Slice: begemot_query_factors

The theme of the request determined ((http://wiki.yandex-team.ru/jandekspoisk/zarubezhnyjjinternet/dmozqueryClassifier1 The rule of the sorcerer Dmoztheme)), only a few of the most popular topics are taken into account.

The most likely theme of the request determined ((http://wiki.yandex-team.ru/jandekspoisk/zarubezhnyjjinternet/dmozqueryClassifier1 The rule of the sorcerer DmozTheme)), only the most popular topics are taken into account (but there are more than in the DMOZQUREMES factor). The factor contains the likelihood of a correspondence of the request of the theme, but for each topic, its own interval is taken on the segment [0..1]

The popularity of the request

The degree of centralization of the points from which the request is set

Geographical distribution of the request

The severity of the querial tasks at different times of the day

The average length of the logical session in which there was a request

The average number of found on request

The request is set mainly at night

The request is set mainly in the morning

The request is given mainly in the afternoon

The request is set mainly in the evening

The popularity of the request within the country

The degree of centralization of the points from which the request is set (inside the country)

Geographical distribution of the request within the country

The hour at which this request is given the most

The degree of severity of the querial tasks at different times of the day (inside the country)

The musicality of the request. The results of the sorcerer Anton Konygin.

The commercial request for the dictionary of phrases from Direct: 0 - maximum commercial, 1 - minimal.

The measure of 'commercial' request. It is a comprehensively calculated Matrixnet factor formula for the procurement vocabulary in direct + for user queries + add. Intensive dictionaries. Requests with intensity to buy a factor seeks to -> 1 commodity requests -> 0.6 with intensity cannot buy, reviews, etc. -> 0 ((http://wiki.yandex-team.ru/Faktorydljanovogokatorazaprosov Factors of the Classifier))) (HTTP : //wiki.yandex-team.ru/jandekspoisk/antispam/antiseo/klassifikATORCHESSKIXZAPROSOV STUNITURE OF HIM))

Rank 'navigation'

Binar non -profit request: Querynoncommerciality> 0.965.

Are there any words from Yweb/Pornofilter/Porno.query.

Classifier of Porn Causions, another dictionary than Pornoquery

Whether the request of blog vocabulary contains

Is the request for navigation, on the clicking of the answers

Request is not in Russian

Request about the video

Requestful factors - the result of work ((http://wiki.yandex-team.ru/jandekspoisk/kachestvopoiska/obshayaformula/tekushhiekomponenty/georegionality classifier of geolocalization of the request)))))))))))))

R- Georelevan - regional results in the issuance could be useful, but nothing more

V- geovital - regional issuance is of fundamental importance

Requestful factors - the result of the work ((http://wiki.yandex-team.ru/poiskovajaplatforma/lingvistika/zaprosnyjefefactory/localizovannyjezaprosya classifier of the request of the request)) - a new version of factors [328] - [328] - [328]: u - u - u - u - u - u - uceleless sites the request is meaningless;

Запросные факторы - результат работы ((http://wiki.yandex-team.ru/PoiskovajaPlatforma/Lingvistika/ZaprosnyjeFactory/LocalizovannyjeZaprosy классификатора геолокализованности запроса)) - новая версия факторов [328]-[330]: R - георелевантные - региональные результаты в issuing could be useful, but nothing more;

Requestful factors - the result of the work ((http://wiki.yandex-team.ru/poiskovajaplatforma/lingvistika/zaprosnyjefefactory/localizovannyjezaprosy classifier of the request of the request)) - a new version of factors [328]: Vegetable fundamental importance.

Regional and navigation request - in the user region there are one or more navigation results on it

The result of the work of the lexical classifier of requests predicting the likelihood of click on the page 3973 page

The result of the work of the lexical classifier of requests predicting the likelihood of click on the theme of 3561

The prediction of the share of “good” (at least two different cities and frequency> = 10) references to the request with geography in Turkey

When responding to a request, the results are important in close proximity ([pharmacies], [children's clinic])

When answering a request, the results within the city are important (the bulk of localized queries)

When responding to a request, the results from the region, the region of the user ([airport], [dairy]) are important

It launches on the basic search under the name ISGEO the maximum weight of the meters of the gelator in the request. A geo-object is understood as an object of the category GEO, Geo1, Geoaddr, Geoaddr1, Landmark, Landmark1 (see ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects kaovsky allocation))))))))))))))))))))))))))))))). wiki.yandex-team.ru/arsengadzhikurbanov/wares Read more))

It launches on the basic searches under the name ISMUSIC the maximum weight of the Music or Music1 category met in the request. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/warees more))))))))))))))))))))))

The request is the name of the organization (example: Gazprom, Gazprom) ((http://wiki.yandex-team.ru/arsengadzhikurbanov/warees Description))

It launches on the basic search under the name ISHUM the maximum weight of the enclosed object of the Hum or Hum1 category in the request. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#ishum more)))))

It launches on the basic search under the name ISTEXT the maximum weight of the TEXT or Text1 category of the category of the category met in the request. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#istext more)))

It launches on the basic search under the name Ispicture the maximum weight of the Picture or Picture1 category of the category of the category of the category in the request. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#ispicture))))))))))))))))))

Returns the maximum degree of household objects in the request under the name Wminone. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#minone more)))))

Returns the maximum degree of household objects in the request under the name Wmaxone. (See ((http://wiki.yandex-team.ru/alekseysokirko/queryobjects SOM-OV)))). ((http://wiki.yandex-team.ru/arsengadzhikurbanov/Wares#maxone more)))))))

Country classifier of localization - how much the request implies the context of the country

= 1 - v. Download formula. Class requests: download/watch online/play/photo/listen

The result of the classifier of the request - in the request there are words from the corresponding dictionary. brand

Medication Dictionary

question

Specific request for Moscow

organization

porn

trips

0 or 1, depending on the presence in the request of the clearly expressed intent Need_photo from the variety

0 or 1, depending on the presence in the request of the clearly expressed intent Need_map from the variety

0 or 1 - whether the request is matured by the tickt

0 or 1 - whether the request is matured by the tickt

0 or 1 - whether the request is matured by the tickt

There is a typo in the request

Double logarithm of the probability of a request for a language model of the Erratum typo service

Summary Skorov words of a request for a language model 3GRAMS-YANDEX-DIRECT.

Summary of the Skorov words of the request by the Web-Mt language model.

The angle in the Depth Nodes space, counted only by words (min for all)

Prediction of a share of requests with geography on a bag of words built for request

The value of the classifier of localization for request

The request is a Urle with an accuracy of the points and testing characters - the ISURL sorcerer's rule is used

Relev_locale == ru

Relev_locale == ua

relev_locale == by

relev_locale == kz

relev_locale == tr

relev_locale == id

relev_locale == world

The result of the work of Adult Rules for the Sorcerer.

Porn query classification result from Wizard (iad_vw flag, based on Vowpal Wabbit)

The probability that the VK.com host is popular for this request in accordance with the corresponding DSSM model.

The probability that the Onliner.by host is popular for this request according to the corresponding DSSM model.

The probability that the Rambler.ru host is popular for this request in accordance with the corresponding DSSM model.

The likelihood that the ExpertCen.ru host is popular for this request in accordance with the corresponding DSSM model.

The probability that the Sunhome.ru host is popular for this request according to the corresponding DSSM model.

The average value of News for the year. It is calculated in offline.

ADDTIME average value for the year. It is calculated in offline.

The average value of TXTHIRELSY for the year. It is calculated in offline.

The average TEXTLIKE value is for the year. It is calculated in offline.

The average Hasnoallwordstersy value for the year. It is calculated in offline.

The average value of ISFORUM for the year. It is calculated in offline.

The average value of Haspayments for the year. It is calculated in offline.

The average value is Yabarhostavgtime2 for the year. It is calculated in offline.

The average value of Yabarurlvisitors for the year. It is calculated in offline.

The average value of QueryDowneronlyClickRate for the year. It is calculated in offline.

The average value of Dateraage for the year. It is calculated in offline.

The average value of the LonGestText for the year. It is calculated in offline.

The average value is DifferentinTernallinks for the year. It is calculated in offline.

The average value of QueryDowneronlyClickRate_Rreg is for a year. It is calculated in offline.

The average ISHUB value is for the year. It is calculated in offline.

The average ISTARGETBUSSINESSCARD for the year. It is calculated in offline.

The average value is BM25_0 on request per year. It is calculated in offline.

The average value of BOCM for the year. It is calculated in offline.

The average ISindexpage is for the year. It is calculated in offline.

The average value of queriesavgcm2 for the year. It is calculated in offline.

The average value of BrowserhostDownloadProbabolyity for the year. It is calculated in offline.

The average value of Regbrowseruserhub for the year. It is calculated in offline.

Auxtitlebm25 average value for the year. It is calculated in offline.

The average value of QuryurlCorrectedctrxFactor for the year. It is calculated in offline.

The average value is QueryTodocallsumfcountTextbM11Norm16384 for the year. It is calculated in offline.

The average value of the XFDTSHOWALLSUMWFSUMWBODYMINWIDESIZE for the year. It is calculated in offline.

Maintened by clicks ISMainPage value for the year. It is calculated in offline.

Main -heated clicks of the Yabarurlavgtime value for the year. It is calculated in offline.

Maintened by clicks DifferentinTernallinks for the year. It is calculated in offline.

Malpanized Dwelltime-AMI Value of Urldomainfraction for the year. It is calculated in offline.

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

The main components of the requesting Embling from the DSSMCTRNOMINER model

Predicted by demand and country, using a DSSM model, the length of the click from this country.

The average for the year for the year predicted using the neural network.

ADDTIME ADDTIME is predicted using a neural network for a year.

The average Txthirelesy value predicted using a neural network for the year.

The average Textlike is predicted using a neural network for the year.

The average HasnoallwordStrsy is predicted using a neural network for a year.

The average ISFORUM is predicted using a neural network for the year.

The average Haspayments is predicted using a neural network for the year.

The average value of Yabarhostavgtime2 for the year for the year.

The average yabarurlvisitors is predicted using a neural network for the year.

The average value of QueryDowneronlyClickRate for the year for the year.

The average Dateraage value for the year for a year predicted using a neural network.

The average LonGestText is predicted using a neural network for the year.

The average DifferentinTernallinks for the year for the year.

The average value of QueryDowneronlyClickRate_Rreg is predicted using a neural network for a year.

The average ISTARGETBUSSINESSCARD is predicted using a neural network for the year.

The average BOCM value predicted using a neural network for the year.

The average ISindEXPAGE is predicted using a neural network for the year.

The average value of QueriesavGCM2 for the year for the year predicted using a neural network.

The average BrowserhostdowLoadProbabolyti is predicted using a neural network for the year.

The average value of Regbrowseruserhub for the year for a year predicted using a neural network.

The average AuxtitlebM25 average value for the year for the year.

The average value of QueryurlCorrectrxFactor for the year for the year.

The average value of QueryTodoCallsumfcountTextbM11Norm16384 for the year for the year.

The average value of the XFDTSHOWALSUMWFSUMWBODYMINWINDOWSIZE for the year for the year.

The value of the ISMAINPAGE with clicks predicted using the neural network with clicks on request for the year.

A mid Yabarurlavgtime value predicted using a neural network with clicks for a year.

DiffferentinTernallinks, which is predicted using a neural network, is a weighted net with clicks for a year.

The Malue Network DwellTime-AMI predicted using the neural network is the value of Urldomainfraction for the year.

The prediction of the probability that the request is localized in accordance with the regionality5 rule.

The factor is an analogue of LongQuery (the sum of the IDF words of the request), but with the 'correct' accounting of synonyms. Specifically, a minimum of IDF (i.e. the most frequent) of synonyms and words is selected.

Characterizes the request for the degree of change from removing a fixed word ('site' for Kirilitsa), DSSM model DSSMBOOSTINGXFONESEAMSARD is used

Characterizes the request for the degree of change from the addition of a fixed word (number of some year), DSSM model DSSMBOOSTINGXFONESEAMSARD is used

Characterizes a request for the degree of change from the addition of a fixed word ('online' for Kirilitsa), DSSM model DSSMBOOSTINGXFONESEAMSARD is used

For each word offline, the average Hasnotr meaning is calculated for 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average ISLJ value is calculated for 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average BCLMLITE value is calculated for 3 months. Further, in all words of the request, a minimum of this value is taken.

For each word offline, the average DBM40 value is calculated for 3 months. Further, for all non -feet, the words of the request are taken as a maximum of this value.

For each word offline, the average ISDESKTOPREQUEST value is calculated for 3 months. Further, for all non -feet, the words of the request are taken as a maximum of this value.

For each word offline, the average value of RLQAVGHASNOLLWORDSTRSYN is calculated at the request for 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average DSSMAGGRETEDANNREG value is calculated at the request for 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average meaning of MetanumurlSperhostfixed is calculated in demands in 3 months. Further, in all words of the request, the maximum of this value is taken.

For each word offline, the average value of MaxsdisnavmxqueryMax is calculated at the request for 3 months. Further, for all non -feet, the words of the request are taken as a maximum of this value.

DSSM prediction of google specificity for query

The average value of Randomlogqueryavgaddtime of the closest KNN queries.

The average value of RandomlogqueryavgtXthirelsy nearest KNN queries.

The average value of Randomlogqueryavgtextlike nearest KNN queries.

The average value of Randomlogqueryavgisforum of the closest KNN queries.

The average value of Randomlogqueryavghaspayments closest to KNN queries.

The average value of Randomlogqueryavgdiferentinternallinks of the nearest KNN queries.

The average value of RandomlogqueryavgistargetbussinessCard of the nearest KNN queries.

The average value is RandomlogqueryavgquerytododoCallsumfcountTextbM11NORM16384 of the nearest KNN queries.

The average value is Randomlogqueryavgxfdtshowallsumwfsumwbodyminwindowsize closest KNN queries.

request came from yandsearch (rearr.is_desktop == 1) (https://wiki.yandex-team.ru/users/lpshka/platformfeatures)

request came from touchsearch (rearr.is_mobile == 1) (https://wiki.yandex-team.ru/users/lpshka/platformfeatures)

request came from padsearch (rearr.is_tablet == 1) (https://wiki.yandex-team.ru/users/lpshka/platformfeatures)

Median dwelltai request in history. Dwelltaym is cut to 6000. The request is normalized by doppelgangers

The number of shows of the request with more than one click in history. The request is normalized by doppelgangers

The share of shows with more than one click from all shows in history. The request is normalized by doppelgangers

The average value for the query factor according to Querytotext linguosting data is calculated in the LingBoostqueryFeatures Hotemaway Rules

The average value for the query factor according to Querytotextbyxfdtshowknn linguosting data is calculated in the LingBoostqueryFeatures Hotemail Rules

SUM / (SUM + 10) for the requested factor according to the XFDTSHOW linguosting data, is calculated in the LingBoostqueryFeatures Hotemaura

Quantile 0.1 For the request factor according to the XFDTSHOW linguosting data, is calculated in the LingBoostqueryFeatures Hotemaway Rules

Quantile 0.1 for the request factor according to the XFDTSHOWKNN linguosting data, is calculated in the LingBoostqueryFeatures Hotemaway Rules

Quantile 0.9 for the quantity factor according to the XFDTSHOWKNN linguosting data, is calculated in the LingBoostqueryFeatures Hotemaway Rules

SUM / (SUM + 10) for the requested factor according to QFUF linguosting data, is calculated in the LingBoostqueryFeatures Hotemaura

The average value for the quoted factor according to QFUF lingvobusting data is calculated in the LingBoostqueryFeatures Hotemail Rules

Relev_locale == Us

25% time quantile from the previous request to the current one. The request is normalized by doppelgangers

The request was recognized as having intent to copyright objects protected by anti-Pirate memorandum.

Transferred Ethos predicted classifier for marking on the relevance of video.

Reduced ETHOS predictive classifier, trained in a synthetic sample 'Request characteristic of the pirate site' VS ', the request is characteristic of the site far from this'

In this slot, there was no zero feature

DSSM regression for the separation of memorandum and no requests

Transferred Ethos predicted classifier trained to distinguish a memorandum queries from a random

DSSM regression of embeds to separate Pirato specific and no requests

Manual formula of the classifier of the pa*of the request of the request

Manual formula of the classifier M*Morandum of request

Time has passed after a sporting event in the request

Time remaining before the sporting event from the request

Time has passed after a sports event between several teams

Time remaining before a sports event between several teams

The difference between the current date, and the date in the request (1 - request about today's date, 0 - about the date 10 days ago and younger)

The difference between the current date, and the date in the request (1 - a request for today's date, 0 - about the date after 10 days and older)

There is one date in the request

Weekday query probability

The average ISBLOG value for the year. It is calculated in offline.

The request has the name of the series and a serial marker (series, series, all seasons).

Is serial query with season and episode specified

Function from number of episodes between requested and last released episodes

The query getting into the TOP 11 clusters on Kernel metric based on DSSM proximity.

The request mentions the product category. Not used (depreded)

The request mentions a vendor. Not used (depreded)

The presence of an album in a lemmetized request

DSSM distance from request to word match

DSSM distance from request to word hockey

DSSM distance from request to word broadcast

DSSM distance from request to word championship

DSSM distance from request to word series

DSSM distance from request to word today

DSSM distance from request to word last

DSSM Distance from request to the word accident

DSSM distance from request to word news

DSSM distance from request to word death

DSSM distance from request to word football

DSSM distance from request to word day

DSSM distance from request to word postcard

DSSM distance from request to word khl

DSSM distance from request to word UFC

DSSM distance from request to word results

DSSM distance from request to word schedule

DSSM distance from request to word fresh

DSSM distance from request to word biography

DSSM distance from request to word yesterday

DSSM distance from request to word forecast

Requests got into the 34th cluster based on CTR-DSSM.

DSSM distance from request to word coronavirus

DSSM distance from request to word buy

DSSM distance from request to word price

DSSM distance from request to word product

DSSM distance from the request to the word description

DSSM distance from request to word delivery

DSSM distance from request to word is paid

DSSM distance from request to word analogue

DSSM distance from request to word sale

DSSM distance from the request to the word installment

DSSM distance from request to word rent

DSSM distance from request to word catalog

DSSM distance from request to word store

Dssm distance from request to word watch

DSSM distance from request to word movie

DSSM distance from request to word season

DSSM distance from request to word free

The likelihood that the selected hosts from the Construction category are popular for this request according to the corresponding DSSM model

The likelihood that the selected hosts from the Wear category are popular for this request according to the corresponding DSSM model

The likelihood that the selected hosth hosts category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosts from the Food category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosts from the Furniture category are popular for this request according to the corresponding DSSM model

The likelihood that the selected hosts from the Travel category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hostplace hostplace category are popular for this request according to the corresponding DSSM model

The likelihood that the selected hosts from the Book category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosts from the Electron category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosts from the Board category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosts from the Service category are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosto hosto are popular for this request in accordance with the corresponding DSSM model

The likelihood that the selected hosts from the Cinema category are popular for this request in accordance with the corresponding DSSM model

Matter coating of the request of the dictionary with the names of the series

Matter coating of a request from a dictionary with films names

DSSM distance from request to word treatment

DSSM distance from request to word pharmacy

DSSM distance from request to the word cold

DSSM distance from request to word diet

DSSM distance from request to word ointment

DSSM distance from request to word means

DSSM distance from request to word recipe

DSSM distance from request to word belly

DSSM Prediction of money spent on a request

Prediction of the classifier that the request is a medical

Prediction of the classifier that the request is a legal

Prediction of the classifier that the request is financial

The prediction of the classifier is that the request of SOS topics

The prediction of the classifier that the request is navigation

The prediction of the classifier is that the request is white (that is, it is not worth showing tin on it)

DSSM prediction of the complexity of the request

Are there any quotes in the request

Is there a minus

Is there a plus word in the request

Is the request completely with a quote

Indicator - is the request of the SEO -request

Indicator - is the request of the SEO -request from the list No. 1

Indicator - is the request of the SEO request from the list No. 2

IsCSQuery FI_IS_CS_QUERY

Request contains full name from FIO Rules

Request contains a word song or track

Musical request from Alice

The prediction of the classifier is that upon request you need to show URL from a mobile market

Prediction of the classifier to especially commercial requests

Prediction of the CP Classifier Requests

Requested classifier of programmatic queries V2

Classifier of a request for equipment repair services: office, household, computers, phones

Phonenumberrule in the Hippo Parisilo phone number in the request.

Request classifier to Pro-Hoogle Expertise.

The request is Latinic.

Whether the request is porn.

Constant 1

Spare factor for the rapid adding functionality

Spare factor for the rapid adding functionality

Spare factor for the rapid adding functionality

Spare factor for the rapid adding functionality

Spare factor for the rapid adding functionality

Spare factor for the rapid adding functionality

Prediction of the CP Classifier Requests

Prediction of the requesting classifier of violence

Prediction of the requesting classifier of weapons or drugs

The prediction of the classifier is that a culinary request request

The prediction of the classifier is that the request is strong international and culinary

The presence of an official word in a lemmetized request

The presence of Wikipedia word in a lemmetized request

The presence in the lemmetized request of the word not and similar in meaning

The presence in a lemmetized request to buy words, price and similar in meaning

Musical request for the script of Arab Alice

1/(1 + number of words of the request), attributes are not taken into account

Is an attribute request (looks at rearr = attrs)

Classifier of a request for pornographic for Indonesian

Classifier of requests for the requirement only English -speaking pages

Classifier of requests for the requirement of mixed English -speaking and Russian -speaking pages

The classifier of requests for the requirement of either only English -speaking pages, or English -speaking and Russian -speaking pages together