Tag: TG_UNIMPLEMENTED

Remapped mascot feature TrafgraphOutAllSE_share_m

Remapped mascot feature NoExtClicksShare

A model trained on a PRS-Law PRS to predict BERT, trained on sinsig_ce with threshold value 0.5, using a chain of regions to the country

URL feature computed from rapid clicks spy_log counters with decay of 1 day

URL feature computed from rapid clicks spy_log counters with decay of 0.5 days

Finance or law host quality for new marks.

Finance or law host quality for new marks for experiments.

Factor for host in list of documentation cs hosts for experiments

URL feature computed from rapid clicks spy_log counters with decay of 3 days

URL feature computed from rapid clicks spy_log counters with decay of 14 days

Age of rapid clicks spy_log update, in seconds

Freshness of rapid clicks spy_log update

OffersBase feature for ecoboost.

OffersBase feature for ecoboost.

OffersBase feature for ecoboost.

OffersBase feature for ecoboost.

OffersBase feature for ecoboost.

OffersBase feature for ecoboost.

Business kernel.

Business kernel.

Num of Sovetnik URLS

Implementation of the algorithm described in the article ((http://wiki.yandex-team.ru//h.yandex.net/?http%3A%2F%2FreseRosoft.microsoft.com%2Fen-US%2FPEOPLIULIUUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUUP032-LIUUUU .pdf http://research.microsoft.com/en-us/people/tyliu/fp032-liu.pdf)) by large regions (tube)

Aries is a service

Host feature computed at the request time from rapid clicks spy_log counters with decay of 3 days

Host feature computed at the request time from rapid clicks spy_log counters with decay of 3 days

Host feature computed at the request time from rapid clicks spy_log counters with decay of 14 days

Host feature computed at the request time from rapid clicks spy_log counters with decay of 14 days

Host feature computed at the request time from rapid clicks spy_log counters with decay of 3 days

Host feature computed at the request time from rapid clicks spy_log counters with decay of 14 days

Host feature computed from rapid clicks spy_log counters with decay of 3 days

Host feature computed from rapid clicks spy_log counters with decay of 3 days

Host feature computed from rapid clicks spy_log counters with decay of 14 days

Host feature computed from rapid clicks spy_log counters with decay of 14 days

Host feature computed from rapid clicks spy_log counters with decay of 3 days

Host feature computed from rapid clicks spy_log counters with decay of 14 days

Is legal video distributor

Average value of feature OneProductProbability

Average value of feature ManyProductsProbability

Average value of feature PayDetectorPredict

Aries is a partner

The document is Shopinshop

The factor according to the name from the original request is considered according to the contents of the document. Algorithm: Chain0wcm

For Ural from Ytier, it is known that he has low -quality content

For Ural from Ytier, it is known that he has a content of acceptable quality

For Ural from Ytier, it is known that he has good quality content

For Urla from Ytier, it is known that he has an excellent content content

The host has an ecom purchase.

The host has a purchase by Visit Log.

URL is a product on the market.

URL is a product on the market and has Offerid.

URL is Shopinshopcpa.

Owner has a purchase by Visit Log.

In the offer from the new Parser, the PartnerOfferContent Available field is exhibited.

In an offer from the new Parser, the PartnerOfferContent Available Field == True.

The value of PURCHASETOTALPredict, calculated in the Hippo.

The value of serpsummarysurpluspredict, calculated in the hippo.

Requestwith120d3ClickpartPredict value, calculated in the Hippo.

FIN_LAW.DSSM Predictions URL + TITLE.

The value of Halfecompredict, calculated in the Hippo.

A sign that the document was received by machine translation

Prediction of Med_with_Trash.DSSM (Medic. Document model with Tresh Valley in Lern) Models for URL + Title.

Factor for name from the original request text of the document. Algorithm Cosinematchmaxpredical.

The factor for all the name from the original request Aggregation on all extensions. Type of aggregation for extensions: the greatest value of the factor; It is considered according to the contents of the document. Algorithm: Chain0wcm

The factor for all the name from the original request Aggregation on all extensions. Type of aggregation for extensions: the greatest value of the factor; It is considered according to the contents of the document. The minimum window size, which includes all the words of the request. It is normalized for the number of words in the request.

The factor for all the name from the original request Aggregation on all extensions. Type of aggregation for extensions: the greatest value of the factor; The text of the document. Algorithm Cosinematchmaxpredical.

A document is a short video (Tiktok, Reels, Shorts).

Document-telegram channel in web format.

Document - post in a telegram.

The prediction of the total time spent to the end of the session, provided that this pair is implemented by the request-document

The prediction of the contribution of this pair request-document to the timetable

The prediction of the percentage of the length of the track, which will be lost subject to the implementation of this pair of the request

Neural model of content quality for medical subjects

Neural model of content quality for financial and legal topics

Neural model of content quality for financial and legal topics (for exposures)

Neural model of content quality for SOS topics

Neural model of content quality for SOS subjects (for ex -)

Prediction of the time of the session, provided that this pair is requested by the request-document

Document - this is a selection of Tiktok /Tag

Document - this is a selection of Tictock /Discovery

Document - this is a selection of Tiktok /Music

URL is a channel/fasting from a verified social network account

DSSM, predicting whether the site is a facial

Rank of the quality of texts on the host. The higher, the greater the likelihood that the host is full of articles - a rewriting, a bad copy of the content ordered on the exchanges of content. Burning stronger as the before the aggregation.

Static URL factor in browser logs for the maximum period. Rank, based on only UBLP meters, which allows you to find many SBR losses

Document DSSM model Language Classifier Rus.

Document DSSM model Language Classifier Eng.

Document DSSM model Language Classifier Other.

DSSM Prediction to determine Alice's irrelevant answers

DSSM model trained on click odd pool

DSSM model trained on click personalization pool

DSSM model trained on click triangle pool

Document has Fio from original request

PAGE Quality 1 experiments factor

Page Quality 2 experiments factor

Page Quality 3 experiments factor

Page Quality 4 experiments factor

Page Quality 5 experiments factor

The document came from Webtier1

The document came from Platinum0

Neural document model for finding unexpected tin

Medical host quality fresh.

1 if host include js from mc.yandex.ru

Is site official

Quality link from good sites estimation

Weight sum of each non-unique nevasca shingle

Nevasca shingle quantity in last week

Greentraffic share (aka direct visits). Desktop

Greentraffic share (aka direct visits). Mobile

Greentraffic absolute (desktop)

Visits averaged by user

Medical host quality for metric.

Levenstein’s distance between the request and Url of the type of YouTubecom/Watch normalized to the maximum of the length of the request and Url

The length of the largest total setting between Url and the request normalized for the length of the request

Sigmoid rationed the value of a textual classifier of porn according to Toloka Porn

Binarized value of a textual classifier text classifier according to Toloka estimates

The value of the text classifier of porn according to the classifier of the web and add. dictionaries

Binarized with the use of networks, the value of a textual classifier of porn according to the estimates of the web and additional classifier. dictionaries

The presence of obscene vocabulary in the request. 0 - absent, 0.5 - non -seated, 1 - hard

The presence of porn markers in the request (0 - is, 1/3 - no, 1 - request 'gray')

Documentary classifier of porn, features according to the text of the document

Documentary classifier of porn, features on Ural document

Documentary classifier of porn, features according to the pictures of the document (information is taken from the picture index)

Documentary classifier of porn, features by video of the document (information is taken from the video index)

The document came from Quickmed

Medical host quality.

The document has a turbo page for Desktop platforms. Updates on top of the base are delivered through SaAS.

Page quality aggregated by host (avg).

Is this host adapted to mobile devices

The factor for the filtered original request: the DSSM state from the request is calculated without words to the initial request, after which the threshold is cut off. Into aircraft association of the URLs, Title, Body, Links, Correctedctr, LongClick, OneClick, Browserpagerank, Splitdwelltime, SampleperiodDayFrc, SimpleClick, Yabarvisits, Yabartime. The algorithm for aggregation of the scales of words: BM15FLOG (BM15 Aggregation of Logarithm of Construction of Words). Normalization coefficient 0.001.

The factor for the filtered original request: the DSSM state from the request is calculated without words to the initial request, after which the threshold is cut off. It is considered to be composational stream, consisting of an tokenized Url and a title of a document. The algorithm for aggregation of the scales of words: BM15FLOGW0. Normalization coefficient 0.0001.

It is considered as (10-x) where X is the return of the document in days (continuously). Uses the data of the Robotaddtime dates

The difference between the current date and the date of the document, determined by the Robotaddtime, 1 - the date is equal to the current, 0 - the document of 10 days or more, or the date is not determined

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

The host contains pirate videos protected by anti-pirate memorandum.

Stevenson

Stevenson

Stevenson

Stevenson

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

Reduced ETHOS predictive classifier, trained in the synthetic sample 'Request characteristic of the pirate site' VS '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

Bans of Antispam from erf

Aggregated by the closest to the Host Urlam Logavg statistics of the ISMOBILEREQUEST factor

Aggregated by the closest on the Host Urlam Logavg-statistics of the NanobtaniumQuerywordtitle5ndist2maxmax factor.

Bans on gsm of Antispam from erf

Bans on fresh of Antispam from erf

A model trained to predict an assessment of the USSR-DUMP-20190719 PRS-20190720 ALL-8-YEARS [T> 0.25] Crossentropy 20K 0.25 -s 0.8 -z 1.

Neuron document model for finding unexpected tin (for ex -)

Features calculated on url with request multitokens expansion

Features calculated on url with request multitokens expansion

The value of the freshness detector calculated in the hippo. Always 0 with a detector value less than the threshold.

Maximum acoustic score

Minimum acoustic score

Average acoustic score

Standard deviation of the acoustic score

Median of the acoustic score

Winner's acoustic score

ASR acoustic model type (0 == undefined, 1 == seq2seq, 2 == grapheme)

Duration of the audio request

Word LSTM trained on toloka data (music_ambient_sound prediction)

Result of Granet rule has form for music key words

Result of Granet rule has form for music_sing_song

Word LSTM trained on scenarios (VINS) data (music_sing_song prediction)

Word LSTM trained on toloka data (music_sing_song prediction)

Word LSTM trained on scenarios (VINS) data (find_poi prediction)

Word LSTM trained on toloka data (find_poi prediction)

Word LSTM trained on scenarios (VINS) data (open_site_or_app prediction)

Word LSTM trained on toloka data (open_site_or_app prediction)

Word LSTM trained on scenarios (VINS) data (how_much prediction)

Word LSTM trained on toloka data (how_much prediction)

Word LSTM trained on scenarios (VINS) data (translate prediction)

Word LSTM trained on toloka data (translate prediction)

Word LSTM trained on scenarios (VINS) data (tv_broadcast prediction)

Word LSTM trained on toloka data (tv_broadcast prediction)

Word LSTM trained on scenarios (VINS) data (convert prediction)

Word LSTM trained on toloka data (convert prediction)

Word LSTM trained on scenarios (VINS) data (create_reminder prediction)

Word LSTM trained on toloka data (create_reminder prediction)

Word LSTM trained on scenarios (VINS) data (tv_stream prediction)

Word LSTM trained on toloka data (tv_stream prediction)

Word LSTM trained on scenarios (VINS) data (recite_a_poem prediction)

Word LSTM trained on toloka data (recite_a_poem prediction)

Result of Granet rule has form for player_dislike

Max percent of query words in DirectOffer

Mean percent of query words in DirectOffer

Min percent of query words in DirectOffer

Max percent of DirectOffer words in query

Mean percent of DirectOffer words in query

Min percent of DirectOffer words in query

Max percent of query words in DirectOffer prefix (query length)

Mean percent of query words in DirectOffer prefix (query length)

Min percent of query words in DirectOffer prefix (query length)

Max percent of query words in DirectOffer prefix (2X query length)

Mean percent of query words in DirectOffer prefix (2X query length)

Min percent of query words in DirectOffer prefix (2X query length)

Max percent of query words in DirectTitle

Mean percent of query words in DirectTitle

Min percent of query words in DirectTitle

Max percent of DirectTitle words in query

Mean percent of DirectTitle words in query

Min percent of DirectTitle words in query

Max percent of query words in DirectTitle prefix (query length)

Mean percent of query words in DirectTitle prefix (query length)

Min percent of query words in DirectTitle prefix (query length)

Max percent of query words in DirectTitle prefix (2X query length)

Mean percent of query words in DirectTitle prefix (2X query length)

Min percent of query words in DirectTitle prefix (2X query length)

Max percent of query words in DirectInfo

Mean percent of query words in DirectInfo

Min percent of query words in DirectInfo

Max percent of DirectInfo words in query

Mean percent of DirectInfo words in query

Min percent of DirectInfo words in query

Max percent of query words in DirectInfo prefix (query length)

Mean percent of query words in DirectInfo prefix (query length)

Min percent of query words in DirectInfo prefix (query length)

Max percent of query words in DirectInfo prefix (2X query length)

Mean percent of query words in DirectInfo prefix (2X query length)

Min percent of query words in DirectInfo prefix (2X query length)

Factoid for answer was taken from right docs

The value of Purchasetotalpredict, calculated in the Hippo.

The value of serpsummarysurpluspredict, calculated in the hippo.

Requestwith120d3ClickpartPredict value, calculated in the Hippo.

The value of Halfecompredict, calculated in the Hippo.

The value of the requester detector of the parasites calculated in the hippo.

Classifier of one product in the request

Classifier of several goods in the request

The value of the Proxima DCG-5, calculated in the hippo.

The request is fresh

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 remaining before a sports event between several teams

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)

Function from number of episodes between requested and last released episodes

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

The request mentions a vendor. Not used (depreded)

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

DSSM Prediction of money spent on a request

DSSM prediction of the complexity of the request

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

Musical request from Alice

Prediction of the classifier to especially commercial requests

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

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

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

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

Blindr-5212

Blindr-5212

Blindr-5212

Blindr-5212

Blindr-5212

Blindr-5212

Blindr-5212

Blindr-5212

Blindr-5212

Blindr-5212

Weighted rate of samohod in WEB L3 top-10

Rate of samohod in WEB L3 top-10

Rate of samohod in WEB L2

Organization wizard text markers ('on the map', 'address', etc)

ImagequeryDlrelevancev5 from a quick source.

Number of user serp feedbacks in UGC DB

Number of user feedbacks in UGC DB

sinsig predict for 0-th web document

sinsig predict for 1-th web document

sinsig predict for 2-th web document

sinsig predict for 3-th web document

sinsig predict for 4-th web document

sinsig predict for 5-th web document

sinsig predict for 6-th web document

sinsig predict for 7-th web document

sinsig predict for 8-th web document

sinsig predict for 9-th web document

The factor according to the berty model is calculated in the marketing service of the berts

The factor according to the berty model is calculated in the marketing service of the berts

The factor according to the berty model is calculated in the marketing service of the berts

DSSM Distillation BERT Models for Assessment Target.

DSSM Distillation BERT Models on Click Target.

DSSM Distillation BERT Models on Has_cpa_Click Target.

DSSM Distillation BERT Models for CPA Target.

DSSM Distillation BERT Models for Billed_cpa Target.

DSSM-LIKE Neural network on the ADS/Pytorch framework (king) with markete target

Median dwelltai requests 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

Pool fold number - is used for auxiliary model training only

Blender factor SurfDepthNodesGradientDispMax.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation on all extensions. Type of aggregation for extensions: the greatest balanced value of the factor; It is normalized for the maximum weight of expansion. It is considered according to the heading of the document. The average weight of the anntations among those in which the request was an accurate tuning.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation by TOP-10 (by the value of the factor) extensions. Type of aggregation for extensions: the smallest balanced value of the factor; The maximum weight of the extension. It is considered according to the heading of the document. The algorithm for aggregation of words weights is BCLMMIXPLAIN: a linear mixture of annotation BCLM weights and balanced Positionless weights of the word, then the former meters are aggregated through BM15. Normalization coefficient 10^(-5).

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation by TOP-10 (by the value of the factor) extensions. Type of aggregation for extensions: an abstract by square of expansion weight, multiplied by the value of the factor; normalized for the total weight of extensions. It is considered according to the heading of the document. The average weight of the anntations among those in which the request was an accurate tuning.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation on all extensions. Type of aggregation for extensions: the greatest balanced value of the factor; It is considered according to the heading of the document. The average weight of the anntations among those in which the request was an accurate tuning.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation on all extensions. Type of aggregation for extensions: the greatest value of the factor; It is considered according to the heading of the document. The weight of the hit is multiplied by 1/ (1 + the position of the word in the sentence) an algorithm for aggregation of the scales of words: BM15. Normalization coefficient 0.01.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation on all extensions. Type of aggregation for extensions: the greatest value of the factor; It is considered according to the heading of the document. The degree of covering the words of the request in the exact form.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation by TOP-10 (by the value of the factor) extensions. Type of aggregation for extensions: the smallest balanced value of the factor; It is considered according to the heading of the document. The degree of coating of the words of the request is accurate to the form (without synonyms).

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation on all extensions. Type of aggregation for extensions: the greatest balanced value of the factor; normalized for the total weight of extensions. It is considered according to the heading of the document. The average weight of the anntations among those in which the request was an accurate tuning.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation on all extensions. Type of aggregation for extensions: an abstract by square of expansion weight, multiplied by the value of the factor; normalized for the total weight of extensions. It is considered according to the heading of the document. The average weight of the anntations among those in which the request was an accurate tuning.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation on all extensions. Type of aggregation for extensions: the greatest balanced value of the factor; It is normalized for the maximum weight of expansion. It is considered according to the heading of the document. Algorithm Cosinematchmaxpredical.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation by TOP-10 (by the value of the factor) extensions. Type of aggregation for extensions: the smallest balanced value of the factor; normalized for the total weight of extensions. It is considered according to the heading of the document. The average weight of the anntations among those in which the request was an accurate tuning.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation on all extensions. Transferred the total weight of the extensions.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation on all extensions. The average weight of extensions.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation on all extensions. The minimum expansion weight.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation on all extensions. Transferred number of extensions according to the X / (X + 10) algorithm.

Linguistic boosting factor. Type of extensions: Kinopoisksuggest (extensions of the textual orgate to text saddles). Aggregation by TOP-10 (by the value of the factor) extensions. Transferred number of extensions according to the X / (X + 10) algorithm.

L2 factor similar to Doppurlosctrlog5Decay30 only with fixed OS on iOS

LVT from VH player.

The series is a children's cartoon.

Whether the user was closed in Urlu in the last week. Data on Yandexuid and Puid.

The last deeply viewed host user. Data on Yandexuid and Puid.

The last deeply viewed host from the series from the request. Data on Yandexuid and Puid.

Does it contain the name of the words that indicate that the voice acting is original or in English.

The exact coincidence of the name of the series in the request and in the document (excluding synonyms).

The last deeply viewed host for any series. Data on Yandexuid and Puid.

The host of the last deeply viewed porn document. Data on Yandexuid and Puid.

The maximum cosine of the current document with documents from history closed shorter than 30 seconds

The maximum cosine of the current document with documents from history, clicks longer than 180 seconds

The maximum cosine of the current document with documents from history, clicks longer than 30 seconds

The maximum number of user likes among the document tags

The maximum number of user dizlash among document tags

The maximum number of user's likes among the tags of the document in 30 days

The maximum number of user dizlash among the document tags in 30 days

The maximum number of user likes among the tags of the document in 7 days

The maximum number of user dizlash among the document tags in 7 days

The maximum number of user's likes among the tags of the document in 8 hours

The maximum number of user dizlash among the document tags in 8 hours

The maximum number of user's likes among the document tags in 2 hours

The maximum number of user dizlash among the document tags in 2 hours

The total number of user likes by the document tags

The total number of user dyslaists by tags of the document

The total number of user's likes by tags of the document in 30 days

The total number of user dyslaists by tags of the document in 30 days

The total number of user's likes by tags of the document in 7 days

The total number of user dyslaists by tags of the document in 7 days

The total number of user flows by tags of the document in 8 hours

The total number of user dyslaists by tags of the document in 8 hours

The total number of user flows by tags of the document in 2 hours

The total number of user dyslaists by tags of the document in 2 hours

The presence of a subscription Yandex+ user

The presence of a three -month subscription Yandex+ user

The availability of a user's movie

The presence of a cinema post+amedias from the user

The availability of the Yandex+ subscription document

The availability of a document on a three -month subscription Yandex+

Accessibility of the document on the cinema subscription

Availability of a document on the cinema -POSISK+Amediatek

Availability of a document without subscription

The presence of a user subscription by which you can see the document

The accessibility of the document to the user

The document was added as a candidate from the dock2dok to a viewed document

how many times the user like the documents with this actor

how many times the user like the documents with this actor, with a fading of 30 days

how many times the user like the documents with this actor, with a fading of 7 days

how many times the user like the documents with this actor, with a fading of 8 hours

How many times the user dizulated with this actor

how many times the user dizulated the documents with this actor, with a fading of 30 days

how many times the user dizulated the documents with this actor, with fading 7 days

how many times the user dizulated the documents with this actor, with a fading of 8 hours

how many times the user discouraged documents with this actor

how many times the user discouraged documents with this actor, with a fading of 30 days

how many times the user discouraged documents with this actor, with a fading of 7 days

how many times the user discouraged documents with this actor, with a fading of 8 hours

how many times the user like the documents with this director

how many times the user like the documents with this director, with a fading of 30 days

how many times the user like the documents with this director, with a fading 7 days

how many times the user like the documents with this director, with a fading of 8 hours

How many times the user dizulated with this director

How many times the user dizulated with this director, with a fading of 30 days

how many times the user dizulated the documents with this director, with fading 7 days

How many times the user dizulated with this director, with a fading of 8 hours

how many times the user discouraged documents with this director

how many times the user discouraged documents with this director, with a fading of 30 days

how many times the user discouraged documents with this director, with a fading of 7 days

how many times the user discouraged documents with this director, with a fading of 8 hours

how many times the user like documents with this genre

how many times the user like documents with this genre, with a fading of 30 days

how many times the user like documents with this genre, with a fading 7 days

how many times the user like documents with this genre, with a fading of 8 hours

how many times the user dizulated documents with this genre

how many times the user dizulated documents with this genre, with a fading of 30 days

how many times the user dizulated documents with this genre, with a fading of 7 days

how many times the user dizulated the documents with this genre, with a fading of 8 hours

how many times the user discouraged documents with this genre

how many times the user discouraged documents with this genre, with a fading of 30 days

how many times the user discouraged documents with this genre, with a fading of 7 days

how many times the user discouraged documents with this genre, with a fading of 8 hours

how many times the user like the documents removed in this country

how many times the user like the documents removed in this country, with a fading of 30 days

how many times the user like the documents removed in this country, with a fading of 7 days

how many times the user like the documents removed in this country, with a fading of 8 hours

how many times the user dizulated the documents removed in this country

How many times the user dizzyked the documents removed in this country, with a fading of 30 days

How many times the user dizulated the documents removed in this country, with a fading of 7 days

How many times the user dizzyked the documents removed in this country, with a fading of 8 hours

how many times the user dropped the documents removed in this country

how many times the user dropped the documents removed in this country, with a fading of 30 days

how many times the user dropped the documents removed in this country, with a fading of 7 days

how many times the user dropped the documents removed in this country, with a fading of 8 hours

how many times the user watched at least 30 seconds a document with this actor

How many times the user watched at least 30 seconds a document with this actor, with a fading of 30 days

How many times the user watched at least 30 seconds a document with this actor, with a fading of 7 days

How many times the user watched at least 30 seconds a document with this actor, with a fading of 8 hours

how many times the user looked deeply with this actor

How many times the user looked deeply with this actor, with a fading of 30 days

How many times the user looked deeply with this actor, with a fading of 7 days

how many times the user looked deeply with this actor, with a fading of 8 hours

how many times the user began to watch a document with this actor

how many times the user began to watch a document with this actor, with a fading of 30 days

how many times the user began to watch a document with this actor, with a fading of 7 days

how many times the user began to watch a document with this actor, with a fading of 8 hours

how many times the user watched at least 30 seconds a document with this actor

How many times the user watched at least 30 seconds a document with this actor, with a fading of 30 days

How many times the user watched at least 30 seconds a document with this actor, with a fading of 7 days

How many times the user watched at least 30 seconds a document with this actor, with a fading of 8 hours

how many times the user looked deeply with this actor

How many times the user looked deeply with this actor, with a fading of 30 days

How many times the user looked deeply with this actor, with a fading of 7 days

how many times the user looked deeply with this actor, with a fading of 8 hours

how many times the user looked deeply with this actor

How many times the user looked deeply with this actor, with a fading of 30 days

How many times the user looked deeply with this actor, with a fading of 7 days

how many times the user looked deeply with this actor, with a fading of 8 hours

how many times the user watched at least 30 seconds a document with this director

How many times the user watched at least 30 seconds a document with this director, with a fading of 30 days

How many times the user watched at least 30 seconds a document with this director, with a fading of 7 days

How many times the user watched at least 30 seconds a document with this director, with a fading of 8 hours

How many times the user looked deeply with this director

How many times the user looked deeply with this director, with a fading of 30 days

How many times the user looked deeply with this director, with a fading of 7 days

how many times the user looked deeply with this director, with a fading of 8 hours

how many times the user began to watch a document with this director

how many times the user began to watch a document with this director, with a fading of 30 days

how many times the user began to watch a document with this director, with a fading of 7 days

how many times the user began to watch a document with this director, with a fading of 8 hours

how many times the user watched at least 30 seconds a document with this director

How many times the user watched at least 30 seconds a document with this director, with a fading of 30 days

How many times the user watched at least 30 seconds a document with this director, with a fading of 7 days

How many times the user watched at least 30 seconds a document with this director, with a fading of 8 hours

How many times the user looked deeply with this director

How many times the user looked deeply with this director, with a fading of 30 days

How many times the user looked deeply with this director, with a fading of 7 days

how many times the user looked deeply with this director, with a fading of 8 hours

How many times the user looked deeply with this director

How many times the user looked deeply with this director, with a fading of 30 days

How many times the user looked deeply with this director, with a fading of 7 days

how many times the user looked deeply with this director, with a fading of 8 hours

how many times the user watched at least 30 seconds a document with this genre

How many times the user watched at least 30 seconds a document with this genre, with a fading of 30 days

How many times the user watched at least 30 seconds a document with this genre, with a fading of 7 days

How many times the user watched at least 30 seconds a document with this genre, with a fading of 8 hours

how many times the user looked deeply with this genre

How many times the user looked deeply with this genre, with a fading of 30 days

How many times the user looked deeply with this genre, with a fading of 7 days

how many times the user looked deeply with this genre, with a fading of 8 hours

how many times the user began to watch a document with this genre

how many times the user began to watch a document with this genre, with a fading of 30 days

how many times the user began to watch a document with this genre, with a fading of 7 days

how many times the user began to watch a document with this genre, with a fading of 8 hours

how many times the user watched at least 30 seconds a document with this genre

How many times the user watched at least 30 seconds a document with this genre, with a fading of 30 days

How many times the user watched at least 30 seconds a document with this genre, with a fading of 7 days

How many times the user watched at least 30 seconds a document with this genre, with a fading of 8 hours

how many times the user looked deeply with this genre

How many times the user looked deeply with this genre, with a fading of 30 days

How many times the user looked deeply with this genre, with a fading of 7 days

how many times the user looked deeply with this genre, with a fading of 8 hours

how many times the user looked deeply with this genre

How many times the user looked deeply with this genre, with a fading of 30 days

How many times the user looked deeply with this genre, with a fading of 7 days

how many times the user looked deeply with this genre, with a fading of 8 hours

how many times the user watched at least 30 seconds a document shot in this country

How many times the user watched at least 30 seconds a document shot in this country, with a fading of 30 days

How many times the user watched at least 30 seconds a document shot in this country, with a fading of 7 days

How many times the user watched at least 30 seconds a document shot in this country, with a fading of 8 hours

how many times the user looked deeply on the document removed in this country

How many times the user looked deeply with the document shot in this country, with a fading of 30 days

How many times the user looked deeply with the document shot in this country, with a fading of 7 days

how many times the user looked deeply with the document shot in this country, with a fading of 8 hours

how many times the user began to watch the document removed in this country

How many times the user began to watch the document shot in this country, with a fading of 30 days

How many times the user began to watch the document shot in this country, with a fading of 7 days

how many times the user began to watch the document shot in this country, with a fading of 8 hours

how many times the user watched at least 30 seconds a document shot in this country

How many times the user watched at least 30 seconds a document shot in this country, with a fading of 30 days

How many times the user watched at least 30 seconds a document shot in this country, with a fading of 7 days

How many times the user watched at least 30 seconds a document shot in this country, with a fading of 8 hours

how many times the user looked deeply on the document removed in this country

How many times the user looked deeply with the document shot in this country, with a fading of 30 days

How many times the user looked deeply with the document shot in this country, with a fading of 7 days

how many times the user looked deeply with the document shot in this country, with a fading of 8 hours

how many times the user looked deeply on the document removed in this country

How many times the user looked deeply with the document shot in this country, with a fading of 30 days

How many times the user looked deeply with the document shot in this country, with a fading of 7 days

how many times the user looked deeply with the document shot in this country, with a fading of 8 hours

How many times the user looked at at least 30 seconds of the document of this author, with a fading of 30 days

how many times the user looked at at least 30 seconds of the document of this author, with a fading of 8 hours

how many times the user began to watch the document of this author

how many times the user began to watch the document of this author, with a fading of 30 days

how many times the user began to watch the document of this author, with a fading of 8 hours

how many times the user looked deeply on the document of this author

How many times the user looked deeply on the document of this author, with a fading of 30 days

the amount by tags of the document, how many times the user looked at at least 30 seconds of the document of this tag, with a fading of 8 hours

the amount by tags of the document, how many times the user began to watch a document of this tag, with a fading of 30 days

the amount by tags of the document, how many times the user began to watch a document of this tag, with a fading of 8 hours

Maximum by tags of the document, how many times the user began to watch a document of this tag, with a fading of 30 days

Maximum by tags of the document, how many times the user began to watch a document of this tag, with a fading of 8 hours

at least by tags of the document, how many times the user looked at at least 30 seconds of a document of this tag, with a fading of 30 days

at least by tags of the document, how many times the user looked at at least 30 seconds of a document of this tag, with a fading of 8 hours

at least by tags of the document, how many times the user looked deeply on the document of this tag, with a fading of 8 hours

at least by tags of the document, how many times the user began to watch a document of this tag

at least by tags of the document, how many times the user began to watch a document of this tag, with a fading of 30 days

at least by tags of the document, how many times the user began to watch a document of this tag, with a fading of 8 hours

Type of user interface - desktop

The category is dedicated to the video block

The category is dedicated to films

The category is dedicated to the series

The category is dedicated to sports

DOT Product pn_vhs#et_history_dssm user embedding and category

The number of likes by the user on Y. Muzyk

The number of likes by the user on the Y. Musiness in 30 days

The number of likes by the user on the Y. Musiness in 7 days

The number of likes by the user on the Y. Musics in 8 hours

The number of likes by the user on the Y. Musiness in 2 hours

The number of likes from Y. Music at Tag

The number of likes from Y. Music at Tag in 30 days

The number of likes from Y. Music at Tag in 7 days

The number of likes from Y. Music at Tag in 8 hours

The user made Tag Like on Y. Musiness

The user made Tag Like on Y. Music in 30 days

The user made Tag Like on Y. Music in 7 days

The user made Tag Like on Y. Music in 8 hours

The user made Tag Like on Y. Music in 2 hours

The number of dizlaiks for the user on Y. Muzyk

The number of dizlaiks for the user on Ya. Musics in 30 days

The number of dizlaiks for the user on Ya. Musics in 7 days

The number of dizlaiks for the user on Ya. Musics in 8 hours

The number of dizlaiks for the user on Ya. Musics in 2 hours

The number of dizlaiks from the I. Musics at Tag

The number of dizlaiks from the I. Musicians at Tag in 30 days

The number of dizlaiks from the I. Musicians at Tag in 7 days

The number of dizlaiks from the I. Musicians at Tag in 8 hours

The user put a dizlaik Tag on Y. Musiness

DOT Product pn_vhs#et_history_dssm user embedding and category V1

Type of user interface - touch

The number of authors in this category

The share of muzzle documents

The presence of an avatar in the public

A number of unique words in a request.

Sigmoid, the sum of the deviations of the terms of the request

Sigmoid, the sum of the number of news articles for all query terms

Sigmoid, the sum of the 'peaks' of the query terms

The maximum (normalized) deviation for the term request, refined by sigmoid

The maximum number of news articles for 72 hours for the term of request for the term of request

The maximum 'peak' maximum maximum

The share of unique words from the request through the Terms, to which the response from the RTMR table was received

Implementation of the algorithm described in the article ((http://wiki.yandex-team.ru//h.yandex.net/?http%3A%2F%2FreseRosoft.microsoft.com%2Fen-US%2FPEOPLIULIUUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUUP032-LIUUUU .pdf http://research.microsoft.com/en-us/people/tyliu/fp032-liu.pdf)) by large regions (tube)

Implementation of the algorithm described in the article ((http://wiki.yandex-team.ru//h.yandex.net/?http%3A%2F%2FreseRosoft.microsoft.com%2Fen-US%2FPEOPLIULIUUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUULYUUP032-LIUUUU .pdf http://research.microsoft.com/en-us/people/tyliu/fp032-liu.pdf)) by large regions (tube)

Rank of the quality of texts on the host. The higher 342 200 224 the greater the likelihood that the host is full of articles - a rewriting, a bad copy of the content ordered on the exchanges of content. Burning stronger as the before the aggregation.

Rank of the quality of texts on the host. The higher 342 200 224 the greater the likelihood that the host is full of articles - a rewriting, a bad copy of the content ordered on the exchanges of content. Burning stronger as the before the aggregation.

RecDssmSpyTitleDomainEmb12Dot

RecCFSharpDomainDot

Query-MainContentKeywords similarity, target: logDwellTime

Dssm Boosting ScoreAvgNearest5Weighted aggregation for XfOneSe model over 1-means centroids.

Linguistic boosting factor.

Linguistic boosting factor.

The factor according to the name from the original request is considered according to the contents of the document. Algorithm: Chain0wcm

The factor according to the name from the original request is considered according to the contents of the document. The minimum window size, which includes all the words of the request. It is normalized for the number of words in the request.

Factor for name from the original request text of the document. Algorithm Cosinematchmaxpredical.

The factor for all the name from the original request Aggregation on all extensions. Type of aggregation for extensions: the greatest value of the factor; It is considered according to the contents of the document. Algorithm: Chain0wcm

The factor for all the name from the original request Aggregation on all extensions. Type of aggregation for extensions: the greatest value of the factor; It is considered according to the contents of the document. The minimum window size, which includes all the words of the request. It is normalized for the number of words in the request.

The factor for all the name from the original request Aggregation on all extensions. Type of aggregation for extensions: the greatest value of the factor; The text of the document. Algorithm Cosinematchmaxpredical.

The ratio of the weight amount of the similarity of the documents of those who fell into PRS to all similar to this

The percentage of documents similar to these PRS

The total weight of documents similar to PRS

The number of documents similar to PRS

MacStmal weight of documents similar to this in PRS

Cluster size (found using a search in depth) which includes a document

The weight of similar documents in PRS compared to LEFT DOC

Distance to Left DOC inside its cluster

Similar documents, type of similarity: mutualserp. The ratio of the weight amount of the similarity of the documents of those who fell into PRS to all similar to this

Similar documents, type of similarity: mutualserp. The percentage of documents similar to these PRS

Similar documents, type of similarity: mutualserp. The total weight of similar documents that fell in PRS

Similar documents, type of similarity: mutualserp. The number of similar documents in PRS

Similar documents, type of similarity: mutualserp. The maximum weight among similar documents that fell in PRS

Similar documents, type of similarity: mutualserp. Cluster size (found using a search in depth) which includes a document

Similar documents, type of similarity: mutualserp. The number of documents from PRS has at least one similar document on the same PRS

Similar documents, type of similarity: mutualserp. The weight of similar documents in PRS compared to LEFT DOC

Similar documents, type of similarity: mutualserp. Distance to Left DOC inside its cluster

Meta:Nonzero metafactor on web_itditp:AddTimeMP(542)

Meta:Resid metafactor on web_itditp:DssmLogDwellTimeBigramsDot(606)

SD:DFT_GREATER_CNT metafactor on web_itditp:DssmBoostingXfOneSeAmSsHardDot(657)

Meta:Resid metafactor on web_itditp:SynS1(33)

Meta:Rmse metafactor on web_itditp:RusWordsInText(49)

Meta:EpsHashShare metafactor on web_itditp:UrlNGramsModel(103)

Meta:Nonzero metafactor on web_itditp:YabarHostSearchTraffic(151)

Meta:Nonzero metafactor on web_itditp:OwnerClicksPCTR_Reg(253)

SD:DFT_SUM_WF_NORM_SUM_W metafactor on web_itditp:Hops(382)

Meta: nonzero metafactor on web_itditp: yabarurlavgtime (394)

SD:DFT_SUM_WF_NORM_SUM_W metafactor on web_itditp:AvgTitleCapitalLettersRatio(584)

Meta:ResidMin metafactor on web_itditp:DssmLogDwellTimeBigramsDot(606)

MutualSerp:DFT_MAX metafactor on web_itditp:DssmBoostingXfWtd45Dot(632)

SD:DFT_MAX metafactor on web_itditp:RandomLogHostDssmBoostingCtrKMeans1ScoreScaledSumWeightedQEPerc25(705)

SD:DFT_SUM_WF_NORM_COUNT metafactor on web_itditp:RandomLogHostDssmVkPopularityPerc25(709)

Points if url has image in snippet in SaaS. The same as snippet in QuerySearchResponse rearrange

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=0.1days, dwelltime less than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=0.1days, dwelltime more than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=0.5days, dwelltime less than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=0.5days, dwelltime more than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=3days, dwelltime less than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=3days, dwelltime more than 120sec)

fadingEmbedding.Norm * CalcFading(fadingEmbedding.FadingCoef, requestTimestamp - fadingEmbedding.LastUpdTimestamp), (model=LogDwellTimeBigrams, fadingCoef=0.1days, dwelltime less than 120sec)

fadingEmbedding.Norm * CalcFading(fadingEmbedding.FadingCoef, requestTimestamp - fadingEmbedding.LastUpdTimestamp), (model=LogDwellTimeBigrams, fadingCoef=0.1days, dwelltime more than 120sec)

fadingEmbedding.Norm * CalcFading(fadingEmbedding.FadingCoef, requestTimestamp - fadingEmbedding.LastUpdTimestamp), (model=LogDwellTimeBigrams, fadingCoef=0.5days, dwelltime less than 120sec)

fadingEmbedding.Norm * CalcFading(fadingEmbedding.FadingCoef, requestTimestamp - fadingEmbedding.LastUpdTimestamp), (model=LogDwellTimeBigrams, fadingCoef=0.5days, dwelltime more than 120sec)

fadingEmbedding.Norm * CalcFading(fadingEmbedding.FadingCoef, requestTimestamp - fadingEmbedding.LastUpdTimestamp), (model=LogDwellTimeBigrams, fadingCoef=3days, dwelltime less than 120sec)

fadingEmbedding.Norm * CalcFading(fadingEmbedding.FadingCoef, requestTimestamp - fadingEmbedding.LastUpdTimestamp), (model=LogDwellTimeBigrams, fadingCoef=3days, dwelltime more than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=0.1days, dwelltime less than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=0.1days, dwelltime more than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=0.5days, dwelltime less than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=0.5days, dwelltime more than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=3days, dwelltime less than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=3days, dwelltime more than 120sec)

fadingEmbedding.Norm * CalcFading(fadingEmbedding.FadingCoef, requestTimestamp - fadingEmbedding.LastUpdTimestamp), (model=LogDwellTimeBigrams, fadingCoef=0.1days, dwelltime less than 120sec)

fadingEmbedding.Norm * CalcFading(fadingEmbedding.FadingCoef, requestTimestamp - fadingEmbedding.LastUpdTimestamp), (model=LogDwellTimeBigrams, fadingCoef=0.1days, dwelltime more than 120sec)

fadingEmbedding.Norm * CalcFading(fadingEmbedding.FadingCoef, requestTimestamp - fadingEmbedding.LastUpdTimestamp), (model=LogDwellTimeBigrams, fadingCoef=0.5days, dwelltime less than 120sec)

fadingEmbedding.Norm * CalcFading(fadingEmbedding.FadingCoef, requestTimestamp - fadingEmbedding.LastUpdTimestamp), (model=LogDwellTimeBigrams, fadingCoef=0.5days, dwelltime more than 120sec)

fadingEmbedding.Norm * CalcFading(fadingEmbedding.FadingCoef, requestTimestamp - fadingEmbedding.LastUpdTimestamp), (model=LogDwellTimeBigrams, fadingCoef=3days, dwelltime less than 120sec)

fadingEmbedding.Norm * CalcFading(fadingEmbedding.FadingCoef, requestTimestamp - fadingEmbedding.LastUpdTimestamp), (model=LogDwellTimeBigrams, fadingCoef=3days, dwelltime more than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=0.1days, dwelltime less than 120sec)

Cosine similarity between document and fading embedding of documents from RTMR user_history (model=LogDwellTimeBigrams, fadingCoef=0.1days, dwelltime more than 120sec)