CN111652741A - User preference analysis method and device and readable storage medium - Google Patents
User preference analysis method and device and readable storage medium Download PDFInfo
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Abstract
The invention relates to artificial intelligence, and discloses a user preference analysis method, which comprises the following steps: acquiring a user static data set, and screening a first characteristic data set from the user static data set by using a correlation analysis method; calculating the first characteristic data set according to the user preference static model to obtain a static preference weight; acquiring a user dynamic data set, and combining the user dynamic data set with the first characteristic data set to generate a second characteristic data set; calculating the second characteristic data set according to the user preference dynamic model to obtain a dynamic preference weight; and establishing a user preference analysis model according to the feature data set and the weight, and executing user preference analysis. The invention also relates to a block chain technology, and the user static data set and the user dynamic data set can be stored in the block chain. The invention also provides a user preference analysis device, electronic equipment and a storage medium. The invention can solve the problem of waste of user characteristic information during user preference analysis.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method and a device for analyzing user preference, electronic equipment and a readable storage medium.
Background
With the rapid development of network technology, user consumption gradually shifts from traditional offline to online, making analysis of the online behavior of the user crucial. Currently, the research on the online behavior of the user is mainly based on the online browsing record. And e.g. analyzing the user behavior according to the online browsing records of the user and recommending the user insurance scheme.
However, this method is very random and not systematic enough, and cannot accurately perform targeted analysis on the correlation characteristics of each user, that is, each user adopts the same analysis means (such as online browsing records), neglects the correlation characteristics of the user and the acceptance of the product, and analyzes according to the online browsing records, although there is a certain degree of intellectualization, due to the uniform method, the method does not achieve the due effect on the premise of occupying a large amount of computing resources, and does not effectively combine the correlation information of the user, which results in the waste of the correlation information characteristics of the user.
Disclosure of Invention
The invention provides a user preference analysis method, a user preference analysis device, electronic equipment and a computer-readable storage medium, and mainly aims to fully utilize all relevant data of a user and improve the accuracy of the user preference analysis when the user preference analysis is carried out.
In order to achieve the above object, the present invention provides a method for analyzing user preferences, comprising:
acquiring a user information data set, wherein the user information data set comprises a user static data set and a user dynamic data set, and denoising the user information data set to obtain a standard data set;
obtaining a user static data set in the standard data set, and screening the user static data set by using a correlation analysis method to obtain a first characteristic data set;
analyzing and calculating the first characteristic data set according to a pre-constructed user preference static model to obtain a static preference weight;
acquiring a user dynamic data set in the standard data set, and combining the user dynamic data set with the first characteristic data set to generate a second characteristic data set;
analyzing and calculating the second characteristic data set according to a pre-constructed user preference dynamic model to obtain a dynamic preference weight;
and establishing a user preference analysis model according to the first characteristic data set and the corresponding static preference weight, and the second characteristic data set and the corresponding dynamic preference weight, and executing user preference analysis according to the user preference analysis model.
Optionally, the denoising processing on the user information data set to obtain a standard data set includes:
denoising the user information data set by adopting the following formula:
s(x)＝f(x)-*e(x)
where s (x) is the standard data set, f (x) is the user information data set, and e (x) is noise, which is the standard deviation of the noise figure.
Optionally, the screening the user static data set to obtain the first feature data set by using a correlation analysis method includes:
classifying the data in the user static data set according to the attributes of the data, and selecting a data set A with one attribute in a traversal mode;
calculating the correlation degree value r between the data set A and the data B of the preset behavior by using the following formulaA,B：
Wherein N represents the number of data in the data set A, aiAnd biFor any two data in the data set A in i, σA、σBRespectively represent data AStandard deviation from data B;
and screening the user static data set according to the correlation degree value to obtain first characteristic data.
Optionally, the user preference static model is:
wherein y represents the static preference weight, xnRepresenting data in the first characteristic data, and a is a model parameter.
Optionally, the user preference dynamic model is:
z＝b+bx′1+…bx′n
wherein P is the dynamic preference weight, z is and x'nFunction in a linear relationship, x'nAnd b is the data in the second characteristic data set and is the model parameter.
Optionally, the user preference analysis model is:
wherein D is a user preference analysis result data set, xiiFor the first feature data set, α is the static preference weight, yiFor the second feature data set β is the dynamic preference weight and m is the number of data of the first feature data set.
In order to solve the above problem, the present invention also provides a user preference analysis apparatus, comprising:
the system comprises a static characteristic screening module, a data processing module and a characteristic screening module, wherein the static characteristic screening module is used for acquiring a user information data set, the user information data set comprises a user static data set and a user dynamic data set, denoising processing is carried out on the user information data set to obtain a standard data set, the user static data set in the standard data set is acquired, and a first characteristic data set is screened from the user static data set by using a correlation analysis method;
the dynamic feature screening module is used for analyzing and calculating the first feature data set according to a pre-constructed user preference static model to obtain a static preference weight, obtaining a user dynamic data set in the standard data set, combining the user dynamic data set with the first feature data set to generate a second feature data set, and analyzing and calculating the second feature data set according to the pre-constructed user preference dynamic model to obtain a dynamic preference weight;
and the preference analysis module is used for establishing a user preference analysis model according to the first characteristic data set and the corresponding static preference weight as well as the second characteristic data set and the corresponding dynamic preference weight, and executing user preference analysis according to the user preference analysis model.
Optionally, the static feature screening module screens the user static data set to obtain a first feature data set by:
classifying the data in the user static data set according to the attributes of the data, and selecting a data set A with one attribute in a traversal mode;
calculating the correlation degree value r between the data set A and the data B of the preset behavior by using the following formulaA,B：
Wherein N represents the number of data in the data set A, aiAnd biFor any two data in the data set A in i, σA、σBRespectively representing the standard deviation of the data A and the data B;
and screening the user static data set according to the correlation degree value to obtain first characteristic data.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the user preference analysis method described above.
In order to solve the above problems, the present invention also provides a computer-readable storage medium comprising a storage data area storing data created according to the use of blockchain nodes and a storage program area storing a computer program implementing the user preference analysis method described above when executed by a processor.
In the embodiment of the invention, the user static data set and the user dynamic data set are obtained, the user static data set is screened by using a correlation analysis method to obtain the first characteristic data set which is more relevant to the user preference analysis, and further the embodiment of the invention combines the user dynamic data set and the first characteristic data set into the second characteristic data set, so that all relevant data of a user are fully utilized. Therefore, the user preference analysis method, the user preference analysis device, the electronic equipment and the computer-readable storage medium in the embodiments of the present invention can accurately analyze the receiving capabilities of different users for products by using the correlation characteristic information of the users, and solve the problems of the waste of the correlation information of the users and the high algorithm complexity in analyzing the correlation information of the users.
Drawings
Fig. 1 is a flowchart illustrating a user preference analysis method according to an embodiment of the present invention;
FIG. 2 is a block diagram of an apparatus for analyzing user preferences according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device implementing a user preference analysis method according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The execution subject of the user preference analysis method provided by the embodiment of the present application includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the user preference analysis method may be performed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
The invention provides a user preference analysis method. Fig. 1 is a schematic flow chart of a user preference analysis method according to an embodiment of the present invention. The method may be performed by an apparatus, which may be implemented by software and/or hardware.
In this embodiment, the user preference analysis method includes:
s1, obtaining a user information data set, wherein the user information data set comprises a user static data set and a user dynamic data set, and denoising the user information data set to obtain a standard data set.
Specifically, the user static data set comprises user personal basic information and asset condition basic information. The personal basic information comprises the name, age, gender and the like of the user, and the asset condition basic information comprises the asset conditions held by the user, such as held vehicle information, house property information, purchased insurance, financial product information and the like. The user dynamic data set comprises recent online behaviors of the user, such as online browsing records of the user, data of behaviors such as online complaints and claims, and the like, and data of complaints and claims on the line, such as complaints of an insurance company on a certain forum or website before three days of the small plum, or requests of the insurance company to carry out the claims on the line before one day of the small plum.
In the embodiment of the present invention, the user information data set may be obtained from a block chain.
Preferably, the embodiment of the present invention performs denoising processing on the user information data set according to the following denoising algorithm:
s(x)＝f(x)-*e(x)
wherein s (x) is the standard data set after denoising, f (x) is the user information data, e (x) is noise, and is the standard deviation of the noise coefficient.
And S2, obtaining a user static data set in the standard data set, and screening the user static data set by using a correlation analysis method to obtain a first characteristic data set.
In detail, in the embodiment of the present invention, the screening of the user static data set by using the correlation analysis method to obtain the first feature data includes:
classifying the data in the user static data set according to the attributes of the data, and selecting a data set A with one attribute in a traversal mode;
calculating the correlation degree value r between the data set A and the data B of the preset behavior by using the following formulaA,B：
Wherein N represents the number of data in the data set A, aiAnd biFor any two data in the data set A in i, σA、σBRespectively, data A and data B, wherein r is the standard deviationA,B∈[-1,1]；
And screening the user static data set according to the correlation degree value to obtain first characteristic data.
In the embodiment of the invention:
if r isA,B>0, then A, B are positively correlated;
if r isA,B<0, then A, B negative correlation;
if r isA,B0, A, B is zero-correlated;
if r isA,BAt ± 1, A, B is completely relevant.
The embodiment of the invention further provides a correlation degree value r between the data of each attribute and the data B of the preset behaviorA,BAnd screening out zero correlation and negative correlation data to obtain the first characteristic data set.
The preset behavior may be different according to different application scenarios, for example, when analyzing the preference analysis of the user for the application product, the preset behavior may be an application behavior.
For example, the user information data set may be classified into various attributes such as name, age, gender, a vehicle in possession, and a vehicle not in possession, and when the preset behavior is an insurance behavior, zero correlation or negative correlation may be calculated between data of the attributes of the name and gender and insurance behavior data, and positive correlation or complete correlation may be calculated between data of the attributes of the age and the vehicle in possession and insurance behavior data, and thus, in the embodiment of the present invention, data of the age and whether the vehicle in possession may be selected as the first feature data set.
And S3, analyzing and calculating the first feature data set according to the pre-constructed user preference static model to obtain a static preference weight.
Preferably, in the embodiment of the present invention, the user preference static model is:
wherein y represents the static preference weight, xnRepresenting data in the first characteristic data, and a is a model parameter.
And S4, acquiring a user dynamic data set in the standard data set, and combining the user dynamic data set with the first characteristic data set to generate a second characteristic data set.
And S5, analyzing and calculating the second feature data set according to the pre-constructed user preference dynamic model to obtain a dynamic preference weight.
Preferably, in the embodiment of the present invention, the user preference dynamic model is:
z＝b+bx′1+…bx′n
wherein P is the dynamic preference weight, z is and x'nFunction in a linear relationship, x'nAnd b is the data in the second characteristic data set and is the model parameter.
S6, establishing a user preference analysis model according to the first characteristic data set and the corresponding static preference weight, and the second characteristic data set and the corresponding dynamic preference weight, and executing user preference analysis according to the user preference analysis model.
Preferably, in the embodiment of the present invention, the user preference analysis model is:
wherein D is a user preference analysis result data set, xiFor the first feature data set, α is the static preference weight, yiFor the second feature data set β is the dynamic preference weight and m is the number of data of the first feature data set.
In the embodiment of the invention, the user static data set and the user dynamic data set are obtained, the user static data set is screened by using a correlation analysis method to obtain the first characteristic data set which is more relevant to the user preference analysis, and further the embodiment of the invention combines the user dynamic data set and the first characteristic data set into the second characteristic data set, so that all relevant data of a user are fully utilized.
Fig. 2 is a functional block diagram of the user preference analyzing apparatus according to the present invention.
The user preference analyzing apparatus 100 of the present invention may be installed in an electronic device. According to the implemented functions, the user preference analysis device may include a static feature filtering module 101, a dynamic feature filtering module 102, and a preference analysis module 103. A module according to the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the static feature screening module 101 is configured to obtain a user information data set, where the user information data set includes a user static data set and a user dynamic data set, perform denoising processing on the user information data set to obtain a standard data set, obtain the user static data set in the standard data set, and screen the user static data set by using a correlation analysis method to obtain a first feature data set;
the dynamic feature screening module is used for analyzing and calculating 102 the first feature data set according to a pre-constructed user preference static model to obtain a static preference weight, obtaining a user dynamic data set in the standard data set, combining the user dynamic data set with the first feature data set to generate a second feature data set, and analyzing and calculating the second feature data set according to the pre-constructed user preference dynamic model to obtain a dynamic preference weight;
the preference analysis module 103 is configured to establish a user preference analysis model according to the first feature data set and the corresponding static preference weight, and the second feature data set and the corresponding dynamic preference weight, and execute user preference analysis according to the user preference analysis model.
In detail, the specific implementation steps of each module of the user preference analysis device are as follows:
the static feature screening module 101 obtains a user information data set, where the user information data set includes a user static data set and a user dynamic data set, performs denoising processing on the user information data set to obtain a standard data set, obtains the user static data set in the standard data set, and screens the user static data set by using a correlation analysis method to obtain a first feature data set.
Specifically, the user static data set comprises user personal basic information and asset condition basic information. The personal basic information comprises the name, age, gender and the like of the user, and the asset condition basic information comprises the asset conditions held by the user, such as held vehicle information, house property information, purchased insurance, financial product information and the like. The user dynamic data set comprises recent online behaviors of the user, such as online browsing records of the user, data of behaviors such as online complaints and claims, and the like, and data of complaints and claims on the line, such as complaints of an insurance company on a certain forum or website before three days of the small plum, or requests of the insurance company to carry out the claims on the line before one day of the small plum.
In the embodiment of the invention, the user information data set is acquired from a block chain.
Preferably, the embodiment of the present invention performs denoising processing on the user information data set according to the following denoising algorithm:
s(x)＝f(x)-*e(x)
wherein s (x) is the standard data set after denoising, f (x) is the user information data, e (x) is noise, and is the standard deviation of the noise coefficient.
In detail, in the embodiment of the present invention, the screening of the user static data set by using the correlation analysis method to obtain the first feature data includes:
classifying the data in the user static data set according to the attributes of the data, and selecting a data set A with one attribute in a traversal mode;
calculating the correlation degree value r between the data set A and the data B of the preset behavior by using the following formulaA,B：
Wherein N represents the number of data in the data set A, aiAnd biFor any two data in the data set A in i, σA、σBRespectively, data A and data B, wherein r is the standard deviationA,B∈[-1,1]；
And screening the user static data set according to the correlation degree value to obtain first characteristic data.
In the embodiment of the invention:
if r isA,B>0, then A, B are positively correlated;
if r isA,B<0, then A, B negative correlation;
if r isA,B0, A, B is zero-correlated;
if r isA,BAt ± 1, A, B is completely relevant.
The embodiment of the invention further provides a correlation degree value r between the data of each attribute and the data B of the preset behaviorA,BAnd screening out zero correlation and negative correlation data to obtain the first characteristic data set.
The preset behavior may be different according to different application scenarios, for example, when analyzing the preference analysis of the user for the application product, the preset behavior may be an application behavior.
For example, the user information data set may be classified into various attributes such as name, age, gender, a vehicle in possession, and a vehicle not in possession, and when the preset behavior is an insurance behavior, it may be calculated that data of the attributes of the name and the gender and data of the insurance behavior are zero-correlation or negative-correlation, and data of the attributes of the age and the vehicle in possession and data of the insurance behavior are positive-correlation or complete-correlation, so that the data of the age and whether the vehicle in possession may be selected as the first feature data set according to the embodiment of the present invention.
The dynamic feature screening module 102 analyzes and calculates the first feature data set according to a pre-constructed user preference static model to obtain a static preference weight, obtains a user dynamic data set in the standard data set, combines the user dynamic data set with the first feature data set to generate a second feature data set, and analyzes and calculates the second feature data set according to a pre-constructed user preference dynamic model to obtain a dynamic preference weight.
Preferably, in the embodiment of the present invention, the user preference static model is:
wherein y represents the static preference weight, xnRepresenting data in the first characteristic data, and a is a model parameter.
And acquiring a user dynamic data set in the standard data set, and combining the user dynamic data set and the first characteristic data set to generate a second characteristic data set.
Preferably, in the embodiment of the present invention, the user preference dynamic model is:
z＝b+bx′1+…bx′n
wherein P is the dynamic preference weight, z is and x'nFunction in a linear relationship, x'nAnd b is the data in the second characteristic data set and is the model parameter.
The preference analysis module 103 establishes a user preference analysis model according to the first feature data set and the corresponding static preference weight, and the second feature data set and the corresponding dynamic preference weight, and performs user preference analysis according to the user preference analysis model.
Preferably, in the embodiment of the present invention, the user preference analysis model is:
wherein D is a user preference analysis result data set, xiFor the first feature data set, α is the static preference weight, yiFor the second feature data set β is the dynamic preference weight and m is the number of data of the first feature data set.
Fig. 3 is a schematic structural diagram of an electronic device implementing the user preference analysis method according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as a user preference analysis program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, for example a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes for user preference analysis, etc., but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), micro processors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (e.g., performing user preference analysis, etc.) stored in the memory 11 and calling data stored in the memory 11.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 3 only shows an electronic device with components, and it will be understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component such as one or more dc or ac power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used to establish a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The user preference analysis 12 stored by the memory 11 in the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement:
acquiring a user information data set, wherein the user information data set comprises a user static data set and a user dynamic data set, and denoising the user information data set to obtain a standard data set;
obtaining a user static data set in the standard data set, and screening the user static data set by using a correlation analysis method to obtain a first characteristic data set;
analyzing and calculating the first characteristic data set according to a pre-constructed user preference static model to obtain a static preference weight;
acquiring a user dynamic data set in the standard data set, and combining the user dynamic data set with the first characteristic data set to generate a second characteristic data set;
analyzing and calculating the second characteristic data set according to a pre-constructed user preference dynamic model to obtain a dynamic preference weight;
and establishing a user preference analysis model according to the first characteristic data set and the corresponding static preference weight, and the second characteristic data set and the corresponding dynamic preference weight, and executing user preference analysis according to the user preference analysis model.
Specifically, the specific implementation method of the processor 10 for the above instruction may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, which is not repeated herein.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a non-volatile computer-readable storage medium. The computer-readable medium may include: any entity or device capable of carrying said computer program code, a recording medium, a usb-disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
In an embodiment of the present invention, the non-volatile computer-readable storage medium includes a storage data area storing data created according to use of a blockchain node and a storage program area storing a computer program that implements the user preference analysis method described above when executed by a processor.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and there may be other divisions when the actual implementation is performed.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for analyzing user preferences, the method comprising:
acquiring a user information data set, wherein the user information data set comprises a user static data set and a user dynamic data set, and denoising the user information data set to obtain a standard data set;
obtaining a user static data set in the standard data set, and screening the user static data set by using a correlation analysis method to obtain a first characteristic data set;
analyzing and calculating the first characteristic data set according to a pre-constructed user preference static model to obtain a static preference weight;
acquiring a user dynamic data set in the standard data set, and combining the user dynamic data set with the first characteristic data set to generate a second characteristic data set;
analyzing and calculating the second characteristic data set according to a pre-constructed user preference dynamic model to obtain a dynamic preference weight;
and establishing a user preference analysis model according to the first characteristic data set and the corresponding static preference weight, and the second characteristic data set and the corresponding dynamic preference weight, and executing user preference analysis according to the user preference analysis model.
2. The method of analyzing user preferences according to claim 1, wherein the denoising the user information data set to obtain a standard data set comprises:
denoising the user information data set by adopting the following formula:
s(x)＝f(x)-*e(x)
where s (x) is the standard data set, f (x) is the user information data set, and e (x) is noise, which is the standard deviation of the noise figure.
3. The method of analyzing user preferences of claim 1, wherein the filtering a first feature data set from the user static data set using a relevance analysis method comprises:
classifying the data in the user static data set according to the attributes of the data, and selecting a data set A with one attribute in a traversal mode;
calculating the correlation degree value r between the data set A and the data B of the preset behavior by using the following formulaA，B：
Wherein N represents the number of data in the data set A, aiAnd biFor any two data in the data set A in i, σA、σBRespectively representing the standard deviation of the data A and the data B;
and screening the user static data set according to the correlation degree value to obtain first characteristic data.
4. The user preference analysis method of claim 1, wherein the user preference static model is:
wherein y represents the static preference weight, xnRepresenting data in the first characteristic data, and a is a model parameter.
5. The user preference analysis method of claim 1, wherein the user preference dynamic model is:
z＝b+bx′1+…bx′n
wherein P is the dynamic preference weight, z is and x'nFunction in a linear relationship, x'nAnd b is the data in the second characteristic data set and is the model parameter.
6. The user preference analysis method of any of claims 1 to 5, wherein the user preference analysis model is:
wherein D is a user preference analysis result data set, xiFor the first feature data set, α is the static preference weight, yiβ is the dynamic preference weight for the second feature data set, and m is the number of data for the first feature data set.
7. An apparatus for analyzing user preference, the apparatus comprising:
the static characteristic screening module is used for obtaining a user information data set, wherein the user information data set comprises a user static data set and a user dynamic data set, denoising is carried out on the user information data set to obtain a standard data set, the user static data set in the standard data set is obtained, and a first characteristic data set is screened from the user static data set by using a correlation analysis method;
the dynamic feature screening module is used for analyzing and calculating the first feature data set according to a pre-constructed user preference static model to obtain a static preference weight, obtaining a user dynamic data set in the standard data set, combining the user dynamic data set with the first feature data set to generate a second feature data set, and analyzing and calculating the second feature data set according to the pre-constructed user preference dynamic model to obtain a dynamic preference weight;
and the preference analysis module is used for establishing a user preference analysis model according to the first characteristic data set and the corresponding static preference weight as well as the second characteristic data set and the corresponding dynamic preference weight, and executing user preference analysis according to the user preference analysis model.
8. The user preference analysis apparatus of claim 7, wherein the static feature filtering module filters a first feature data set from the user static data set by:
classifying the data in the user static data set according to the attributes of the data, and selecting a data set A with one attribute in a traversal mode;
calculating the correlation degree value r between the data set A and the data B of the preset behavior by using the following formulaA，B：
Wherein N represents the number of data in the data set A, aiAnd biFor any two data in the data set A in i, σA、σBRespectively representing the standard deviation of the data A and the data B;
and screening the user static data set according to the correlation degree value to obtain first characteristic data.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a user preference analysis method as claimed in any one of claims 1 to 6.
10. A computer-readable storage medium comprising a stored data area storing data created according to the use of blockchain nodes and a stored program area storing a computer program, characterized in that the computer program, when executed by a processor, implements a user preference analysis method according to any one of claims 1 to 6.
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