CN106056407A - Online banking user portrait drawing method and equipment based on user behavior analysis - Google Patents
Online banking user portrait drawing method and equipment based on user behavior analysis Download PDFInfo
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- CN106056407A CN106056407A CN201610385405.0A CN201610385405A CN106056407A CN 106056407 A CN106056407 A CN 106056407A CN 201610385405 A CN201610385405 A CN 201610385405A CN 106056407 A CN106056407 A CN 106056407A
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- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0224—Discounts or incentives, e.g. coupons or rebates based on user history
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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
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Abstract
The invention relates to a user portrait drawing method and user portrait drawing equipment, in particular to a method and equipment for acquiring user portrait data through analyzing network surfing behaviors of online banking users. According to the user portrait drawing method and the user portrait drawing equipment, weighted scores of webpage feature points are obtained through analyzing network surfing behaviors of the online banking users, feature tags of the feature points with high scores are counted, user portrait data can be obtained, feature indexes such as user information, behaviors, financial management preferences and risk tolerance are subjected to quantification processing, so that user groups can be classified for portrait drawing from multiple dimensions, thus financial enterprises can have a more intuitive understanding of requirements of the users for various kinds of different products, and send product promotion information in a targeted manner.
Description
Technical field
The present invention relates to a kind of user and draw a portrait method and apparatus, a kind of Net silver based on user behavior analysis is used
Family portrait method and apparatus.
Background technology
Along with constantly weeding out the old and bring forth the new of the Internet financial product, the product of various different characteristics impacts the sight line of user,
The user buying finance product the most increasingly has multiformity.For the customer, each finance product is respectively arranged with feature, one by one
Read product description not only to waste time and energy, be also not easily found oneself product applicable.For bank, in the face of differentiation
Numerous users, unified send identical product promotion information, not only to expend substantial amounts of financial resources, be also easy to be taken as rubbish letter
Breath is complained；Promote suitable finance product to purpose client to become to be increasingly difficult to.Therefore, how the financial company such as bank draws
Lead client and be quickly found the real product being suitable for oneself, reduce enterprise and promote cost, just become the most meaningful.
Summary of the invention
It is an object of the invention to provide a kind of Net silver user based on user behavior analysis and draw a portrait method and apparatus, by point
The internet behavior of analysis Net silver user obtains user's representation data, by user profile, behavior, financing preference, risk are born energy
The characteristic indexs such as power carry out quantification treatment so that user group is able to carry out classification portrait from various dimensions, so that finance enterprise
Industry more intuitively understands user's demand to various different products, sends product promotion information targetedly.
Net silver user based on user behavior analysis of the present invention draws a portrait method, comprises the steps:
A) feature tag is extracted: the key word of extraction representative products feature is as feature tag from finance product description, and
Giving initial score value and initial weight value to each feature tag, the set of whole feature tags constitutes tag database；
B) webpage is labelled: extracts the characteristic point of Net silver visible page, chooses at least one feature tag from tag database
It is mapped to characteristic point；
C) extract user characteristics behavior: obtain the Operation Log of whole user, extract user characteristics behavior therein, give user
Characteristic behavior specific behavior weighted value；All set constituting action data bases of user characteristics behavior；
D) statistics score: obtain the Operation Log of specific user's behavior, one by one Net silver visible page in read operation daily record
Characteristic point and be mapped to the feature tag on this feature point, obtains the initial score value of feature tag from tag database, with
Time read when this specific user operates visible page the characteristic behavior for this feature point, subordinate act data base obtains this feature
The behavior weighted value of behavior；Initial score value is multiplied with behavior weighted value, obtains behavior score value；By same under this specific user's item
Behavior score value under feature tag is added, and obtains this specific user behavior total score about this feature label；Repeat this step,
Until obtaining the behavior total score under this specific user all feature tags item；
E) representation data generates: choose the behavior total score some feature tags more than threshold value, by the behavior of each feature tag
Total score is multiplied with the initial weight value of this feature label, obtains the representation data of this feature label.
In step D, when obtaining the Operation Log of specific user's behavior, Net silver visible page in read operation daily record one by one
Stay time, and give this visible page and stay time positively related duration weighted value, initial score value and behavior weighted value phase
The result taken advantage of is multiplied with duration weighted value again, obtains behavior score value.
In step E) first by step D) behavior total score under whole feature tag items of obtaining is according to arranging from low to high
Sequence, selects one of them numerical value suddenlyd change as threshold value.
In step E) first by step D) behavior total score under whole feature tag items of obtaining is according to arranging from low to high
Sequence, and be added the most one by one, until being added the gained sum special ratios more than or equal to the highest behavior total score, participate in
Numerical value the highest in the behavior total score being added is as threshold value.
Net silver user based on user behavior analysis draws a portrait equipment, including:
Tag database, for the feature tag of the representative products feature that storage is extracted from finance product description, this feature
Label includes at least initial score value and two parameters of initial weight value；
Tag processor, for extracting the characteristic point of Net silver visible page, and chooses at least one feature from tag database
Label mapping is to characteristic point；
Behavior database, is used for storing user characteristics behavior, and this user characteristics behavior at least has one parameter of behavior weighted value；
Data processor, for obtaining the Operation Log of specific user's behavior, the Net silver visual page in read operation daily record one by one
The characteristic point in face and be mapped to the feature tag on this feature point, obtains initial point of feature tag from tag database
Value, reads the characteristic behavior for this feature point when this specific user operates visible page simultaneously, obtains in subordinate act data base
The behavior weighted value of this feature behavior；Initial score value is multiplied with behavior weighted value, obtains behavior score value；By this specific user's item
Under behavior score value under same feature tag be added, obtain this specific user behavior total score about this feature label；
Portrait maker；For choosing the behavior total score some feature tags more than threshold value, by the behavior of each feature tag
Total score is multiplied with the initial weight value of this feature label, obtains the representation data of this feature label；Representation data is collected defeated
Go out the user of specific format and draw a portrait file.
Owing to using technique scheme, the financial company such as bank can by analyze the internet behavior of Net silver user to
The indexs such as family information, behavior, financing preference, risk tolerance carry out quantification treatment so that user group can be from various dimensions
Carrying out classifying and describing, so that enterprise is easier to the demand finding user to product, the precision marketing for financial company carries
For the portrait of user accurately.
Accompanying drawing explanation
Fig. 1 is the FB(flow block) of one embodiment of the invention.
Detailed description of the invention
Net silver user based on user behavior analysis of the present invention draws a portrait method, comprises the steps:
A) feature tag is extracted: the key word of extraction representative products feature is as feature tag from finance product description, such as:
Distribution unit, issuing date, in the time limit, it is contemplated that income, investment threshold, for user, dividend mode etc..And give each feature tag
Giving initial score value and initial weight value, the set of whole feature tags constitutes tag database.
B) webpage is labelled: extracts the characteristic point of Net silver visible page, chooses at least one feature from tag database
Label mapping is to characteristic point；One characteristic point can map a feature tag, it is also possible to maps multiple feature tag.Described
Characteristic point is all territory elements that can affect user's attention rate, such as word, icon, frame, operation on visible page
Prompt, messagewindow etc..These territory elements often provide single, independent information content, have user and determine also
Unique attention rate.Such as issue the title of unit on the Net silver page, provide a user with single, independent as a characteristic point
Information content, has user simultaneously and determines and unique attention rate.
C) extract user characteristics behavior: obtain the Operation Log of whole user, extract user characteristics behavior therein, give
User characteristics behavior specific behavior weighted value；All set constituting action data bases of user characteristics behavior；User characteristics row
For including the users such as click, input through keyboard, touch, page scroll, the page slides, key point stops, critical events redirects
Inner directed behavior acts on web page characteristics point or the behavior of webpage.Height according to user's independent desire determines its behavior weighted value,
Such as, the independent desire of click behavior is apparently higher than browsing and page scroll, and the behavior weighted value of therefore click is high
In browsing and page scroll；The independent desire of input through keyboard is again higher than click, and therefore the behavior weighted value of input through keyboard is high
In click.The weighted value of concrete each user characteristics behavior is calculated by a large amount of statistical datas.
D) statistics score: obtain the Operation Log of specific user's behavior, the Net silver visual page in read operation daily record one by one
The characteristic point in face and be mapped to the feature tag on this feature point, obtains initial point of feature tag from tag database
Value, reads the characteristic behavior for this feature point when this specific user operates visible page simultaneously, obtains in subordinate act data base
The behavior weighted value of this feature behavior；Initial score value is multiplied with behavior weighted value, obtains behavior score value；By this specific user's item
Under behavior score value under same feature tag be added, obtain this specific user behavior total score about this feature label；Repeat
This step, until obtaining the behavior total score under this specific user all feature tags item；During concrete operations, choose a certain specific
The Operation Log of user's a period of time, reads web page on whole Net silver one by one, analyzes characteristic point therein and is mapped to this
Feature tag in characteristic point, obtains the behavior total score of all feature tags by above-mentioned steps.The row of a certain feature tag
For total score, this user of the highest expression is the highest for the attention rate of this feature label, and such as conservative user the most more lays particular stress on money
The safety of gold, its use during Net silver due to frequent operation enjoy a good reputation, the finance product of the type that breaks even, this user Xiang Xiayou
The behavior total score of the feature tags such as pass breaks even, low-risk is the highest；Otherwise, pursue under user's item of high yield, high yield,
The behavior total score of the feature tags such as earning rate is the highest.
E) representation data generates: choose the behavior total score some feature tags more than threshold value, by each feature tag
Behavior total score is multiplied with the initial weight value of this feature label, obtains the representation data of this feature label.The setting of threshold value can
To be filtered out by feature tag relatively low for a large amount of scores, obtain some feature tags representative, that score is higher for retouching
State user characteristics.Initial weight value arrange can the finally interference of correction data and error, obtain directly perceived, draw a portrait number accurately
According to.
Obtaining after user's representation data, the financial department such as bank, can be by extract should before distribution finance product
The product description of finance product, analyzes and formulates the feature list of this product, by the spy of user's representation data Yu finance product
Levy list comparison, choose and wherein there is the user of high consistency be the purpose client of this product, can be to these users
Pushed information or preferentially show the information of this product these users use Net silver when.
Above-described embodiment, in step D, when obtaining the Operation Log of specific user's behavior, net in read operation daily record one by one
The stay time of silver visible page, and give this visible page and stay time positively related duration weighted value, initial score value with
The result that behavior weighted value is multiplied is multiplied with duration weighted value again, obtains behavior score value.Stay time is described from another angle
User is to the attention rate of characteristic point in the page.
Choosing of threshold value can have multiple method, in step E) first by step D) under whole feature tag items of obtaining
Behavior total score, according to sorting from low to high, selects one of them numerical value suddenlyd change as threshold value.Such as, behavior total score is from low
To height sequence sequence when, the numeric distribution of low side between 1 20, high-end numeric distribution 300 1000 it
Between, then between 20 and 300, there is a sudden change, select 300 as threshold value.So can get rid of except microcosmic disturbs, can protect again
Stop the more than enough feature tag with certain weight to draw a portrait for describing user.
The another kind of method that threshold value is chosen, in step E) first by step D) behavior under whole feature tag items of obtaining
Total score is according to sorting from low to high, and is added the most one by one, until being added gained sum more than or equal to the highest behavior
The special ratios of total score, such as 30%, participate in numerical value the highest in the behavior total score being added as threshold value.This method is same
A part of feature tag minimum for behavior total score can be got rid of by sample.
As it is shown in figure 1, Net silver user based on user behavior analysis of the present invention draws a portrait equipment, including:
Tag database 1, for the feature tag of the representative products feature that storage is extracted from finance product description, this feature
Label includes at least initial score value and two parameters of initial weight value；
Tag processor, for extracting the characteristic point of Net silver visible page 3, and chooses at least one feature from tag database
Label mapping is to characteristic point；
Behavior database 2, is used for storing user characteristics behavior, and this user characteristics behavior at least has behavior weighted value one ginseng
Number；
Data processor 6, for obtaining the Operation Log of specific user's behavior, the Net silver visual page in read operation daily record one by one
The characteristic point in face and be mapped to the feature tag on this feature point, obtains initial point of feature tag from tag database
Value, reads the characteristic behavior for this feature point when this specific user operates visible page simultaneously, obtains in subordinate act data base
The behavior weighted value of this feature behavior；Initial score value is multiplied with behavior weighted value, obtains behavior score value；By this specific user's item
Under behavior score value under same feature tag be added, obtain this specific user behavior total score about this feature label；
Portrait maker 4；For choosing the behavior total score some feature tags more than threshold value, by the behavior of each feature tag
Total score is multiplied with the initial weight value of this feature label, obtains the representation data of this feature label；Representation data is collected defeated
Go out the user of specific format and draw a portrait file 5.
Claims (5)
1. Net silver user based on user behavior analysis draws a portrait method, it is characterised in that comprise the steps:
A) feature tag is extracted: the key word of extraction representative products feature is as feature tag from finance product description, and
Giving initial score value and initial weight value to each feature tag, the set of whole feature tags constitutes tag database；
B) webpage is labelled: extracts the characteristic point of Net silver visible page, chooses at least one feature tag from tag database
It is mapped to characteristic point；
C) extract user characteristics behavior: obtain the Operation Log of whole user, extract user characteristics behavior therein, give user
Characteristic behavior specific behavior weighted value；All set constituting action data bases of user characteristics behavior；
D) statistics score: obtain the Operation Log of specific user's behavior, one by one Net silver visible page in read operation daily record
Characteristic point and be mapped to the feature tag on this feature point, obtains the initial score value of feature tag from tag database, with
Time read when this specific user operates visible page the characteristic behavior for this feature point, subordinate act data base obtains this feature
The behavior weighted value of behavior；Initial score value is multiplied with behavior weighted value, obtains behavior score value；By same under this specific user's item
Behavior score value under feature tag is added, and obtains this specific user behavior total score about this feature label；Repeat this step,
Until obtaining the behavior total score under this specific user all feature tags item；
E) representation data generates: choose the behavior total score some feature tags more than threshold value, by the behavior of each feature tag
Total score is multiplied with the initial weight value of this feature label, obtains the representation data of this feature label.
The most according to claim 1, Net silver user based on user behavior analysis draws a portrait method, it is characterised in that: in step D,
When obtaining the Operation Log of specific user's behavior, the stay time of Net silver visible page in read operation daily record one by one, and give
This visible page and stay time positively related duration weighted value, the result that initial score value is multiplied with behavior weighted value again with duration
Weighted value is multiplied, and obtains behavior score value.
Net silver user based on user behavior analysis the most according to claim 1 or claim 2 draws a portrait method, it is characterised in that: in step
Rapid E) first by step D) behavior total score under whole feature tag items of obtaining according to sorting from low to high, select wherein one
The numerical value of individual sudden change is as threshold value.
Net silver user based on user behavior analysis the most according to claim 1 or claim 2 draws a portrait method, it is characterised in that: in step
Rapid E) first by step D) behavior total score under whole feature tag items of obtaining is according to sorting from low to high, and from low to high
Being added one by one, until being added the gained sum special ratios more than or equal to the highest behavior total score, the behavior participating in being added is total
Numerical value the highest in score value is as threshold value.
5. Net silver user based on user behavior analysis draws a portrait equipment, it is characterised in that including:
Tag database, for the feature tag of the representative products feature that storage is extracted from finance product description, this feature
Label includes at least initial score value and two parameters of initial weight value；
Tag processor, for extracting the characteristic point of Net silver visible page, and chooses at least one feature from tag database
Label mapping is to characteristic point；
Behavior database, is used for storing user characteristics behavior, and this user characteristics behavior at least has one parameter of behavior weighted value；
Data processor, for obtaining the Operation Log of specific user's behavior, the Net silver visual page in read operation daily record one by one
The characteristic point in face and be mapped to the feature tag on this feature point, obtains initial point of feature tag from tag database
Value, reads the characteristic behavior for this feature point when this specific user operates visible page simultaneously, obtains in subordinate act data base
The behavior weighted value of this feature behavior；Initial score value is multiplied with behavior weighted value, obtains behavior score value；By this specific user's item
Under behavior score value under same feature tag be added, obtain this specific user behavior total score about this feature label；
Portrait maker；For choosing the behavior total score some feature tags more than threshold value, by the behavior of each feature tag
Total score is multiplied with the initial weight value of this feature label, obtains the representation data of this feature label；Representation data is collected defeated
Go out the user of specific format and draw a portrait file.
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