WO2018000281A1 - Système et procédé d'apprentissage de représentation de portrait d'utilisateur à base de réseau neuronal profond - Google Patents

Système et procédé d'apprentissage de représentation de portrait d'utilisateur à base de réseau neuronal profond Download PDF

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Publication number
WO2018000281A1
WO2018000281A1 PCT/CN2016/087773 CN2016087773W WO2018000281A1 WO 2018000281 A1 WO2018000281 A1 WO 2018000281A1 CN 2016087773 W CN2016087773 W CN 2016087773W WO 2018000281 A1 WO2018000281 A1 WO 2018000281A1
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user
neural network
deep neural
user portrait
statement
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PCT/CN2016/087773
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English (en)
Chinese (zh)
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邱楠
杨新宇
王昊奋
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深圳狗尾草智能科技有限公司
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Priority to PCT/CN2016/087773 priority Critical patent/WO2018000281A1/fr
Priority to CN201680001741.9A priority patent/CN106489159A/zh
Publication of WO2018000281A1 publication Critical patent/WO2018000281A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking

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  • the invention relates to big data, artificial intelligence and machine learning, in particular to a user image representation learning system and method based on deep neural network.
  • social networks have grown rapidly and the number of users has exploded.
  • people are more likely to use social networks as a public media platform to meet social needs and specific interests.
  • the current social network products can not meet the demand very well, the information published by various users is mixed, users need to identify the information they are interested in. If the deep learning extraction feature is selected for the information trend and distribution characteristics in the specific domain of the social network, it is more favorable to obtain the information characteristics of the user.
  • user portraits are mainly implemented by means of statistics, which often ignores some of the hidden information of the user.
  • the feature can be abstracted and refined, the feature representation is more concise and more precise, and the deeper hidden information can be extracted.
  • the invention discloses a user image representation learning system based on a deep neural network, which comprises: an intent recognition module for identifying a user's use function according to the received sentence; and a feature vector extraction module for inputting the sentence text through deep learning
  • the context relationship is modeled, and the feature information extracted by the user is extracted; and the user portrait learning module is used to continuously update the user portrait through the iterative training of the feature information and the supervision information.
  • the method further includes a statement receiving module, configured to receive a statement input by the user.
  • a statement receiving module configured to receive a statement input by the user.
  • the user-entered statement contains statements for unstructured data processing and statements that can be converted to structured data.
  • the unstructured data refers to statement text input by a user.
  • the structured data refers to data after converting an input sentence into an entity and a relationship.
  • the function of identifying the user includes multiple functions.
  • the invention also discloses a user image representation learning method based on deep neural network, wherein the package. Including: identifying the user's use function; extracting the feature information of the use function; and continuously updating the user's portrait through the iterative training of the feature information and the supervisory information.
  • the statement further includes receiving a user input.
  • the user-entered statement includes statements for unstructured data processing and statements that can be converted to structured data.
  • the unstructured data refers to statement text input by a user.
  • the structured data refers to data after converting an input sentence into an entity and a relationship.
  • the supervisory information is combined when updating the user's portrait.
  • FIG. 1 is a block diagram of a user image representation learning system based on a deep neural network according to an embodiment of the present invention
  • FIG. 2 is a flow chart of a method for user portrait representation learning based on a deep neural network according to an embodiment of the present invention.
  • the user portrait representation learning system 100 includes a sentence receiving module 101, an intent recognition module 102, a feature vector extraction module 103, and a user portrait learning module 104.
  • the statement receiving module is configured to receive a statement input by the user, and send the statement to the user after receiving the statement of the user.
  • the identification module 102 is configured to identify a function that the user wants to use according to a user's sentence, such as a function that the user wants to chat, order, or tune, and the like, and may be one or more of a plurality of functions.
  • structured data refers to data after converting an input statement into an entity and a relationship.
  • the chat input sentence can be used as unstructured data
  • the on-demand input statement data can extract the corresponding on-demand entity.
  • the statement entered by the user contains statements for unstructured data processing and statements that can be converted to structured data.
  • the statement text entered by the user is set to unstructured data.
  • the feature vector extraction module 103 models the context relationship of the input sentence text by deep learning according to the unstructured data, and extracts the feature information value of the user using the function.
  • the user portrait learning module 104 continuously updates the user portrait by iteratively training the feature information and the supervisory information.
  • structured data deep learning is used to model the relationship between entities, and then the user image is updated with a small amount of supervised information to finally obtain high-quality user images.
  • structured data refers to data after converting an input statement into an entity and a relationship.
  • step S201 the user inputs the sentence text; the user's intention is determined based on the sentence text for input.
  • the intent information of the user is obtained, for example, the user inputs a chat statement, an on-demand statement, or a training statement.
  • Feature extraction is performed on the obtained user intent information, mainly by extracting feature vectors through deep learning.
  • step S203 extracting a feature vector of the user intention information, such as a chat party vector, an on-demand vector or a training vector, and the like.
  • step S204 the user portrait is updated, and the user image is continuously updated through the iterative training of the feature information and the supervision information.
  • the present invention is described in the following three intents as an embodiment, but it should be understood that the present invention is not limited to the input of the three intentions (i.e., chattering intention, on-demand intention, or tuning intention), or Other types of intent input.
  • the statement text entered by the user is set to unstructured data.
  • the context relationship of the input sentence text is modeled by deep learning, and the feature information of the function used by the user is extracted.
  • the feature information value the user image is updated in combination with a small amount of supervision information.
  • structured data use deep learning to model the relationship between entities, and then update the user's portrait with a small amount of supervised information to achieve high quality.
  • User portrait refers to data after converting an input statement into an entity and a relationship.
  • the present invention proposes a user image representation learning system and method for deep neural networks, which analyzes user characteristics and updates user images to obtain high quality user images.

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Abstract

L'invention concerne un système d'apprentissage de représentation de portrait d'utilisateur à base de réseau neuronal profond, comprenant : un module de reconnaissance d'intention (102) utilisé pour reconnaître une fonction d'utilisation d'un utilisateur selon une déclaration reçue; un module d'extraction de vecteur caractéristique (103) utilisé pour modéliser la relation de contexte d'un texte ou la relation entre des entités grâce à un apprentissage profond, puis extraire des informations caractéristiques de l'utilisateur grâce à des informations textuelles saisies par l'utilisateur; et un module d'apprentissage de portrait d'utilisateur (104) utilisé pour mettre à jour en continu un portrait d'utilisateur grâce à un apprentissage itératif des informations caractéristiques et des informations de supervision. Grâce à l'apprentissage d'un portrait d'utilisateur dans un mode d'apprentissage profond, les caractéristiques du portrait d'utilisateur peuvent être extraites de manière abstraite, la représentation de caractéristique est plus concise et précise, et un niveau profond d'informations implicites peut être extrait.
PCT/CN2016/087773 2016-06-29 2016-06-29 Système et procédé d'apprentissage de représentation de portrait d'utilisateur à base de réseau neuronal profond WO2018000281A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
PCT/CN2016/087773 WO2018000281A1 (fr) 2016-06-29 2016-06-29 Système et procédé d'apprentissage de représentation de portrait d'utilisateur à base de réseau neuronal profond
CN201680001741.9A CN106489159A (zh) 2016-06-29 2016-06-29 一种基于深度神经网络的用户画像表示学习系统及方法

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PCT/CN2016/087773 WO2018000281A1 (fr) 2016-06-29 2016-06-29 Système et procédé d'apprentissage de représentation de portrait d'utilisateur à base de réseau neuronal profond

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CN108828948A (zh) * 2018-07-15 2018-11-16 大国创新智能科技(东莞)有限公司 基于深度学习的人工智能作战方法和机器人系统
CN109918162A (zh) * 2019-02-28 2019-06-21 集智学园（北京）科技有限公司 一种可学习的海量信息高维图形交互式展示方法
CN112364008A (zh) * 2020-11-20 2021-02-12 国网江苏省电力有限公司营销服务中心 一种面向电力物联网智能终端的设备画像构建方法
CN112908481A (zh) * 2021-03-18 2021-06-04 马尚斌 一种自动化个人健康评估及管理方法及系统
CN113282757A (zh) * 2021-07-14 2021-08-20 国网电子商务有限公司 基于电商领域表示模型的端到端三元组提取方法及系统
CN113609851A (zh) * 2021-07-09 2021-11-05 浙江连信科技有限公司 心理学上想法认知偏差的识别方法、装置及电子设备

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CN108804704A (zh) * 2018-06-19 2018-11-13 北京顶象技术有限公司 一种用户深度画像方法及装置
CN109783733B (zh) * 2019-01-15 2020-11-06 腾讯科技（深圳）有限公司 用户画像生成装置及方法、信息处理装置及存储介质
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