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WO2018188378A1 - Procédé et dispositif de marquage d'étiquette pour application, terminal et support d'informations lisible par ordinateur - Google Patents

Procédé et dispositif de marquage d'étiquette pour application, terminal et support d'informations lisible par ordinateur Download PDF

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Publication number
WO2018188378A1
WO2018188378A1 PCT/CN2017/118709 CN2017118709W WO2018188378A1 WO 2018188378 A1 WO2018188378 A1 WO 2018188378A1 CN 2017118709 W CN2017118709 W CN 2017118709W WO 2018188378 A1 WO2018188378 A1 WO 2018188378A1
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WIPO (PCT)
Prior art keywords
tag
application
feature word
preference
feature
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PCT/CN2017/118709
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English (en)
Chinese (zh)
Inventor
潘岸腾
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广州优视网络科技有限公司
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Publication of WO2018188378A1 publication Critical patent/WO2018188378A1/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
    • G06F16/10—File systems; File servers
    • G06F16/14—Details of searching files based on file metadata
    • G06F16/148—File search processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/13—File access structures, e.g. distributed indices

Definitions

  • the present invention relates to the field of information processing technologies, and in particular, to a method, an apparatus, a terminal, and a computer readable storage medium for labeling an application.
  • a first embodiment of the present invention provides a method for labeling an application, including:
  • the corresponding one or more tags are selected from the tag library in a preset manner for the new application to be marked.
  • a second embodiment of the present invention provides an apparatus for labeling an application, including:
  • the feature word information extracting unit is configured to extract the feature word information from the application description information of each application in the preset application library, and extract the feature word information from the application description information of the new application to be labeled;
  • a feature word information determining unit of the tag configured to combine corresponding feature word information of the plurality of applications having the same tag as the feature word information of the tag;
  • a first preference determining unit configured to determine a first preference of each tag pair for each feature word to which it belongs
  • a second preference determining unit configured to determine, according to the first preference and the extracted feature word information of the new application, a second preference of the new application to each tag in the tag library;
  • the label labeling unit is configured to select a corresponding one or more labels from the label library according to the second preference to label the new application.
  • the feature word information extracting unit is configured to perform word segmentation processing on the application description information to extract the feature words, and calculate a probability that each of the feature words appears as a weight of the feature word to which the feature word belongs.
  • the feature word information includes a feature word and a weight of the feature word to which the application belongs.
  • the feature word information determining unit of the tag combines the corresponding feature word information of the plurality of applications having the same tag, and the method for the feature word information of the tag includes:
  • the feature words obtained after the combination and the weight of each of the feature words on the label are used as feature word information of the label.
  • the method for calculating the weight of each feature word on the label by the feature word information determining unit of the tag is as follows:
  • f t,j represents the weight of the feature word j on the label t
  • w i,j denotes the weight of the feature word j for the application i having the tag t in the preset application library
  • A represents a set of applications with a tag t in the preset application library
  • W represents a feature word set belonging to an application in the application set A
  • n the number of applications in the application set A
  • m represents the number of feature words in the feature word set W.
  • the method for determining, by the first preference determining unit, the first preference includes:
  • p t,j represents the first preference of the tag t for the feature word j
  • f t,j represents the weight of the feature word j on the label t
  • s j represents the probability that the feature word j appears in all the feature word sets extracted from the respective application description information of all applications in the preset application library, wherein:
  • w i,j represents the weight of the feature word j on the application i in the preset application library
  • AA represents a collection of all applications in the preset application library
  • Aw represents a set of all feature words extracted from the respective application description information of all applications
  • n the number of applications in the application set AA
  • m represents the number of feature words in the feature word set Aw.
  • the method for determining, by the second preference determining unit, the second preference includes:
  • the second preference of the new application for each tag in the tag library is calculated by the following formula:
  • r i,t represents a second preference of the new application i for the tag t
  • p t,j represents the first preference of the tag t for the feature word j
  • w i,j represents the weight of the feature word j extracted from the application description information of the new application i to the new application i;
  • AM represents the obtained set of all feature words attributed to the tag t
  • m represents the number of feature words in the feature word set attributed to the tag t.
  • the method for determining, by the second preference determining unit, the second preference of the new application to each tag in the tag library comprises:
  • the second preference is determined by the following formula:
  • r i,t represents a second preference of the new application i for the tag t
  • p t,j represents the first preference of the tag t for the feature word j
  • w i,j represents the weight of the feature word j extracted from the application description information of the new application i for the new application i;
  • Topic t represents a selected set of subject feature words attributed to the tag t;
  • m represents the number of feature words in the set of subject feature words belonging to the tag t.
  • the second preference determining unit selects, according to a first preference degree of each of the feature words belonging to each tag, a certain number of feature words as a topic feature of the corresponding tag according to a preset manner.
  • Word methods include:
  • the second preference determining unit selects, according to a first preference degree of each of the feature words belonging to each tag, a certain number of feature words as a topic feature of the corresponding tag according to a preset manner.
  • Word methods include:
  • a plurality of feature words corresponding to the plurality of first preference degrees greater than or equal to the first preset preference threshold are selected as the topic feature words.
  • the method for the label labeling unit to select the corresponding one or more labels from the label library according to the second preference to mark the new application according to the second preference includes:
  • the first preference is ranked by the first one or more tags to the new application.
  • the method for the label labeling unit to select the corresponding one or more labels from the label library according to the second preference to mark the new application according to the second preference includes:
  • the one or more tags corresponding to one or more second preference degrees greater than or equal to the second preset preference threshold are selected for the new application.
  • the embodiment of the present invention further provides a terminal, including a memory and a processor, where the memory stores computer readable instructions, and when the computer readable instructions are executed by the processor, the application provided by the embodiment of the present invention is implemented.
  • the method of labeling labels are described in detail below.
  • the embodiment of the present invention further provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed, a method for labeling an application provided by an embodiment of the present invention is implemented.
  • a method, device, terminal and computer readable storage medium for labeling an application according to an embodiment of the present invention by using an application known in the library and a tag thereof, using features and core functions for introducing an application
  • Application description information and word segmentation technology establishes an association between the new application to be tagged and the tag in the tag library, enabling automatic identification of one or more tags for new application annotation, reducing labor costs Improves accuracy and productivity.
  • FIG. 1 is a flowchart of a method for labeling an application according to an embodiment of the present invention
  • FIG. 2 is a schematic block diagram of an apparatus for labeling an application according to an embodiment of the present invention.
  • An embodiment of the present invention provides a terminal, where the terminal includes a memory, a processor, and a device for labeling an application.
  • the memory, the processor, and other components are electrically connected directly or indirectly to implement data transmission or interaction.
  • the device for labeling an application includes at least one software that can be stored in the memory or firmware in an operating system (OS) of the terminal in the form of software or firmware.
  • functional module The processor is configured to execute the executable module stored in the memory when the execution instruction is received, thereby implementing a corresponding function application, such as the method for labeling the application provided by the embodiment.
  • the terminal may further include more, less, or completely different components than the above, and is not limited herein.
  • FIG. 1 is a flowchart of a method for labeling an application according to an embodiment of the present invention, and the method is applicable to the foregoing terminal. As shown in FIG. 1, the method for labeling an application of the present invention includes the following steps S1 to S6.
  • S1 Extract feature word information from application description information of each application in the preset application library.
  • the application library is usually preset when developing the application market or the application store, and the third-party applications downloaded from the application market or the application store are saved in the preset application library.
  • third-party applications provided by the app store or the app market have one or more tags from the tag library that is preset when developing the app store or app market. The tag is used to identify the categories of various apps. Or content, easy for users to find. .
  • each application in the preset application library has application description information, which is used to introduce the characteristics and core functions of the application, so that the user can understand the application and generate interest in the application.
  • the method provided by the present invention may first perform word segmentation on the application description information to extract feature words, and then count the probability of occurrence of each feature word as the weight of the feature word to which the feature word belongs.
  • the feature word information described in step S1 includes the feature word and the weight of the feature word to which it belongs.
  • Word segmentation technology can be used to process word segmentation of application description information.
  • the extracted feature words are words obtained after word segmentation processing, or keywords.
  • the feature word information extracted from the description information of an application i is written as w i
  • w1: pci1, w2: pci2, w3: pci3, ... represent feature words and corresponding weights, for example w1 represents a feature word, and pci1 represents the weight of the feature word on the application i.
  • the application description information of the application is: "The input method with the precise typing and the most personalized interface, and the versatile input method".
  • the feature words obtained after the segmentation of the description information are: "typing and precision” , interface, personality, input method, possession, omnipotence, input method.”
  • the characteristic word information of "Sogou Pinyin Input Method” is:
  • the same feature words in the feature word information corresponding to each application having the same tag may be merged into one feature word, and the feature word obtained after the combination may be used as the feature word of the tag.
  • the weight of each of the feature words on the tag is then determined.
  • the feature words obtained after the combination and the weight of each of the feature words on the label are used as feature word information of the label.
  • Each existing application in the preset application library has one or more tags, and the feature information attributed to the application is extracted in the description information of each application, and the corresponding applications of multiple applications having the same tag are The feature word information is merged, and the feature word information obtained after the combination is used as the feature word information of the tag.
  • the feature word information of the tag similarly includes the feature word and the weight of the feature word on the tag.
  • the same plurality of feature words may be combined into one feature word, and the weight of each feature word on the label is calculated as follows:
  • f t,j represents the weight of the feature word j on the label t
  • w i,j denotes the weight of the feature word j for the application i having the tag t in the preset application library
  • A represents a set of applications with a tag t in the preset application library
  • W represents a feature word set belonging to an application in the application set A
  • n the number of applications in the application set A
  • m represents the number of feature words in the feature word set W.
  • the weight of each feature word on a certain tag is the probability that each feature word appears in the feature word set of the application belonging to the application set having the tag.
  • S3 Determine a first preference of each tag pair for each feature word that belongs to it.
  • the degree of association between words here the first preference degree of each feature word belonging to each tag pair is used as the degree of association, and the method for determining the first preference degree is as follows:
  • p t,j represents the first preference of the tag t for the feature word j
  • f t,j denotes the weight of the feature word j on the tag t, that is, the probability of occurrence in the feature word set of the application belonging to the application set having the tag t;
  • s j represents the probability that the feature word j appears in all the feature word sets extracted from the respective application description information of all applications in the preset application library, wherein:
  • w i,j represents the weight of the feature word j on the application i in the preset application library
  • AA represents a collection of all applications in the preset application library
  • Aw represents a set of all feature words extracted from the respective application description information of all applications
  • n the number of applications in the application set AA
  • m represents the number of feature words in the feature word set Aw.
  • the implementation step here is the same as the implementation method of step S1.
  • the feature word information is extracted from the application description information of the new application to be labeled, and the feature word information includes the feature word and the weight of the feature word for the new application to which the feature word belongs. Can also be recorded as w i
  • w i ⁇ w1:pci1,w2:pci2,w3:pci3,... ⁇ .
  • step S1 For other related descriptions, refer to the description of step S1, and the description is not repeated here.
  • S5 Determine, according to the first preference and the extracted feature word information of the new application, a second preference of the new application to each tag in the tag library.
  • the association degree of the new application with each tag in the tag library can be established.
  • the second preference of each tag in the tag library is used as the degree of association by the new application, and the method for determining the second preference is as follows:
  • r i,t represents a second preference of the new application i for the tag t
  • p t,j represents the first preference of the tag t for the feature word j
  • w i,j represents the weight of the feature word j extracted from the application description information of the new application i for the new application i;
  • AM represents the obtained set of all feature words attributed to the tag t
  • m represents the number of feature words in the feature word set attributed to the tag t.
  • the new application i is regarded as a combination of different feature words j extracted from the application description information of the new application i, and the first preference of each feature word belonging to the new application i by superimposing the tag t The degree value is obtained, and the second preference of the new application i for the tag t is obtained. Note that if a certain feature word attributed to the new application i is not in the feature word set belonging to the tag t, the first preference of the tag t for the feature word is zero.
  • the number of feature words in the feature word set attributed to the tag t when the number of feature words in the feature word set attributed to the tag t is large, the number of times of searching and accumulating is also large, which causes a large amount of calculation.
  • a preferred embodiment is described.
  • a part of the feature words may be filtered out from the feature word set belonging to the tag t according to the size of the first preference value, and the feature words corresponding to the smaller first preference value may be filtered out.
  • the number of feature words in the feature word set belonging to the tag t is reduced, and the amount of calculation can be reduced.
  • a certain number of feature words are selected as the topic feature words of the corresponding tags according to a preset manner, and a certain number of feature words are selected as corresponding tags according to a first preference degree of each feature word belonging to each tag.
  • the preset manner of the topic feature words may be: selecting a certain number of feature words ranked first in the first preference degree as the topic feature according to the order of the first preference degree of each feature word belonging to the tag t from the label t.
  • the word may also preset a first preset preference threshold, and select a plurality of feature words corresponding to the plurality of first preference degrees that are greater than or equal to the first preset preference threshold as the topic feature words.
  • it may be defined according to the data situation and the business scenario, for example, selecting 50, 100, 200 or other values; then determining the second preference, as follows:
  • r i,t represents a second preference of the new application i for the tag t
  • p t,j represents the first preference of the tag t for the feature word j
  • w i,j represents the weight of the feature word j extracted from the application description information of the new application i for the new application i;
  • Topic t represents a selected set of subject feature words attributed to the tag t;
  • m represents the number of feature words in the set of subject feature words belonging to the tag t.
  • S6 Select the corresponding one or more tags from the tag library according to the second preference to label the new application.
  • the first preference or the first one or more labels of the second preference ranking may be selected for the new application according to the order in which the new application applies the second preference value of each label from large to small.
  • the number of labels to be labeled may be defined according to the data situation and the business scenario, and may be any number between 1-5, such as 1, 2, 5, etc., or more.
  • a second preset preference threshold may be set, and one or more labels corresponding to one or more second preferences equal to or greater than the second preset preference threshold are selected to mark the new application.
  • a method for labeling an application according to the present invention by applying an application known in the library and a tag thereof, using a description and a word segmentation technique for introducing characteristics and core functions of the application, and applying a new application to the tag to be labeled Establishes an association with the tags in the preset tag library, which automatically finds one or more tags suitable for new application annotation, reduces labor costs, and improves the accuracy of labeling new applications. And work efficiency.
  • FIG. 2 is a schematic block diagram of an apparatus for labeling an application according to an embodiment of the present invention. As shown in FIG. 2, the apparatus for labeling an application of the present invention includes:
  • the feature word information extracting unit is configured to extract feature word information from the application description information of each application in the preset application library, and extract feature word information from the application description information of the new application to be tagged.
  • step S1 may be performed by the feature word information extracting unit.
  • the feature word information determining unit of the tag is configured to merge the corresponding feature word information of the plurality of applications having the same tag as the feature word information of the tag.
  • the description of the feature word information determining unit of the tag may refer to the detailed description of step S2 shown in FIG. 1, that is, step S2 may be performed by the feature word information determining unit of the tag.
  • the first preference determination unit is configured to determine a first preference for each of the feature words to which each tag belongs.
  • the description about the first preference determination unit may refer to the detailed description of step S3 shown in FIG. 1, that is, step S3 may be performed by the first preference determination unit.
  • the second preference determining unit is configured to determine a second preference of the new application for each tag in the tag library based on the first preference and the extracted feature word information of the new application.
  • step S4 may be performed by the second preference determination unit.
  • the label labeling unit is configured to select a corresponding one or more labels from the label library according to the second preference to mark the new application.
  • step S5 may be performed by the label labeling unit.
  • the method for extracting the feature word information from the application description information of each application in the preset application library may include: first performing word segmentation on the application description information to extract the feature words, and then The probability of occurrence of each feature word is counted as the weight of the feature word to which it belongs.
  • the feature word information determining unit of the tag merges the corresponding feature word information of the plurality of applications having the same tag, and the method as the feature word information of the tag may include: corresponding to each application having the same tag The same feature words in the feature word information are merged into one feature word, and the feature word obtained after the combination is used as the feature word of the tag. The weight of each of the feature words on the label is then determined. The feature words obtained after the combination and the weight of each of the feature words on the tag are then used as feature word information of the tag.
  • the feature word information determining unit of the tag is configured to merge the same plurality of feature words into one feature word in the merging process, and the method for calculating the weight of each feature word on the tag is as follows:
  • f t,j represents the weight of the feature word j on the label t
  • w i,j denotes the weight of the feature word j for the application i having the tag t in the preset application library
  • A represents a set of applications with a tag t in the preset application library
  • W represents a feature word set belonging to an application in the application set A
  • n the number of applications in the application set A
  • m represents the number of feature words in the feature word set W.
  • the method for determining, by the first preference determining unit, the first preference may include:
  • the first preference is determined by the following calculation formula as follows:
  • p t,j represents the first preference of the tag t for the feature word j
  • f t,j represents the weight of the feature word j on the label t
  • s j represents the probability that the feature word j appears in all the feature word sets extracted from the respective application description information of all applications in the preset application library, wherein:
  • w i,j represents the weight of the feature word j on the application i in the preset application library
  • AA represents a collection of all applications in the preset application library
  • Aw represents a set of all feature words extracted from the respective application description information of all applications
  • n the number of applications in the application set AA
  • m represents the number of feature words in the feature word set Aw.
  • the method for determining, by the second preference determining unit, the second preference may include:
  • the second preference is determined by the following formula:
  • r i,t represents a second preference of the new application i for the tag t
  • p t,j represents the first preference of the tag t for the feature word j
  • w i,j represents the weight of the feature word j extracted from the application description information of the new application i for the new application i;
  • AM represents the obtained set of all feature words attributed to the tag t
  • m represents the number of feature words in the feature word set attributed to the tag t.
  • the method for determining, by the second preference degree determining unit, the second preference of the new application to each tag in the tag library comprises: firstly, according to each tag pair The first preference of each feature word selects a certain number of feature words as the topic feature words of the corresponding tags in a preset manner, and then determines the second preference degrees.
  • the first preference number is selected as the topic feature word, or Presetting a first preset preference threshold, and selecting a plurality of feature words corresponding to the plurality of first preference degrees that are greater than or equal to the first preset preference threshold as the topic feature words.
  • it can be customized according to the data situation and the business scenario, for example, 50, 100, 200 or other values are selected.
  • the second preference can be determined by the following formula:
  • r i,t represents a second preference of the new application i for the tag t
  • p t,j represents the first preference of the tag t for the feature word j
  • w i,j represents the weight of the feature word j extracted from the application description information of the new application i for the new application i;
  • Topic t represents a selected set of subject feature words attributed to the tag t;
  • m represents the number of feature words in the set of subject feature words belonging to the tag t.
  • the label labeling unit may select the corresponding one or more labels from the label library according to the second preference degree to label the new application. on.
  • the preset manner of selecting one or more labels from the tag library can be performed in various ways.
  • the first preference or the first one or more labels of the second preference ranking may be selected for the new application according to the order in which the new application applies the second preference value of each label from large to small.
  • the number of labels to be labeled may be defined according to the data situation and the business scenario, and may be any number between 1-5, such as 1, 2, 5, etc., or more.
  • a second preset preference threshold may be set, and one or more labels corresponding to one or more second preferences equal to or greater than the second preset preference threshold are selected to mark the new application. . .
  • a device for labeling an application by using an application known in the library and a tag thereof, using a description and a word segmentation technique for introducing characteristics and core functions of the application, a new application to be tagged
  • a computer program product for providing a method for labeling an application according to an embodiment of the present invention comprising a computer readable storage medium storing program code, the program code comprising instructions for executing the application described in the foregoing method embodiment
  • program code comprising instructions for executing the application described in the foregoing method embodiment
  • the functions may be stored in a computer readable storage medium if implemented in the form of a software functional unit and sold or used as a standalone product.
  • the technical solution of the present invention which is essential or contributes to the prior art, or a part of the technical solution, may be embodied in the form of a software product, which is stored in a storage medium, including
  • a number of instructions are used to cause a computer device (which may be a personal computer, smart tablet, smartphone, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.
  • the foregoing storage medium includes various media that can store program codes, such as a USB flash drive, a removable hard disk, a read only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.
  • the method, the device, the terminal and the computer readable storage medium for labeling an application provided by the embodiment of the present invention utilize an application description for introducing an application feature and a core function by using an application known in the library and a tag thereof
  • Information and word segmentation technology establishes an association between the new application to be tagged and the tag in the preset tag library, enabling automatic identification of one or more tags for new applications, reducing manual Cost, improved accuracy and productivity

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Abstract

L'invention concerne un procédé et un dispositif de marquage d'une étiquette pour une application, un terminal et un support d'informations lisible par ordinateur. Le procédé consiste : à extraire des informations de mots caractéristiques à partir d'informations de description d'application de chaque application dans une bibliothèque d'applications prédéfinie (S1) ; à fusionner les informations de mots caractéristiques correspondantes d'une pluralité d'applications présentant la même étiquette et à utiliser les informations de mots caractéristiques fusionnées en tant qu'informations de mots caractéristiques de l'étiquette (S2) ; à déterminer un premier degré de préférence de chaque étiquette sur chaque mot caractéristique appartenant à l'étiquette (S3) ; à extraire des informations de mots caractéristiques à partir d'informations de description d'application de nouvelles applications qui doivent être marquées au moyen d'étiquettes (S4) ; à déterminer un second degré de préférence de la nouvelle application sur chaque étiquette dans la bibliothèque d'étiquettes sur la base du premier degré de préférence et des informations de mots caractéristiques extraites des nouvelles applications (S5) ; et selon le second degré de préférence, à sélectionner une ou plusieurs étiquettes correspondantes à partir de la bibliothèque d'étiquettes dans un mode prédéfini afin de marquer la nouvelle application (S6).
PCT/CN2017/118709 2017-04-10 2017-12-26 Procédé et dispositif de marquage d'étiquette pour application, terminal et support d'informations lisible par ordinateur WO2018188378A1 (fr)

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CN106951571B (zh) * 2017-04-10 2021-06-22 阿里巴巴（中国）有限公司 一种给应用标注标签的方法和装置
CN107506398B (zh) * 2017-08-02 2020-01-24 杭州东信北邮信息技术有限公司 一种为图书添加标签属性的方法
CN108363550A (zh) * 2017-12-28 2018-08-03 中兴智能交通股份有限公司 一种缓存数据更新和存储的方法和装置
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