, they can do something more useful than write posts for social networks.

But, from a skeptical point of view, it is unlikely that Yandex will miss the moment to promote its commercial capabilities and their necessity, in particular the context.

I always perceive such news very positively. Because in addition to SEO optimization, you have a large area for content strategic actions, and this takes SEO to a new level. They stop treating him as something strange and incomprehensible. In the form people are familiar with, SEO is fucked up, it used to be like that, but time passes, and the outdated perception remains.

The logic is this: Previously, there were many web studios on the market that did SEO, and some simply pretended to do it, but took a budget for it. The latter predominated in number. This is why there is an opinion that SEO is a scam. Time passes, each update of the algorithm displaces those who “pretended”, and the outdated perception of people still remains.

The new Korolev algorithm logically continues the changes in Yandex search in recent years. Greater emphasis on neural networks, analysis of the entire content of the page, and not just the headings.

A very important point is the analysis of other search queries that bring users to the current page, which allows you to more accurately determine the relevance of the content and the relationship between search queries.

To summarize: search quality will improve. And that's great.

Goodbye SEO texts

I wonder how the new algorithm will perform in real life. It takes time to evaluate both the adequacy of semantic output and ranking priorities.

Definitely, search will now have to do a better job of handling non-standard and rare queries if the network really sees more meaning behind keywords. I really hope so, because this is another step towards “Goodbye, SEO texts.” However, the network will need to be trained. This doesn't seem to be a joke.

I just tried the search for "Movie Boy with a Scar on His Forehead" and got a lot of references to the movie "Scarface" in the search results. That is keywords Still, meaning is still prevailing.

And only if I find the Harry Potter pages I need in the search results and spend a significant amount of time on them, the machine will understand what meaning I put into the request and will clarify the search results for the next time. At least that's how it should be. The learning process will not be quick, but in any case it is good step to the future.

A little closer to business...

Today, in response to the request “Buy a cabinet with a sliding door,” I persistently receive ovens and a bunch of unnecessary things (blinds, swing doors, etc.).

The essence of the algorithm is to determine additional properties of the document at the URL indexing stage, expressing in numerical form the correspondence of the page text to previously known and frequently used phrases. It is stated that the innovation will affect low-frequency queries, which make up about a third of the search results.

Due to the lack of statistics on such “rare” queries, the quality of search for them suffers. In fact, this algorithm will pull out of oblivion documents that do not directly contain a long query, but are close in meaning to the user’s query.

It is important for marketers and SEO specialists that their optimized sites compete not only with each other, but also with with sites that have not been touched by the optimizer at all.

Of course, this only applies to low-frequency requests, and estimating the share of requests as 1/3 of the flow is an upper estimate. But in the near future, some sites may experience an outflow of low-frequency traffic. At the same time, it is pointless to make any numerical forecasts.

In my opinion, the idea itself of constructing various indices made up of labeled n-grams (and this is what Yandex claims) lies on the surface. For example, one of the main features of the statoperator crawler is the construction of an index based on n-grams.

N-grams are more informative than individual words, they are amenable to classification and allow you to significantly expand the number of factors for constructing a search by meaning. I'm glad that Yandex is moving in the right direction and high level implements current methods to increase the speed and quality of search.

Opinion of Dmitry Sevalnev, head of the SEO and advertising department at “