CN107392645A - Usage mining method, apparatus and its equipment - Google Patents
Usage mining method, apparatus and its equipment Download PDFInfo
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- CN107392645A CN107392645A CN201710471331.7A CN201710471331A CN107392645A CN 107392645 A CN107392645 A CN 107392645A CN 201710471331 A CN201710471331 A CN 201710471331A CN 107392645 A CN107392645 A CN 107392645A
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Abstract
The present invention proposes a kind of usage mining method, apparatus and its equipment, wherein, method includes：Processing is carried out to user's history behavioural information and obtains the frequent item set for including multigroup correlation behavior；The behavioural information in active user's preset time is handled according to frequent item set, calculates corresponding with every group of correlation behavior of active user support and confidence level, and at least one set of target association behavior of the condition of satisfaction is filtered out according to predetermined threshold value；The first relevant parameter and the second relevant parameter of active user is calculated according to the goal behavior information related to target association behavior and its corresponding trigger parameter；The result and predetermined threshold value handled according to preset algorithm the first relevant parameter and the second relevant parameter determines whether active user is targeted customer.Thereby, it is possible to precisely excavate targeted customer, the possible behavior in future of Accurate Prediction user, so as to the commodity that recommended user likes, the search experience of user is lifted, lifts the conversion ratio of businessman.
Description
Technical field
The present invention relates to Internet technical field, more particularly to a kind of usage mining method, apparatus and its equipment.
Background technology
With the development of internet, people are in the epoch of an information explosion.Compared to past absence of information, face
To the information data of magnanimity at this stage, screening to information and it is filtered into weigh the important indicator of a system quality.
In correlation technique, based on user's history behavior, according to marking of the user to commodity or the click to commodity, check
The behavior of behavior and the purchase of item property carries out the modes such as the algorithm of commercial product recommending using collaborative filtering to user.So
And aforesaid way can not precisely excavate user, and prepare following probable behavior of prediction user.
The content of the invention
The purpose of the present invention is intended to one of technical problem at least solving in correlation technique to a certain extent.
Therefore, first purpose of the present invention is to propose a kind of usage mining method, for solving in the prior art simultaneously
The problem of user can not precisely be excavated, and prepare the probable behavior in prediction user's future.
Second object of the present invention is to propose a kind of usage mining device.
Third object of the present invention is to propose another usage mining device.
Fourth object of the present invention is to propose a kind of computer program product.
The 5th purpose of the present invention is to propose a kind of computer-readable recording medium.
For the above-mentioned purpose, first aspect present invention embodiment proposes a kind of usage mining method, including：User is gone through
History behavioural information is handled, and obtains the frequent item set for including multigroup correlation behavior；According to the frequent item set to active user
Behavioural information in preset time is handled, and calculates corresponding with every group of correlation behavior of active user support and confidence
Spend, and at least one set of target association behavior of the condition of satisfaction is filtered out according to predetermined threshold value；According to the target association behavior
Related goal behavior information, trigger parameter corresponding with the target association behavior is obtained, according to the trigger parameter and institute
The first relevant parameter that goal behavior information calculates active user is stated, and according to the trigger parameter and the behavioural information meter
Calculate the second relevant parameter of active user；First relevant parameter and second relevant parameter are carried out according to preset algorithm
Processing, determines whether active user is targeted customer according to result and predetermined threshold value.
The usage mining method of the embodiment of the present invention, by handling user's history behavioural information, obtain comprising more
The frequent item set of group correlation behavior, and the behavioural information in active user's preset time is handled according to frequent item set, count
Calculate corresponding with every group of correlation behavior of active user support and confidence level and the condition of satisfaction is filtered out according to predetermined threshold value
At least one set of target association behavior, then according to the goal behavior information related to target association behavior, acquisition and target association
Trigger parameter corresponding to behavior, the first relevant parameter of active user is calculated according to trigger parameter and goal behavior information, and
The second relevant parameter of active user is calculated according to trigger parameter and behavioural information, finally the first association is joined according to preset algorithm
Number and the second relevant parameter are handled, and determine whether active user is that target is used according to result and predetermined threshold value
Family.Thereby, it is possible to precisely excavate targeted customer, the possible behavior in future of Accurate Prediction user, like so as to recommended user
Commodity, the search experience of user is lifted, lift the conversion ratio of businessman.
In addition, usage mining method according to the above embodiment of the present invention can also have technical characteristic additional as follows：
Alternatively, it is described that user's history behavioural information is handled, the frequent item set for including multigroup correlation behavior is obtained,
Including：User's history behavioural information is handled using the distributed algorithm based on hadoop frameworks, acquisition includes multigroup pass
The frequent item set of connection behavior.
Alternatively, the basis goal behavior information related to the target association behavior, obtain and closed with the target
Trigger parameter corresponding to connection behavior, associated according to the trigger parameter with the first of goal behavior information calculating active user
Parameter, and according to the trigger parameter and the second relevant parameter of behavioural information calculating active user, including：According to
The related goal behavior information of the target association behavior, obtains number of clicks corresponding with the target association behavior, according to
The amount of showing of the number of clicks and the goal behavior information calculates the first clicking rate of active user, and according to the point
Hit the second clicking rate of the amount of the showing calculating active user of number and the behavioural information.
Alternatively, the basis goal behavior information related to the target association behavior, obtain and closed with the target
Trigger parameter corresponding to connection behavior, associated according to the trigger parameter with the first of goal behavior information calculating active user
Parameter, and according to the trigger parameter and the second relevant parameter of behavioural information calculating active user, including：According to
The related goal behavior information of the target association behavior, conversion number corresponding with the target association behavior is obtained, according to
The click volume of the conversion number and the goal behavior information calculates the first conversion ratio of active user, and according to described turn
Change the second conversion ratio of the click volume calculating active user of number and the behavioural information.
It is alternatively, described that first relevant parameter and second relevant parameter are handled according to preset algorithm,
Determine whether active user is targeted customer according to result and predetermined threshold value, including：Calculate the first association ginseng
The ratio of number and second relevant parameter；By the ratio compared with predetermined threshold value, the ratio is known if comparing
More than or equal to predetermined threshold value, it is determined that active user is targeted customer.
For the above-mentioned purpose, second aspect of the present invention embodiment proposes a kind of usage mining device, including：Processing obtains
Module, for handling user's history behavioural information, obtain the frequent item set for including multigroup correlation behavior；Calculating sifting mould
Block, for being handled according to the frequent item set the behavioural information in active user's preset time, calculating and active user
Every group of correlation behavior corresponding to support and confidence level, and filter out according to predetermined threshold value at least one set of target of the condition of satisfaction
Correlation behavior；Obtain computing module, for according to the goal behavior information related to the target association behavior, acquisition with it is described
Trigger parameter corresponding to target association behavior, the of active user is calculated according to the trigger parameter and the goal behavior information
One relevant parameter, and according to the trigger parameter and the second relevant parameter of behavioural information calculating active user；Processing
Determining module, for being handled according to preset algorithm first relevant parameter and second relevant parameter, according to place
Reason result and predetermined threshold value determine whether active user is targeted customer.
The usage mining device of the embodiment of the present invention, by handling user's history behavioural information, obtain comprising more
The frequent item set of group correlation behavior, and the behavioural information in active user's preset time is handled according to frequent item set, count
Calculate corresponding with every group of correlation behavior of active user support and confidence level and the condition of satisfaction is filtered out according to predetermined threshold value
At least one set of target association behavior, then according to the goal behavior information related to target association behavior, acquisition and target association
Trigger parameter corresponding to behavior, the first relevant parameter of active user is calculated according to trigger parameter and goal behavior information, and
The second relevant parameter of active user is calculated according to trigger parameter and behavioural information, finally the first association is joined according to preset algorithm
Number and the second relevant parameter are handled, and determine whether active user is that target is used according to result and predetermined threshold value
Family.Thereby, it is possible to precisely excavate targeted customer, the possible behavior in future of Accurate Prediction user, like so as to recommended user
Commodity, the search experience of user is lifted, lift the conversion ratio of businessman.
In addition, usage mining device according to the above embodiment of the present invention can also have technical characteristic additional as follows：
Alternatively, the processing acquisition module is specifically used for：Using the distributed algorithm based on hadoop frameworks to user
Historical behavior information is handled, and obtains the frequent item set for including multigroup correlation behavior.
Alternatively, the acquisition computing module is specifically used for：According to the goal behavior related to the target association behavior
Information, number of clicks corresponding with the target association behavior is obtained, according to the number of clicks and the goal behavior information
The amount of showing calculate the first clicking rate of active user, and gauge is showed according to the number of clicks and the behavioural information
Calculate the second clicking rate of active user.
Alternatively, the acquisition computing module is specifically additionally operable to：According to the target line related to the target association behavior
For information, conversion number corresponding with the target association behavior is obtained, is believed according to the conversion number and the goal behavior
The click volume of breath calculates the first conversion ratio of active user, and according to the conversion number and the click volume of the behavioural information
Calculate the second conversion ratio of active user.
Alternatively, the processing determining module is specifically used for：Calculate first relevant parameter and associate ginseng with described second
Several ratio；By the ratio compared with predetermined threshold value, know that the ratio is more than or equal to predetermined threshold value if comparing,
Then determine that active user is targeted customer.
For the above-mentioned purpose, third aspect present invention embodiment proposes another usage mining device, including processor
And memory；Wherein, the processor by read the executable program code stored in the memory run with it is described
Program corresponding to executable program code, for realizing a kind of usage mining method, methods described includes：To user's history row
Handled for information, obtain the frequent item set for including multigroup correlation behavior；Active user is preset according to the frequent item set
Behavioural information in time is handled, and calculates corresponding with every group of correlation behavior of active user support and confidence level, and
At least one set of target association behavior of the condition of satisfaction is filtered out according to predetermined threshold value；According to related to the target association behavior
Goal behavior information, trigger parameter corresponding with the target association behavior is obtained, according to the trigger parameter and the target
Behavioural information calculates the first relevant parameter of active user, and is calculated currently according to the trigger parameter and the behavioural information
The second relevant parameter of user；First relevant parameter and second relevant parameter are handled according to preset algorithm,
Determine whether active user is targeted customer according to result and predetermined threshold value.
For the above-mentioned purpose, fourth aspect present invention embodiment proposes a kind of computer program product, when the calculating
When instruction in machine program product is by computing device, a kind of usage mining method is performed, methods described includes：To user's history
Behavioural information is handled, and obtains the frequent item set for including multigroup correlation behavior；It is pre- to active user according to the frequent item set
If the behavioural information in the time is handled, corresponding with every group of correlation behavior of active user support and confidence level are calculated,
And at least one set of target association behavior of the condition of satisfaction is filtered out according to predetermined threshold value；According to related to the target association behavior
Goal behavior information, corresponding with target association behavior trigger parameter is obtained, according to the trigger parameter and the mesh
The first relevant parameter that behavioural information calculates active user is marked, and is calculated and worked as according to the trigger parameter and the behavioural information
The second relevant parameter of preceding user；According to preset algorithm to first relevant parameter and second relevant parameter at
Reason, determines whether active user is targeted customer according to result and predetermined threshold value.
For the above-mentioned purpose, fifth aspect present invention embodiment proposes a kind of computer-readable recording medium, deposits thereon
Contain computer program, it is characterised in that the computer program realizes a kind of usage mining method when being executed by processor, described
Method includes：User's history behavioural information is handled, obtains the frequent item set for including multigroup correlation behavior；According to the frequency
Numerous item collection is handled the behavioural information in active user's preset time, is calculated corresponding with every group of correlation behavior of active user
Support and confidence level, and filter out according to predetermined threshold value at least one set of target association behavior of the condition of satisfaction；According to institute
The related goal behavior information of target association behavior is stated, trigger parameter corresponding with the target association behavior is obtained, according to institute
State the first relevant parameter that trigger parameter and the goal behavior information calculate active user, and according to the trigger parameter and
The behavioural information calculates the second relevant parameter of active user；According to preset algorithm to first relevant parameter and described
Two relevant parameters are handled, and determine whether active user is targeted customer according to result and predetermined threshold value.
The additional aspect of the present invention and advantage will be set forth in part in the description, and will partly become from the following description
Obtain substantially, or recognized by the practice of the present invention.
Brief description of the drawings
Of the invention above-mentioned and/or additional aspect and advantage will become from the following description of the accompanying drawings of embodiments
Substantially and it is readily appreciated that, wherein：
Fig. 1 is the schematic flow sheet of usage mining method according to an embodiment of the invention；
Fig. 2 is the schematic flow sheet of usage mining method in accordance with another embodiment of the present invention；
Fig. 3 is the structural representation of usage mining device according to an embodiment of the invention；
Fig. 4 is the structural representation of usage mining device in accordance with another embodiment of the present invention.
Embodiment
Embodiments of the invention are described below in detail, the example of the embodiment is shown in the drawings, wherein from beginning to end
Same or similar label represents same or similar element or the element with same or like function.Below with reference to attached
The embodiment of figure description is exemplary, it is intended to for explaining the present invention, and is not considered as limiting the invention.
Below with reference to the accompanying drawings the usage mining method, apparatus and its equipment of the embodiment of the present invention are described.
Specifically, commending system of the prior art can not precisely excavate user, and prepare prediction user not
Come probable behavior, cause businessman conversion ratio reduce the problems such as.
In order to solve the above problems, the present invention proposes a kind of usage mining method, can precisely excavate targeted customer, accurately
The possible behavior in future of user is predicted, so as to the commodity that recommended user likes, the search experience of user is lifted, lifts businessman's
Conversion ratio.It is specific as follows：
Fig. 1 is the schematic flow sheet of usage mining method according to an embodiment of the invention, as shown in figure 1, the user
Method for digging comprises the following steps：
Step 101, user's history behavioural information is handled, obtains the frequent item set for including multigroup correlation behavior.
Specifically, in actual applications, user's history behavioural information can be handled by a variety of modes, so as to
Obtain the frequent item set for including multigroup correlation behavior.Selection setting can be carried out as needed, as a kind of example, using based on
The distributed algorithm of hadoop frameworks is handled user's history behavioural information, obtains the frequent episode for including multigroup correlation behavior
Collection.Wherein, Hadoop frameworks are a distributed parallel programming frameworks of increasing income for realizing correlation computations model, and user can borrow
Help Hadoop to write program, the program write is run on computer group, so as to realize the processing to mass data.
Thus, traditional frequent-item algorithm is on the one hand improved in the embodiment of the present invention, on the other hand based on hadoop
Framework realizes that distributed algorithm to frequent item set mining, compared to traditional mining algorithm performance height, is not limited by machine internal memory and cpu
System.
It is understood that Internet user's amount is big, using distributed algorithm, excavated for user's history behavioural information
The frequent item set of multigroup correlation behavior is included, improves processing speed.
Wherein, user's history behavioural information can also be obtained by a variety of modes, as a kind of example, pass through system day
Will collects user's history behavioural information (such as user is in electric business website or search behavior of search engine).
It should be noted that in embodiments of the present invention, frequent item set is generally referred to as two frequent item sets, for example includes
One group of correlation behavior A and B frequent item set, it will also be appreciated that to can after handling user's history behavioural information
To obtain the frequent item set comprising one group or multigroup correlation behavior.
For example, most of users search for iPhone in Taobao can also search for Huawei's mobile phone, that is, search for apple hand
The behavior of machine and the behavior of search Huawei mobile phone are one group of correlation behaviors, and the frequent item set of acquisition includes the row of search iPhone
For this group of correlation behavior of behavior with search Huawei mobile phone.
Step 102, the behavioural information in active user's preset time is handled according to frequent item set, calculated and current
Support and confidence level corresponding to every group of correlation behavior of user, and filter out according to predetermined threshold value at least one set of the condition of satisfaction
Target association behavior.
Specifically, it is necessary to obtain in active user's preset time after the frequent item set comprising multigroup correlation behavior is obtained
Behavioural information, wherein, preset time can need to carry out selection setting according to practical application, for example, one day, a week or
Person one month etc..
Further, corresponding with every group of correlation behavior of active user support and confidence level are calculated, is shown as one kind
Example, support and confidence level corresponding to every group of correlation behavior are calculated by formula (1) and (2)：
Wherein, f1 represents one in correlation behavior；F2 represents another in correlation behavior；F represents that active user is pre-
If the behavioural information total amount in the time.
Further, filter out support and confidence level is less than every group of correlation behavior of predetermined threshold value, wherein predetermined threshold value
It can be needed to carry out selection setting according to practical application.Meet preset threshold condition at least it is understood that filtering out
One group of target association behavior.
Step 103, according to the goal behavior information related to target association behavior, obtain corresponding with target association behavior
Trigger parameter, the first relevant parameter of active user is calculated according to trigger parameter and goal behavior information, and joined according to triggering
Number and behavioural information calculate the second relevant parameter of active user.
Specifically, many modes can be used according to the goal behavior information related to target association behavior, acquisition and mesh
Trigger parameter corresponding to correlation behavior is marked, ginseng is associated with the first of goal behavior information calculating active user according to trigger parameter
Number, and according to trigger parameter and the second relevant parameter of behavioural information calculating active user, be illustrated below：
The first example, according to the goal behavior information related to target association behavior, obtain and target association behavior pair
The number of clicks answered, the first clicking rate of active user is calculated according to the amount of showing of number of clicks and goal behavior information, and
The second clicking rate of active user is calculated according to the amount of showing of number of clicks and behavioural information.
Specifically, the related goal behavior information of the target association behavior of displaying has showing for many i.e. goal behavior information
Amount is a lot, and user can select to click on one or more, or without clicking on.Can be according to target association behavior pair
The number of clicks answered calculates the first clicking rate of active user.
It is understood that, it is necessary to calculate target association behavior correlation after at least one set of target association behavior filtered out
Goal behavior information, this group of correlation behavior target of behavior for continuing to search for the behavior of iPhone and search for Huawei mobile phone
, it is necessary to after obtaining the behavior of search iPhone and the behavior of search Huawei mobile phone exemplified by correlation behavior, believe for iPhone
The number of clicks of breath and the number of clicks for Huawei's cellphone information.
Further, the first clicking rate of active user is calculated according to the amount of showing of number of clicks and goal behavior information,
Number of clicks divided by total information content that shows are obtained into the first clicking rate, then showing according to number of clicks and behavioural information
Amount calculates, that is, is not limited to goal behavior information, is directed to the amount of showing of all behavioural informations, calculates the second clicking rate.
Second of example, according to the goal behavior information related to target association behavior, obtain and target association behavior pair
The conversion number answered, the first conversion ratio of active user is calculated according to the click volume of conversion number and goal behavior information, and
The second conversion ratio of active user is calculated according to the click volume of conversion number and behavioural information.
Step 104, the first relevant parameter and the second relevant parameter are handled according to preset algorithm, according to result
And predetermined threshold value determines whether active user is targeted customer.
Specifically, preset algorithm has many kinds, can need to be selected according to practical application, as a kind of example, meter
Calculate the ratio of the first relevant parameter and the second relevant parameter.
Further, by ratio compared with predetermined threshold value, know that ratio is more than or equal to predetermined threshold value if comparing,
Then determine that active user is targeted customer.
In summary, the usage mining method of the embodiment of the present invention, by handling user's history behavioural information, obtain
The frequent item set for including multigroup correlation behavior is taken, and the behavioural information in active user's preset time is carried out according to frequent item set
Processing, calculate corresponding with every group of correlation behavior of active user support and confidence level and satisfaction is filtered out according to predetermined threshold value
At least one set of target association behavior of condition, then according to the goal behavior information related to target association behavior, acquisition and mesh
Trigger parameter corresponding to correlation behavior is marked, ginseng is associated with the first of goal behavior information calculating active user according to trigger parameter
Number, and calculate according to trigger parameter and behavioural information the second relevant parameter of active user, finally according to preset algorithm to the
One relevant parameter and the second relevant parameter are handled, according to result and predetermined threshold value determine active user whether be
Targeted customer.Thereby, it is possible to precisely excavate targeted customer, the possible behavior in future of Accurate Prediction user, so as to recommended user
The commodity liked, the search experience of user is lifted, lift the conversion ratio of businessman.
Fig. 2 is the schematic flow sheet of usage mining method in accordance with another embodiment of the present invention, as shown in figure 1, the use
Family method for digging comprises the following steps：
Step 201, user's history behavioural information is handled using the distributed algorithm based on hadoop frameworks, obtained
Include the frequent item set of multigroup correlation behavior.
Specifically, distributed algorithm is realized to frequent item set mining based on hadoop frameworks, compared to traditional mining algorithm
Performance is high, is not limited by machine internal memory and cpu.
Wherein, Hadoop frameworks are a distributed parallel programming frameworks of increasing income for realizing correlation computations model, user
Program can be write by Hadoop, the program write is run on computer group, so as to realize to mass data
Processing.
It should be noted that in embodiments of the present invention, frequent item set is generally referred to as two frequent item sets, for example includes
One group of correlation behavior A and B frequent item set, it will also be appreciated that to can after handling user's history behavioural information
To obtain the frequent item set comprising one group or multigroup correlation behavior.
Step 202, the behavioural information in active user's preset time is handled according to frequent item set, calculated and current
Support and confidence level corresponding to every group of correlation behavior of user, and filter out according to predetermined threshold value at least one set of the condition of satisfaction
Target association behavior.
It should be noted that step S202 description is corresponding with above-mentioned steps S102, thus to step S202 retouch
The description with reference to above-mentioned steps S102 is stated, will not be repeated here.
Step 203, according to the goal behavior information related to target association behavior, obtain corresponding with target association behavior
Number is converted, according to the first conversion ratio of the click volume of conversion number and goal behavior information calculating active user, and according to
Convert the second conversion ratio of the click volume calculating active user of number and behavioural information.
It is understood that, it is necessary to calculate target association behavior correlation after at least one set of target association behavior filtered out
Goal behavior information, this group of correlation behavior target of behavior for continuing to search for the behavior of iPhone and search for Huawei mobile phone
, it is necessary to after obtaining the behavior of search iPhone and the behavior of search Huawei mobile phone exemplified by correlation behavior, believe for iPhone
The conversion number of breath and the conversion number for Huawei's cellphone information.
Wherein, conversion can be user purchase or be register also be to provide the contact methods such as wechat cell-phone number can
It is considered once to convert.
It should be noted that can be that all related goal behavior information all convert in target association behavior, can also
It is Partial Conversion, or does not convert.
Further, the first conversion ratio of active user is calculated according to the click volume of conversion number and goal behavior information,
Number will be converted divided by total number of clicks obtains the first conversion ratio, then according to conversion number and the click volume of behavioural information
Calculate, that is, be not limited to goal behavior information, be directed to the click volume of all behavioural informations, calculate the second conversion ratio.
Step 204, the ratio of the first conversion ratio and the second conversion ratio is calculated, by ratio compared with predetermined threshold value,
Know that ratio is more than or equal to predetermined threshold value if comparing, it is determined that active user is targeted customer.
Specifically, the first conversion ratio will be calculated with the ratio of the second conversion ratio compared with predetermined threshold value, it is general pre-
Gating limit value is 1, and it is targeted customer that active user is determined when ratio is more than or equal to predetermined threshold value.That is, the use of prediction is represented
Family behavior is more accurate.
Thereby, it is possible to precisely excavate targeted customer, the possible behavior in future of Accurate Prediction user, so as to which recommended user likes
Joyous commodity, the search experience of user is lifted, lift the conversion ratio of businessman.
Based on above-described embodiment, the influence of number of clicks of the user behavior on different training sets can also be tied, to enter
One step precisely excavates targeted customer.Specifically, the conversion ratio of target association behavior is identified with jrate, and clki identifies i-th of user
The number of clicks of behavior, the influence of clicking rate of the user behavior on different training sets is tied by below equation (3).
It will also be appreciated that a such as user clicks on and once converted once, conversion ratio is exactly 100 percent, separately
One user clicks on five conversions three times, conversion ratio be 60 percent, it is necessary to for above-mentioned conversion ratio in this case or
Clicking rate is smoothed, with the accuracy of the further possible behavior in future for improving prediction user.
Specifically, introduce logistic regression and smoothing processing is done to clicking rate or conversion ratio, reduce number of clicks (conversion time
Number) the problem of confidence does not cause.As a kind of example, handled by formula (4)：
Thus, the usage mining method of the embodiment of the present invention, it is not necessary to do the training of model, performance cost is smaller (especially
In the case of being mass data).And favorable expandability, newly-generated data can be added rapidly in model, tell on.Thus,
Targeted customer, the possible behavior in future of Accurate Prediction user, so as to the commodity that recommended user likes, lifting can precisely be excavated
The search experience of user, lift the conversion ratio of businessman.
Corresponding with the usage mining method that above-mentioned several embodiments provide, a kind of embodiment of the invention also provides one kind
Usage mining device, the usage mining provided due to usage mining device provided in an embodiment of the present invention with above-mentioned several embodiments
Method is corresponding, thus foregoing usage mining method embodiment be also applied for the present embodiment offer usage mining dress
Put, be not described in detail in the present embodiment.
Fig. 3 is the structural representation of usage mining device according to an embodiment of the invention.As shown in figure 3, the user
Excavating gear includes：Handle acquisition module 11, calculating sifting module 12, obtain computing module 13 and processing determining module 14.
Wherein, acquisition module 11 is handled, for handling user's history behavioural information, acquisition includes multigroup associated line
For frequent item set.
Calculating sifting module 12, at according to frequent item set to the behavioural information in active user's preset time
Reason, corresponding with every group of correlation behavior of active user support and confidence level are calculated, and satisfaction is filtered out according to predetermined threshold value
At least one set of target association behavior of condition.
Computing module 13 is obtained, for according to the goal behavior information related to target association behavior, obtaining and being closed with target
Trigger parameter corresponding to connection behavior, the first relevant parameter of active user is calculated according to trigger parameter and goal behavior information, with
And the second relevant parameter of active user is calculated according to trigger parameter and behavioural information.
Determining module 14 is handled, for being handled according to preset algorithm the first relevant parameter and the second relevant parameter,
Determine whether active user is targeted customer according to result and predetermined threshold value.
Further, processing acquisition module 11 is specifically used for：Using the distributed algorithm based on hadoop frameworks to user
Historical behavior information is handled, and obtains the frequent item set for including multigroup correlation behavior.
Further, computing module 13 is obtained to be specifically used for：According to the goal behavior information related to target association behavior,
Number of clicks corresponding with target association behavior is obtained, the first of active user is calculated according to number of clicks and goal behavior information
Clicking rate, and according to number of clicks and the second clicking rate of behavioural information calculating active user.
Further, computing module 13 is obtained specifically to be additionally operable to：Believed according to the goal behavior related to target association behavior
Breath, conversion number corresponding with target association behavior is obtained, calculates active user's according to conversion number and goal behavior information
First conversion ratio, and according to conversion number and the second conversion ratio of behavioural information calculating active user.
Further, processing determining module 14 is specifically used for：Calculate the first relevant parameter and second relevant parameter
Ratio；By ratio compared with predetermined threshold value, know that ratio is more than or equal to predetermined threshold value if comparing, it is determined that current to use
Family is targeted customer.
In summary, the usage mining device of the embodiment of the present invention, by handling user's history behavioural information, obtain
The frequent item set for including multigroup correlation behavior is taken, and the behavioural information in active user's preset time is carried out according to frequent item set
Processing, calculate corresponding with every group of correlation behavior of active user support and confidence level and satisfaction is filtered out according to predetermined threshold value
At least one set of target association behavior of condition, then according to the goal behavior information related to target association behavior, acquisition and mesh
Trigger parameter corresponding to correlation behavior is marked, ginseng is associated with the first of goal behavior information calculating active user according to trigger parameter
Number, and calculate according to trigger parameter and behavioural information the second relevant parameter of active user, finally according to preset algorithm to the
One relevant parameter and the second relevant parameter are handled, according to result and predetermined threshold value determine active user whether be
Targeted customer.Thereby, it is possible to precisely excavate targeted customer, the possible behavior in future of Accurate Prediction user, so as to recommended user
The commodity liked, the search experience of user is lifted, lift the conversion ratio of businessman.
The present invention proposes a kind of usage mining device, and Fig. 4 is the knot of usage mining in accordance with another embodiment of the present invention
Structure schematic diagram.As shown in figure 4, memory 21, processor 22 and being stored on memory 21 and can be run on processor 22
Computer program.
Processor 22 realizes the usage mining method provided in above-described embodiment when performing described program.
Further, usage mining device also includes：
Communication interface 23, for the communication between memory 21 and processor 22.
Memory 21, for depositing the computer program that can be run on processor 22.
Memory 21 may include high-speed RAM memory, it is also possible to also including nonvolatile memory (non-volatile
Memory), a for example, at least magnetic disk storage.
Processor 22, the usage mining method described in above-described embodiment is realized during for performing described program.
If memory 21, processor 22 and the independent realization of communication interface 23, communication interface 21, memory 21 and processing
Device 22 can be connected with each other by bus and complete mutual communication.The bus can be industry standard architecture
(Industry Standard Architecture, referred to as ISA) bus, external equipment interconnection (Peripheral
Component, referred to as PCI) bus or extended industry-standard architecture (Extended Industry Standard
Architecture, referred to as EISA) bus etc..The bus can be divided into address bus, data/address bus, controlling bus etc..
For ease of representing, only represented in Fig. 4 with a thick line, it is not intended that an only bus or a type of bus.
Optionally, in specific implementation, if memory 21, processor 22 and communication interface 23, are integrated in chip piece
Upper realization, then memory 21, processor 22 and communication interface 23 can complete mutual communication by internal interface.
Processor 22 is probably a central processing unit (Central Processing Unit, referred to as CPU), or
Specific integrated circuit (Application Specific Integrated Circuit, referred to as ASIC), or by with
It is set to the one or more integrated circuits for implementing the embodiment of the present invention.
The present invention proposes a kind of computer program product, when the instruction in computer program product is by computing device,
Perform the usage mining method described in above-described embodiment.
The present invention proposes a kind of computer-readable recording medium, is stored thereon with computer program, it is characterised in that the meter
Calculation machine program realizes the usage mining method described in above-described embodiment when being executed by processor.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or the spy for combining the embodiment or example description
Point is contained at least one embodiment or example of the present invention.In this manual, to the schematic representation of above-mentioned term not
Identical embodiment or example must be directed to.Moreover, specific features, structure, material or the feature of description can be with office
Combined in an appropriate manner in one or more embodiments or example.In addition, in the case of not conflicting, the skill of this area
Art personnel can be tied the different embodiments or example and the feature of different embodiments or example described in this specification
Close and combine.
In addition, term " first ", " second " are only used for describing purpose, and it is not intended that instruction or hint relative importance
Or the implicit quantity for indicating indicated technical characteristic.Thus, define " first ", the feature of " second " can be expressed or
Implicitly include at least one this feature.In the description of the invention, " multiple " are meant that at least two, such as two, three
It is individual etc., unless otherwise specifically defined.
Any process or method described otherwise above description in flow chart or herein is construed as, and represents to include
Module, fragment or the portion of the code of the executable instruction of one or more the step of being used to realize custom logic function or process
Point, and the scope of the preferred embodiment of the present invention includes other realization, wherein can not press shown or discuss suitable
Sequence, including according to involved function by it is basic simultaneously in the way of or in the opposite order, carry out perform function, this should be of the invention
Embodiment person of ordinary skill in the field understood.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution system, device or equipment (such as computer based system including the system of processor or other can be held from instruction
The system of row system, device or equipment instruction fetch and execute instruction) use, or combine these instruction execution systems, device or set
It is standby and use.For the purpose of this specification, " computer-readable medium " can any can be included, store, communicate, propagate or pass
Defeated program is for instruction execution system, device or equipment or the dress used with reference to these instruction execution systems, device or equipment
Put.The more specifically example (non-exhaustive list) of computer-readable medium includes following：Electricity with one or more wiring
Connecting portion (electronic installation), portable computer diskette box (magnetic device), random access memory (RAM), read-only storage
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device, and portable optic disk is read-only deposits
Reservoir (CDROM).In addition, computer-readable medium, which can even is that, to print the paper of described program thereon or other are suitable
Medium, because can then enter edlin, interpretation or if necessary with it for example by carrying out optical scanner to paper or other media
His suitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each several part of the present invention can be realized with hardware, software, firmware or combinations thereof.Above-mentioned
In embodiment, software that multiple steps or method can be performed in memory and by suitable instruction execution system with storage
Or firmware is realized.Such as, if realized with hardware with another embodiment, following skill well known in the art can be used
Any one of art or their combination are realized：With the logic gates for realizing logic function to data-signal from
Logic circuit is dissipated, the application specific integrated circuit with suitable combinational logic gate circuit, programmable gate array (PGA), scene can compile
Journey gate array (FPGA) etc..
Those skilled in the art are appreciated that to realize all or part of step that above-described embodiment method carries
Suddenly it is that by program the hardware of correlation can be instructed to complete, described program can be stored in a kind of computer-readable storage medium
In matter, the program upon execution, including one or a combination set of the step of embodiment of the method.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing module, can also
That unit is individually physically present, can also two or more units be integrated in a module.Above-mentioned integrated mould
Block can both be realized in the form of hardware, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized in the form of software function module and as independent production marketing or in use, can also be stored in a computer
In read/write memory medium.
Storage medium mentioned above can be read-only storage, disk or CD etc..Although have been shown and retouch above
Embodiments of the invention are stated, it is to be understood that above-described embodiment is exemplary, it is impossible to be interpreted as the limit to the present invention
System, one of ordinary skill in the art can be changed to above-described embodiment, change, replace and become within the scope of the invention
Type.
Claims (13)
- A kind of 1. usage mining method, it is characterised in that comprise the following steps：User's history behavioural information is handled, obtains the frequent item set for including multigroup correlation behavior；The behavioural information in active user's preset time is handled according to the frequent item set, calculated every with active user Support and confidence level corresponding to correlation behavior are organized, and at least one set of target association of the condition of satisfaction is filtered out according to predetermined threshold value Behavior；According to the goal behavior information related to the target association behavior, triggering corresponding with the target association behavior is obtained Parameter, the first relevant parameter of active user is calculated according to the trigger parameter and the goal behavior information, and according to institute State trigger parameter and the behavioural information calculates the second relevant parameter of active user；First relevant parameter and second relevant parameter are handled according to preset algorithm, according to result and Predetermined threshold value determines whether active user is targeted customer.
- 2. the method as described in claim 1, it is characterised in that it is described that user's history behavioural information is handled, obtain bag Frequent item set containing multigroup correlation behavior, including：User's history behavioural information is handled using the distributed algorithm based on hadoop frameworks, acquisition includes multigroup association The frequent item set of behavior.
- 3. the method as described in claim 1, it is characterised in that the basis target line related to the target association behavior For information, trigger parameter corresponding with the target association behavior is obtained, is believed according to the trigger parameter and the goal behavior Breath calculates the first relevant parameter of active user, and calculates active user's according to the trigger parameter and the behavioural information Second relevant parameter, including：According to the goal behavior information related to the target association behavior, click on corresponding with the target association behavior is obtained Number, the first clicking rate of active user is calculated according to the amount of showing of the number of clicks and the goal behavior information, and The second clicking rate of active user is calculated according to the amount of showing of the number of clicks and the behavioural information.
- 4. the method as described in claim 1, it is characterised in that the basis target line related to the target association behavior For information, trigger parameter corresponding with the target association behavior is obtained, is believed according to the trigger parameter and the goal behavior Breath calculates the first relevant parameter of active user, and calculates active user's according to the trigger parameter and the behavioural information Second relevant parameter, including：According to the goal behavior information related to the target association behavior, conversion corresponding with the target association behavior is obtained Number, the first conversion ratio of active user is calculated according to the click volume of the conversion number and the goal behavior information, and The second conversion ratio of active user is calculated according to the click volume of the conversion number and the behavioural information.
- 5. the method as described in claim 1, it is characterised in that it is described according to preset algorithm to first relevant parameter and institute State the second relevant parameter to be handled, determine whether active user is targeted customer according to result and predetermined threshold value, Including：Calculate the ratio of first relevant parameter and second relevant parameter；By the ratio compared with predetermined threshold value, know that the ratio is more than or equal to predetermined threshold value if comparing, really It is targeted customer to determine active user.
- A kind of 6. usage mining device, it is characterised in that including：Acquisition module is handled, for handling user's history behavioural information, obtains the frequent episode for including multigroup correlation behavior Collection；Calculating sifting module, for being handled according to the frequent item set the behavioural information in active user's preset time, Corresponding with every group of correlation behavior of active user support and confidence level are calculated, and the condition of satisfaction is filtered out according to predetermined threshold value At least one set of target association behavior；Computing module is obtained, for according to the goal behavior information related to the target association behavior, obtaining and the target Trigger parameter corresponding to correlation behavior, calculate active user according to the trigger parameter and the goal behavior information first are closed Join parameter, and the second relevant parameter of active user is calculated according to the trigger parameter and the behavioural information；Determining module is handled, at according to preset algorithm to first relevant parameter and second relevant parameter Reason, determines whether active user is targeted customer according to result and predetermined threshold value.
- 7. device as claimed in claim 6, it is characterised in that the processing acquisition module is specifically used for：User's history behavioural information is handled using the distributed algorithm based on hadoop frameworks, acquisition includes multigroup association The frequent item set of behavior.
- 8. device as claimed in claim 6, it is characterised in that the acquisition computing module is specifically used for：According to the goal behavior information related to the target association behavior, click on corresponding with the target association behavior is obtained Number, the first clicking rate of active user is calculated according to the amount of showing of the number of clicks and the goal behavior information, and The second clicking rate of the amount of the showing active user calculated according to the number of clicks and the behavioural information.
- 9. device as claimed in claim 6, it is characterised in that the acquisition computing module is specifically additionally operable to：According to the goal behavior information related to the target association behavior, conversion corresponding with the target association behavior is obtained Number, the first conversion ratio of active user is calculated according to the click volume of the conversion number and the goal behavior information, and The second conversion ratio of active user is calculated according to the click volume of the conversion number and the behavioural information.
- 10. device as claimed in claim 6, it is characterised in that the processing determining module is specifically used for：Calculate the ratio of first relevant parameter and second relevant parameter；By the ratio compared with predetermined threshold value, know that the ratio is more than or equal to predetermined threshold value if comparing, really It is targeted customer to determine active user.
- 11. a kind of usage mining device, it is characterised in that including processor and memory；Wherein, the processor can perform by reading the executable program code stored in the memory to run with described Program corresponding to program code, for realize as described in any in claim 1-5 based on usage mining method.
- 12. a kind of computer program product, when the instruction in the computer program product is by computing device, perform as weighed Profit requires the usage mining method any one of 1-5.
- 13. a kind of computer-readable recording medium, is stored thereon with computer program, it is characterised in that the computer program quilt The usage mining method as any one of claim 1-5 is realized during computing device.
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| CN201710471331.7A CN107392645A (en) | 2017-06-20 | 2017-06-20 | Usage mining method, apparatus and its equipment |
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