CN110782128B - User occupation label generation method and device and electronic equipment - Google Patents
User occupation label generation method and device and electronic equipment Download PDFInfo
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- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
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- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
Abstract
The invention discloses a user occupation label generation method, a device, electronic equipment and a computer readable medium, which comprise the following steps: acquiring user information data, wherein the user information data at least comprises label data of a relevant person of the user on the user and occupation classification information data of the user; training the user information data by adopting a machine learning method, and constructing a user occupation classification prediction model; acquiring current user information data, wherein the current user information data at least comprises tag data of the current user, which is related to the current user, of a person associated with the current user; substituting the current user information into the user occupation prediction model to predict the current user occupation classification; and generating the current user occupation label based on the current user occupation classification. The invention can improve the judging capability of the user occupation by constructing the user occupation classification prediction model, and is beneficial to the financial platform to avoid economic loss caused by occupation information deletion.
Description
Technical Field
The present invention relates to the field of computer information processing, and in particular, to a method and apparatus for generating a user professional label, an electronic device, and a computer readable medium.
Background
The financial platform has a large number of users with missing information, and the missing user information enables the financial platform to deviate from the evaluation of the credit limit of the users and the repayment capability of the users, so that risk control of the financial platform is not facilitated. If the professional information of the user is lost, the financial platform is difficult to accurately estimate the income level of the user, further the user qualification is difficult to accurately estimate, the user qualification judgment is wrong, bad clients are divided into good clients, the situation that loan is difficult to withdraw exists, and the economic loss of the financial platform is caused.
The judging accuracy of the prior art on the user occupation is not high, and the coverage rate of the user occupation label in the user label system is also not high.
Disclosure of Invention
The technical problem to be solved by the invention is to improve the accuracy of professional judgment of the user.
An aspect of the present invention provides a user occupation label generation method, which is characterized by comprising: acquiring user information data, wherein the user information data at least comprises label data of a relevant person of the user on the user and occupation classification information data of the user; training the user information data by adopting a machine learning method, and constructing a user occupation classification prediction model; acquiring current user information data, wherein the current user information data at least comprises tag data of the current user, which is related to the current user, of a person associated with the current user; substituting the current user information into the user occupation prediction model to predict the current user occupation classification; and generating the current user occupation label based on the current user occupation classification.
According to a preferred embodiment of the present invention, the acquiring user information data further includes: and integrating tag data of the associated persons of different identity marks of the user on the user.
According to a preferred embodiment of the present invention, further comprising: integrating different identity marks of the users by taking the users as nodes and the relationship among the users as edges, and constructing a knowledge graph of the user identity mark information; identifying all associated persons of different identity marks of the user based on the identity mark information knowledge graph; and extracting the label data of all the associated persons for the user.
According to a preferred embodiment of the present invention, further comprising: the different identities may include a communication network number, an identity number, a device number, and a social account number of the user.
According to a preferred embodiment of the present invention, the acquiring current user information data further includes: and acquiring the current user information data by using the user identity information knowledge graph.
According to a preferred embodiment of the present invention, further comprising: acquiring at least one identity of the current user; using the user identity information knowledge graph to identify all the identities of the current user; and extracting tag data of the current user by the associated persons of all the identity marks of the current user.
According to a preferred embodiment of the present invention, further comprising: setting a mapping relation table of occupation classification and occupation labels; and establishing the current user occupation label according to the current user occupation classification by using the mapping relation table.
According to a preferred embodiment of the present invention, further comprising: the method of machine learning may further include a neural network model.
A second aspect of the present invention provides a user occupation label generating apparatus, comprising: the data acquisition module is used for acquiring user information data, wherein the user information data comprises label data of the user by the associated person of the user and occupation classification information data of the user; the model construction module is used for training the user information data by adopting a machine learning method and constructing a user occupation classification prediction model; the input module is used for acquiring current user information data, wherein the current user information data comprises label data of the current user, which is related to the current user, of a person associated with the current user; the model use module is used for substituting the current user information into the user occupation prediction model to predict the current user occupation classification; and the label generating module is used for generating the current user occupation label based on the current user occupation classification.
According to a preferred embodiment of the present invention, the data acquisition module further includes: and the data integration unit is used for integrating the label data of the related persons with different identity marks of the user on the user.
According to a preferred embodiment of the present invention, the data acquisition module is further configured to integrate different identities of the users with the users as nodes and the relationships between the users as edges, so as to construct the user identity information knowledge graph; all the associated persons used for identifying different identity marks of the user based on the identity mark information knowledge graph; and the tag data of all the associated persons to the user are extracted.
According to a preferred embodiment of the present invention, further comprising: the different identities may include a communication network number, an identity number, a device number, and a social account number of the user.
According to a preferred embodiment of the present invention, the input module further includes: and the current user information data is acquired by utilizing the user identity information knowledge graph.
According to a preferred embodiment of the present invention, the input module is further configured to obtain at least one identity of the current user; using the user identity information knowledge graph to identify all the identities of the current user; and extracting tag data of the current user by the associated persons of all the identity marks of the current user.
According to a preferred embodiment of the present invention, the user occupation label generating apparatus further includes: the mapping relation table is used for setting the occupation classification and the occupation label; and establishing the current user occupation label according to the current user occupation classification by using the mapping relation table.
According to a preferred embodiment of the present invention, further comprising: the method of machine learning may further include a neural network model.
A third aspect of the present invention provides an electronic apparatus, wherein the electronic apparatus includes: a processor; the method comprises the steps of,
a memory storing computer executable instructions that, when executed, cause the processor to perform the method of any of the claims.
A fourth aspect of the invention provides a computer readable storage medium storing one or more programs which when executed by a processor implement the method of any one of the claims.
The technical scheme of the invention has the following beneficial effects:
the user occupation label generation method can collect label data of the user associated person on the user and construct a user occupation classification prediction model. The occupation of the user is predicted by matching the keywords, so that the judging capability of the occupation of the user is improved, and the financial platform is facilitated to avoid economic losses caused by loss of occupation information.
The user occupation label generation method can efficiently predict the occupation of nearly millions of users, and increase the coverage rate of user occupation labels in a user label system while increasing the accuracy of occupation judgment.
Drawings
In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects achieved more clear, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted, however, that the drawings described below are merely illustrative of exemplary embodiments of the present invention and that other embodiments of the drawings may be derived from these drawings by those skilled in the art without undue effort.
FIG. 1 is a flow chart of a user occupation label generating method of the present invention;
FIG. 2 is a schematic diagram of a knowledge graph of user identity information in the user professional tag generation method of the invention;
FIG. 3 is a schematic diagram of the architecture of the user occupation label generating apparatus of the present invention;
FIG. 4 is a schematic diagram of a structural framework of the user professional tag generating electronic device of the present invention;
fig. 5 is a schematic diagram of a computer readable storage medium of the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals in the drawings denote the same or similar elements, components or portions, and thus a repetitive description thereof will be omitted.
The features, structures, characteristics or other details described in a particular embodiment do not exclude that may be combined in one or more other embodiments in a suitable manner, without departing from the technical idea of the invention.
In the description of specific embodiments, features, structures, characteristics, or other details described in the present invention are provided to enable one skilled in the art to fully understand the embodiments. However, it is not excluded that one skilled in the art may practice the present invention without one or more of the specific features, structures, characteristics, or other details.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various devices, elements, components or portions, this should not be limited by these terms. These words are used to distinguish one from the other. For example, a first device may also be referred to as a second device without departing from the spirit of the invention.
The term "and/or" and/or "includes all combinations of any of the associated listed items and one or more.
FIG. 1 is a flow chart of a user occupation label generating method of the present invention; as shown in fig. 1, the user occupation label generating method of the present invention includes:
s101: and acquiring user information data, wherein the user information data at least comprises label data of the associated person of the user on the user and occupation classification information data of the user.
Wherein, the obtaining the user information data further includes: and integrating tag data of the associated persons of different identity marks of the user on the user.
Wherein, still include: integrating different identity marks of the users by taking the users as nodes and the relationship among the users as edges, and constructing a knowledge graph of the user identity mark information;
identifying all associated persons of different identity marks of the user based on the identity mark information knowledge graph;
and extracting the label data of all the associated persons for the user.
Wherein, still include: the different identities may include a communication network number, an identity number, a device number, and a social account number of the user.
S102: training the user information data by adopting a machine learning method, and constructing a user occupation classification prediction model.
Wherein the method of machine learning further may comprise a neural network model.
The neural network model comprises an input layer, a hidden layer and an output layer, and each layer of the neural network comprises at least one neuron. The data processing process of the neural network model is as follows: and inputting data into the neurons of the input layer, and performing certain conversion on the data through an activation function to finally obtain an output result.
In the invention, user information data is used as a training sample when training a neural network model. The method specifically comprises at least the step of carrying out label data of a sample user on the user and the step of carrying out occupation classification data of the user, the step of adjusting the network structure of the model based on the occupation classification data of the user, and finally achieving certain precision and accuracy.
Training the neural network model through the user information data, wherein the trained neural network model is used for constructing a user occupation classification prediction model.
In order to improve the accuracy of the user occupation classification prediction model in predicting the user occupation, the number of training samples needs to be large enough, and the model needs to be trained irregularly during the use of the user occupation classification prediction model.
S103: and acquiring current user information data, wherein the current user information data at least comprises tag data of the current user, which is related to the current user, of the associated person of the current user.
Wherein, the obtaining the current user information data further includes: and acquiring the current user information data by using the user identity information knowledge graph.
Wherein, still include: acquiring at least one identity of the current user; using the user identity information knowledge graph to identify all the identities of the current user; and extracting tag data of the current user by the associated persons of all the identity marks of the current user.
S104: substituting the current user information into the user occupation prediction model to predict the current user occupation classification.
S105: and generating the current user occupation label based on the current user occupation classification.
Wherein the method of the invention further comprises: setting a mapping relation table of occupation classification and occupation labels; and establishing the current user occupation label according to the current user occupation classification by using the mapping relation table.
The method of the invention is further illustrated by way of example.
The user occupation information of the financial platform is missing, and the financial platform predicts the occupation of the user by constructing a user occupation classification prediction model in order to improve the judging capability of the user occupation.
The input data of the user occupation classification prediction model is label data of the user associated person to the user, and the output data is the user occupation.
The training data of the user occupation classification prediction model is constructed, and historical user data with perfect occupation information in a financial platform can be selected.
In order to improve the accuracy of the user occupation classification prediction model, more and more accurate keywords need to be obtained from various channels for matching with the occupation of the user.
More and more accurate keywords are acquired, and labels of related persons with different identity marks of the user to the user are required to be integrated.
Further comprises: and integrating different identity marks of the users by taking the users as nodes and the relationship among the users as edges, and constructing the user identity mark information knowledge graph. FIG. 2 is a schematic diagram of a knowledge graph of user identity information in the user professional tag generation method of the invention; as shown in fig. 2, the device number, the communication network number, the social account number, the identity number and other different identities of the user a are integrated.
To obtain more and more accurate keywords, all contacts of the user are identified by using a user identity information knowledge graph, and then tag data of the user by the associated person is extracted, so that more and more accurate keywords are obtained. As shown in fig. 2, the user identification information knowledge graph is utilized to identify the communication network number identification of the user, and the first-degree contact A, B of the user is identified through the communication network number identification, so that the label D, E of the first-degree contact a of the user to the user and the label A, B, C of the user B to the user are extracted.
The step of identifying the equipment number, the social account number, the identity number and other different identity marks through the user identity mark information knowledge graph, further identifying the associated person of the user, and further extracting the tag data of the associated person to the user is similar to the step of extracting the tag data of the associated person to the user through the communication network number.
The method of the invention can also carry out statistical analysis on the simultaneous occurrence of a plurality of labels of the same user.
After label data of a large number of users and professional classification information data of the users are obtained, training the large number of data by adopting a machine learning method to construct a user professional classification prediction model. The method of the present invention, the method of machine learning may further comprise a neural network model.
After the user occupation classification prediction model is built, when the financial platform predicts the occupation of the current user by using the user occupation classification prediction model, the current user information data is required to be acquired first, and the current user information data comprises label data of the current user, wherein the label data comprises label data of the current user and related people of the current user.
The tag data of the current user can be acquired by the association person of the current user through the established user identity identification information knowledge graph. Further comprises: acquiring at least one identity of a current user; using the knowledge graph of the user identity information to identify all the identities of the current user; and extracting tag data of the current user of the associated persons of all the identity marks of the current user.
The acquisition of tag data for the current user is described in further detail in connection with fig. 3 for the current user's correspondents.
The financial platform acquires the communication network number identity of the current user, and further identifies the identity numbers, the equipment numbers, the social account numbers and other identity marks through the user identity information knowledge graph; finally, all the labels of the relevant persons on the current user are extracted.
And substituting the current user information into the user occupation prediction model to predict the current user occupation classification.
Setting a mapping relation table of the professional classification and the professional label after acquiring the professional classification of the current user; and establishing a current user occupation label according to the current user occupation classification by using the mapping relation table.
By the method, nearly ten millions of users can be predicted efficiently, and the coverage rate of the professional user tag in the user tag system is increased by 8% while the prediction accuracy is improved.
The user occupation label generation method can collect label data of the user associated person on the user and construct a user occupation classification prediction model. The occupation of the user is predicted by matching the keywords, so that the judging capability of the occupation of the user is improved, and the financial platform is facilitated to avoid economic losses caused by loss of occupation information.
The user occupation label generation method can efficiently predict the occupation of nearly millions of users, and increase the coverage rate of user occupation labels in a user label system while increasing the accuracy of occupation judgment.
Those skilled in the art will appreciate that all or part of the steps implementing the above-described embodiments are implemented as a program (computer program) executed by a computer data processing apparatus. The above-described method provided by the present invention can be implemented when the computer program is executed. Moreover, the computer program may be stored in a computer readable storage medium, which may be a readable storage medium such as a magnetic disk, an optical disk, a ROM, a RAM, or a storage array composed of a plurality of storage media, for example, a magnetic disk or a tape storage array. The storage medium is not limited to a centralized storage, but may be a distributed storage, such as cloud storage based on cloud computing.
The following describes apparatus embodiments of the invention that may be used to perform method embodiments of the invention. Details described in the embodiments of the device according to the invention should be regarded as additions to the embodiments of the method described above; for details not disclosed in the embodiments of the device according to the invention, reference may be made to the above-described method embodiments.
It will be appreciated by those skilled in the art that the modules in the embodiments of the apparatus described above may be distributed in an apparatus as described, or may be distributed in one or more apparatuses different from the embodiments described above with corresponding changes. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
FIG. 3 is a schematic diagram of the architecture of the user occupation label generating apparatus of the present invention; as shown in fig. 3, the apparatus 300 of the present invention includes: a data acquisition module 301, a model construction module 302, an input module 303, a model use module 304, and a tag generation module 305.
The data acquisition module is used for acquiring user information data, wherein the user information data at least comprises label data of the user by the associated person of the user and occupation classification information data of the user.
And the model construction module is used for training the user information data by adopting a machine learning method and constructing a user occupation classification prediction model.
The input module is used for acquiring current user information data, wherein the current user information data at least comprises tag data of the current user, which is related to the current user, of the associated person of the current user.
And the model use module is used for substituting the current user information into the user occupation prediction model to predict the current user occupation classification.
And the label generating module is used for generating the current user occupation label based on the current user occupation classification.
The data acquisition module of the present invention comprises: and the data integration unit is used for integrating the label data of the related persons with different identity marks of the user on the user.
The data acquisition module is also used for integrating different identity marks of the users by taking the users as nodes and the relationship among the users as edges to construct the user identity mark information knowledge graph; identifying all associated persons of different identity marks of the user based on the identity mark information knowledge graph; and extracting the label data of all the associated persons for the user.
The different identity marks can comprise communication network numbers, identity numbers, equipment numbers and social account numbers of the users.
Wherein the input module further comprises: and the current user information data is acquired by utilizing the user identity information knowledge graph.
The input module is further used for acquiring at least one identity of the current user; using the user identity information knowledge graph to identify all the identities of the current user; and extracting tag data of the current user by the associated persons of all the identity marks of the current user.
The user occupation label generating device can be used for setting a mapping relation table of occupation classification and occupation labels; and establishing the current user occupation label according to the current user occupation classification by using the mapping relation table.
The user occupation label generating device of the present invention further comprises: the method of machine learning may further include a neural network model.
The following describes an embodiment of an electronic device according to the present invention, which may be regarded as a specific physical implementation of the above-described embodiment of the method and apparatus according to the present invention. Details described in relation to the embodiments of the electronic device of the present invention should be considered as additions to the embodiments of the method or apparatus described above; for details not disclosed in the embodiments of the electronic device of the present invention, reference may be made to the above-described method or apparatus embodiments.
Fig. 4 is a schematic diagram of a structural framework of the user occupation label generating electronic device of the present invention. An electronic device 400 according to this embodiment of the present invention is described below with reference to fig. 4. The electronic device 400 shown in fig. 4 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 4, the electronic device 400 is embodied in the form of a general purpose computing device. The components of electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one memory unit 420, a bus 430 connecting the different system components (including memory unit 420 and processing unit 410), a display unit 440, and the like.
Wherein the storage unit stores program code that is executable by the processing unit 410 such that the processing unit 410 performs the steps according to various exemplary embodiments of the present invention described in the electronic prescription stream processing method section above in this specification. For example, the processing unit 410 may perform the steps shown in fig. 1.
The memory unit 420 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 4201 and/or cache memory 4202, and may further include Read Only Memory (ROM) 4203.
The storage unit 420 may also include a program/utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 430 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 400 may also communicate with one or more external devices 500 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 400, and/or any device (e.g., router, modem, etc.) that enables the electronic device 400 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 450. Also, electronic device 400 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 460. The network adapter 460 may communicate with other modules of the electronic device 400 via the bus 430. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 400, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the exemplary embodiments described herein may be implemented in software, or may be implemented in software in combination with necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a computer readable storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the above-mentioned method according to the present invention. The computer program, when executed by a data processing device, enables the computer readable medium to carry out the above-described method of the present invention, namely: acquiring user information data, wherein the user information data comprises label data of a relevant person of the user on the user and occupation classification information data of the user; training the user information data by adopting a machine learning method, and constructing a user occupation classification prediction model; acquiring current user information data, wherein the current user information data comprises tag data of a relevant person of the current user on the current user; substituting the current user information into the user occupation prediction model to predict the current user occupation classification; and generating the current user occupation label based on the current user occupation classification.
The computer program may be stored on one or more computer readable media, as shown in fig. 5. The computer readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in accordance with embodiments of the present invention may be implemented in practice using a general purpose data processing device such as a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
The above-described specific embodiments further describe the objects, technical solutions and advantageous effects of the present invention in detail, and it should be understood that the present invention is not inherently related to any particular computer, virtual device or electronic apparatus, and various general-purpose devices may also implement the present invention. The foregoing description of the embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. A user occupation label generation method, comprising:
integrating different identity marks of the users by taking the users as nodes and the relationship among the users as edges, and constructing a user identity mark information knowledge graph; identifying the associated persons of different identity marks of the user based on the identity mark information knowledge graph; extracting tag data of the associated person to the user; acquiring user information data by using the user identity information knowledge graph; the user information data at least comprises label data of the user by the associated person of the user and occupation classification information data of the user;
training a neural network model by taking historical user information data with perfect professional information in a platform as training samples, wherein the samples at least comprise label data and professional classification information data of related persons of sample users on the users, and adjusting the network structure of the neural network model based on the professional classification information data of the users to construct a user professional classification prediction model;
acquiring current user information data by using the user identity information knowledge graph, wherein the current user information data at least comprises tag data of the current user by a related person of the current user, and carrying out statistical analysis on simultaneous occurrence of a plurality of tag data of the same user; acquiring at least one identity of the current user, identifying all the identities of the current user by using the user identity information knowledge graph, and extracting tag data of the current user by the associated persons of all the identities of the current user;
substituting the current user information data into the user occupation classification prediction model to predict the current user occupation classification, wherein input data in the user occupation classification prediction model is label data of a user associated person to a user, and output data is user occupation;
and generating the current user occupation label based on the current user occupation classification.
2. The method as recited in claim 1, further comprising:
the different identity marks comprise communication network numbers, identity numbers, equipment numbers and social account numbers of users.
3. The method as recited in claim 1, further comprising:
setting a mapping relation table of occupation classification and occupation labels;
and establishing the current user occupation label according to the current user occupation classification by using the mapping relation table.
4. A method according to any one of claims 1-3, wherein the neural network model further comprises:
the neural network comprises at least one neuron, wherein the data is input to the neurons of the input layer and then converted through an activation function, and finally an output result is obtained.
5. A user occupation label generating apparatus, comprising:
a data acquisition module comprising: the method is used for integrating different identity marks of the users by taking the users as nodes and the relationship among the users as edges, and constructing a knowledge graph of the user identity mark information; identifying the associated persons of different identity marks of the user based on the identity mark information knowledge graph; extracting tag data of the associated person to the user; acquiring user information data by using the user identity information knowledge graph; the user information data at least comprises label data of the user by the associated person of the user and occupation classification information data of the user;
the model construction module is used for training the user information data by adopting a machine learning method and constructing a user occupation classification prediction model, and comprises the following steps: training a neural network model by taking historical user information data with perfect professional information in a platform as training samples, wherein the samples at least comprise label data and professional classification information data of related persons of sample users on the users, and adjusting the network structure of the neural network model based on the professional classification information data of the users to construct a user professional classification prediction model;
the input module is used for acquiring current user information data by utilizing a user identity identification information knowledge graph, inputting the current user information data, wherein the current user information data at least comprises label data of a current user, which are obtained by a related person of the current user, and carrying out statistical analysis on simultaneous occurrence of a plurality of label data of the same user; the method comprises the steps of obtaining at least one identity of a current user, identifying all the identities of the current user by using a user identity information knowledge graph, and extracting tag data of the current user by associated persons of all the identities of the current user;
the model use module is used for substituting the current user information data into the user occupation classification prediction model to predict the current user occupation classification, wherein the input data in the user occupation classification prediction model is label data of a user associated person to a user, and the output data is user occupation;
and the label generating module is used for generating the current user occupation label based on the current user occupation classification.
6. The apparatus as recited in claim 5, further comprising:
the different identity marks comprise communication network numbers, identity numbers, equipment numbers and social account numbers of users.
7. The apparatus of claim 5, wherein the user occupation label generating means further comprises:
the mapping relation table is used for setting the occupation classification and the occupation label;
and establishing the current user occupation label according to the current user occupation classification by using the mapping relation table.
8. The apparatus of any one of claims 5-7, further comprising:
the method of machine learning further includes a neural network model.
9. An electronic device, wherein the electronic device comprises:
a processor; the method comprises the steps of,
a memory storing computer executable instructions that, when executed, cause the processor to perform the method of any of claims 1-4.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-4.
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