CN114398412A - Information processing method and server applied to user analysis - Google Patents

Information processing method and server applied to user analysis Download PDF

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CN114398412A
CN114398412A CN202111659441.9A CN202111659441A CN114398412A CN 114398412 A CN114398412 A CN 114398412A CN 202111659441 A CN202111659441 A CN 202111659441A CN 114398412 A CN114398412 A CN 114398412A
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user portrait
big data
data service
portrait label
service session
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吴启琦
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Suzhou Zhongtuo Internet Information Technology Co ltd
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    • G06F16/2457—Query processing with adaptation to user needs
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    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2465—Query processing support for facilitating data mining operations in structured databases
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Abstract

The application relates to an information processing method and a server applied to user analysis, wherein a basic user portrait label stored in advance in a user portrait label set is loaded to an AI model to adjust the user portrait label to obtain a derivative user portrait label; the derived user portrait label covers the basic user portrait label to optimize the user portrait label set, so that in the process of optimizing the user portrait label set, the optimization efficiency of the user portrait label set is improved on the basis of compatibility accuracy, and the portrait utilization rate can be improved as much as possible by integrating the basic user portrait label.

Description

Information processing method and server applied to user analysis
Technical Field
The present application relates to the field of user analysis and information processing technologies, and in particular, to an information processing method and a server applied to user analysis.
Background
With the continuous progress of new generation information technology, user analysis is also developed to a certain extent. However, in practical applications, how to improve the optimization efficiency of the user portrait tab set and improve the portrait utilization as much as possible is a technical problem that needs to be further improved.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides an information processing method and a server applied to user analysis.
The application provides an information processing method applied to user analysis, which is applied to an information processing server and comprises the following steps:
determining whether the user representation tag set contains a basic user representation tag of a first to-be-processed big data service session;
on the premise that the user portrait label set contains a basic user portrait label of the first to-be-processed big data service session, loading the basic user portrait label of the first to-be-processed big data service session, which is stored in advance in the user portrait label set, to an AI model for user portrait label adjustment, so as to obtain a derivative user portrait label of the first to-be-processed big data service session;
overlaying a base user portrait label of the first big data service session to be processed with the help of a derivative user portrait label of the first big data service session to be processed, so as to optimize the user portrait label set;
the AI model is trained according to basic user portrait tags and associated user portrait tags of a plurality of second big data service sessions to be processed in advance, in the training process of the AI model, the basic user portrait tags of the second big data service sessions to be processed are regarded as input, the associated user portrait tags of the second big data service sessions to be processed are regarded as reference of the AI model, and variables of the AI model are optimized until a set training expectation is met.
In some optional embodiments, after the deriving the derived user representation label of the first pending big data service session, the method further comprises:
inputting the first big data service session to be processed into a trained first user portrait label identification model to carry out user portrait label identification, so as to obtain a related user portrait label of the first big data service session to be processed;
and overlaying the derivative user portrait label of the first to-be-processed big data service session by means of the associated user portrait label of the first to-be-processed big data service session to optimize the user portrait label set, wherein the user portrait label set is optimized by using the user portrait label of the first to-be-processed big data service session which is obtained recently.
In some optional embodiments, the inputting the first to-be-processed big data service session into a trained first user portrait label recognition model for user portrait label recognition to obtain an associated user portrait label of the first to-be-processed big data service session includes:
and on the premise that the information processing server is in a blank window period, inputting the first to-be-processed big data service session into a trained first user portrait label recognition model for user portrait label recognition, and obtaining a related user portrait label of the first to-be-processed big data service session.
In some optional embodiments, the AI model includes a first network layer and a second network layer, where the step of loading a base user portrait tag of the first to-be-processed big data service session, which is stored in the user portrait tag set in advance, to an AI model for user portrait tag adjustment to obtain a derivative user portrait tag of the first to-be-processed big data service session includes:
inputting the basic user portrait label of the first to-be-processed big data service session, which is stored in the user portrait label set in advance, into the first network layer for user portrait label decoding to obtain a transitional user portrait label of the first to-be-processed big data service session;
and inputting the transitional user portrait label of the first big data service session to be processed into the second network layer for user portrait label identification to obtain a derivative user portrait label of the first big data service session to be processed.
In some optional embodiments, the AI model includes a first network layer and a third network layer, where the step of loading a base user portrait tag of the first to-be-processed big data service session, which is stored in the user portrait tag set in advance, to an AI model for user portrait tag adjustment to obtain a derivative user portrait tag of the first to-be-processed big data service session includes:
inputting the basic user portrait label of the first to-be-processed big data service session, which is stored in the user portrait label set in advance, into the first network layer for user portrait label decoding to obtain a transitional user portrait label of the first to-be-processed big data service session;
splicing the first big data service session to be processed and the transition user portrait label of the first big data service session to be processed to obtain a spliced user portrait label of the first big data service session to be processed;
and inputting the spliced user portrait label of the first big data service session to be processed into the third network layer for user portrait label identification to obtain a derivative user portrait label of the first big data service session to be processed.
The application also provides an information processing server, which comprises a memory, a processor and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor reads the computer program from the memory and runs the computer program to realize the method.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when run, implements the above-described method.
The technical scheme provided by the embodiment of the application can have the following beneficial effects.
In the embodiment of the application, a basic user portrait label which is stored in advance in a user portrait label set is loaded to an AI model to adjust the user portrait label, so that a derivative user portrait label is obtained; the derived user portrait label covers the basic user portrait label to optimize the user portrait label set, so that in the process of optimizing the user portrait label set, the optimization efficiency of the user portrait label set is improved on the basis of compatibility accuracy, and the portrait utilization rate can be improved as much as possible by integrating the basic user portrait label.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flowchart of an information processing method applied to user analysis according to an embodiment of the present application.
Fig. 2 is a schematic diagram of a hardware structure of an information processing server according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Referring to fig. 1, an embodiment of the present application provides a flowchart of an information processing method applied to user analysis, which is applied to an information processing server.
Step 100, determining whether the user portrait label set contains a basic user portrait label of the first to-be-processed big data service session.
Step 200, on the premise that the user portrait label set contains the basic user portrait label of the first to-be-processed big data service session, loading the basic user portrait label of the first to-be-processed big data service session, which is stored in advance in the user portrait label set, to an AI model for user portrait label adjustment, so as to obtain a derivative user portrait label of the first to-be-processed big data service session.
Step 300, overlaying the base user portrait label of the first to-be-processed big data service session by means of the derivative user portrait label of the first to-be-processed big data service session so as to optimize the user portrait label set.
In the embodiment of the application, the AI model is trained according to basic user portrait tags and associated user portrait tags of a plurality of second to-be-processed big data service sessions in advance, in the training process of the AI model, the basic user portrait tags of the plurality of second to-be-processed big data service sessions are regarded as input, the associated user portrait tags of the plurality of second to-be-processed big data service sessions are regarded as references of the AI model, and variables of the AI model are optimized until a set training expectation is met.
For some optional design considerations, after obtaining the derived user representation label of the first pending big data service session, the method further comprises: inputting the first big data service session to be processed into a trained first user portrait label identification model to carry out user portrait label identification, so as to obtain a related user portrait label of the first big data service session to be processed; and overlaying the derivative user portrait label of the first to-be-processed big data service session by means of the associated user portrait label of the first to-be-processed big data service session to optimize the user portrait label set, wherein the user portrait label set is optimized by using the user portrait label of the first to-be-processed big data service session which is obtained recently.
For some optional design considerations, the inputting the first to-be-processed big data service session into a trained first user portrait label recognition model for user portrait label recognition to obtain an associated user portrait label of the first to-be-processed big data service session includes: and on the premise that the information processing server is in a blank window period, inputting the first to-be-processed big data service session into a trained first user portrait label recognition model for user portrait label recognition, and obtaining a related user portrait label of the first to-be-processed big data service session.
For some optional design ideas, the AI model includes a first network layer and a second network layer, where the base user portrait tag of the first to-be-processed big data service session, which is stored in the user portrait tag set in advance, is loaded to the AI model for user portrait tag adjustment, so as to obtain a derivative user portrait tag of the first to-be-processed big data service session, and the method includes: inputting the basic user portrait label of the first to-be-processed big data service session, which is stored in the user portrait label set in advance, into the first network layer for user portrait label decoding to obtain a transitional user portrait label of the first to-be-processed big data service session; and inputting the transitional user portrait label of the first big data service session to be processed into the second network layer for user portrait label identification to obtain a derivative user portrait label of the first big data service session to be processed.
For some optional design ideas, the AI model includes a first network layer and a third network layer, where the base user portrait tag of the first to-be-processed big data service session, which is stored in the user portrait tag set in advance, is loaded to the AI model for user portrait tag adjustment, so as to obtain a derivative user portrait tag of the first to-be-processed big data service session, and the method includes: inputting the basic user portrait label of the first to-be-processed big data service session, which is stored in the user portrait label set in advance, into the first network layer for user portrait label decoding to obtain a transitional user portrait label of the first to-be-processed big data service session; splicing the first big data service session to be processed and the transition user portrait label of the first big data service session to be processed to obtain a spliced user portrait label of the first big data service session to be processed; and inputting the spliced user portrait label of the first big data service session to be processed into the third network layer for user portrait label identification to obtain a derivative user portrait label of the first big data service session to be processed.
In summary, when the method is applied to the embodiment of the application, the basic user portrait label stored in advance in the user portrait label set is loaded to the AI model to adjust the user portrait label, so that the derivative user portrait label is obtained; the derived user portrait label covers the basic user portrait label to optimize the user portrait label set, so that the optimization efficiency of the user portrait label set is improved on the basis of compatibility accuracy in the process of optimizing the user portrait label set, and the utilization rate of portrait can be improved as much as possible by integrating the basic user portrait label
On the basis, please refer to fig. 2 in combination, the present application further provides a schematic diagram of a hardware structure of the information processing server 20, which specifically includes a memory 210, a processor 220, a network module 230, and an information processing apparatus applied to user analysis. The memory 210, the processor 220, and the network module 230 are electrically connected directly or indirectly to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 210 stores therein an information processing apparatus for user analysis, which includes at least one software functional module that can be stored in the memory 210 in the form of software or firmware (firmware), and the processor 220 executes software programs and modules stored in the memory 210.
The Memory 210 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 210 is used for storing a program, and the processor 220 executes the program after receiving an execution instruction.
The processor 220 may be an integrated circuit chip having data processing capabilities. The Processor 220 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The network module 230 is used for establishing a communication connection between the information processing server 20 and other communication terminal devices through a network, so as to implement transceiving operation of network signals and data. The network signal may include a wireless signal or a wired signal.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
It is well known to those skilled in the art that with the development of electronic information technology such as large scale integrated circuit technology and the trend of software hardware, it has been difficult to clearly divide the software and hardware boundaries of a computer system. As any of the operations may be implemented in software or hardware. Execution of any of the instructions may be performed by hardware, as well as by software. Whether a hardware implementation or a software implementation is employed for a certain machine function depends on non-technical factors such as price, speed, reliability, storage capacity, change period, and the like. Accordingly, it will be apparent to those skilled in the art of electronic information technology that a more direct and clear description of one embodiment is provided by describing the various operations within the embodiment. Knowing the operations to be performed, the skilled person can directly design the desired product based on considerations of said non-technical factors.
The present application may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present application.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present application may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry can execute computer-readable program instructions to implement aspects of the present application by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present application are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the application is defined by the appended claims.

Claims (7)

1. An information processing method applied to user analysis, which is applied to an information processing server, comprises the following steps:
determining whether the user representation tag set contains a basic user representation tag of a first to-be-processed big data service session;
on the premise that the user portrait label set contains a basic user portrait label of the first to-be-processed big data service session, loading the basic user portrait label of the first to-be-processed big data service session, which is stored in advance in the user portrait label set, to an AI model for user portrait label adjustment, so as to obtain a derivative user portrait label of the first to-be-processed big data service session;
overlaying a base user portrait label of the first big data service session to be processed with the help of a derivative user portrait label of the first big data service session to be processed, so as to optimize the user portrait label set;
the AI model is trained according to basic user portrait tags and associated user portrait tags of a plurality of second big data service sessions to be processed in advance, in the training process of the AI model, the basic user portrait tags of the second big data service sessions to be processed are regarded as input, the associated user portrait tags of the second big data service sessions to be processed are regarded as reference of the AI model, and variables of the AI model are optimized until a set training expectation is met.
2. The method of claim 1, wherein after the deriving the derived user representation label for the first pending big data service session, the method further comprises:
inputting the first big data service session to be processed into a trained first user portrait label identification model to carry out user portrait label identification, so as to obtain a related user portrait label of the first big data service session to be processed;
and overlaying the derivative user portrait label of the first to-be-processed big data service session by means of the associated user portrait label of the first to-be-processed big data service session to optimize the user portrait label set, wherein the user portrait label set is optimized by using the user portrait label of the first to-be-processed big data service session which is obtained recently.
3. The method of claim 2, wherein inputting the first pending big data service session into a trained first user portrait label recognition model for user portrait label recognition to obtain an associated user portrait label for the first pending big data service session comprises:
and on the premise that the information processing server is in a blank window period, inputting the first to-be-processed big data service session into a trained first user portrait label recognition model for user portrait label recognition, and obtaining a related user portrait label of the first to-be-processed big data service session.
4. The method according to any one of claims 1 to 3, wherein the AI model comprises a first network layer and a second network layer, and wherein the step of loading the base user portrait tag of the first pending big data service session, which is stored in the user portrait tag set in advance, to the AI model for user portrait tag adjustment to obtain the derivative user portrait tag of the first pending big data service session comprises:
inputting the basic user portrait label of the first to-be-processed big data service session, which is stored in the user portrait label set in advance, into the first network layer for user portrait label decoding to obtain a transitional user portrait label of the first to-be-processed big data service session;
and inputting the transitional user portrait label of the first big data service session to be processed into the second network layer for user portrait label identification to obtain a derivative user portrait label of the first big data service session to be processed.
5. The method according to any one of claims 1 to 3, wherein the AI model comprises a first network layer and a third network layer, and wherein the step of loading the base user portrait tag of the first pending big data service session, which is stored in the user portrait tag set in advance, to the AI model for user portrait tag adjustment to obtain the derivative user portrait tag of the first pending big data service session comprises:
inputting the basic user portrait label of the first to-be-processed big data service session, which is stored in the user portrait label set in advance, into the first network layer for user portrait label decoding to obtain a transitional user portrait label of the first to-be-processed big data service session;
splicing the first big data service session to be processed and the transition user portrait label of the first big data service session to be processed to obtain a spliced user portrait label of the first big data service session to be processed;
and inputting the spliced user portrait label of the first big data service session to be processed into the third network layer for user portrait label identification to obtain a derivative user portrait label of the first big data service session to be processed.
6. An information processing server, comprising a memory, a processor, and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor implements the method of any one of claims 1-5 by reading the computer program from the memory and running it.
7. A computer-readable storage medium, on which a computer program is stored which, when executed, implements the method of any of claims 1-5.
CN202111659441.9A 2021-12-31 2021-12-31 Information processing method and server applied to user analysis Withdrawn CN114398412A (en)

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Application publication date: 20220426