CN106557472B - Method and device for establishing user database - Google Patents
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- CN106557472B CN106557472B CN201510617067.4A CN201510617067A CN106557472B CN 106557472 B CN106557472 B CN 106557472B CN 201510617067 A CN201510617067 A CN 201510617067A CN 106557472 B CN106557472 B CN 106557472B
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Abstract
The embodiment of the application discloses a method for establishing a user database and a device applying the method, which can avoid the bloated user database. The establishing method comprises the following steps: acquiring at least two user attribute values according to the user identity parameters; merging the user attribute values into user behavior parameters according to a preset merging model; and storing the user identity parameters and the user behavior parameters into a user database after the user identity parameters and the user behavior parameters are associated. According to the technical scheme provided by the embodiment of the application, the embodiment of the application reduces the space required by storing the user attribute value and avoids the bloated user database by combining the user attribute values associated with each user.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a method and an apparatus for establishing a user database.
Background
The user database is a widely used concept in the technical field of internet, and is used for associating and storing user identity information and certain user behavior information such as the number of user vehicles, so that the efficiency of user information collection is greatly improved. And with the rapid development of internet technology, the demand of user databases is increasing.
Currently, the method of building a user database generally comprises the steps of:
s1, determining a user group corresponding to the user database, and acquiring identity information of users in the user group;
s2, acquiring different types of required user attribute values according to the identity information of the user, wherein the user attribute values can be the vehicle ownership of the user or the house ownership of the user;
and S3, associating the user identity information with the user attribute value, and storing the user identity information and the user attribute value in a user database together.
However, to ensure the integrity of the user database data, all user attribute values need to be stored in the user database. With the continuous increase of the user attribute value types associated with each user, the storage space required by the associated user attribute value is larger and larger, and the user database is bloated.
Disclosure of Invention
The embodiment of the application aims to provide a method and a device for establishing a user database, which can avoid the user database from being too bloated.
In order to solve the foregoing technical problem, an embodiment of the present application provides a method for establishing a user database, including:
acquiring at least two user attribute values according to the user identity parameters;
merging the user attribute values into user behavior parameters according to a preset merging model;
and storing the user identity parameters and the user behavior parameters into a user database after the user identity parameters and the user behavior parameters are associated.
In order to solve the foregoing technical problem, an embodiment of the present application provides an apparatus for establishing a user database, including:
the acquisition module is used for acquiring at least two user attribute values according to the user identity parameters;
the merging module is used for merging the user attribute values into user behavior parameters according to a preset merging model;
and the association module is used for associating the user identity parameters with the user behavior parameters and then storing the user identity parameters and the user behavior parameters into a user database.
According to the technical scheme provided by the embodiment of the application, the embodiment of the application reduces the space required by storing the user attribute value and avoids the bloated user database by combining the user attribute values associated with each user.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is a conceptual diagram of an exemplary system architecture to which embodiments of the present application may be applied;
FIG. 2 is a flowchart of a method for establishing a user database according to an embodiment of the present application;
fig. 3 is a specific flowchart of normalization processing on a user attribute value in the method for establishing a user database in the embodiment of the present application;
fig. 4 is a specific flowchart illustrating merging of the user attribute values into user behavior parameters according to a preset merging model in the method for establishing a user database according to the embodiment of the present application;
fig. 5 is a specific flowchart of generating a user behavior parameter according to a preset common factor in the method for establishing a user database in the embodiment of the present application;
fig. 6 is a specific flowchart of obtaining a user behavior parameter according to a preset weighting model in the method for establishing a user database in the embodiment of the present application;
fig. 7 is a block diagram of an apparatus for creating a user database according to an embodiment of the present application.
Detailed Description
The embodiment of the application provides a method and a device for establishing a user database. By combining the user attribute values associated with each user, the space required by storing the user attribute values is reduced, and the bloated size of a user database can be avoided.
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Such a social network may be adapted, for example, to FACEBOOK, TWITTER, YOUTUBE, L ink, wechat, microblogging, etc., or even to encompass currently available instant messaging type networks.
In one implementation, for example, the network node has both social and payment attributes. The social network may give the node basic bibliographic information, for example, including basic attributes associated with people, specifically, for example, including, in addition to basic natural attributes associated with people, social relationship attributes between people and organizations associated with people. Basic payment bibliographic information may be given to nodes in the payment network, including, for example, accounts and their corresponding virtualized currency or equivalents, and even direct redemption coupons representing real world currency. The virtualized currency or equivalent may have some conversion relationship with the redemption ticket. In this way, the wealth attributes of the network world can be characterized in a manner associated with the network nodes. Through the integration of the social network and the payment network, the network nodes with social relations can further execute the virtual function of payment.
FIG. 1 illustrates an exemplary system architecture concept that may be applicable herein.
As shown in fig. 1, the system includes one or more servers 12, one or more client devices 13, and a network 11 for connecting the servers 12 and the client devices 13. The server 12 may receive a transaction request sent by each client device 13 through the network 11, and obtain each transaction information corresponding to the transaction request from each corresponding client device 13, so as to complete a transaction process according to each transaction information. The server 12 may be a single server or a server cluster composed of a plurality of servers. The client device 39 may be a personal computer, laptop, wireless telephone, Personal Digital Assistant (PDA), smart watch, or other computer and communication device.
The server and client devices may each include some basic components, such as a bus, processing means, storage means, one or more input/output means, and communication interfaces, etc. the bus may include one or more wires for enabling communication between the components of the server and client devices, the processing means may include various types of processors or microprocessors for executing instructions, processing processes or threads, the storage means may include dynamic memory, such as Random Access Memory (RAM), or static memory, such as Read Only Memory (ROM), for storing static information, and mass storage including magnetic or optical recording media and corresponding drives, etc. the input means may be a keyboard, mouse, stylus, touch screen, voice recognition means, or biometric means, etc. the output means may be a display, printer, or speaker, etc. for outputting information, the communication interfaces may be used to enable the server or client device to communicate with other systems or devices, the optical communication interfaces may be connected to the client network by wired, wireless, or other connections, to enable the devices, servers, or devices to communicate with each other, the devices, the internet may include a dedicated network application software for controlling the internet, or any combination of mobile applications including internet, mobile network (lan, NFC, or bluetooth (lan) for implementing the functions of the internet.
Fig. 2 is a flowchart of a method for establishing a user database in the embodiment of the present application. The execution subject of the method may be the server 12 for storing the database.
The establishing method comprises the following steps:
and S10, acquiring at least two user attribute values according to the user identity parameters.
In the embodiment of the application, the identity information of all users in the user group can be acquired according to the user group faced by the user database. The user identity parameter can be a user identity ID with unique attributes, such as an identity card number, an enterprise work card number or a passport number.
And further, querying a related attribute value database through the user identity parameter, and acquiring an attribute value associated with the user identity parameter.
For example, a business may build a database of employees for its employees, storing user attribute values such as age, salary treatment, job position, etc. for the employees. The identity information of the employees needing to be put in storage needs to be acquired, and the identity information can be embodied by the enterprise employee card number of each employee. And going to corresponding databases through the work card number to obtain corresponding user attribute values.
For another example, a credit database of residents is to be established in a certain region, and the users store attribute values related to the credits of the residents, where the user attribute values may be financial scenario contents such as default amount and default duration, or non-financial default contents such as default amount of car rental and house rental. The method for establishing the user database provided in the embodiment of the present application may also be applied, and details are not described herein.
In the embodiment of the present application, taking the server 12 for storing the database as an execution main body of the establishing method as an example, in the process of establishing the user database, the server 12 may establish communication with a server and a client that store other data, so as to collect the user attribute value. With the assistance of the network 10, the acquisition of the user attribute values may be started before the method is started, or may be started in real time, which is not described herein.
And S20, normalizing the user attribute values.
Still taking the establishment of the credit database of the residents as an example, the obtained multiple related signal contents such as default amount, default duration and the like are different in the embodiment form of the user attribute value, and the magnitude of the obtained multiple related signal contents is also different greatly. By normalizing the attribute values, all the user attribute values are positioned in the same dimension and have the same adjustment direction.
For example, the pre-acquired user attribute values include a default amount and a default duration, the default amount may be 1000 yuan, and the default duration may be 30 days. Both of them can be changed to be varied in the range of 0 to 1 by the normalization processing, thereby unifying the dimension and the adjustment direction thereof.
With reference to fig. 3, in the embodiment of the present application, a process of performing normalization processing on a user attribute value specifically includes the following steps:
s21, obtaining the mean value and standard deviation of all user attribute values;
and S22, subtracting the mean value from each user attribute value, and dividing the result by the standard deviation to obtain a normalization result.
All the user attribute values can be made to conform to the standard normal distribution through the foregoing steps. All user attribute values that fit the standard normal distribution are between 0 and 1, i.e., all user attribute values are adjusted to be within the same dimension.
Of course, the process of normalizing the user attribute values is not limited to the foregoing steps, and other manners may also be adopted, which only needs to ensure the uniformity of the dimension and the adjustment direction of each user attribute value, for example, each user attribute value may be adjusted within a dimension of 0 to 10, which is not described herein again.
And S30, merging the user attribute values into user behavior parameters according to a preset merging model.
After all the user attribute values are normalized in step S20, the normalized user attribute values are merged to obtain the user behavior parameters.
Still taking the establishment of an enterprise employee database as an example, according to a preset merging model, the obtained user attribute values such as the working years, salary treatment, working posts and the like are merged into a user behavior parameter.
The employee enrollment information is comprehensively reflected through one user behavior parameter, the storage space required by a plurality of user attribute values is reduced, and the user database is prevented from being too large.
Of course, if a credit database of the residents is to be established as an example, a plurality of related signal contents such as default amount, default duration and the like can be obtained in advance as the user attribute values. And then, the acquired user attribute values are combined into a user behavior parameter, and the credit of residents is comprehensively embodied through the user behavior parameter.
With reference to fig. 4, in the embodiment of the present application, a process of merging user attribute values into user behavior parameters according to a preset merging model specifically includes the following steps:
and S31, converting each user attribute value into the sum of a preset number of linear factor functions of preset common factors and preset special factors.
Take the user attribute value as X, the preset common factor as F, and the preset special factor as an example. If the number of the user attribute values X is 5 and the number of the preset common factors F is 3, each user attribute value can be identified by the sum of the linear factor function of the 3 common factors F and the corresponding preset special factor.
In this embodiment of the present application, the linear factor function in each user attribute value is not limited to only summing the common factors F, and further includes a preset load factor a multiplied by each common factor, and the step of obtaining the linear factor function that converts each user attribute value into the preset number of preset common factors specifically includes:
firstly, acquiring a preset factor load A corresponding to a preset common factor F in each user attribute value;
and secondly, multiplying and summing each preset common factor F and the corresponding preset factor load A to obtain a linear factor function corresponding to the user attribute value.
And finally, converting each user attribute value into the sum of the corresponding linear factor function and the preset special factor.
Through the steps, the following steps are obtained: user attribute value Xi＝ai1*F1+ai2*F2+ai3*F3+i，(i＝1,2,3…p)。
The model of the user attribute value X may be represented by a matrix, which is specifically as follows:
and satisfies the following conditions:
(1)m≤p；
(2) the preset common factor F and the preset special factor are not related;
(3) each preset common factor F is uncorrelated and has a variance of 1;
(4) each preset special factor is uncorrelated and has a variance of 1.
Of course, with the adjustment of the user attribute value X, the preset common factor F and the preset special factor, the above conditions are not limited, and for example, the adjustment may be unified so that each preset common factor F is uncorrelated and has a variance of 5.
In the embodiment of the application, the user attribute value X is described by the common factor F and the preset special factor, and the number of the common factors F is smaller than that of the user attribute values X. And the method only needs to store less public factors F, and avoids the bloated state of the user database compared with the method of storing more user attribute values X.
And S32, generating user behavior parameters according to the preset public factors.
The preset public factor F consisting of the reverse attribute value X, the load factor A and the preset special factor is obtained by reversely calculating the expression of the user attribute value X, and the number of the preset public factor F is smaller than that of the user attribute value X, so that the generation efficiency of the user behavior parameters is improved.
Taking the user attribute value X as an example to embody the data of the enterprise employee, the user attribute value X may include the contents of the year of employment, salary treatment, job post, and the like. The preset common factor F may include the working capability and working attitude thereof. These may also represent the value of an employee.
Similarly, taking the example that the user attribute value X is used for embodying the credit content of the user, the user attribute value X may include the default amount, the default duration, and other contents. The preset common factor F may include contents such as a user's willingness to perform, and a user's ability to perform. Because both the user's will of performing and the ability of performing cannot be measured directly, the user's credit can be embodied well after the two are combined.
In conclusion, the user behavior parameters obtained based on the preset common factor F have high accuracy while the generation efficiency is high.
With reference to fig. 5, in the embodiment of the present application, a process of generating a user behavior parameter according to a preset common factor specifically includes the following steps:
s321, summarizing all preset factor loads to obtain a factor load matrix.
In the embodiment of the present application, the preset load factors of the same user attribute value Xi are used as one row of the factor load matrix, and the preset load factors of each row are changed according to the preset common factor F and are sequentially arranged, so as to obtain the factor load matrix a described in the foregoing.
And S322, performing factor rotation on the factor load matrix to obtain the rotated factor load.
The factor rotation process may include both orthogonal rotation and skew rotation. Taking the orthogonal rotation as an example, the process specifically includes:
and performing orthogonal transformation on the factor load matrix A, and multiplying the factor load matrix A by an orthogonal matrix to the right, so that the rotated factor load matrix B has more distinct practical significance. Of course, the orthogonal matrix may be chosen differently, for example, it may be a maximum variance rotation method. By carrying out orthogonal transformation on the factor load matrix A, the sum of the variances of each row of elements of the factor load matrix A is maximized, so that the factor load on the same row is as close to 1 or 0 as possible, and the factor load has bipolar differentiation.
And S323, correcting the linear factor function according to the rotated factor load to obtain a corrected factor function.
And S324, converting each preset public factor into a linear attribute function of all user attribute values according to the corrected factor function.
AF + is assigned to the user attribute value X. After the correction, the linear property function of the preset common factor F can be obtained by a method such as a regression method, a bartlett method, or an anderson-rubin method.
For example, if the influence of a preset special factor is not considered, it can be simply considered that F ═ a-1X, which is not described herein.
Because the same user attribute value X may have a larger load on a plurality of preset common factors F, or a plurality of user attribute values X may have a larger load on the same preset common factor F, that is, the preset common factor has a more obvious influence on a plurality of user attribute values. By means of factor rotation, each user attribute value X has a large load on only one common factor F, and the loads on the rest preset common factors F are small and at most reach a medium size. Then, for each preset common factor (i.e. each column of the load matrix), the load on part of the user attribute values X is larger, and the load on other variables is smaller, so that the loads on the same column are separated to the two poles close to 1 and 0 as much as possible. The relation between each common factor and the variables with larger loads is highlighted, and the meaning of the preset common factor F can be reasonably explained through the user attribute values X with larger loads, namely the preset common factor F embodies the use of the user attribute values X with larger loads.
And S325, weighting all the preset public factors according to the preset weighting model to obtain the user behavior parameters.
Still taking the example that the user attribute value X is used for embodying the credit content of the user, the user attribute value X may include the default amount, the default duration, and the like. The preset common factor F may include contents such as a user's willingness to perform, and a user's ability to perform. After the user's fulfillment will and fulfillment ability are weighted, user behavior parameters are obtained, which may be used to prompt the user's credit.
With reference to fig. 6, in the embodiment of the present application, the process of weighting all preset common factors according to a preset weighting model to obtain a user behavior parameter specifically includes the following steps:
s3251, acquiring the weighting ratio of each preset common factor according to a preset weighting model.
And S3252, multiplying each preset common factor by the corresponding weighting ratio and summing to obtain the final common factor.
In the embodiment of the present application, the preset weighting model may be configured according to the location of the user database. Still taking the example that the user attribute value X is used for embodying the credit content of the user, the user attribute value X may include the default amount, the default duration, and the like. The preset common factor F may include contents such as a user's willingness to perform, and a user's ability to perform. If the user database has a large demand for the user performance capability, the weighting ratio of the user performance capability can be increased, and the weighting ratio of the user performance will be reduced, so that the final public factor meeting the demand is obtained.
And S3253, converting the final common factor into a user behavior parameter according to a preset protocol model.
In the embodiment of the application, still taking the case that the user attribute value X is used for embodying the credit content of the user as an example, the preset protocol model may be a credit score protocol model, for example, a sesame score protocol model, and the common factor is converted into the user behavior parameter conforming to the credit score protocol.
Certainly, the preset protocol model is not limited to the credit score protocol model, and the preset protocol model may be adaptively adjusted according to the change of the user attribute value X, which is not described herein again.
Of course, in other embodiments of the present application, the method for establishing the user database may not include the step S20. For example, when all the user attribute values are located in the same dimension, it is obviously unnecessary to perform normalization processing on the user attribute values; or the user attribute values are in the same scale, or all the user attribute values can be covered by a larger data range, which is not described herein.
And S40, storing the user identity parameters and the user behavior parameters into a user database after the user identity parameters and the user behavior parameters are associated.
According to the embodiment of the application, the user attribute values associated with each user are combined to obtain a user behavior parameter capable of accurately reflecting the user attribute values, so that the space required by the user attribute values with larger storage magnitude is reduced, and the bloated state of a user database is avoided.
Fig. 7 is a block diagram of a user database creation apparatus in an embodiment of the present application. Since the device is based on the above establishment method, specific details of the device can refer to the above establishment method, and are not described herein again.
The user database establishing device comprises:
an obtaining module 21, configured to obtain at least two user attribute values according to the user identity parameter;
the merging module 22 is configured to merge the user attribute values into user behavior parameters according to a preset merging model;
and the association module 23 is configured to store the user identity parameter and the user behavior parameter in a user database after associating the user identity parameter and the user behavior parameter.
In the embodiment of the application, the user identity parameter comprises a user identity card number.
In this embodiment of the application, the obtaining module 21 is specifically configured to:
and querying an attribute value database according to the user identity parameters to obtain user attribute values associated with the user identity parameters.
In an embodiment of the present application, the apparatus further includes a normalization module, specifically configured to:
after the obtaining module 21 obtains at least two user attribute values according to the user identity parameters, the merging module 22 performs normalization processing on the user attribute values before merging the user attribute values into the user behavior parameters according to a preset merging model;
the merging module 22 is specifically configured to:
and merging the normalized user attribute values into user behavior parameters according to a preset merging model.
In an embodiment of the present application, the normalization module is specifically configured to:
acquiring the mean value and the standard deviation of all user attribute values;
the mean value is subtracted from each user attribute value and divided by its standard deviation.
In this embodiment of the application, the merging module 22 is specifically configured to:
the summing unit is used for converting each user attribute value into the sum of the linear factor function of the preset public factors and the preset special factors in the preset number;
and the generating unit is used for generating the user behavior parameters according to the preset public factors.
In an embodiment of the present application, the summing unit is specifically configured to:
acquiring a preset factor load corresponding to a preset common factor in each user attribute value;
multiplying and summing each preset public factor and the corresponding preset factor load to obtain a linear factor function corresponding to the user attribute value;
and converting the user attribute value into the sum of a linear factor function and a preset special factor.
In an embodiment of the present application, the generating unit is specifically configured to:
summarizing all preset factor loads to obtain a factor load matrix;
performing factor rotation on the factor load matrix to obtain a rotated factor load;
correcting the linear factor function according to the rotated factor load to obtain a corrected factor function;
converting each preset public factor into a linear attribute function of all user attribute values according to the corrected factor function;
and weighting all the preset public factors according to a preset weighting model to obtain the user behavior parameters.
In an embodiment of the present application, the generating unit is specifically configured to:
acquiring the weighting ratio of each preset public factor according to a preset weighting model;
multiplying each preset public factor by the corresponding weighting ratio and summing to obtain a final public factor;
and converting the final common factor into a user behavior parameter according to a preset protocol model.
In the embodiment of the application, the preset protocol model includes a sesame score protocol model.
According to the embodiment of the application, the user attribute values associated with each user are combined to obtain a user behavior parameter capable of accurately reflecting the user attribute values, so that the space required by the user attribute values with larger storage magnitude is reduced, and the bloated state of a user database is avoided.
The present invention has been described with reference to methods and apparatus (systems) according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions in combination with the information-sensing device. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, 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, in conjunction with the information-sensing device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (6)
1. A method for creating a user database, comprising:
acquiring at least two user attribute values according to the user identity parameters;
merging the user attribute values into user behavior parameters according to a preset merging model;
after the user identity parameters and the user behavior parameters are associated, storing the user identity parameters and the user behavior parameters into a user database;
merging the user attribute values into user behavior parameters according to a preset merging model, wherein the merging comprises the following steps:
converting each user attribute value into the sum of a linear factor function of preset public factors and preset special factors of a preset number;
generating the user behavior parameters according to the preset public factors;
converting each user attribute value into the sum of a preset number of linear factor functions of preset common factors and preset special factors, specifically comprising:
acquiring a preset factor load corresponding to a preset common factor in each user attribute value;
multiplying and summing each preset public factor and the corresponding preset factor load to obtain a linear factor function corresponding to the user attribute value;
converting the user attribute value into the sum of the linear factor function and a preset special factor;
generating the user behavior parameter according to the preset public factor, specifically comprising:
summarizing all the preset factor loads to obtain a factor load matrix;
performing factor rotation on the factor load matrix to obtain a rotated factor load;
correcting the linear factor function according to the rotated factor load to obtain a corrected factor function;
converting each preset public factor into a linear attribute function of all user attribute values according to the corrected factor function;
weighting all preset public factors according to a preset weighting model to obtain user behavior parameters;
wherein, according to the preset weighting model, weighting all the preset public factors to obtain the user behavior parameters, which specifically comprises:
acquiring the weighting ratio of each preset public factor according to a preset weighting model;
multiplying each preset public factor by the corresponding weighting ratio and summing to obtain a final public factor;
and converting the final common factor into a user behavior parameter according to a preset protocol model.
2. The method according to claim 1, wherein obtaining at least two user attribute values according to the user identity parameter specifically comprises:
and querying an attribute value database according to the user identity parameters to obtain user attribute values associated with the user identity parameters.
3. The method of claim 1, wherein after obtaining at least two user attribute values according to the user identity parameter, before merging the user attribute values into the user behavior parameter according to a preset merging model, the method further comprises:
carrying out normalization processing on the user attribute values;
merging the user attribute values into user behavior parameters according to a preset merging model, which specifically comprises the following steps:
and merging the normalized user attribute values into user behavior parameters according to a preset merging model.
4. An apparatus for building a user database, comprising:
the acquisition module is used for acquiring at least two user attribute values according to the user identity parameters;
the merging module is used for merging the user attribute values into user behavior parameters according to a preset merging model;
the association module is used for associating the user identity parameters with the user behavior parameters and then storing the user identity parameters and the user behavior parameters into a user database;
the merging module is specifically configured to:
the summation unit is used for converting each user attribute value into the sum of the linear factor function of preset public factors and preset special factors in preset quantity;
the generating unit is used for generating the user behavior parameters according to the preset public factors;
the summing unit is specifically configured to:
acquiring a preset factor load corresponding to a preset common factor in each user attribute value;
multiplying and summing each preset public factor and the corresponding preset factor load to obtain a linear factor function corresponding to the user attribute value;
converting the user attribute value into the sum of the linear factor function and a preset special factor;
the generating unit is specifically configured to:
summarizing all the preset factor loads to obtain a factor load matrix;
performing factor rotation on the factor load matrix to obtain a rotated factor load;
correcting the linear factor function according to the rotated factor load to obtain a corrected factor function;
converting each preset public factor into a linear attribute function of all user attribute values according to the corrected factor function;
weighting all preset public factors according to a preset weighting model to obtain user behavior parameters;
the generating unit is specifically configured to:
acquiring the weighting ratio of each preset public factor according to a preset weighting model;
multiplying each preset public factor by the corresponding weighting ratio and summing to obtain a final public factor;
and converting the final common factor into a user behavior parameter according to a preset protocol model.
5. The apparatus of claim 4, wherein the acquisition module is specifically configured to:
and querying an attribute value database according to the user identity parameters to obtain user attribute values associated with the user identity parameters.
6. The apparatus of claim 4, further comprising a normalization module, specifically configured to:
after the acquisition module acquires at least two user attribute values according to the user identity parameters, the merging module performs normalization processing on the user attribute values before merging the user attribute values into user behavior parameters according to a preset merging model;
the merging module is specifically configured to:
and merging the normalized user attribute values into user behavior parameters according to a preset merging model.
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