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MX2016012272A - Intencion de cliente en entorno de busqueda integrada. - Google Patents

Intencion de cliente en entorno de busqueda integrada.

Info

Publication number
MX2016012272A
MX2016012272A MX2016012272A MX2016012272A MX2016012272A MX 2016012272 A MX2016012272 A MX 2016012272A MX 2016012272 A MX2016012272 A MX 2016012272A MX 2016012272 A MX2016012272 A MX 2016012272A MX 2016012272 A MX2016012272 A MX 2016012272A
Authority
MX
Mexico
Prior art keywords
search
intent
local
trained
derived
Prior art date
Application number
MX2016012272A
Other languages
English (en)
Inventor
Yan An
Raff Paul
Huang Xiao
Cai Yichao
Ma Rui
Aidan Crook C
Original Assignee
Microsoft Technology Licensing Llc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Microsoft Technology Licensing Llc filed Critical Microsoft Technology Licensing Llc
Publication of MX2016012272A publication Critical patent/MX2016012272A/es

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G06F16/90335—Query processing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/285—Clustering or classification
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

La arquitectura que opera en combinación con una estructura de búsqueda integrada para obtener la intención de usuario asociada a una consulta de búsqueda, y entonces con base en la intención obtenida, elegir el método de búsqueda: una búsqueda local en el dispositivo local actual desde el cual se inicia la búsqueda, una búsqueda no local de las fuentes de datos que no sean el dispositivo local, o tanto la búsqueda local como la búsqueda no local. El contexto de consulta puede obtenerse para evaluar mejor la intención de consulta. La arquitectura emplea los modelos predictivos instruidos con posibles características que permiten la predicción de una intención singular (o grado de intención) en el entorno de búsqueda integrada. Los modelos pueden instruirse usando las características históricas y en tiempo real. Un clasificador se instruye usando las características. La intención de usuario entonces se obtiene con base en la salida del clasificador y en consecuencia se realiza la búsqueda.
MX2016012272A 2014-03-26 2014-03-26 Intencion de cliente en entorno de busqueda integrada. MX2016012272A (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2014/074110 WO2015143639A1 (en) 2014-03-26 2014-03-26 Client intent in integrated search environment

Publications (1)

Publication Number Publication Date
MX2016012272A true MX2016012272A (es) 2016-11-30

Family

ID=54193882

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2016012272A MX2016012272A (es) 2014-03-26 2014-03-26 Intencion de cliente en entorno de busqueda integrada.

Country Status (10)

Country Link
US (1) US20170039269A1 (es)
EP (1) EP3123356A4 (es)
JP (1) JP6446057B2 (es)
KR (1) KR20160136321A (es)
CN (1) CN105264528A (es)
AU (1) AU2014388153B2 (es)
CA (1) CA2940014A1 (es)
MX (1) MX2016012272A (es)
RU (1) RU2662410C2 (es)
WO (1) WO2015143639A1 (es)

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US10726204B2 (en) * 2018-05-24 2020-07-28 International Business Machines Corporation Training data expansion for natural language classification
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CN111382218B (zh) * 2018-12-29 2023-09-26 北京嘀嘀无限科技发展有限公司 一种兴趣点（poi）检索的系统和方法
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CN113792136B (zh) * 2021-08-25 2024-06-04 北京库睿科技有限公司 文本数据多样化推荐式搜索方法和系统
US12242817B1 (en) * 2023-11-20 2025-03-04 Ligilo Inc. Artificial intelligence models in an automated chat assistant determining workplace accommodations

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Also Published As

Publication number Publication date
EP3123356A1 (en) 2017-02-01
RU2662410C2 (ru) 2018-07-25
CA2940014A1 (en) 2015-10-01
CN105264528A (zh) 2016-01-20
US20170039269A1 (en) 2017-02-09
AU2014388153B2 (en) 2020-01-02
JP6446057B2 (ja) 2018-12-26
RU2016137962A (ru) 2018-03-27
RU2016137962A3 (es) 2018-03-27
KR20160136321A (ko) 2016-11-29
EP3123356A4 (en) 2017-09-06
JP2017509086A (ja) 2017-03-30
AU2014388153A1 (en) 2016-09-08
WO2015143639A1 (en) 2015-10-01

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