CN117113143A - User classification method, device, equipment and medium - Google Patents
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Abstract
The application discloses a user classification method, device, equipment and medium, and relates to the field of big data or finance. The method comprises the following steps: creating an index template, wherein the index template is used for representing the user category; filling in user information into an index template to obtain a first user template, wherein the first user template is a template of an unknown user category; the first user template and a second user template are entered into the natural language processing tool to perform user classification on the first user template, the second user template being a template of a known user class. Therefore, user classification is executed according to the natural language processing tool, the efficiency and accuracy of user classification can be improved, personalized marketing strategies are formulated according to users of different classifications, proper financial products and high-quality services are recommended, customer satisfaction is improved, customer retention rate is increased, and bank profit maximization is finally achieved.
Description
Technical Field
The present application relates to the field of big data or finance, and in particular, to a user classification method, apparatus, device and medium.
Background
For banks, the user groups are reasonably divided, and the method has obvious commercial value and strategic significance. After the user groups are divided, the bank can provide customized bank products and services for different user groups, so that the utilization rate of resources and the satisfaction degree of users are improved.
Currently, a method for classifying user groups is generally that related technicians manually screen and classify user information stored in a banking system. However, the manual classification method is time-consuming, labor-consuming and low in efficiency, so that the working efficiency of related technicians is reduced, and the accuracy of user classification is also reduced.
Disclosure of Invention
In view of the above, the embodiments of the present application provide a user classification method, apparatus, device, and medium, which can improve the accuracy of user classification and the working efficiency of related technicians.
The embodiment of the application discloses the following technical scheme:
in a first aspect, the present application provides a method for classifying users, the method comprising:
creating an index template, wherein the index template is used for representing the user category;
filling user information into the index template to obtain a first user template, wherein the first user template is a template of an unknown user class;
the first user model and a second user template are input into a natural language processing tool to perform user classification on the first user template, the second user template being a template of a known user class.
Optionally, the method for acquiring the user information specifically includes:
acquiring original information, wherein the original information comprises one or more of a user name, a user age, a user gender, a user work, a user credit rating, a user default record and a user commodity purchase;
and desensitizing the original information to obtain user information.
Optionally, the desensitizing the original information to obtain user information includes:
and carrying out salt adding treatment on the original information to obtain user information.
Optionally, the filling the user information into the index template includes:
collecting a user information image, wherein the user information image comprises user information;
performing optical character recognition on the user information image to obtain user information;
filling the user information into the index template.
Optionally, the collecting the user information image includes:
acquiring an original image;
and carrying out normalization processing on the original image to obtain a user information image, wherein the normalization processing comprises carrying out one or more of angle correction normalization, scaling filling normalization, resolution normalization and clipping normalization on the original image.
In a second aspect, the present application provides a user classification apparatus, the apparatus comprising: the device comprises a creation module, an acquisition module and an execution module;
the creation module is used for creating an index template, and the index template is used for representing the user category;
the acquisition module is used for filling user information into the index template to acquire a first user template, wherein the first user template is a template of an unknown user class;
the execution module is used for inputting the first user model and a second user model into a natural language processing tool to execute user classification on the first user model, wherein the second user model is a model with known user classification.
Optionally, the acquiring module specifically includes: the device comprises a first acquisition module, a second acquisition module and a third acquisition module;
the first acquisition module is used for acquiring a user information image, wherein the user information image contains user information;
the second acquisition module is used for carrying out optical character recognition on the user information image to obtain user information;
and the third acquisition module is used for filling the user information into the index template.
Optionally, the first obtaining module specifically includes: the device comprises a first acquisition sub-module and a second acquisition sub-module;
the first acquisition sub-module is used for acquiring an original image;
the second obtaining sub-module is configured to perform normalization processing on the original image to obtain a user information image, where the normalization processing includes performing one or more of angle correction normalization, scaling filling normalization, resolution normalization, and clipping normalization on the original image.
In a third aspect, the present application provides a user classification device, comprising: a memory and a processor;
the memory is used for storing programs;
the processor is configured to implement the steps of the user classification method when executing the computer program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the user classification method described above.
Compared with the prior art, the application has the following beneficial effects:
the application discloses a user classification method, a device, equipment and a medium, wherein the method comprises the following steps: creating an index template, wherein the index template is used for representing the user category; filling in user information into an index template to obtain a first user template, wherein the first user template is a template of an unknown user category; the first user model and a second user template are entered into the natural language processing tool to perform user classification, the second user template being a template of a known user class. Therefore, user classification is executed according to the natural language processing tool, the efficiency and accuracy of user classification can be improved, personalized marketing strategies are formulated according to users of different classifications, proper financial products and high-quality services are recommended, customer satisfaction is improved, customer retention rate is increased, and bank profit maximization is finally achieved.
Drawings
In order to more clearly illustrate this embodiment or the technical solutions of the prior art, the drawings that are required for the description of the embodiment or the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a user classification method according to an embodiment of the present application;
fig. 2 is a schematic diagram of a user classification device according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a computer readable medium according to an embodiment of the present application;
fig. 4 is a schematic hardware structure of a server according to an embodiment of the present application.
Detailed Description
Currently, a method for identifying a target user is generally that a related technician performs manual screening through user information stored in a banking system. However, the manual method is time-consuming, labor-consuming and low in efficiency, so that the working efficiency of related technicians is reduced, and the accuracy of user classification is also reduced.
In view of the above, the present application provides a user classification method, apparatus, device and medium, where the method includes: creating an index template, wherein the index template is used for representing the user category; filling in user information into an index template to obtain a first user template, wherein the first user template is a template of an unknown user category; the first user model and a second user template are entered into the natural language processing tool to perform user classification, the second user template being a template of a known user class. Therefore, user classification is executed according to the natural language processing tool, the efficiency and accuracy of user classification can be improved, personalized marketing strategies are formulated according to users of different classifications, proper financial products and high-quality services are recommended, customer satisfaction is improved, customer retention rate is increased, and bank profit maximization is finally achieved.
In order to make the present application better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, the flowchart of a user classification method according to an embodiment of the present application is shown. The method comprises the following steps:
s101: an index template is created.
First, a concise and clear index template is created, wherein the index template comprises information indexes of assets, liability records, transaction records and credit reports of the user, such as user name, user age, user gender, user work, user credit rating, user default records, user commodity purchase and the like.
In some examples, the metrics templates may be embodied in question-and-answer form, such as:
the Question: user age, gender, job, deposit, credit rating, record of no violations, purchase of money-like products, ask the user whether the user is a first or second user?
Answer: the user is a category-one.
In other examples, the index template may also be embodied in tabular form, such as shown in table 1 below:
TABLE 1
It should be noted that, the present application is not limited to the specific index template and the number of user categories.
S102: filling user information into the index template to obtain a first user template.
The first user template is a template which is obtained by randomly filling target information of a target user into the index template and has structuring and readability. It should be noted that, since the first user template is only a template after the user information is filled in, the first user template is not known about the classification result of the user.
In some examples, the first user template may be a prompt word template for a prompt, where prompt refers to a given piece of text or sentence that is used to initiate and direct the machine learning model to generate a particular type, topic, or format of output. It can be expressed specifically as:
the Question: user-a, age-40, gender-female, work-teacher, deposit-100 tens of thousands, credit rating-high, record of whether there is a violation-none, purchase-3-money of financial products, ask the user whether the user is a first category user or a second category user?
Answer: the user is a category user.
In other examples, the first user template may also be represented as table 2 below:
TABLE 2
The user information may be directly input manually by the user, or may be extracted from an image including the user information. Specifically, optical character recognition can be performed on the user information image to obtain user information; and filling the user information into the index template, thereby obtaining a first user template.
It will be appreciated that the acquired image containing the user information (i.e., the original image) may be pre-processed, such as normalized, and then subjected to optical character recognition to obtain the user information.
Specifically, the normalization processing is a processing method for processing the original image according to a certain standard, thereby facilitating the subsequent acquisition of user information. By way of example, the normalization process may refer to one or more of a normalization process that angle-rectifies (i.e., rotates) the original image, a normalization process that scales and fills the original image, a normalization process that unifies the resolution of the original image, a normalization process that unifies the cropping of the original image, and so forth. It should be noted that, the present application is not limited to a specific normalization processing operation.
It will be appreciated that other pre-processing methods, such as noise processing, graying processing, etc., may be performed in addition to normalization processing. Illustratively, the graying process refers to an operation of converting a color image into a gray image, and after converting into the gray image, contrast can be visually increased, highlighting an area of user information of the original image. The noise processing can be based on methods such as a mean value filtering algorithm, a median value filtering algorithm, a Gaussian filtering algorithm and the like. The present application is not limited to a specific pretreatment method.
In some specific implementations, since the user information needs to be kept secret, the method for obtaining the user information may specifically be as follows:
the method comprises the steps of firstly, obtaining original information, wherein the original information comprises one or more of a user name, a user age, a user gender, a user work, a user credit rating, a user default record and a user commodity purchase; and secondly, desensitizing the original information to obtain the user information. For example, the above-described desensitization processing may be a salification processing method, which is a method of disagreeing a hashed result with a hashed result using the original user information by inserting a specific character string at an arbitrary fixed position of the user information.
S103: the first user template and the second user template are input into a natural language processing tool to perform user classification on the first user template.
The second user template is a template of known user categories.
In some examples, the second user template may be expressed as:
the Question: user-a, age-35, gender-female, job-lawyer, deposit-150 tens of thousands, credit rating-high, record of whether there is a violation-none, purchase-6-money of financial products, ask the user whether the user is a first category user or a second category user?
Answer: the user is a first category of users.
In other examples, the first user template may also be represented as table 3 below:
TABLE 3 Table 3
After the first user template and the second user template are obtained, the first user template and the second user template can be input into a natural language processing tool to execute user classification on the first user template.
It will be appreciated that, for a specific user category, the user may be distinguished by a first category user, a second category user, or may be distinguished by other categorization manners, which is not limited to the present application.
In summary, the application discloses a user classification method, which comprises the following steps: creating an index template, wherein the index template is used for representing the user category; filling in user information into an index template to obtain a first user template, wherein the first user template is a template of an unknown user category; the first user model and a second user template are entered into the natural language processing tool to perform user classification, the second user template being a template of a known user class. Therefore, user classification is executed according to the natural language processing tool, the efficiency and accuracy of user classification can be improved, personalized marketing strategies are formulated according to users of different classifications, proper financial products and high-quality services are recommended, customer satisfaction is improved, customer retention rate is increased, and bank profit maximization is finally achieved.
Referring to fig. 2, which is a user classification device according to an embodiment of the present application, the user classification device 200 includes: a creation module 201, an acquisition module 202, and an execution module 203;
specifically, the creating module 201 is configured to create an index template, where the index template is used to characterize a user class; an obtaining module 202, configured to fill in user information into an index template to obtain a first user template, where the first user template is a template of an unknown user class; an execution module 203 for inputting the first user template and a second user template into the natural language processing tool to perform user classification on the first user template, the second user template being a template of a known user class.
In some specific implementations, the acquisition module 201 specifically includes: the device comprises a first acquisition module, a second acquisition module and a third acquisition module;
the first acquisition module is used for acquiring a user information image, wherein the user information image contains user information; the second acquisition module is used for carrying out optical character recognition on the user information image to obtain user information; and the third acquisition module is used for filling the user information into the index template.
In some specific implementations, the first acquisition module specifically includes: the device comprises a first acquisition sub-module and a second acquisition sub-module;
the first acquisition sub-module is used for acquiring an original image; the second acquisition sub-module is used for carrying out normalization processing on the original image to acquire the user information image, wherein the normalization processing comprises one or more of angle correction normalization, scaling normalization, proportion filling normalization, resolution normalization and clipping normalization on the original image.
In some specific implementations, the device for acquiring the user information specifically includes:
the first user module is used for acquiring original information, wherein the original information comprises one or more of a user name, a user age, a user gender, a user work, a user credit rating, a user default record and a user purchasing commodity; and the second user module is used for carrying out desensitization processing on the original information so as to acquire the user information.
In some specific implementations, the second user module is specifically configured to: and carrying out salt adding treatment on the original information to obtain the user information.
In summary, the application discloses a user classification device, which comprises a creation module, an acquisition module and an execution module. Therefore, user classification is executed according to the natural language processing tool, the efficiency and accuracy of user classification can be improved, personalized marketing strategies are formulated according to users of different classifications, proper financial products and high-quality services are recommended, customer satisfaction is improved, customer retention rate is increased, and bank profit maximization is finally achieved.
The user classification method, the device, the equipment and the medium provided by the application can be used in the big data field or the financial field. The foregoing is merely exemplary, and the application fields of the user classification method, the device, the equipment and the medium provided by the application are not limited.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
Referring to FIG. 3, a schematic diagram of a computer readable medium according to an embodiment of the present application is shown. The computer readable medium 300 has stored thereon a computer program 311, which computer program 311, when executed by a processor, implements the steps of the user classification method of fig. 1 described above.
It should be noted that in the context of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It should be noted that the machine-readable medium according to the present application may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above. The computer readable storage medium can be, for example, but 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 of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-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 computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and 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 computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
Referring to fig. 4, which is a schematic diagram of a hardware structure of a server according to an embodiment of the present application, the server 400 may have a relatively large difference due to different configurations or performances, and may include one or more central processing units (central processing units, CPU) 422 (e.g., one or more processors) and a memory 432, and one or more storage media 430 (e.g., one or more mass storage devices) storing application programs 440 or data 444. Wherein memory 432 and storage medium 430 may be transitory or persistent storage. The program stored on the storage medium 430 may include one or more modules (not shown), each of which may include a series of instruction operations on a server. Still further, the central processor 422 may be configured to communicate with the storage medium 430 and execute a series of instruction operations in the storage medium 430 on the server 400.
The server 400 may also include one or more power supplies 426, one or more wired or wireless network interfaces 450, one or more input/output interfaces 458, and/or one or more operating systems 441, such as Windows ServerTM, mac OS XTM, unixTM, linuxTM, freeBSDTM, and the like.
The steps performed by the user classification method in the above embodiment may be based on the server structure shown in fig. 4.
It should also be noted that, according to an embodiment of the present application, the process of the user classification method described in the flowchart of fig. 1 may be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow diagram of fig. 1 described above.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
While several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in the present application is not limited to the specific combinations of technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the spirit of the disclosure. Such as the above-mentioned features and the technical features disclosed in the present application (but not limited to) having similar functions are replaced with each other.
Claims (10)
1. A method of classifying users, the method comprising:
creating an index template, wherein the index template is used for representing the user category;
filling user information into the index template to obtain a first user template, wherein the first user template is a template of an unknown user class;
the first user template and a second user template are input into a natural language processing tool to perform user classification on the first user template, the second user template being a template of a known user class.
2. The method according to claim 1, wherein the method for obtaining the user information specifically comprises the following steps:
acquiring original information, wherein the original information comprises one or more of a user name, a user age, a user gender, a user work, a user credit rating, a user default record and a user commodity purchase;
and desensitizing the original information to obtain user information.
3. The method of claim 2, wherein desensitizing the original information to obtain user information comprises:
and carrying out salt adding treatment on the original information to obtain user information.
4. The method of claim 1, wherein said filling user information into said index template comprises:
collecting a user information image, wherein the user information image comprises user information;
performing optical character recognition on the user information image to obtain user information;
filling the user information into the index template.
5. The method of claim 4, wherein the acquiring the user information image comprises:
acquiring an original image;
and carrying out normalization processing on the original image to obtain a user information image, wherein the normalization processing comprises carrying out one or more of angle correction normalization, scaling filling normalization, resolution normalization and clipping normalization on the original image.
6. A user classification device, the device comprising: the device comprises a creation module, an acquisition module and an execution module;
the creation module is used for creating an index template, and the index template is used for representing the user category;
the acquisition module is used for filling user information into the index template to acquire a first user template, wherein the first user template is a template of an unknown user class;
the execution module is used for inputting the first user template and a second user template into a natural language processing tool to execute user classification on the first user template, wherein the second user template is a template with a known user class.
7. The apparatus of claim 6, wherein the obtaining module specifically comprises: the device comprises a first acquisition module, a second acquisition module and a third acquisition module;
the first acquisition module is used for acquiring a user information image, wherein the user information image contains user information;
the second acquisition module is used for carrying out optical character recognition on the user information image to obtain user information;
and the third acquisition module is used for filling the user information into the index template.
8. The apparatus of claim 7, wherein the first acquisition module specifically comprises: the device comprises a first acquisition sub-module and a second acquisition sub-module;
the first acquisition sub-module is used for acquiring an original image;
the second obtaining sub-module is configured to perform normalization processing on the original image to obtain a user information image, where the normalization processing includes performing one or more of angle correction normalization, scaling filling normalization, resolution normalization, and clipping normalization on the original image.
9. A user classification device, comprising: a memory and a processor;
the memory is used for storing programs;
the processor being adapted to execute the program to carry out the steps of the method according to any one of claims 1 to 5.
10. A computer storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method according to any of claims 1 to 5.
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| CN202311074343.8A CN117113143A (en) | 2023-08-24 | 2023-08-24 | User classification method, device, equipment and medium |
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| CN202311074343.8A CN117113143A (en) | 2023-08-24 | 2023-08-24 | User classification method, device, equipment and medium |
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