CN111652741B - User preference analysis method, device and readable storage medium - Google Patents
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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 static preference weight; acquiring a user dynamic data set, combining the user dynamic data set with the first characteristic data set, and generating a second characteristic data set; calculating a second characteristic data set according to the user preference dynamic model to obtain dynamic preference weights; and establishing a user preference analysis model according to the characteristic data set and the weight, and executing user preference analysis. The present invention also relates to blockchain techniques in which user static data sets and user dynamic data sets may be stored. The invention also provides a user preference analysis device, electronic equipment and a storage medium. The invention can solve the problem of wasting the user characteristic information when analyzing the user preference.
Description
Technical Field
The present invention relates to the field of artificial intelligence technologies, and in particular, to a method and apparatus for analyzing user preference, an electronic device, and a readable storage medium.
Background
With the rapid development of network technology, consumer consumption gradually changes from traditional offline to online, making analysis of online behavior of the consumer critical. At present, the research on the online behavior mode of the user is mainly based on online browsing records for analysis. And if the user behavior is analyzed according to the browsing records on the user line, recommending the user insurance scheme.
However, the method is quite random and not systematic enough, and cannot accurately analyze the correlation characteristics of each user in a targeted manner, namely, each user adopts the same analysis means (such as online browsing records), ignores the correlation characteristics of the user and the acceptance of products, analyzes according to the online browsing records, and has a certain degree of intelligentization, but the method is uniform, so that the due effect is not achieved on the premise of occupying a large amount of calculation resources, and the user correlation information is not effectively combined, so that the user correlation information characteristics are wasted.
Disclosure of Invention
The invention provides a user preference analysis method, a device, electronic equipment and a computer readable storage medium, which mainly aim to fully utilize all relevant data of a user and improve the accuracy of user preference analysis when the user preference analysis is carried out.
In order to achieve the above object, the present invention provides a user preference analysis method, including:
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;
acquiring 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 static preference weight;
acquiring a user dynamic data set in the standard data set, combining the user dynamic data set with the first characteristic data set, and generating a second characteristic data set;
analyzing and calculating the second characteristic data set according to a pre-constructed user preference dynamic model to obtain dynamic preference weights;
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 is performed on the user information data set to obtain a standard data set, including:
denoising the user information data set by adopting the following formula:
s(x)＝f(x)-ε*e(x)
where s (x) is the standard data set, f (x) is the user information data set, e (x) is noise, and ε is the standard deviation of the noise figure.
Optionally, the screening the user static dataset by using a correlation analysis method to obtain a first feature dataset includes:
classifying the data in the user static data set according to the attribute of the data, and selecting a data set A with one attribute in a traversing mode;
calculating a correlation degree value r between the data set A and the data B of the preset behavior by using the following formula A,B ：
Wherein N represents the number of data in the data set A, a i And b i For any two data in the dataset A in i, σ A 、σ B Respectively representing standard deviations 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.
Optionally, the user preference static model is:
wherein α represents the static preference weight, x n And representing data in the first characteristic data, wherein a is a model parameter.
Optionally, the user preference dynamic model is:
z＝+bx 1 ′ +…bx ′ n
wherein β is the dynamic preference weight and z is the sum of x ′ n Function in linear relationship, x ′ n And b is a model parameter for the data in the second characteristic data set.
Optionally, the user preference analysis model is:
wherein D is a user preference analysis result data set, x ii For the first feature data set, α is the static preference weight, y i And for the second characteristic data set, beta is the dynamic preference weight, and m is the data number of the first characteristic data set.
In order to solve the above problems, the present invention also provides a user preference analysis apparatus, the apparatus comprising:
the static feature screening module is used for acquiring a user information data set, wherein the user information data set comprises a user static data set and a user dynamic data set, denoising the user information data set to obtain a standard data set, acquiring the 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 feature data set;
the dynamic feature screening module is used for analyzing and calculating the first feature data set according to a pre-built user preference static model to obtain static preference weight, acquiring 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-built user preference dynamic model to obtain 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 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 static feature screening module screens the user static data set for a first feature data set by:
classifying the data in the user static data set according to the attribute of the data, and selecting a data set A with one attribute in a traversing mode;
calculating a correlation degree value r between the data set A and the data B of the preset behavior by using the following formula A,B ：
Wherein N represents the number of data in the data set A, a i And b i For any two data in the dataset A in i, σ A 、σ B Respectively representing standard deviations 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-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
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-mentioned problems, the present invention also provides a computer-readable storage medium including a storage data area storing data created according to use of blockchain nodes and a storage program area storing a computer program, wherein the computer program when executed by a processor implements the user preference analysis method described above.
The embodiment of the invention acquires the user static data set and the user dynamic data set, screens the user static data set by utilizing a correlation analysis method to obtain the first characteristic data set which is more relevant to user preference analysis, and further forms the second characteristic data set by the user dynamic data set and the first characteristic data set, so that all relevant data of a user are fully utilized. Therefore, the user preference analysis method, the device, the electronic equipment and the computer readable storage medium can accurately analyze the receiving capability of different users to products by utilizing the correlation characteristic information of the users, and solve the problems of waste of the correlation information of the users and excessive complexity of algorithms when 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 schematic block diagram of a user preference analysis apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device for implementing a user preference analysis method according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The execution subject of the user preference analysis method provided by the embodiment of the application includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the user preference analysis method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end 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. Referring to fig. 1, a flowchart of a user preference analysis method according to an embodiment of the invention is shown. The method may be performed by an apparatus, which may be implemented in software and/or hardware.
In this embodiment, the user preference analysis method includes:
s1, 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.
Specifically, the user static data set includes user personal basic information and asset condition basic information. The personal basic information includes the name, age, sex, etc. of the user, and the asset condition basic information includes the asset condition held by the user, such as held vehicle information, house property information, purchased insurance, financial product information, etc. The user dynamic data set includes recent online behavior of the user, such as online browsing records of the user, data of online complaints, claims and other behaviors, such as data of online complaints of some insurance company in some forum or website before Li San days or data of online request of insurance company to execute claims and the like before Li Yitian days.
In the embodiment of the invention, the user information data set can be obtained from a blockchain.
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)-ε*e(x)
wherein s (x) is the denoised standard data set, f (x) is the user information data, e (x) is noise, and epsilon is the standard deviation of noise coefficients.
S2, acquiring 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 to obtain the first feature data from the user static data set by using a correlation analysis method includes:
classifying the data in the user static data set according to the attribute of the data, and selecting a data set A with one attribute in a traversing mode;
calculating a correlation degree value r between the data set A and the data B of the preset behavior by using the following formula A,B ：
Wherein N represents the number of data in the data set A, a i And b i For any two data in the dataset A in i, σ A 、σ B Respectively representing standard deviation of data A and data B, wherein r A,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, the following steps are included:
if r A,B >0, A, B positive correlation;
if r A,B <0, A, B is inversely related;
if r A,B =0, A, B zero correlation;
if r A,B = ±1, A, B are completely correlated.
The embodiment of the invention further relates to a correlation degree value r between the data of each attribute and the data B of the preset behavior A,B And screening out the data with zero correlation and negative correlation to obtain the first characteristic data set.
The preset behavior may be different according to different application scenarios, for example, in analyzing the preference analysis of the user's insurance product, the preset behavior may be the insurance behavior.
For example, the user information data set may be classified into various attributes such as name, age, sex, vehicle in possession, vehicle in absence of possession, etc., and when the preset behavior is an insuring behavior, zero correlation or negative correlation between data of name and sex attributes and insuring behavior data may be calculated, while positive correlation, even complete correlation, between data of the attributes of the age, the vehicle in possession and insuring behavior data may be calculated, so that the embodiment of the present invention may filter out the data of the age and whether the vehicle in possession is taken as the first feature data set.
S3, analyzing and calculating the first characteristic data set according to the pre-constructed user preference static model to obtain static preference weight.
Preferably, in the embodiment of the present invention, the user preference static model is:
wherein α represents the static preference weight, x n And representing data in the first characteristic data, wherein a is a model parameter.
S4, acquiring a user dynamic data set in the standard data set, and collecting the user dynamic data set and the first characteristic data set together to generate a second characteristic data set.
S5, analyzing and calculating the second characteristic data set according to the pre-constructed user preference dynamic model to obtain dynamic preference weight.
Preferably, in an embodiment of the present invention, the user preference dynamic model is:
z＝b+bx′ 1 +…bx′ n
wherein β is the dynamic preference weight and z is the sum of x ′ n Function in linear relationship, x ′ n And b is a model parameter for the data in the second characteristic data set.
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 an embodiment of the present invention, the user preference analysis model is:
wherein D is a user preference analysis result data set, x i For the first feature data set, α is the static preference weight, y i And for the second characteristic data set, beta is the dynamic preference weight, and m is the data number of the first characteristic data set.
The embodiment of the invention acquires the user static data set and the user dynamic data set, screens the user static data set by utilizing a correlation analysis method to obtain the first characteristic data set which is more relevant to user preference analysis, and further forms the second characteristic data set by the user dynamic data set and the first characteristic data set, so that all relevant data of a user are fully utilized.
As shown in fig. 2, a functional block diagram of the user preference analysis apparatus of the present invention.
The user preference analysis apparatus 100 of the present invention may be installed in an electronic device. The user preference analysis means may comprise a static feature screening module 101, a dynamic feature screening module 102, a preference analysis module 103, depending on the implemented functionality. The module of the present invention may also be referred to as a unit, meaning a series of computer program segments capable of being executed by the processor of the electronic device and of performing fixed functions, stored in the memory of the electronic device.
In the present embodiment, the functions concerning 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 configured to perform analysis and calculation 102 on the first feature data set according to a pre-built user preference static model to obtain a static preference weight, obtain a user dynamic data set in the standard data set, combine the user dynamic data set with the first feature data set to generate a second feature data set, and perform analysis and calculation on the second feature data set according to the pre-built 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 weights, and the second feature data set and the corresponding dynamic preference weights, and perform 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 filtering 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 filters the user static data set by using a correlation analysis method to obtain a first feature data set.
Specifically, the user static data set includes user personal basic information and asset condition basic information. The personal basic information includes the name, age, sex, etc. of the user, and the asset condition basic information includes the asset condition held by the user, such as held vehicle information, house property information, purchased insurance, financial product information, etc. The user dynamic data set includes recent online behavior of the user, such as online browsing records of the user, data of online complaints, claims and other behaviors, such as data of online complaints of some insurance company in some forum or website before Li San days or data of online request of insurance company to execute claims and the like before Li Yitian days.
In the embodiment of the invention, the user information data set is acquired from a blockchain.
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)-ε*e(x)
wherein s (x) is the denoised standard data set, f (x) is the user information data, e (x) is noise, and epsilon is the standard deviation of noise coefficients.
In detail, in the embodiment of the present invention, the screening to obtain the first feature data from the user static data set by using a correlation analysis method includes:
classifying the data in the user static data set according to the attribute of the data, and selecting a data set A with one attribute in a traversing mode;
calculating a correlation degree value r between the data set A and the data B of the preset behavior by using the following formula A,B ：
Wherein N represents the number of data in the data set A, a i And b i For any two data in the dataset A in i, σ A 、σ B Respectively representing standard deviation of data A and data B, wherein r A,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, the following steps are included:
if r A,B >0, A, B positive correlation;
if r A,B <0, A, B is inversely related;
if r A,B =0, A, B zero correlation;
if r A,B = ±1, A, B are completely correlated.
The embodiment of the invention further relates to a correlation degree value r between the data of each attribute and the data B of the preset behavior A,B And screening out the data with zero correlation and negative correlation to obtain the first characteristic data set.
The preset behavior may be different according to different application scenarios, for example, in analyzing the preference analysis of the user's insurance product, the preset behavior may be the insurance behavior.
For example, the user information data set may be classified into various attributes such as name, age, sex, vehicle in possession, vehicle in absence of possession, etc., and when the preset behavior is an insuring behavior, zero correlation or negative correlation between data of name and sex attributes and insuring behavior data may be calculated, while positive correlation, even complete correlation, between data of the attributes of the age, the vehicle in possession and insuring behavior data may be calculated, so that the embodiment of the present invention may filter out the data of the age and whether the vehicle in possession is taken as the first feature data set.
The dynamic feature screening module 102 performs analysis and calculation on the first feature data set according to a pre-built user preference static model to obtain static preference weights, 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 performs analysis and calculation on the second feature data set according to the pre-built user preference dynamic model to obtain dynamic preference weights.
Preferably, in the embodiment of the present invention, the user preference static model is:
wherein α represents the static preference weight, x n And representing data in the first characteristic data, wherein a is a model parameter.
And acquiring a user dynamic data set in the standard data set, and collecting the user dynamic data set and the first characteristic data set together to generate a second characteristic data set.
Preferably, in an embodiment of the present invention, the user preference dynamic model is:
z＝b+bx′ 1 +Φbx′ n
wherein β is the dynamic preference weight and z is the sum of x ′ n Function in linear relationship, x ′ n And b is a model parameter for the data in the second characteristic data set.
The preference analysis module 103 establishes a user preference analysis model according to the first feature data set and the corresponding static preference weights, and the second feature data set and the corresponding dynamic preference weights, and performs user preference analysis according to the user preference analysis model.
Preferably, in an embodiment of the present invention, the user preference analysis model is:
wherein D is a user preference analysis result data set, x i For the first feature data set, α is the static preference weight, y i For the second feature data set, β is the dynamic preference weight, m is the first feature numberNumber of data of the dataset.
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, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided 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 for storing application software installed in the electronic device 1 and various types of data, such as a code for user preference analysis, etc., but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 11 (for example, performs user preference analysis, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being 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 may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source 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 implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or 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, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The user preference analysis 12 stored by the memory 11 in the electronic device 1 is a combination of instructions which, 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;
acquiring 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 static preference weight;
acquiring a user dynamic data set in the standard data set, combining the user dynamic data set with the first characteristic data set, and generating a second characteristic data set;
analyzing and calculating the second characteristic data set according to a pre-constructed user preference dynamic model to obtain dynamic preference weights;
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 above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the integrated modules/units of the electronic device 1 may be stored in a non-volatile computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U 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 blockchain nodes and a storage program area storing a computer program which when executed by a processor implements the user preference analysis method described above.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
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 characteristics 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 evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. A method of user preference analysis, 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, denoising the user information data set to obtain a standard data set, the user static data set comprises user personal basic information and asset condition basic information, and the user dynamic data set comprises recent online behaviors of a user;
acquiring 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 static preference weight;
acquiring a user dynamic data set in the standard data set, combining the user dynamic data set with the first characteristic data set, and generating a second characteristic data set;
analyzing and calculating the second characteristic data set according to a pre-constructed user preference dynamic model to obtain dynamic preference weights;
establishing a user preference analysis model according to the first characteristic data set, the corresponding static preference weight and the second characteristic data set, the corresponding dynamic preference weight, and executing user preference analysis according to the user preference analysis model;
wherein the user preference static model is:
wherein α represents the static preference weight, x n Representing data in the first feature data set, a being a model parameter;
the user preference dynamic model is:
z＝+bx′ 1 +…bx′ n
wherein β is the dynamic preference weight, z is equal to x' n Function in linear relationship, x' n B is a model parameter for the data in the second feature data set;
the user preference analysis model is:
wherein D is a user preference analysis result data set, x i For the first feature data set, α is the static preference weight, y i For the second feature data set, β is the dynamic preferenceAnd m is the data number of the first characteristic data set.
2. The method of claim 1, wherein 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)-ε*e(x)
where s (x) is the standard data set, f (x) is the user information data set, e (x) is noise, and ε is the standard deviation of the noise figure.
3. The method of claim 1, wherein the filtering the first feature data set from the user static data set using correlation analysis comprises:
classifying the data in the user static data set according to the attribute of the data, and selecting a data set A with one attribute in a traversing mode;
calculating the correlation degree value r between the data set A and the data set B of the preset behavior by using the following formula A,B ：
Wherein N represents the number of data in the data set A, a i And b i For any two data in the dataset A in i, σ A 、σ B Respectively representing standard deviations of the data set A and the data set B;
and screening the user static data set according to the correlation degree value to obtain a first characteristic data set.
4. A user preference analysis apparatus, the apparatus comprising:
the static feature screening module is used for acquiring 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 acquired, a first feature data set is screened from the user static data set by using a correlation analysis method, the user static data set comprises user personal basic information and asset condition basic information, and the user dynamic data set comprises recent online behaviors of a user;
the dynamic feature screening module is used for analyzing and calculating the first feature data set according to a pre-built user preference static model to obtain static preference weight, acquiring 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-built user preference dynamic model to obtain dynamic preference weight;
the preference analysis module is used for establishing a user preference analysis model according to the first characteristic data set, the corresponding static preference weight, the second characteristic data set and the corresponding dynamic preference weight, and executing user preference analysis according to the user preference analysis model;
wherein the user preference static model is:
wherein α represents the static preference weight, x n Representing data in the first feature data set, a being a model parameter;
the user preference dynamic model is:
z＝b+bx′ 1 +…bx′ n
wherein β is the dynamic preference weight, z is equal to x' n Function in linear relationship, x' n B is a model parameter for the data in the second characteristic data set;
the user preference analysis model is:
wherein D is a user preference analysis result data set, x i For the first feature data set, α is the static preference weight, y i And for the second characteristic data set, beta is the dynamic preference weight, and m is the data number of the first characteristic data set.
5. The user preference analysis apparatus of claim 4, wherein the static feature screening module screens the user static data set for a first feature data set by:
classifying the data in the user static data set according to the attribute of the data, and selecting a data set A with one attribute in a traversing mode;
calculating the correlation degree value r between the data set A and the data set B of the preset behavior by using the following formula A,B ：
Wherein N represents the number of data in the data set A, a i And b i For any two data in the dataset A in i, σ A 、σ B Respectively representing standard deviations of the data set A and the data set B;
and screening the user static data set according to the correlation degree value to obtain a first characteristic data set.
6. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
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 of any one of claims 1 to 3.
7. A computer readable storage medium comprising a storage data area storing data created according to use of blockchain nodes and a storage program area storing a computer program, the computer program when executed by a processor implementing the user preference analysis method according to any one of claims 1 to 3.
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