CN110135976A - User's portrait generation method, device, electronic equipment and computer-readable medium - Google Patents
User's portrait generation method, device, electronic equipment and computer-readable medium Download PDFInfo
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
Abstract
Generation method, device, electronic equipment and computer-readable medium this disclosure relates to which a kind of user based on banking operation model draws a portrait.This method comprises: generating various dimensions characteristic by user's banking operation data, the various dimensions characteristic includes duration dimension data, behavior dimension data, frequency dimension data and attribute dimensions data；The various dimensions characteristic is inputted in banking operation model, the behavioural characteristic index of the user is obtained；And based on the behavioural characteristic index by determining that the credit of the user is drawn a portrait in preset multidimensional quadrant label.This disclosure relates to user based on banking operation model draw a portrait generation method, device, electronic equipment and computer-readable medium, user portrait of the user in time series can be obtained, more targeted can provide personalized financial service for user according to the related company of time and user's portrait.
Description
Technical field
This disclosure relates to computer information processing field, in particular to a kind of user based on banking operation model
Portrait generation method, device, electronic equipment and computer-readable medium.
Background technique
User's portrait is also known as user role, delineates having for target user, connection user's demand and design direction as a kind of
Effect tool, user's portrait are widely used in each field.User's portrait is applied in electric business field,
Under big data era background, user information is full of in a network, and each specifying information of user is abstracted into label, utilizes these
Label embodies user image, to provide targeted service for user.
User's portrait is substantially generated by big data at present, according to mass users data, extracts the feature of user,
Then enterprise according to their own needs classifies to user, formulates different user tags.But in the essential characteristic of user
Certain parts be constant, for example gender, age, some essential characteristics often change as time goes by, such as
The hobby of user, the work of user, the exercise habit of user etc..Dependent on some basic numbers when generating portrait in the prior art
According to, the feature for generating portrait is single, can not system comprehensively react the feature of user.
Therefore, it is necessary to it is a kind of new by the user of banking operation model draw a portrait generation method, device, electronic equipment and based on
Calculation machine readable medium.
Above- mentioned information are only used for reinforcing the understanding to the background of the disclosure, therefore it disclosed in the background technology part
It may include the information not constituted to the prior art known to persons of ordinary skill in the art.
Summary of the invention
It is set in view of this, the disclosure provides a kind of draw a portrait generation method, device, electronics of user based on banking operation model
Standby and computer-readable medium can obtain user portrait of the user in time series, according to the pass of time and user's portrait
It is that enterprise more targeted can provide personalized financial service for user.
Other characteristics and advantages of the disclosure will be apparent from by the following detailed description, or partially by the disclosure
Practice and acquistion.
According to the one side of the disclosure, a kind of user's portrait generation method based on banking operation model, this method are proposed
It include: to generate various dimensions characteristic by user's banking operation data, the various dimensions characteristic includes duration number of dimensions
According to, behavior dimension data, frequency dimension data and attribute dimensions data；The various dimensions characteristic is inputted into banking operation
In model, the behavioural characteristic index of the user is obtained；And based on the behavioural characteristic index by preset multidimensional quadrant mark
The credit portrait of the user is determined in label.
In one embodiment of the present disclosure, further includes: pass through the banking operation data and at least one machine of historical user
Device learning model establishes the banking operation model, the banking operation data include at least one banking operation and it is described at least
One banking operation corresponding time.
In one embodiment of the present disclosure, various dimensions characteristic is generated by user's banking operation data, comprising: really
Determine banking operation and its corresponding time；By the banking operation according to its corresponding time-sequencing；And by sequence after
The banking operation and the attribute data generate the various dimensions characteristic.
In one embodiment of the present disclosure, by described in the banking operation and attribute data generation after sequence
Various dimensions characteristic includes: based on each banking operation, when by the interval of first banking operation and end banking operation
Between determine the duration dimension data；And/or the behavior dimension is determined by the time corresponding to the end banking operation
Data；The frequency dimension data are determined by the quantity of multiple banking operations；And pass through the amount of money in the attribute data
Determine the attribute dimensions data.
In one embodiment of the present disclosure, the various dimensions characteristic is inputted in banking operation model, obtains institute
The behavioural characteristic index for stating user includes: that the various dimensions characteristic is carried out branch mailbox coding；Described in after branch mailbox is encoded
Various dimensions characteristic inputs in the banking operation model；The banking operation model carries out the various dimensions characteristic
Cluster and grouping are with the behavioural characteristic index of the determination user.
In one embodiment of the present disclosure, the banking operation model to the various dimensions characteristic carry out cluster and
Grouping with the behavioural characteristic index of the determination user include: the banking operation model according to behavioural characteristic index to described more
Dimensional characteristics data are clustered and are grouped with the behavioural characteristic index of the determination user.
In one embodiment of the present disclosure, the banking operation model is special to the various dimensions according to behavioural characteristic index
It includes: the banking operation model according to behavior that sign data, which are clustered and be grouped with the behavioural characteristic index of the determination user,
Characteristic index carries out Unsupervised clustering to the various dimensions characteristic with the behavioural characteristic index of the determination user；And/or
The banking operation model carries out decision tree grouping described in determination to the various dimensions characteristic according to behavioural characteristic index
The behavioural characteristic index of user.
In one embodiment of the present disclosure, based on the behavioural characteristic index by being determined in preset multidimensional quadrant label
The credit portrait of the user includes: to determine that user's is pre- in preset Multidimensional Value quadrant based on the behavioural characteristic index
Forward price value quadrant；And determine that the multidimensional quadrant label of the user is drawn with the determination credit according to the expectancy quadrant
Picture.
In one embodiment of the present disclosure, pass through the banking operation data of historical user and at least one machine learning mould
It includes: the banking operation data and unsupervised learning model foundation first by historical user that type, which establishes the banking operation model,
Model；And/or pass through banking operation model described in the first model output data and supervised learning model foundation.
In one embodiment of the present disclosure, pass through the banking operation data of historical user and at least one machine learning mould
It includes: at least one banking operation for obtaining the historical data and at least one described gold that type, which establishes the banking operation model,
Melt the behavior corresponding time；Multiple history various dimensions features are generated respectively based on the time corresponding to each banking operation and its
Data；Based on each history various dimensions characteristic, training set data and test set data are generated respectively；Pass through multiple training
Collection data and multiple test set data and at least one machine learning model establish multiple user characteristics models；And by described
Banking operation model described in multiple user characteristics model foundations.
In one embodiment of the present disclosure, pass through multiple training set datas and multiple test set data and at least one machine
It includes: respectively to the institute in multiple training set datas and multiple test set data that device learning model, which establishes multiple user characteristics models,
The each dimension data for stating history multi-dimensional data carries out branch mailbox coding；And the training set data after encoding branch mailbox inputs institute
It states at least one machine learning model, the test set data after being encoded by branch mailbox are verified to establish the multiple user
Characteristic model.
In one embodiment of the present disclosure, pass through banking operation model packet described in the multiple user characteristics model foundation
It includes: the banking operation model is established by multi-task learning method based on the multiple user characteristics model.
According to the one side of the disclosure, a kind of user's portrait generating means based on banking operation model, the device are proposed
It include: characteristic module, for generating various dimensions characteristic, the various dimensions characteristic by user's banking operation data
According to including duration dimension data, behavior dimension data, frequency dimension data and attribute dimensions data；Model computation module is used for
The various dimensions characteristic is inputted in banking operation model, the behavioural characteristic index of the user is obtained；And user draws
As module, for determining that the credit of the user is drawn a portrait based on the behavioural characteristic index.
In one embodiment of the present disclosure, further includes: model training module, for the banking operation by historical user
Data and at least one machine learning model establish the banking operation model, and the banking operation data include at least one gold
Melt the behavior time corresponding at least one described banking operation.
In one embodiment of the present disclosure, the characteristic module includes: time quantum, for determining banking operation
And its corresponding time；Sequencing unit is used for the banking operation according to its corresponding time-sequencing；And feature unit,
For passing through the banking operation and the attribute data generation various dimensions characteristic after sequence.
In one embodiment of the present disclosure, the feature unit includes: duration subelement, for based on each finance
Behavior determines the duration dimension data by the interval time of first banking operation and end banking operation；And/or behavior
Unit, for determining the behavior dimension data by the time corresponding to the end banking operation；And/or frequency is single
Member, for determining the frequency dimension data by the quantity of the banking operation；And/or attribute subelement, for passing through
It states the amount of money in attribute data and determines the attribute dimensions data.
In one embodiment of the present disclosure, the model computation module includes: processing unit, is used for the various dimensions
Characteristic carries out branch mailbox coding；Model unit inputs the gold for the various dimensions characteristic after encoding branch mailbox
Melt in behavior model；Computing unit is clustered and is grouped to the various dimensions characteristic for the banking operation model
With the behavioural characteristic index of the determination user.
In one embodiment of the present disclosure, the computing unit is also used to the banking operation model according to behavior spy
Sign index is clustered and is grouped with the behavioural characteristic index of the determination user to the various dimensions characteristic.
In one embodiment of the present disclosure, the computing unit is also used to the banking operation model according to behavior spy
It levies index and Unsupervised clustering is carried out with the behavioural characteristic index of the determination user to the various dimensions characteristic；And/or institute
It states banking operation model and decision tree grouping is carried out with the determination use to the various dimensions characteristic according to behavioural characteristic index
The behavioural characteristic index at family.
In one embodiment of the present disclosure, user's portrait module, is also used to exist based on the behavioural characteristic index
The expectancy quadrant of user is determined in preset Multidimensional Value quadrant；And the use is determined according to the expectancy quadrant
The multidimensional quadrant label at family is with the determination credit portrait.
In one embodiment of the present disclosure, the model training module includes: cluster cell, for passing through historical user
Banking operation data and the first model of unsupervised learning model foundation；And/or decision package, for passing through first model
Banking operation model described in output data and supervised learning model foundation.
In one embodiment of the present disclosure, the model training module includes: behavior taxon, described for obtaining
At least one banking operation of historical data time corresponding at least one described banking operation；Data processing unit is used for
Multiple history various dimensions characteristics are generated respectively based on the time corresponding to each banking operation and its；Data classification list
Member generates training set data and test set data for being based on each history various dimensions characteristic respectively；Disaggregated model list
Member, it is special for establishing multiple users by multiple training set datas and multiple test set data and at least one machine learning model
Levy model；And conjunctive model unit, for passing through banking operation model described in the multiple user characteristics model foundation.
In one embodiment of the present disclosure, the disaggregated model unit includes: coded sub-units, for respectively to multiple
Each dimension data of the history multi-dimensional data in training set data and multiple test set data carries out branch mailbox coding；With
And training subelement, it inputs at least one described machine learning model, passes through for the training set data after encoding branch mailbox
Test set data after branch mailbox coding are verified to establish the multiple user characteristics model.
In one embodiment of the present disclosure, the conjunctive model unit is also used to based on the multiple user characteristics mould
Type establishes the banking operation model by multi-task learning method.
According to the one side of the disclosure, a kind of electronic equipment is proposed, which includes: one or more processors；
Storage device, for storing one or more programs；When one or more programs are executed by one or more processors, so that one
A or multiple processors realize such as methodology above.
According to the one side of the disclosure, it proposes a kind of computer-readable medium, is stored thereon with computer program, the program
Method as mentioned in the above is realized when being executed by processor.
According to user's portrait generation method, device, electronic equipment and the computer based on banking operation model of the disclosure
Readable medium, generates various dimensions characteristic by user's banking operation data, and the various dimensions characteristic is inputted finance
In behavior model, the behavioural characteristic index of the user is obtained；And based on the behavioural characteristic index by preset multidimensional as
The mode for determining the credit portrait of the user in label is limited, user portrait of the user in time series can be obtained, according to
The related company of time and user's portrait more targeted can provide personalized financial service for user.
It should be understood that the above general description and the following detailed description are merely exemplary, this can not be limited
It is open.
Detailed description of the invention
Its example embodiment is described in detail by referring to accompanying drawing, above and other target, feature and the advantage of the disclosure will
It becomes more fully apparent.Drawings discussed below is only some embodiments of the present disclosure, for the ordinary skill of this field
For personnel, without creative efforts, it is also possible to obtain other drawings based on these drawings.
Fig. 1 is a kind of user's portrait generation method based on banking operation model shown according to an exemplary embodiment
Application block diagram.
Fig. 2 is a kind of user's portrait generation method based on banking operation model shown according to an exemplary embodiment
Flow chart.
Fig. 3 is a kind of user's portrait generation method based on banking operation model shown according to an exemplary embodiment
Schematic diagram.
Fig. 4 is a kind of user's portrait generation method based on banking operation model shown according to an exemplary embodiment
Schematic diagram.
Fig. 5 is a kind of user's portrait generation method based on banking operation model shown according to an exemplary embodiment
Flow chart.
Fig. 6 is a kind of user's portrait generation method based on banking operation model shown according to an exemplary embodiment
Schematic diagram.
Fig. 7 is a kind of user's portrait generating means based on banking operation model shown according to an exemplary embodiment
Block diagram.
Fig. 8 is the block diagram of a kind of electronic equipment shown according to an exemplary embodiment.
Specific embodiment
Example embodiment is described more fully with reference to the drawings.However, example embodiment can be real in a variety of forms
It applies, and is not understood as limited to embodiment set forth herein；On the contrary, thesing embodiments are provided so that the disclosure will be comprehensively and complete
It is whole, and the design of example embodiment is comprehensively communicated to those skilled in the art.Identical appended drawing reference indicates in figure
Same or similar part, thus repetition thereof will be omitted.
In addition, described feature, structure or characteristic can be incorporated in one or more implementations in any suitable manner
In example.In the following description, many details are provided to provide and fully understand to embodiment of the disclosure.However,
It will be appreciated by persons skilled in the art that can with technical solution of the disclosure without one or more in specific detail,
Or it can be using other methods, constituent element, device, step etc..In other cases, it is not shown in detail or describes known side
Method, device, realization or operation are to avoid fuzzy all aspects of this disclosure.
Block diagram shown in the drawings is only functional entity, not necessarily must be corresponding with physically separate entity.
I.e., it is possible to realize these functional entitys using software form, or realized in one or more hardware modules or integrated circuit
These functional entitys, or these functional entitys are realized in heterogeneous networks and/or processor device and/or microcontroller device.
Flow chart shown in the drawings is merely illustrative, it is not necessary to including all content and operation/step,
It is not required to execute by described sequence.For example, some operation/steps can also decompose, and some operation/steps can close
And or part merge, therefore the sequence actually executed is possible to change according to the actual situation.
It should be understood that although herein various assemblies may be described using term first, second, third, etc., these groups
Part should not be limited by these terms.These terms are to distinguish a component and another component.Therefore, first group be discussed herein below
Part can be described as the second component without departing from the teaching of disclosure concept.As used herein, term " and/or " include associated
All combinations for listing any of project and one or more.
It will be understood by those skilled in the art that attached drawing is the schematic diagram of example embodiment, module or process in attached drawing
Necessary to not necessarily implementing the disclosure, therefore it cannot be used for the protection scope of the limitation disclosure.
Fig. 1 is a kind of user's portrait generation method based on banking operation model shown according to an exemplary embodiment
Application block diagram.
As shown in Figure 1, system architecture 100 may include terminal device 101,102,103, network 104 and server 105.
Network 104 between terminal device 101,102,103 and server 105 to provide the medium of communication link.Network 104 can be with
Including various connection types, such as wired, wireless communication link or fiber optic cables etc..
User can be used terminal device 101,102,103 and be interacted by network 104 with server 105, to receive or send out
Send message etc..Various telecommunication customer end applications can be installed, such as financial platform class is answered on terminal device 101,102,103
With, web browser applications, searching class application, instant messaging tools, mailbox client, social platform software etc..
In embodiment of the disclosure, will by user browse Financial Information platform for, in the disclosure based on finance
User's portrait generation method of behavior model is described in detail.It is noted that the banking operation model in the disclosure is also
It can be applicable in the platform of multiple application scenarios and different merchandise classifications, the application is not limited.
Terminal device 101,102,103 can be the various electronic equipments with display screen and supported web page browsing, packet
Include but be not limited to smart phone, tablet computer, pocket computer on knee and desktop computer etc..
Server 105 can be to provide the server of various services, such as utilize terminal device 101,102,103 to user
The financial platform class website browsed provides the back-stage management server supported.Server 105 can be to the user's received
Banking operation data carry out the processing such as analyzing, and processing result (user's portrait) is fed back to corporate client management terminal.
Server 105 for example can generate various dimensions characteristic, the various dimensions feature by user's banking operation data
Data include duration dimension data, behavior dimension data, frequency dimension data and attribute dimensions data；Server 105 can be such as
The various dimensions characteristic is inputted in banking operation model, the behavioural characteristic index of the user is obtained；Server 105 can
Such as based on the behavioural characteristic index by determining that the credit of the user is drawn a portrait in preset multidimensional quadrant label.
Server 105 can also be established for example by the banking operation data of historical user and at least one machine learning model
The banking operation model, the banking operation data include at least one banking operation and at least one described banking operation pair
The time answered.
Server 105 can be the server of an entity, also may be, for example, multiple server compositions, needs to illustrate
It is that user's portrait generation method provided by the embodiment of the present disclosure based on banking operation model can be executed by server 105,
Correspondingly, user's portrait generating means based on banking operation model can be set in server 105.And be supplied to user into
The finance pages platform end of row Financial Information browsing is normally in terminal device 101,102,103.
According to the user's portrait generation method and device based on banking operation model of the disclosure, pass through user's banking operation
Data generate various dimensions characteristic, and the various dimensions characteristic is inputted in banking operation model, obtains the user's
Behavioural characteristic index；And based on the behavioural characteristic index by the credit of the user determining in preset multidimensional quadrant label
The mode of portrait can obtain user portrait of the user in time series, can according to the related company of time and user's portrait
Personalized financial service is provided for user with more targeted.
Fig. 2 is a kind of user's portrait generation method based on banking operation model shown according to an exemplary embodiment
Flow chart.User based on banking operation model draws a portrait generation method 20 including at least step S202 to S208.
As shown in Fig. 2, generating various dimensions characteristic, the various dimensions by user's banking operation data in S202
Characteristic includes duration dimension data, behavior dimension data, frequency dimension data and attribute dimensions data.
Wherein, banking operation related data can be loaning bill behavior, and debt behavior etc., overdue behavior etc. is with financial wind
The behavior of danger, may also include amount owed data.
In one embodiment, specifically can include: determine banking operation and its corresponding time；The banking operation is pressed
According to its corresponding time-sequencing；And the various dimensions spy is generated with the attribute data by the banking operation after sequence
Levy data.
In one embodiment, the various dimensions spy is generated with the attribute data by the banking operation after sequence
Sign data include: to determine institute by the interval time of first banking operation and end banking operation based on each banking operation
State duration dimension data；And/or the behavior dimension data is determined by the time corresponding to the end banking operation；With/
Or the frequency dimension data are determined by the quantity of multiple banking operations；And/or it is true by the amount of money in the attribute data
The fixed attribute dimensions data.
Currently, RFM model is a kind of User management model, there are three elements for RFM model: the last time consumption
(Recency), consuming frequency (Frequency), spending amount (Monetary).RFM model passes through the recent purchase of a client
Behavior, the population frequency of purchase and how much has been spent this three indexs are bought to describe the situation of the client.It uses in this application
Family administrative model establishes banking operation model to generate user's portrait.
Based on the definition in RFM model, in the present embodiment by user's lend-borrow action and for the relevant time, can will borrow
The relevant behavioral data of loan behavior is divided into, and frequency and credit amount are borrowed or lent money in the last time debt-credit.It more specifically can be according to head
The time interval of secondary lend-borrow action and last time lend-borrow action generates duration characteristics data L (Length)；Pass through last time
The time of lend-borrow action determines behavioural characteristic data R (Recency), determines frequecy characteristic number by the number that lend-borrow action occurs
According to F (Frequency), attributive character data M (Monetary) is determined by the credit amount in nodes ' behavior.
It is noted that in attributive character data, it can be using the amount of money of user's last time debt-credit as attributive character
Data, can also be using credit amount average in the multiple lend-borrow action of user as attributive character data, and concrete condition can be according to model
The difference of focus in calculating and be adjusted, the disclosure is not limited.
Also banking operation data can be generated by debt behavior time corresponding with its of user, and then it is special to generate various dimensions
Data are levied, specific steps are same as above, and the disclosure repeats no more again.
In S204, the various dimensions characteristic is inputted in banking operation model, the behavior for obtaining the user is special
Levy index.
Wherein, the banking operation model is the User management model with temporal characteristics, and traditional RFM model passes through transaction
Three dimensions in link: R, F, M refined user group are the assessments with historical data to user's current state.With various electricity
The development of sub- technology, advertisement and media industry quickly increase, and user has touched more a greater amount of information.In today's society
In, huge variation will occur in a short time for the hobby of user and behavior, and RFM model is only used by historical data analysis
Data obtained from the current state of family have been far from satisfying the demand that market is quickly developed and changed.So disclosure base
Banking operation model is proposed in the User management model of time series, the generating process of concrete model is in the corresponding implementation of Fig. 3
It is described in detail in example.
In one embodiment, the various dimensions characteristic is inputted in banking operation model, obtains the user's
Behavioural characteristic index includes: that the various dimensions characteristic is carried out branch mailbox coding；The various dimensions after branch mailbox is encoded are special
Data are levied to input in the banking operation model；The banking operation model is clustered and is divided to the various dimensions characteristic
Group is with the behavioural characteristic index of the determination user.Wherein, branch mailbox method refers to through the value around investigating come smooth storing data
Value, the data for having same number in different case are indicated with " depth of case ", indicate each bin values with " width of case "
Value interval.The main purpose of branch mailbox is denoising, by continuous data discretization, increases granularity.
More specifically, the banking operation model carries out without prison the various dimensions characteristic according to behavioural characteristic index
Cluster is superintended and directed with the behavioural characteristic index of the determination user；And/or the banking operation model according to behavioural characteristic index to institute
It states various dimensions characteristic and carries out decision tree grouping with the behavioural characteristic index of the determination user.
In S206, based on the behavioural characteristic index by the credit of the user determining in preset multidimensional quadrant label
Portrait.
In one embodiment, determine that user's is pre- in preset Multidimensional Value quadrant based on the behavioural characteristic index
Forward price value quadrant；And determine that the multidimensional quadrant label of the user is drawn with the determination credit according to the expectancy quadrant
Picture.
Fig. 3 is a kind of user's portrait generation method based on banking operation model shown according to an exemplary embodiment
Schematic diagram, wherein each index dimension can be subdivided out different grades by these three indexs according to RFM, and this makes it possible to thin
Divide different classes of user, exists further according to every class user precision marketing.Each dimension for example can be done primary two points in the disclosure
, each dimension be divided into height two classes, 8 groups of users can be obtained in tri- dimensions of RFM in this way.User in this way
It is analyzed, be particularly may be divided into according to different dimensions: is important value client, important holding client, important development client, important
Keep client etc..
And after introducing L-dimensional, it also can be obtained after following a period of time, the behavioural characteristic of user, in the disclosure
For example L-dimensional can also be done primary two points, as current and user after 120 days behavioural characteristic, thus user by working as
8 preceding groups extend to 16 groups, in as 16 quadrants.Certainly, L can also do different differentiations, can be for example by L points
It currently 120 days and 360 days, then user can be divided into 24 dimensions by 8 current dimensions, certainly, is gone back for three dimensions
The dimension of L can be divided into 10 days, 30 days etc., the dimension processing mode of L can be fine according to different application scenarios and to user
The division of change is required and is determined.
Fig. 4 is a kind of user's portrait generation method based on banking operation model shown according to an exemplary embodiment
Schematic diagram.As shown in figure 4, user is extended to 16 groups by 8 current groups, the feelings in as 16 quadrants
Under condition, user can be grouped are as follows: current important value client and the following important value client, current important holding client and future weight
Client is kept, current important development client and the following important development client, current important client and future kept and important keeps
Client etc..
Can be that user determine that different user draws a portrait label according to different quadrant classifications, can for example, active user as
Limit is classified as important holding client, following 30 days show as it is important keep client, then can draw a portrait label according to for user's setting
Client is paid close attention to for emphasis, needs to carry out the preferential measure of more advertisement promotion or more.
It will be clearly understood that the present disclosure describes how to form and use particular example, but the principle of the disclosure is not limited to
These exemplary any details.On the contrary, the introduction based on disclosure disclosure, these principles can be applied to many other
Embodiment.
Fig. 5 is a kind of user's portrait generation method based on banking operation model shown according to an exemplary embodiment
Flow chart.Process shown in fig. 5 is to " by the banking operation data of historical user and the foundation of at least one machine learning model
The detailed description of the banking operation model ".
As shown in figure 5, in S502, obtain the historical data at least one banking operation and at least one described gold
Melt the behavior corresponding time.
In S504, multiple history various dimensions features are generated based on the time corresponding to each banking operation and its respectively
Data.
In S506, it is based on each history various dimensions characteristic, generates training set data and test set data respectively.
Wherein, the basic data of user can be divided into training observation collection according to the time as shown in the table, and training performance collection is surveyed
Examination observation collection, and test performance collection.
In S508, by multiple training set datas and multiple test set data with unsupervised learning model foundation first
Model.Wherein, unsupervised learning model can be k-mean Clustering Model, and k-mean cluster is first to randomly select K user base
Data are as initial cluster centre.Then the distance between each user base data and each seed cluster centre are calculated,
Each object is distributed to the cluster centre nearest apart from it, in cluster data, cluster centre and pair for distributing to them
As just representing a cluster.
In S510, pass through banking operation mould described in the first model output data and supervised learning model foundation
Type.Supervised learning model can be decision-tree model, and decision tree (Decision Tree) is various to happen probability known
On the basis of, the desired value that net present value (NPV) is sought by constituting decision tree is more than or equal to zero probability, in the timing row of the disclosure
For in model, decision tree is a prediction model, that he represents is one between basic data and the probability of basic data generation
Kind mapping relations.A classifier is obtained by study, this classifier can provide classification to emerging basic data.
Fig. 6 is a kind of user's portrait generation method based on banking operation model shown according to an exemplary embodiment
Schematic diagram.As shown in fig. 6, can also will have monitor model and without prison by multi-task learning method during model training
The parameter for superintending and directing model carries out Joint regulation.Multi-task learning (Multi-task learning) is learnt with single task
A kind of (Single-task learning) opposite machine learning method, can learn multiple tasks simultaneously, be generally used for point
Class and recurrence.And multi-task learning then values the connection between task, by combination learning, while to the ginseng of multiple model tasks
Number is learnt, and the difference between task has both been considered, it is also contemplated that the connection between task, this be also multi-task learning most
One of important thought.
Certainly, in order to preferably carry out Coordination Treatment to multiple tasks, also different weights can be set for different tasks,
In embodiment of the disclosure, may be, for example, cluster task setting weight be A, be decision tree task be arranged weight be B, then more
When tasking learning, parameter adjustment can be carried out according to the difference of weight.
It will be appreciated by those skilled in the art that realizing that all or part of the steps of above-described embodiment is implemented as being executed by CPU
Computer program.When the computer program is executed by CPU, above-mentioned function defined by the above method that the disclosure provides is executed
Energy.The program can store in a kind of computer readable storage medium, which can be read-only memory, magnetic
Disk or CD etc..
Further, it should be noted that above-mentioned attached drawing is only the place according to included by the method for disclosure exemplary embodiment
Reason schematically illustrates, rather than limits purpose.It can be readily appreciated that above-mentioned processing shown in the drawings is not indicated or is limited at these
The time sequencing of reason.In addition, be also easy to understand, these processing, which can be, for example either synchronously or asynchronously to be executed in multiple modules.
Following is embodiment of the present disclosure, can be used for executing embodiments of the present disclosure.It is real for disclosure device
Undisclosed details in example is applied, embodiments of the present disclosure is please referred to.
Fig. 7 is a kind of user's portrait generating means based on banking operation model shown according to an exemplary embodiment
Block diagram.As shown in fig. 7, the user based on banking operation model draws a portrait, generating means 70 include: characteristic module 702, model
Computing module 704 and user's portrait module 706.User's portrait generating means 70 based on banking operation model may also include that
Model training module 708.
Characteristic module 702 is used to generate various dimensions characteristic, the various dimensions by user's banking operation data
Characteristic includes duration dimension data, behavior dimension data, frequency dimension data and attribute dimensions data；
Wherein, the characteristic module 702 can include: time quantum, for determine banking operation and its it is corresponding when
Between；Sequencing unit is used for the banking operation according to its corresponding time-sequencing；And feature unit, for passing through sequence
The banking operation and the attribute data afterwards generates the various dimensions characteristic.
Wherein, the feature unit includes: duration subelement, for being based on each banking operation, passes through first finance
The interval time of behavior and end banking operation determines the duration dimension data；And/or behavior subelement, for by described
Time corresponding to the banking operation of end determines the behavior dimension data；And/or frequency subelement, for passing through the finance
The quantity of behavior determines the frequency dimension data；And/or attribute subelement, for true by the amount of money in the attribute data
The fixed attribute dimensions data.
Model computation module 704 is used to input the various dimensions characteristic in banking operation model, obtains the use
The behavioural characteristic index at family；
The model computation module 704 includes: processing unit, for the various dimensions characteristic to be carried out branch mailbox volume
Code；Model unit inputs in the banking operation model for the various dimensions characteristic after encoding branch mailbox；It calculates single
Member is clustered and is grouped with the behavior of the determination user to the various dimensions characteristic for the banking operation model
Characteristic index.
User draws a portrait module 706 for determining that the credit of the user is drawn a portrait based on the behavioural characteristic index.The use
Family portrait module 706 is also used to determine the pre- forward price of user in preset Multidimensional Value quadrant based on the behavioural characteristic index
It is worth quadrant；And determine that the multidimensional quadrant label of the user is drawn a portrait with the determination credit according to the expectancy quadrant.
Model training module 708 by the banking operation data of historical user at least one machine learning model for being built
The banking operation model is found, the banking operation data include at least one banking operation and at least one described banking operation
The corresponding time.
The model training module 708 can include: cluster cell, for passing through the banking operation data and nothing of historical user
The first model of supervised learning model foundation；And/or decision package, for being learned by the first model output data with there is supervision
Practise banking operation model described in model foundation.
According to user's portrait generating means based on banking operation model of the disclosure, it is raw to pass through user's banking operation data
At various dimensions characteristic, the various dimensions characteristic is inputted in banking operation model, the behavior for obtaining the user is special
Levy index；And based on the behavioural characteristic index by determining what the credit of the user was drawn a portrait in preset multidimensional quadrant label
Mode, can obtain user portrait of the user in time series, and the related company drawn a portrait according to the time with user can more have
Personalized financial service is targetedly provided for user.
Fig. 8 is the block diagram of a kind of electronic equipment shown according to an exemplary embodiment.
The electronic equipment 200 of this embodiment according to the disclosure is described referring to Fig. 8.The electronics that Fig. 8 is shown
Equipment 200 is only an example, should not function to the embodiment of the present disclosure and use scope bring any restrictions.
As shown in figure 8, electronic equipment 200 is showed in the form of universal computing device.The component of electronic equipment 200 can wrap
It includes but is not limited to: at least one processing unit 210, at least one storage unit 220, (including the storage of the different system components of connection
Unit 220 and processing unit 210) bus 230, display unit 240 etc..
Wherein, the storage unit is stored with program code, and said program code can be held by the processing unit 210
Row, so that the processing unit 210 executes described in this specification above-mentioned electronic prescription circulation processing method part according to this
The step of disclosing various illustrative embodiments.For example, the processing unit 210 can execute step as shown in Figure 2.
The storage unit 220 may include the readable medium of volatile memory cell form, such as random access memory
Unit (RAM) 2201 and/or cache memory unit 2202 can further include read-only memory unit (ROM) 2203.
The storage unit 220 can also include program/practical work with one group of (at least one) program module 2205
Tool 2204, such program module 2205 includes but is not limited to: operating system, one or more application program, other programs
It may include the realization of network environment in module and program data, each of these examples or certain combination.
Bus 230 can be to indicate one of a few class bus structures or a variety of, including storage unit bus or storage
Cell controller, peripheral bus, graphics acceleration port, processing unit use any bus structures in a variety of bus structures
Local bus.
Electronic equipment 200 can also be with one or more external equipments 300 (such as keyboard, sensing equipment, bluetooth equipment
Deng) communication, can also be enabled a user to one or more equipment interact with the electronic equipment 200 communicate, and/or with make
Any equipment (such as the router, modulation /demodulation that the electronic equipment 200 can be communicated with one or more of the other calculating equipment
Device etc.) communication.This communication can be carried out by input/output (I/O) interface 250.Also, electronic equipment 200 can be with
By network adapter 260 and one or more network (such as local area network (LAN), wide area network (WAN) and/or public network,
Such as internet) communication.Network adapter 260 can be communicated by bus 230 with other modules of electronic equipment 200.It should
Understand, although not shown in the drawings, other hardware and/or software module can be used in conjunction with electronic equipment 200, including but unlimited
In: microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape drive and number
According to backup storage system etc..
Through the above description of the embodiments, those skilled in the art is it can be readily appreciated that example described herein is implemented
Mode can also be realized by software realization in such a way that software is in conjunction with necessary hardware.Therefore, according to the disclosure
The technical solution of embodiment can be embodied in the form of software products, which can store non-volatile at one
Property storage medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) in or network on, including some instructions are so that a calculating
Equipment (can be personal computer, server or network equipment etc.) executes the above method according to disclosure embodiment.
Can with any combination of one or more programming languages come write for execute the disclosure operation program
Code, described program design language include object oriented program language-Java, C++ etc., further include conventional
Procedural programming language-such as " C " language or similar programming language.Program code can be fully in user
It calculates and executes in equipment, partly executes on a user device, being executed as an independent software package, partially in user's calculating
Upper side point is executed on a remote computing or is executed in remote computing device or server completely.It is being related to far
Journey calculates in the situation of equipment, and remote computing device can pass through the network of any kind, including local area network (LAN) or wide area network
(WAN), it is connected to user calculating equipment, or, it may be connected to external computing device (such as utilize ISP
To be connected by internet).
Above-mentioned computer-readable medium carries one or more program, when said one or multiple programs are by one
When the equipment executes, so that the computer-readable medium implements function such as: generating various dimensions by user's banking operation data
Characteristic, the various dimensions characteristic include duration dimension data, behavior dimension data, frequency dimension data and attribute
Dimension data；The various dimensions characteristic is inputted in banking operation model, the behavioural characteristic index of the user is obtained；With
And based on the behavioural characteristic index by determining that the credit of the user is drawn a portrait in preset multidimensional quadrant label.
By the description of above embodiment, those skilled in the art is it can be readily appreciated that example embodiment described herein
It can also be realized in such a way that software is in conjunction with necessary hardware by software realization.Therefore, implemented according to the disclosure
The technical solution of example can be embodied in the form of software products, which can store in a non-volatile memories
In medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) or on network, including some instructions are so that a calculating equipment (can
To be personal computer, server, mobile terminal or network equipment etc.) it executes according to the method for the embodiment of the present disclosure.
It is particularly shown and described the exemplary embodiment of the disclosure above.It should be appreciated that the present disclosure is not limited to
Detailed construction, set-up mode or implementation method described herein；On the contrary, disclosure intention covers included in appended claims
Various modifications and equivalence setting in spirit and scope.
Claims (10)
- The generation method 1. a kind of user based on banking operation model draws a portrait characterized by comprisingVarious dimensions characteristic is generated by user's banking operation data, the various dimensions characteristic includes duration number of dimensions According to, behavior dimension data, frequency dimension data and attribute dimensions data；The various dimensions characteristic is inputted in banking operation model, the behavioural characteristic index of the user is obtained；AndBased on the behavioural characteristic index by determining that the credit of the user is drawn a portrait in preset multidimensional quadrant label.
- 2. the method as described in claim 1, which is characterized in that further include:The banking operation model is established by the banking operation data and at least one machine learning model of historical user, it is described Banking operation data include at least one banking operation time corresponding at least one described banking operation.
- 3. the method as described in claim 1, which is characterized in that generate various dimensions characteristic by user's banking operation data According to, comprising:Determine banking operation and its corresponding time；By the banking operation according to its corresponding time-sequencing；AndPass through the banking operation and the attribute data generation various dimensions characteristic after sequence.
- 4. method as claimed in claim 3, which is characterized in that by sequence after the banking operation and the attribute data Generating the various dimensions characteristic includes:Based on each banking operation, determine that the duration is tieed up by the interval time of first banking operation and end banking operation Degree evidence；And/orThe behavior dimension data is determined by the time corresponding to the end banking operation；And/orThe frequency dimension data are determined by the quantity of multiple banking operations；And/orThe attribute dimensions data are determined by the amount of money in the attribute data.
- 5. a kind of user's credit standing assesses device characterized by comprisingCharacteristic module, for generating various dimensions characteristic, the various dimensions characteristic by user's banking operation data According to including duration dimension data, behavior dimension data, frequency dimension data and attribute dimensions data；Model computation module obtains the row of the user for inputting the various dimensions characteristic in banking operation model It is characterized index；AndUser's portrait module, for determining that the credit of the user is drawn a portrait based on the behavioural characteristic index.
- 6. device as claimed in claim 5, which is characterized in that further include:Model training module, for described in the banking operation data and the foundation of at least one machine learning model by historical user Banking operation model, the banking operation data include that at least one banking operation is corresponding at least one described banking operation Time.
- 7. device as claimed in claim 5, which is characterized in that the characteristic module includes:Time quantum, for determining banking operation and its corresponding time；Sequencing unit is used for the banking operation according to its corresponding time-sequencing；AndFeature unit, for passing through the banking operation and the attribute data generation various dimensions characteristic after sequence According to.
- 8. device as claimed in claim 5, which is characterized in that the feature unit includes:Duration subelement, for being based on each banking operation, when by the interval of first banking operation and end banking operation Between determine the duration dimension data；And/orBehavior subelement, for determining the behavior dimension data by the time corresponding to the end banking operation；And/orFrequency subelement, for determining the frequency dimension data by the quantity of the banking operation；And/orAttribute subelement, for determining the attribute dimensions data by the amount of money in the attribute data.
- 9. a kind of electronic equipment characterized by comprisingOne or more processors；Storage device, for storing one or more programs；When one or more of programs are executed by one or more of processors, so that one or more of processors are real The now method as described in any in claim 1-4.
- 10. a kind of computer-readable medium, is stored thereon with computer program, which is characterized in that described program is held by processor The method as described in any in claim 1-4 is realized when row.
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