CN114418345A - Big data based user analysis method and system - Google Patents
Big data based user analysis method and system Download PDFInfo
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- CN114418345A CN114418345A CN202111637863.6A CN202111637863A CN114418345A CN 114418345 A CN114418345 A CN 114418345A CN 202111637863 A CN202111637863 A CN 202111637863A CN 114418345 A CN114418345 A CN 114418345A
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Abstract
The embodiment of the application discloses a user analysis method and system based on big data. The user analysis method based on big data comprises the following steps: determining historical online user behavior information and historical heat information of at least one interaction event in the historical online user behavior information; determining significance description information of each interaction event in at least one interaction event in historical online user behavior information according to the feature recognition variable data and historical heat information of at least one interaction event; and according to the significance description information of at least one interactive event, adjusting the feature recognition degree of the historical online user behavior information to generate target user behavior information.
Description
Technical Field
The present application relates to the field of big data and user analysis technologies, and in particular, to a user analysis method and system based on big data.
Background
Nowadays, with the continuous development of big data, the application field based on big data is more and more extensive, however, in the actual process, how to accurately and effectively analyze the user is a technical problem that needs to be improved at present.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides a user analysis method and system based on big data.
The application provides a big data-based user analysis method, which comprises the following steps: determining historical online user behavior information and hot user behavior information corresponding to the historical online user behavior information; the hot user behavior information comprises historical hot information of at least one interaction event in the historical online user behavior information; recording the historical online user behavior information and the hot user behavior information; determining service theme information and feature recognition degree information through a big data analysis thread or through a service session; updating a historical popularity quantification result corresponding to the historical popularity information of the at least one interaction event based on the service theme information to obtain an updated popularity quantification result of each interaction event in the at least one interaction event; determining significance description information of each interactive event based on the feature recognition degree information and the updated heat quantification result of each interactive event in the at least one interactive event; and adjusting the feature recognition degree of the historical online user behavior information based on the significance description information of the at least one interaction event to generate target user behavior information.
Optionally, the updating, based on the service theme information, a historical popularity quantization result corresponding to the historical popularity information of the at least one interaction event to obtain an updated popularity quantization result of each interaction event in the at least one interaction event includes: setting an updated heat quantification result of a first interaction event in the at least one interaction event as a differentiation result of a history heat quantification result of the first interaction event and a heat quantification result of an attention item corresponding to the business topic information, and setting an updated heat quantification result of a second interaction event in the at least one interaction event as a null set, wherein the history heat quantification result of the first interaction event is greater than the heat quantification result of the attention item, and the history heat quantification result of the second interaction event is less than the heat quantification result of the attention item.
Optionally, the updating, based on the service theme information, a historical popularity quantization result corresponding to the historical popularity information of the at least one interaction event to obtain an updated popularity quantization result of each interaction event in the at least one interaction event includes: and setting the updated heat quantification result of each interactive event as an evaluation value of a comparison result between the historical heat quantification result of the interactive event and the heat quantification result of the attention item corresponding to the business topic information.
Optionally, the saliency description information comprises local description information; the adjusting the feature recognition degree of the historical online user behavior information based on the significance description information of the at least one interaction event to generate target user behavior information comprises the following steps: and taking the local description information of the at least one interactive event as a model variable of a first set model, and adjusting the feature recognition degree of the at least one interactive event of the historical online user behavior information through a set algorithm to generate target user behavior information.
The embodiment of the application also provides a big data analysis system, 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 technical scheme provided by the embodiment of the application can have the following beneficial effects.
According to the embodiment, for the historical online user behavior information acquired in advance, significance description information can be determined for at least one interactive event in the historical online user behavior information according to the feature recognition variable determined for multiple times and the heat information corresponding to the historical online user behavior information, feature recognition degree adjustment is performed on the interactive event in the historical online user behavior information according to the determined significance description information, target user behavior information is generated, and therefore high-quality target user behavior information can be obtained through adjustment of the historical online user behavior information.
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 a big data based user analysis method according to an embodiment of the present disclosure.
Fig. 2 is a schematic hardware structure diagram of a big data analysis system according to an embodiment of the present disclosure.
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 schematic flow chart of a big data-based user analysis method, which may specifically include the following technical solutions recorded in steps 100 to 300.
For some exemplary embodiments, the historical popularity quantification result corresponding to the historical popularity information of the at least one interaction event recorded in step 200 is updated based on the business topic information, so as to obtain an updated popularity quantification result of each interaction event in the at least one interaction event, which may be specifically described in the following two embodiments.
The first embodiment: setting an updated heat quantification result of a first interaction event in the at least one interaction event as a differentiation result of a history heat quantification result of the first interaction event and a heat quantification result of an attention item corresponding to the business topic information, and setting an updated heat quantification result of a second interaction event in the at least one interaction event as a null set, wherein the history heat quantification result of the first interaction event is greater than the heat quantification result of the attention item, and the history heat quantification result of the second interaction event is less than the heat quantification result of the attention item.
The second embodiment: and setting the updated heat quantification result of each interactive event as an evaluation value of a comparison result between the historical heat quantification result of the interactive event and the heat quantification result of the attention item corresponding to the business topic information.
For some exemplary embodiments, the recorded saliency description information comprises local description information; the adjusting the feature recognition degree of the historical online user behavior information based on the significance description information of the at least one interaction event to generate target user behavior information may specifically include: and taking the local description information of the at least one interactive event as a model variable of a first set model, and adjusting the feature recognition degree of the at least one interactive event of the historical online user behavior information through a set algorithm to generate target user behavior information.
Step 300, determining significance description information of each interactive event based on the feature recognition degree information and the updated heat quantification result of each interactive event in the at least one interactive event; and adjusting the feature recognition degree of the historical online user behavior information based on the significance description information of the at least one interaction event to generate target user behavior information.
In summary, for the history online user behavior information acquired in advance, significance description information may be determined for at least one interaction event in the history online user behavior information according to the feature recognition variable determined multiple times and the heat information corresponding to the history online user behavior information, and then feature recognition degree adjustment may be performed on the interaction event in the history online user behavior information according to the determined significance description information to generate target user behavior information, so that target user behavior information with higher quality may be obtained by adjusting the history online user behavior information.
On the basis, please refer to fig. 2 in combination, the present application further provides a schematic diagram of a hardware structure of a big data analysis system 200, which specifically includes a memory 210, a processor 220, a network module 230, and a big data analysis apparatus. 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 a big data analysis device, which includes at least one software function module that can be stored in the memory 210 in the form of software or firmware (firmware), and the processor 220 executes the software program and the module 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 big data analysis system 200 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 (5)
1. A big data-based user analysis method is characterized by comprising the following steps:
determining historical online user behavior information and hot user behavior information corresponding to the historical online user behavior information; the hot user behavior information comprises historical hot information of at least one interaction event in the historical online user behavior information; recording the historical online user behavior information and the hot user behavior information;
determining service theme information and feature recognition degree information through a big data analysis thread or through a service session; updating a historical popularity quantification result corresponding to the historical popularity information of the at least one interaction event based on the service theme information to obtain an updated popularity quantification result of each interaction event in the at least one interaction event;
determining significance description information of each interactive event based on the feature recognition degree information and the updated heat quantification result of each interactive event in the at least one interactive event; and adjusting the feature recognition degree of the historical online user behavior information based on the significance description information of the at least one interaction event to generate target user behavior information.
2. The method according to claim 1, wherein the updating, based on the service topic information, a historical popularity quantization result corresponding to the historical popularity information of the at least one interaction event to obtain an updated popularity quantization result of each interaction event of the at least one interaction event comprises:
setting an updated heat quantification result of a first interaction event in the at least one interaction event as a differentiation result of a history heat quantification result of the first interaction event and a heat quantification result of an attention item corresponding to the business topic information, and setting an updated heat quantification result of a second interaction event in the at least one interaction event as a null set, wherein the history heat quantification result of the first interaction event is greater than the heat quantification result of the attention item, and the history heat quantification result of the second interaction event is less than the heat quantification result of the attention item.
3. The method according to claim 1, wherein the updating, based on the service topic information, a historical popularity quantization result corresponding to the historical popularity information of the at least one interaction event to obtain an updated popularity quantization result of each interaction event of the at least one interaction event comprises: and setting the updated heat quantification result of each interactive event as an evaluation value of a comparison result between the historical heat quantification result of the interactive event and the heat quantification result of the attention item corresponding to the business topic information.
4. The method of claim 3, wherein the saliency description information comprises local description information; the adjusting the feature recognition degree of the historical online user behavior information based on the significance description information of the at least one interaction event to generate target user behavior information comprises the following steps:
and taking the local description information of the at least one interactive event as a model variable of a first set model, and adjusting the feature recognition degree of the at least one interactive event of the historical online user behavior information through a set algorithm to generate target user behavior information.
5. A big data analysis system is characterized by 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 1-4 by reading the computer program from the memory and running it.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111637863.6A CN114418345A (en) | 2021-12-30 | 2021-12-30 | Big data based user analysis method and system |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111637863.6A CN114418345A (en) | 2021-12-30 | 2021-12-30 | Big data based user analysis method and system |
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| CN114418345A true CN114418345A (en) | 2022-04-29 |
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| CN202111637863.6A Withdrawn CN114418345A (en) | 2021-12-30 | 2021-12-30 | Big data based user analysis method and system |
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- 2021-12-30 CN CN202111637863.6A patent/CN114418345A/en not_active Withdrawn
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