CN108053263A - The method and device of potential user's data mining - Google Patents
The method and device of potential user's data mining Download PDFInfo
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- CN108053263A CN108053263A CN201711467932.7A CN201711467932A CN108053263A CN 108053263 A CN108053263 A CN 108053263A CN 201711467932 A CN201711467932 A CN 201711467932A CN 108053263 A CN108053263 A CN 108053263A
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Abstract
The present invention provides a kind of method and device of potential user's data mining, including：Gathered data sample, according to data sample, statistical modeling data, establish user data mining model, and user data mining model is trained, eventually by input data obtain judging user whether be the potential user for buying enterprise's charging function point judgment value, this method can accurately filter out the highest user group of purchase intention, the form finally targetedly pushed by short message stimulates user to carry out consumer behavior, and saving enterprise is contributed to promote cost, improve enterprise's income.
Description
Technical field
The present invention relates to data statistics field, in particular to a kind of method and device of potential user's data mining.
Background technology
The means that enterprise carries out merchandising are varied, including advertisement, mail, short message etc..Comprehensive cost, acceptance rate,
The factors such as conversion ratio, the promotion way of promotion effect carried out in the form of short message are more prominent.Short message is communicated with client as enterprise
One of most common channel has the advantages that coverage is high, and opening rate is high, and instantaneity is strong.
It, can be irregularly to user with the shape of short message in order to which user is promoted to buy enterprise's Related product (such as data, report)
Formula pushes sales promotion information.Existing method is to carry out user's screening according to indexs such as the region of user or liveness, then is promoted
Short message push is sold, though so there is certain effect, the ratio of end user's purchase volume and short message cost is not optimal, and
Potential user cannot be chosen exactly, waste enterprise's popularization cost, non-for improving enterprise and taking in there is no very big
Value.
The content of the invention
Problem present in for the above-mentioned prior art, the present invention provides a kind of methods of potential user's data mining.
In a first aspect, an embodiment of the present invention provides a kind of method of potential user's data mining, including：
User to be determined is obtained in preset duration to the visit capacity of enterprise's charging function point；
Visit capacity is inputted into trained user data mining model, according to the output quantity of user data mining model, is sentenced
Whether disconnected user is the potential user for buying enterprise's charging function point；
Wherein, the training process of user data mining model includes：
Gathered data sample, data sample include the user of promotion short message sending in a period of time, enterprise's charging function point
Visit capacity and enterprise's charging function point purchase situation；
Initial user data mining model is established, initial user data mining model is
Y=α0+α1x1+α2x2+α3x3+…+αnxn+β
Wherein, the numerical value of y is used to judge whether user is the potential user for buying enterprise's charging function point, and β is default normal
Number, x1、x2、x3、...、xnRespectively user is to the access times of enterprise's difference charging function point, α0、α1、α2、...、αnRespectively
The weight of default enterprise's difference charging function point；
Using modeling data, initial user data mining model is trained, trained user data is obtained and excavates
Model.
With reference to first aspect, an embodiment of the present invention provides the first possible embodiment of first aspect, wherein,
According to data sample, statistical modeling data, modeling data includes the visit for being sent promotion short-message users to enterprise's charging function point
The amount of asking and by send promotion short message client whether buy enterprise's charging function point step after, the instruction of user data mining model
Practice process to further include：
The 80% of modeling data is used as training set, and the 20% of modeling data is used as test set.
The possible embodiment of with reference to first aspect the first, an embodiment of the present invention provides second of first aspect
Possible embodiment, wherein, using modeling data, initial user data mining model is trained, is obtained trained
User data mining model, including：
Linear regression training is carried out to initial user data mining model using training set.
Second of possible embodiment with reference to first aspect, an embodiment of the present invention provides the third of first aspect
Possible embodiment, wherein, linear regression training is carried out to initial user data mining model using training set, including：
Initial user data mining model is trained using least square method.
The possible embodiment of with reference to first aspect the first, an embodiment of the present invention provides the 4th kind of first aspect
Possible embodiment, wherein, using modeling data, initial user data mining model is trained, is obtained trained
After user data mining model step, the training process of user data mining model further includes：
Initial user data mining model is tested using test set.
Second aspect, the embodiment of the present invention additionally provide a kind of device of potential user's data mining, including：
Acquisition module, for obtaining user to be determined in preset duration to the visit capacity of enterprise's charging function point；
For visit capacity to be inputted trained user data mining model, mould is excavated according to user data for output module
The output quantity of type judges whether user is the potential user for buying enterprise's charging function point；
Model building module is used to implement the foundation and training of user data mining model；
Wherein, model building module includes：
Collecting unit, for gathered data sample, data sample includes the user of promotion short message sending in a period of time, enterprise
The visit capacity of industry charging function point and the purchase situation of enterprise's charging function point；
Statistic unit, for according to data sample, statistical modeling data, modeling data to be including being sent promotion short-message users
It visit capacity to enterprise's charging function point and is sent the client of promotion short message and whether buys enterprise's charging function point；
Unit is established, for establishing initial user data mining model, initial user data mining model is
Y=α0+α1x1+α2x2+α3x3+…+αnxn+β
Wherein, the numerical value of y is used to judge whether user is the potential user for buying enterprise's charging function point, and β is default normal
Number, x1、x2、x3、...、xnRespectively user is to the access times of enterprise's difference charging function point, α0、α1、α2、...、αnRespectively
The weight of default enterprise's difference charging function point；
Training unit for utilizing modeling data, is trained initial user data mining model, obtains trained
User data mining model.
With reference to second aspect, an embodiment of the present invention provides the first possible embodiment of second aspect, wherein,
Model building module further includes：
Screening unit is used as test set for screening the 80% of modeling data as training set, the 20% of modeling data.
With reference to the first possible embodiment of second aspect, an embodiment of the present invention provides second of second aspect
Possible embodiment, wherein, training unit includes：
Linear training unit, for carrying out linear regression training to initial user data mining model using training set.
With reference to the first possible embodiment of second aspect, an embodiment of the present invention provides the third of second aspect
Possible embodiment, wherein, model building module further includes：
Test cell, for being tested using test set initial user data mining model.
The third aspect, an embodiment of the present invention provides a kind of computer storage media, for saving as described in second aspect
Device used in computer software instructions.
The embodiment of the present invention brings following advantageous effect：
An embodiment of the present invention provides a kind of method and device of potential user's data mining, including：Gathered data sample,
According to data sample, statistical modeling data establish user data mining model, and user data mining model are trained,
Eventually by input data obtain judging user whether be the potential user for buying enterprise's charging function point judgment value, this
Kind method can accurately filter out the highest user group of purchase intention, finally targetedly pass through the form that short message pushes and pierce
Swash user and carry out consumer behavior, help to save enterprise's popularization cost, improve enterprise's income.
Other features and advantages of the present invention will illustrate in the following description, also, partly become from specification
It obtains it is clear that being understood by implementing the present invention.The purpose of the present invention and other advantages in specification, claim
Specifically noted structure is realized and obtained in book and attached drawing.
For the above objects, features and advantages of the present invention is enable to be clearer and more comprehensible, preferred embodiment cited below particularly, and coordinate
Appended attached drawing, is described in detail below.
Description of the drawings
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution of the prior art
Embodiment or attached drawing needed to be used in the description of the prior art are briefly described, it should be apparent that, in describing below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, can also be obtained according to these attached drawings other attached drawings.
A kind of flow chart of the method for potential user's data mining that Fig. 1 is provided by first embodiment of the invention；
The flow chart of the training process for the user data mining model that Fig. 2 is provided by first embodiment of the invention；
A kind of structure diagram of the device for potential user's data mining that Fig. 3 is provided by second embodiment of the invention；
The structure diagram for the model building module that Fig. 4 is provided by second embodiment of the invention.
Specific embodiment
To make the destination of the embodiment of the present invention, technical solution and advantage clearer, below in conjunction with attached drawing to this hair
Bright technical solution is clearly and completely described, it is clear that and described embodiment is part of the embodiment of the present invention, without
It is whole embodiments.The component of embodiments of the present invention, which are generally described and illustrated herein in the accompanying drawings can be with a variety of
It configures to arrange and design.Therefore, the detailed description of the embodiment of the present invention to providing in the accompanying drawings is not intended to limit below
The scope of claimed invention processed, but it is merely representative of the selected embodiment of the present invention.Based on the embodiments of the present invention,
Those of ordinary skill in the art's all other embodiments obtained without making creative work, belong to this hair
The scope of bright protection.
Embodiment one
A kind of flow chart of the method for potential user's data mining shown in FIG. 1 is participated in, this method is applied to specifically include
Following steps：
Step S101 obtains user to be determined in preset duration to the visit capacity of enterprise's charging function point；
Visit capacity is inputted trained user data mining model, according to user data mining model by step S102
Output quantity judges whether user is the potential user for buying enterprise's charging function point；
The user behavior in certain day (in such as yesterday or a period of time) is counted, that is, the number of charging function point is accessed, by this time
Number is input to as input quantity in the model, is obtained the output quantity of the model, be can determine whether user meets potential payment
The feature of user；If meeting, the user is confirmed as into potential user, and sales promotion information is subjected to short message push.
Wherein, as shown in Fig. 2, the training process of user data mining model includes：
Step S201, gathered data sample, data sample include the user of promotion short message sending in a period of time, and enterprise receives
Take the visit capacity of function point and the purchase situation of enterprise's charging function point；
In general, the Core Feature of product can be set to toll site or go out valuable data as attraction by enterprise
Sell, if day eye looks into website, wherein " looking into boss ", " looking into relation ", " checking company's equity structure " function, " check corporate risk, people
The functions such as member's risk " can trigger the prompting of user purchase function member after SC service ceiling is reached, can after purchase function member
Infinitely check；Similarly, " business standing report report, Dong supervise high credit report " is also bought as worth of data for user.
For using the user for looking into boss, looking into relation, company's equity structure, risk class function, the number used can between
The reversed probability for mirroring its purchase corresponding function；Similarly, for checking that the user of high credit report is supervised in business standing report, Dong,
It is related whether its number checked also finally buys report with it.
According to data sample, statistical modeling data, modeling data includes being sent promotion short-message users to enterprise's charge work(
The visit capacity and whether enterprise's charging function point is bought by the client of transmission promotion short message that energy is put；
Step S202, according to data sample, statistical modeling data, modeling data includes being sent promotion short-message users to enterprise
The visit capacity of industry charging function point and by send promotion short message client whether buy enterprise's charging function point；Modeling data
80% is used as training set, and the 20% of modeling data is used as test set.
Step S203, establishes initial user data mining model, and initial user data mining model is
Y=α0+α1x1+α2x2+α3x3+…+αnxn+β
Wherein, the numerical value of y is used to judge whether user is the potential user for buying enterprise's charging function point, and β is default normal
Number, x1、x2、x3、...、xnRespectively user is to the access times of enterprise's difference charging function point, α0、α1、α2、...、αnRespectively
The weight of default enterprise's difference charging function point；
Step S204 using modeling data, is trained initial user data mining model, obtains trained user
Data mining model is trained initial user data mining model using the least square method of linear regression.Above-mentioned first
In beginning user data mining model, input quantity respectively charges to product work(for history day (such as yesterday or interior for the previous period) user
The access times that can be put carry out initial user data mining model linear regression training, using minimum when linear regression is trained
Square law can easily acquire unknown data using least square method, and cause these data and real data for acquiring
Between error quadratic sum for minimum.
Further, initial user data mining model is tested using test set, for the accurate of test model
Property.
An embodiment of the present invention provides a kind of method of potential user's data mining, including：Gathered data sample, according to number
According to sample, statistical modeling data establish user data mining model, and user data mining model are trained, final logical
Cross input data obtain judging user whether be the potential user for buying enterprise's charging function point judgment value, this method
The highest user group of purchase intention can be accurately filtered out, the form finally targetedly pushed by short message stimulates user
Consumer behavior is carried out, helps to save enterprise's popularization cost, improve enterprise's income.
Embodiment two
It is latent an embodiment of the present invention provides one kind for the method for potential user's data mining that previous embodiment is provided
In the device that user data excavates, a kind of structure diagram of the device of potential user's data mining shown in Figure 3, the device
Including such as lower part：
Acquisition module 11, for obtaining user to be determined in preset duration to the visit capacity of enterprise's charging function point；
Output module 12 for visit capacity to be inputted trained user data mining model, is excavated according to user data
The output quantity of model judges whether user is the potential user for buying enterprise's charging function point；
Model building module 13 is used to implement the foundation and training of user data mining model；
Wherein, as shown in figure 4, model building module 13 includes：
Collecting unit 131, for gathered data sample, data sample includes the use of promotion short message sending in a period of time
Family, the visit capacity of enterprise's charging function point and the purchase situation of enterprise's charging function point；
Statistic unit 132, for according to data sample, statistical modeling data, modeling data to be including being sent promotion short message
Whether user buys enterprise's charging function point to the visit capacity of enterprise's charging function point and by the client of transmission promotion short message；
Unit 133 is established, for establishing initial user data mining model, initial user data mining model is
Y=α0+α1x1+α2x2+α3x3+…+αnxn+β
Wherein, the numerical value of y is used to judge whether user is the potential user for buying enterprise's charging function point, and β is default normal
Number, x1、x2、x3、...、xnRespectively user is to the access times of enterprise's difference charging function point, α0、α1、α2、...、αnRespectively
The weight of default enterprise's difference charging function point；
Training unit 134 for utilizing modeling data, is trained initial user data mining model, is trained
Good user data mining model.
An embodiment of the present invention provides a kind of device of potential user's data mining, including：Gathered data sample, according to number
According to sample, statistical modeling data establish user data mining model, and user data mining model are trained, final logical
Cross input data obtain judging user whether be the potential user for buying enterprise's charging function point judgment value, this method
The highest user group of purchase intention can be accurately filtered out, the form finally targetedly pushed by short message stimulates user
Consumer behavior is carried out, helps to save enterprise's popularization cost, improve enterprise's income.
The embodiment of the present invention additionally provides a kind of computer storage media, for saving as the device of above-described embodiment offer
Computer software instructions used.
It should be noted that in embodiment provided by the present invention, it should be understood that disclosed system and method, it can
To realize by another way.The apparatus embodiments described above are merely exemplary, for example, the unit is drawn
Point, it is only a kind of division of logic function, there can be other dividing mode in actual implementation, in another example, multiple units or group
Part may be combined or can be integrated into another system or some features can be ignored or does not perform.It is described to be used as separation unit
The unit that part illustrates may or may not be physically separate, and the component shown as unit can be or also may be used
Not to be physical location, you can be located at a place or can also be distributed in multiple network element.It can be according to reality
Need some or all of unit therein is selected to realize the destination of this embodiment scheme.
In addition, each functional unit in embodiment provided by the invention can be integrated in a processing unit, also may be used
To be that unit is individually physically present, can also two or more units integrate in a unit.
If the function is realized in the form of SFU software functional unit and is independent production marketing or in use, can be with
It is stored in a computer read/write memory medium.Based on such understanding, technical scheme is substantially in other words
The part contribute to the prior art or the part of the technical solution can be embodied in the form of software product, the meter
Calculation machine software product is stored in a storage medium, is used including some instructions so that a computer equipment (can be
People's computer, server or network equipment etc.) perform all or part of the steps of the method according to each embodiment of the present invention.
And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic disc or CD.
In addition, term " first ", " second ", " the 3rd " are only used for description purpose, and it is not intended that instruction or implying phase
To importance.
Finally it should be noted that：Embodiment described above is only the specific embodiment of the present invention, to illustrate the present invention
Technical solution, rather than its limitations, protection scope of the present invention is not limited thereto, although with reference to the foregoing embodiments to this hair
It is bright to be described in detail, it will be understood by those of ordinary skill in the art that：Any one skilled in the art
In the technical scope disclosed by the present invention, can still modify to the technical solution recorded in previous embodiment or can be light
It is readily conceivable that variation or equivalent substitution is carried out to which part technical characteristic；And these modifications, variation or replacement, do not make
The essence of appropriate technical solution departs from the spirit and scope of technical solution of the embodiment of the present invention, should all cover the protection in the present invention
Within the scope of.Therefore, protection scope of the present invention described should be subject to the protection scope in claims.
Claims (10)
- A kind of 1. method of potential user's data mining, which is characterized in that the described method includes：User to be determined is obtained in preset duration to the visit capacity of enterprise's charging function point；The visit capacity is inputted into trained user data mining model, according to the output of the user data mining model Amount judges whether the user is the potential user for buying enterprise's charging function point；Wherein, the training process of user data mining model includes：Gathered data sample, the data sample include the user of promotion short message sending in a period of time, enterprise's charging function point Visit capacity and enterprise's charging function point purchase situation；According to the data sample, statistical modeling data, the modeling data includes being sent promotion short-message users to enterprise's receipts Whether the visit capacity for taking function point and the client for being sent promotion short message buy enterprise's charging function point；Initial user data mining model is established, the initial user data mining model isY=α0+α1x1+α2x2+α3x3+…+αnxn+βWherein, the numerical value of y is used to judging whether user to be the potential user for buying enterprise charging function point, and β is preset constant, x1、 x2、x3、...、xnRespectively user is to the access times of enterprise's difference charging function point, α0、α1、α2、...、αnIt is respectively default Enterprise's difference charging function point weight；Using the modeling data, the initial user data mining model is trained, obtains trained user data Mining model.
- 2. according to the method described in claim 1, it is characterized in that, according to the data sample, statistical modeling data are described Modeling data includes being sent promotion short-message users to the visit capacity of enterprise's charging function point and is sent the client for promoting short message After whether buying enterprise's charging function point step, the training process of user data mining model further includes：The 80% of the modeling data is used as training set, and the 20% of the modeling data is used as test set.
- 3. according to the method described in claim 2, it is characterized in that, using the modeling data, to the initial user data Mining model is trained, and obtains trained user data mining model, including：Linear regression training is carried out to the initial user data mining model using the training set.
- 4. according to the method described in claim 3, it is characterized in that, the initial user data is excavated using the training set Model carries out linear regression training, including：The initial user data mining model is trained using least square method.
- 5. according to the method described in claim 2, it is characterized in that, using the modeling data, to the initial user data Mining model is trained, after obtaining trained user data mining model step, the training of user data mining model Process further includes：The initial user data mining model is tested using the test set.
- 6. a kind of device of potential user's data mining, which is characterized in that described device includes：Acquisition module, for obtaining user to be determined in preset duration to the visit capacity of enterprise's charging function point；Output module for the visit capacity to be inputted trained user data mining model, is dug according to the user data The output quantity of model is dug, judges whether the user is the potential user for buying enterprise's charging function point；Model building module is used to implement the foundation and training of user data mining model；Wherein, the model building module includes：Collecting unit, for gathered data sample, the data sample includes the user of promotion short message sending in a period of time, enterprise The visit capacity of industry charging function point and the purchase situation of enterprise's charging function point；Statistic unit, for according to the data sample, statistical modeling data, the modeling data to be including being sent promotion short message Whether user buys enterprise's charging function point to the visit capacity of enterprise's charging function point and by the client of transmission promotion short message；Unit is established, for establishing initial user data mining model, the initial user data mining model isY=α0+α1x1+α2x2+α3x3+…+αnxn+βWherein, the numerical value of y is used to judging whether user to be the potential user for buying enterprise charging function point, and β is preset constant, x1、 x2、x3、...、xnRespectively user is to the access times of enterprise's difference charging function point, α0、α1、α2、...、αnIt is respectively default Enterprise's difference charging function point weight；Training unit for utilizing the modeling data, is trained the initial user data mining model, is trained Good user data mining model.
- 7. device according to claim 6, which is characterized in that the model building module further includes：Screening unit is used as test for screening the 80% of the modeling data as training set, the 20% of the modeling data Collection.
- 8. device according to claim 7, which is characterized in that the training unit includes：Linear training unit, for carrying out linear regression instruction to the initial user data mining model using the training set Practice.
- 9. device according to claim 6, which is characterized in that the model building module further includes：Test cell, for being tested using the test set the initial user data mining model.
- 10. a kind of computer storage media, which is characterized in that for saving as the device described in claim 1 to 5 any one Computer software instructions used.
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