Tag: TG_RTMODELS_SERVICE
(51 ranking factors)
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DssmYaMusicASREarlyBindingCe
web_production: 436
DSSM model with early binding, trained on reforming and learned by ASR hypotheses of musical requests for Alice
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DssmYaMusicEarlyBindingCe
web_production: 438
DSSM model with early binding, trained on reforming and learned on musical requests for Alice
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CommercialDssmOddLike
web_production: 812
Finetuned reformulations DSSM to commercial clicked bargain odd-like target from visit log
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AliceClickDssm
web_production: 900
DSSM CLOSE DISCOUNT according to data specific for Alice
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AliceTimespentSuffixSum
web_production: 957
The prediction of the total time spent to the end of the session, provided that this pair is implemented by the request-document
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AliceTimespent
web_production: 958
The prediction of the contribution of this pair request-document to the timetable
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AliceMaxPercentPlayed
web_production: 965
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
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AliceTimespentSum
web_production: 1273
Prediction of the time of the session, provided that this pair is requested by the request-document
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DssmLogDwellTimeBigrams
web_production: 1338
DSSM model trained on clicks. Takes bigrams into account.
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DssmOneClickProbability
web_production: 1405
DSSM model trained on clicks, target=OneClicks/Clicks. Takes bigrams into account.
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DssmQueryDwellTime
web_production: 1406
DSSM model trained on clicks, target=QueryDwellTime stream value. Takes bigrams into account.
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alice_aramusic_dssm
web_production: 1430
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AliceMusicRelevanceDssm
web_production: 1431
DSSM Prediction to determine Alice's irrelevant answers
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DssmCtrNoMiner
web_production: 1504
DSSM model trained on CTRs without miner.
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DssmCtrEngSsHard
web_production: 1855
DSSM model trained on cross language CTRs using serp similarity hard miner.
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ReformulationsLongestClickLogDt
web_production: 1885
DSSM model that predicts the logarithm of the longest click on the Serpa. As negative examples, select Urla from past requests of the same user, and the maximum time between requests is no more than 7 minutes (super -cords for reformulations)
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ReformulationsLongestClickLogDtEarlyBindingDssm
web_production: 1892
DSSM model with early binding, trained in reformulations, which predicts the logarithm of the longest click on the Serpa.
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DssmReformulationsWithExtensions
web_production: 1898
DSSM model trained on a reformal pool, which in the request, in addition to the request itself, receives 4 extensions of the XFDT with the largest weight
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DssmFomula8YearsCe25Prediction
web_production: 1906
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.
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DssmFomula8YearsCe25PredictionRatings
web_production: 1912
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 and an educational study on assessments of relevance.
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AliceAramusicDssmL2
web_l2: 76
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DssmDwelltimeRegChainTrainedEmbedding
web_meta: 297
Model trained on url, title and user regions chain. Target: DwellTime
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QueryWordTitleDistanceToWordOtzyvy
web_meta: 298
The proximity between the title and the word 'reviews', calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordNovosti
web_meta: 299
The proximity between the title and the word 'News', designed using the model from the Factor-1635
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QueryWordTitleDistanceToWordSkolko
web_meta: 300
The proximity between the title and the word 'how much', calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordPochemu
web_meta: 301
The proximity between the title and the word 'why', calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordDelat
web_meta: 302
The proximity between the title and the word 'do', designed using the model from the Factor-1635
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QueryWordTitleDistanceToWordInstagram
web_meta: 303
The proximity between the title and the word 'Instagram', designed using the model from the Factor-1635
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QueryWordTitleDistanceToWordUkraina
web_meta: 304
The proximity between the title and the word 'Ukraine', calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordZnachenie
web_meta: 305
The proximity between the title and the word 'meaning', calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordKrasivye
web_meta: 306
The proximity between the title and the word 'Beautiful', calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordStoit
web_meta: 307
The proximity between the title and the word 'stands', calculated using the model from the Factor-1635
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QueryWordTitleQueryMinWordSimilarity
web_meta: 308
The minimum proximity between the title and the words of the request calculated using the model from the Factor-1635
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QueryWordTitleQueryMaxMinDiff
web_meta: 309
The difference in maximum and minimal proximity between the title and all the words of the request
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QueryWordTitleNearestHalfAvg
web_meta: 310
We sort the words of the proximity to Title and leave half the closest. The meaning of the factor is the average proximity among the remaining (closest to Title)
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QueryWordTitleQueryMinWordSimilarityExcludeNumbers
web_meta: 311
Similarly, fi_query_word_title_Query_min_word_similarity (minimum proximity between the title and the words of the request), but the numbers in the request are not taken into account
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QueryWordTitleCityMaxWordSimilarity
web_meta: 312
The maximum proximity between the title and the words of the city of the user, calculated using the model from the Factor-1635
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QueryWordTitleOblMinWordSimilarity
web_meta: 313
The minimum proximity between the title and the words of the user area calculated using the model from the Factor-1635
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QueryWordTitleOblMaxWordSimilarity
web_meta: 314
The maximum proximity between the title and the words of the user area calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordSerial
web_meta: 321
The proximity between the title and the word 'series', calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordOficialnyj
web_meta: 322
The proximity between the title and the word 'Official', calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordSezon
web_meta: 323
The proximity between the title and the word 'season', calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordRozhdeniya
web_meta: 324
The proximity between the title and the word 'birth', calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordXoroshem
web_meta: 326
The proximity between the title and the word 'good', calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordVk
web_meta: 352
The proximity between the title and the word 'vk', calculated using the model from the Factor-1635
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QueryWordTitleDistanceToWordKupit
web_meta: 353
The proximity between the title and the word 'buy', designed using the model from the Factor-1635
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QueryWordTitleDistanceToWordMuzyku
web_meta: 355
The proximity between the title and the word 'music', designed using the model from the Factor-1635
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FeaturesPersonalModelPers
web_rtmodels: 15
Features personal model pers predict
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FeaturesPersonalModelOdd
web_rtmodels: 16
Features personal model odd predict
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FeaturesPersonalModelTr
web_rtmodels: 17
Features personal model tr predict
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FeaturesPersonalModelSurplus
web_rtmodels: 18
Features personal model surplus predict
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