CN106095915A - The processing method and processing device of user identity - Google Patents
The processing method and processing device of user identity Download PDFInfo
- Publication number
- CN106095915A CN106095915A CN201610405268.2A CN201610405268A CN106095915A CN 106095915 A CN106095915 A CN 106095915A CN 201610405268 A CN201610405268 A CN 201610405268A CN 106095915 A CN106095915 A CN 106095915A
- Authority
- CN
- China
- Prior art keywords
- identity
- user
- behavioral data
- setting
- characteristic vector
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Computer Hardware Design (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The present invention provides the processing method and processing device of a kind of user identity.Its method includes: obtain the behavioral data of pending user；According to identity converter, the behavioral data of described pending user being converted into identity characteristic vector, described identity characteristic vector is for unique behavioral data identifying described pending user；According to identity processor, the identity characteristic vector of described pending user is processed, whether be to set identity to determine the identity of described pending user.By using the technique scheme of the present invention, the behavioral data according to pending user can be realized the identity of pending user is determined, compared with prior art, can effectively ensure the accuracy of the determination of user identity, and it is effectively improved the determination efficiency of user identity, carry out the information pushing relevant to this user identity according to the user identity determined so that follow-up, and then be effectively improved the efficiency of information pushing.
Description
[technical field]
The present invention relates to big technical field of data processing, particularly relate to the processing method and processing device of a kind of user identity.
[background technology]
Along with popularizing that scientific and technological fast development, mobile terminal use, the various application along with mobile terminal the most more come
The most, greatly facilitate the life of people.
Various application service providers, in order to more efficiently promote various service related information to user, need first to determine user
Identity, the most just can ensure that the accuracy of pushed information, to improve pushing efficiency.In prior art, user identity is really
Determining process relatively complicated, such as a lot of application service providers use the mode of excitation user to allow user at some application of registration or clothes
The when of business, actively provide identity information.But the mode of this user identity still cannot ensure the user identity got
Accuracy.
Therefore, existing technique scheme determines the inefficient of user identity.
[summary of the invention]
The invention provides the processing method and processing device of a kind of user identity, to improve the determination efficiency of user identity.
The present invention provides the processing method of a kind of user identity, described method to include:
Obtain the behavioral data of pending user；
According to identity converter, the behavioral data of described pending user being converted into identity characteristic vector, described identity is special
Levy vector for unique behavioral data identifying described pending user；
According to identity processor, the identity characteristic vector of described pending user is processed, described pending to determine
Whether the identity of user is to set identity.
The present invention also provides for the processing means of a kind of user identity, and described device includes:
Acquisition module, for obtaining the behavioral data of pending user；
Conversion module, for according to identity converter the behavioral data of described pending user is converted into identity characteristic to
Amount, described identity characteristic vector is for unique behavioral data identifying described pending user；
Processing module, for the identity characteristic vector of described pending user being processed according to identity processor, with
Whether the identity determining described pending user is to set identity.
The processing method and processing device of the user identity of the present invention, by using technique scheme, it is achieved according to pending
The identity of pending user is determined by the behavioral data of user, compared with prior art, can effectively ensure user's body
The accuracy of the determination of part, and it is effectively improved the determination efficiency of user identity, in order to follow-up according to the user identity determined
Carry out the information pushing relevant to this user identity, and then be effectively improved the efficiency of information pushing.
[accompanying drawing explanation]
Fig. 1 is the flow chart of the processing method embodiment one of the user identity of the present invention.
Fig. 2 is the flow chart of the processing method embodiment two of the user identity of the present invention.
Fig. 3 is the structure chart of the processing means embodiment one of the user identity of the present invention.
Fig. 4 is the structure chart of the processing means embodiment two of the user identity of the present invention.
[detailed description of the invention]
In order to make the object, technical solutions and advantages of the present invention clearer, below in conjunction with the accompanying drawings with specific embodiment pair
The present invention is described in detail.
Fig. 1 is the flow chart of the processing method embodiment one of the user identity of the present invention.As it is shown in figure 1, the present embodiment
The processing method of user identity, specifically may include steps of:
100, the behavioral data of pending user is obtained；
The behavioral data of the pending user of the present embodiment can be that the historical behavior to this pending user is acquired
Obtaining, the behavioral data of pending user is specifically as follows related data, can include a lot in this data
Field.In actual application, the when of gathering the behavioral data of this pending user, can carry out targetedly in conjunction with setting identity
Acquisition.Such as set identity as car owner, when obtaining the behavioral data of this pending user, can tend to obtain this and wait to locate
The behavioral data relevant to car owner of reason user.Such as in these behavioral datas can include a period of time of this pending user
Map retrieval number of times and use natural law, essential information point (point of interest；Poi) retrieval number of times and natural law, public transport
Route retrieval, driving route retrieval, Walking Route inspection and subway line retrieval number of times, retrieval number of times accounting and natural law, user ground
The retrieval number of times of figure use time cumulation duration, user to use each term that map natural law is relevant to car, retrieve number of times with
Accounting in car coordinate indexing total degree and natural law etc..The most each poi can comprise four directions surface information: title, classification, warp
Information such as degree latitude and neighbouring retail shop of restaurant of hotel etc., this poi information can also be referred to as " navigation map information ".Wherein with
The term that car is relevant comprises: gas station, automobile services, sale of automobile, 4S shop, car detailing, Motor Maintenance, automobile decoration,
Carwash, auto parts machinery, Vehicle Inspection field etc..
101, according to identity converter, the behavioral data of pending user is converted into identity characteristic vector；
Owing to the behavioral data of pending user cannot be processed by follow-up identity processor, the present embodiment needs
First converting the behavioral data of this pending user, the identity characteristic vector after conversion identifies pending user for unique
Behavioral data, the identity characteristic vector of such as the present embodiment can use numeral 0 and 1 constitute.
102, according to identity processor, the identity characteristic vector of pending user is processed, to determine pending user
Identity be whether to set identity.
The identity processor of the present embodiment can process with the identity characteristic vector of pending user, it is achieved to pending
Whether the identity of user is identified, be to set identity to determine the identity of pending user.The setting identity of the present embodiment can
Think car owner, or can also be other such as drawing practitioner or certain art speciality practitioner or other machinery or
Practitioner of Engineering etc..
The processing method of the user identity of the present embodiment, by using technique scheme, it is achieved according to pending user
Behavioral data the identity of pending user is determined, compared with prior art, can effectively ensure user identity
The accuracy determined, and be effectively improved the determination efficiency of user identity, in order to follow-up carry out according to the user identity determined
The information pushing relevant to this user identity, and then it is effectively improved the efficiency of information pushing.
Still optionally further, on the basis of the technical scheme shown in above-mentioned Fig. 1, before step 101, it is also possible to include as
Lower step a: according to the first behavior data set and the second behavioral data collection of non-setting identity user of setting identity user, generate
Identity converter.
Specifically, the first behavior data set of the setting identity user of the present embodiment and second row of non-setting identity user
Can also collect in advance for data set, such as, can obtain from each user access record on the internet.First
Behavioral data concentrates the behavioral data including multiple setting identity user, permissible in the behavioral data of each setting identity user
Data including multiple fields.The data of the multiple fields in the behavioral data of each setting identity user are relevant as one
Connection data store, so, in the first behavior data set the behavioral data of the user of multiple setting identity be exactly according to
Family stores one by one.In like manner the second behavioral data concentrates the behavioral data including multiple non-setting identity user, its storage
Mode is with reference to the behavioral data storage mode in above-mentioned first behavior data set, the behavioral data of the user of multiple non-setting identity
Also it is to store one by one according to user.The first behavior data set according to setting identity user sets the row of identity user
For the behavioral data of the non-setting identity user that the second behavioral data of data and non-setting identity user is concentrated, genus can be found out
In setting some general character of behavioral data of identity user, and it is not belonging to the individual character of specific identity user, as such, it is possible to according to
First behavior data set and the second behavioral data collection, generate identity converter.This identity converter is possible not only to according to behavior number
It is identified according to the identity of pending user, identity characteristic vector can also be generated according to recognition result simultaneously.
The quantity of the user of the setting identity that the first behavior data set includes and the second behavioral data collection in the present embodiment
The quantity of the user of the non-setting identity included can be identical, it is also possible to differs；Quantity specifically can be 20,500
Or any number such as 800；And the quantity of the user of setting identity that includes of the first behavior data set and the second behavior number
According to concentrating the quantity of the user of non-setting identity included, the identity converter obtained and identity processor carry out identity convert and
The accuracy that identity processes is the highest.
The setting identity of such as the present embodiment can be car owner, and non-setting identity is non-car owner.According to car owner first
All behavioral datas that second behavioral data of behavioral data collection and non-car owner is concentrated, can train an identity converter, should
Identity converter is capable of the behavioral data of user being converted into a corresponding identity characteristic vector, to simplify behavioral data
Representation, facilitate the follow-up identification carrying out user identity according to identity characteristic vector.
Such as this step a is specifically as follows: use each in the first behavior data set to set the behavior of identity user
The behavioral data of each non-setting identity user that data and the second behavioral data are concentrated, the identity that training is preset converts mould
Type, obtains identity converter.
The identity transformation model of the present embodiment specifically can use random forest (random forest) or gradient to promote
Decision tree (Gradient Boost Decision Tree；GBDT) realize.
For example, it is possible to the behavioral data of the user of the setting identity that the first behavior data set is included and the second behavior number
According to the behavioral data of the user concentrating the non-setting identity included, it is referred to as training data.Then these training datas are utilized to instruct
Practicing random forest or GBDT model, can obtain N decision tree respectively, wherein a tree N of decision tree is by training process
The decision tree number state modulator of middle configuration, can configure according to the actual requirements before training.Every in N decision tree
One tree leaf node be all numbered, the m-th leaf node of such as n-th decision tree be (n, m).To every training data,
It is the probability setting identity user that every tree all can have a leaf node to export this user, if probability is more than specifying threshold value, and can
With by result queue for 1, no person is 0.And owing to the identity of the user of every training data determines that, when output result with
When the identity of the user known is inconsistent, random forest or the GBDT model of correspondence can be adjusted, to adjust final acquisition
N decision tree, improve N the decision tree accuracy to user identity identification.All known by use in training data
Random forest or GBDT model are trained by the behavioral data of user identity, finally give N decision tree and are
The identity converter obtained eventually, the identity of user can be identified according to behavioral data, the most also may be used by this identity converter
To generate only one identity characteristic vector according to recognition result.Such as, for the behavioral data of each user, this N tree
Leaf node can export one N*M vector of acquisition, wherein M is the leaf node of the most decision tree of leaf node number
Number.If n-th tree m-th leaf node discriminative training data be labeled as 1, then ((n-1) * M+m) bit element is 1,
No person is 0, and the element not exporting the leaf node of result corresponding is 0.If the leaf node number of certain decision tree is less than M, exceed
Leaf node number element below is with 0 leaf node supplying disappearance.Due to each behavioral data pair in training data
The identity of the user answered all determines that, so, according to the behavioral data of the user that these identity determine, can train this N certainly
Plan tree is capable of identify that the ability of user identity, and simultaneously for the behavioral data of each user, this N decision tree can be corresponding defeated
Go out a corresponding identity characteristic vector.N decision tree parameter after training all secures, and can accurately realize pending
The identification of user identity, now this N decision tree can form an identity converter.
Still optionally further, on the basis of the technical scheme of above-mentioned embodiment illustrated in fig. 1, before step 102, also may be used
To include step b: according to the first behavior data set, the second behavioral data collection and identity converter, generate identity processor.
Such as, following two steps can be included when this step b implements:
(b1) each in the first behavior data set is set behavioral data and the second behavioral data collection of identity user
In the behavioral data of each non-setting identity user, be separately input in identity converter, obtain the first behavior data set
In each set identity user identity characteristic vector sum second behavioral data concentrate each non-setting identity user
Identity characteristic vector；
(b2) each in the first behavior data set is used to set identity characteristic vector sum second behavior of identity user
The identity characteristic vector of the non-setting identity user of each in data set, the identity that training is preset processes model, obtains identity
Processor.
Owing to identity converter can realize the behavioral data conversion to corresponding identity characteristic vector of user, this enforcement
In example after generating identity converter, each in the first behavior data set can be set the behavioral data of identity user
With the behavioral data of each non-setting identity user that the second behavioral data is concentrated, respectively by identity converter, it is converted into
Characteristic vector, as such, it is possible to obtain first eigenvector set corresponding in the first behavior data set and the second behavioral data collection
Corresponding second feature vector set.Then the characteristic vector in first eigenvector set and second feature vector set is divided
Not as training data, the identity that training is preset processes model, to obtain identity processor.Such as, this identity preset processes
Model is specifically as follows Logic Regression Models.Owing to the characteristic vector in first eigenvector set is the feature of setting user
Vector, so, inputs the characteristic vector in first eigenvector set and processes in model to the identity preset, can calculate
Whether user corresponding to this feature vector is the probability of the user presetting identity, if probability is more than or equal to predetermined probabilities threshold
Value, it may be determined that this user is the user presetting identity；Probability is less than predetermined probabilities threshold value else if, then explanation needs to adjust
This identity preset processes model so that the probit of calculating is more than this predetermined probabilities threshold value.By using first eigenvector
All characteristic vectors in set and second feature vector set, constantly train this identity preset to process model, the most constantly
Ground adjusts this identity preset and processes model, obtains this identity processor.
For example with GBDT model training be decision tree number be set to 3 tree time, the decision tree at training is the most permissible
There are 4 leaf nodes.For the behavioral data of certain user a, leaf node (1,1), (2,2), the differentiation result that (3,4) are given is
Be respectively 1,0,1, the identity characteristic vector of the 0-1 attribute that the behavioral data of the most pending user is corresponding be (1,0,0,0,0,0,
0,0,0,0,0,1), the thick differentiation end value corresponding for providing the leaf node differentiating result is marked.
Still optionally further, on the basis of the technical scheme of above-mentioned embodiment illustrated in fig. 1, step 102 specifically can be wrapped
Include following steps:
(c1) the pending user corresponding to identity characteristic vector of pending user is calculated according to identity processor for setting
The probit of the user of identity；
(c2) judge that whether probit is more than or equal to predetermined probabilities threshold value；If so, determine that pending user is for setting
The user of identity；That otherwise determine this pending user is not the user setting identity.
The probit of the present embodiment can the most rule of thumb be chosen.
The technical scheme of above-described embodiment, compared with prior art, can ensure the standard of the determination of user identity effectively
Really property, and be effectively improved the determination efficiency of user identity, in order to follow-up carry out and this user according to the user identity determined
The information pushing that identity is relevant, and then it is effectively improved the efficiency of information pushing.
Fig. 2 is the flow chart of the processing method embodiment two of the user identity of the present invention.The present embodiment combines above-mentioned reality
Executing the technical scheme of example, to set identity as car owner, the non-identity that sets, as a example by non-car owner, describes technical scheme.
As in figure 2 it is shown, the processing method of the user identity of the present embodiment, specifically may include steps of:
200, the first behavior data set and the second behavioral data collection of non-car owner of car owner are gathered；
Such as, specifically the user data from the Internet can gather this first behavior data set and the second behavioral data
Collection.Wherein the first behavior data set includes that the behavioral data of multiple car owner, the second behavioral data are concentrated and includes multiple non-car owner
Behavioral data.
The behavioral data of the present embodiment includes the behavioral data of the multiple fields relevant to car, is referred to above-mentioned in detail
The record of embodiment, does not repeats them here.
201, the behavioral data that the first behavior data set of car owner and second behavioral data of non-car owner are concentrated is utilized, right
Random forest or GBDT model are trained, and N the decision tree obtained is as identity converter；
Concrete training principle is referred to the record of above-described embodiment, does not repeats them here.N the decision-making finally given
The leaf node number setting the most decision tree of middle period son node number corresponding is M.
202, the behavioral data first behavior data set of car owner and second behavioral data of non-car owner concentrated, adopts respectively
Change with identity converter, obtain the first identity characteristic vector set corresponding to the first behavior data set of car owner and non-car owner
Second Identity of Local vector set corresponding to the second behavioral data collection；
Owing to each behavioral data can uniquely change a corresponding identity characteristic vector by identity transducer.So
In the behavioral data of each car owner in the first behavior data set of car owner and the first identity characteristic vector of car owner one
Identity characteristic vector one_to_one corresponding, the behavioral data of each non-car owner that second behavioral data of non-car owner is concentrated and non-car owner
Second Identity of Local vector in an identity characteristic vector one_to_one corresponding.
203, the first identity characteristic vector set and second row of non-car owner that the first behavior data set of car owner is corresponding are utilized
For the characteristic vector in the Second Identity of Local vector set that data set is corresponding, Logic Regression Models is trained, obtains identity
Processor；
Concrete training principle is referred to the record of above-described embodiment, does not repeats them here.
204, the behavioral data of pending user is obtained；
205, the behavioral data of pending user is inputted to identity processor, calculate this pending user for car owner's
Probit；
206, judge that whether probit is more than or equal to predetermined probabilities threshold value；If so, determine that pending user is car owner；
Otherwise determine that this pending user is non-car owner.
The said method using the present embodiment can also realize the identification of the user to other default identity.
Along with the development of the Internet, going on a journey with car, carwash, the Internet service relevant to car such as Motor Maintenance is gradually by people
Accept.In the internet, applications that these are relevant to car, car owner is the core of all services, therefore obtains car owner's body of the unknown
Part becomes extremely important, especially during promoting service and ownership.Use the said method of the present embodiment, permissible
Get whether pending user is car owner accurately and efficiently, in order to follow-up car owner's identity according to user carries out vehicle phase
The information pushing closed, and then it is effectively improved the efficiency of information pushing.
Fig. 3 is the structure chart of the processing means embodiment one of the user identity of the present invention.As it is shown on figure 3, the present embodiment
The processing means of user identity, specifically may include that acquisition module 10, conversion module 11 and processing module 12.
Wherein acquisition module 10 is for obtaining the behavioral data of pending user；Conversion module 11 is for converting according to identity
The behavioral data of the pending user that acquisition module 10 is obtained by device converts identity characteristic vector, and identity characteristic vector is for unique
Identify the behavioral data of pending user；Processing module 12 is pending for convert conversion module 11 according to identity processor
Whether the identity characteristic vector of user processes, be to set identity to determine the identity of pending user.
The processing means of the user identity of the present embodiment, realizes the realization of the process of user identity by the above-mentioned module of employing
Principle and technique effect, with above-mentioned related method embodiment realize identical, be referred to the record of above-described embodiment in detail,
Do not repeat them here.
Fig. 4 is the structure chart of the processing means embodiment two of the user identity of the present invention.As shown in Figure 4, the present embodiment
The processing means of user identity, on the basis of the technical scheme of above-mentioned embodiment illustrated in fig. 3, further comprises following technology
Scheme.
As shown in Figure 4, the processing means of the user identity of the present embodiment also includes identity converter generation module 13.This body
Part converter generation module 13 is for according to the first behavior data set and the second of non-setting identity user setting identity user
Behavioral data collection, generates identity converter.
Still optionally further, the identity converter generation module 13 in the processing means of the user identity of the present embodiment is concrete
Every for use each behavioral data setting identity user in the first behavior data set and the second behavioral data to concentrate
The behavioral data of one non-setting identity user, the identity transformation model that training is preset, obtain identity converter.Wherein convert mould
Block 11 is specifically used for according to the identity converter generated with identity converter generation module 13, by treating that acquisition module 10 obtains
The behavioral data processing user converts identity characteristic vector.
Still optionally further, as shown in Figure 4, the processing means of the user identity of the present embodiment also includes identity processor
Generation module 14.Wherein identity processor generation module 14 is for according to the first behavior data set, the second behavioral data collection and body
The identity converter that part converter generation module 13 generates, generates identity processor.
Still optionally further, identity processor generation module 14 is specifically for setting each in the first behavior data set
Determine the behavioral data of identity user and the behavioral data of each non-setting identity user of the second behavioral data concentration, the most defeated
Enter to the identity converter of identity converter generation module 13 generation, obtain each in the first behavior data set and set body
The identity characteristic vector of each non-setting identity user that identity characteristic vector sum second behavioral data of part user is concentrated；Adopt
With in the first behavior data set each set identity user identity characteristic vector sum the second behavioral data concentrate each
The identity characteristic vector of individual non-setting identity user, the identity that training is preset processes model, obtains identity processor.
The identity preset of such as the present embodiment processes model and includes Logic Regression Models.
Still optionally further, as shown in Figure 4, in the processing means of the user identity of the present embodiment, processing module 12 is concrete
Including: computing unit 121 and judging unit 122.
Wherein computing unit 121 calculates for the identity processor generated according to identity processor generation module 14 and converts mould
The probit of the user that pending user be setting identity that the identity characteristic vector of pending user that block 11 converts is corresponding；Sentence
Whether the probit that disconnected unit 122 calculates for judging computing unit 121 is more than or equal to predetermined probabilities threshold value；The most true
Fixed pending user is the user setting identity.
The processing means of the user identity of the present embodiment, realizes the realization of the process of user identity by the above-mentioned module of employing
Principle and technique effect, with above-mentioned related method embodiment realize identical, be referred to the record of above-described embodiment in detail,
Do not repeat them here.
In several embodiments provided by the present invention, it should be understood that disclosed system, apparatus and method are permissible
Realize by another way.Such as, device embodiment described above is only schematically, such as, and described unit
Dividing, be only a kind of logic function and divide, actual can have other dividing mode when realizing.
The described unit illustrated as separating component can be or may not be physically separate, shows as unit
The parts shown can be or may not be physical location, i.e. may be located at a place, or can also be distributed to multiple
On NE.Some or all of unit therein can be selected according to the actual needs to realize the mesh of the present embodiment scheme
's.
It addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, it is also possible to
It is that unit is individually physically present, it is also possible to two or more unit are integrated in a unit.Above-mentioned integrated list
Unit both can realize to use the form of hardware, it would however also be possible to employ hardware adds the form of SFU software functional unit and realizes.
The above-mentioned integrated unit realized with the form of SFU software functional unit, can be stored in an embodied on computer readable and deposit
In storage media.Above-mentioned SFU software functional unit is stored in a storage medium, including some instructions with so that a computer
Equipment (can be personal computer, server, or the network equipment etc.) or processor (processor) perform the present invention each
The part steps of method described in embodiment.And aforesaid storage medium includes: USB flash disk, portable hard drive, read only memory (Read-
Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disc or CD etc. various
The medium of program code can be stored.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all essences in the present invention
Within god and principle, any modification, equivalent substitution and improvement etc. done, within should be included in the scope of protection of the invention.
Claims (14)
1. the processing method of a user identity, it is characterised in that described method includes:
Obtain the behavioral data of pending user；
According to identity converter, the behavioral data of described pending user is converted into identity characteristic vector, described identity characteristic to
Amount is for unique behavioral data identifying described pending user；
According to identity processor, the identity characteristic vector of described pending user is processed, to determine described pending user
Identity be whether to set identity.
Method the most according to claim 1, it is characterised in that according to identity converter by the behavior of described pending user
Before data are converted into identity characteristic vector, described method also includes:
The first behavior data set according to setting identity user and the second behavioral data collection of non-setting identity user, generate described
Identity converter.
Method the most according to claim 2, it is characterised in that according to setting the first behavior data set of identity user and non-
Set the second behavioral data collection of identity user, generate described identity converter, specifically include:
Use the behavioral data of the described setting identity user of each in described first behavior data set and described second behavior
The behavioral data of the described non-setting identity user of each in data set, the identity transformation model that training is preset, obtain described
Identity converter.
Method the most according to claim 2, it is characterised in that described identity characteristic vector is carried out according to identity processor
Processing, before determining whether the identity of described pending user is setting identity, described method also includes:
According to described first behavior data set, described second behavioral data collection and described identity converter, generate at described identity
Reason device.
Method the most according to claim 4, it is characterised in that according to described first behavior data set, described second behavior
Data set and described identity converter, generate described identity processor, specifically include:
By behavioral data and the described second behavior number of described for each in described first behavior data set setting identity user
According to the behavioral data of each the described non-setting identity user concentrated, it is separately input in described identity converter, obtains institute
State the second behavioral data collection described in the identity characteristic vector sum of the described setting identity user of each in the first behavior data set
In each described non-setting identity user identity characteristic vector；
Use described in the identity characteristic vector sum of the described setting identity user of each in described first behavior data set second
The identity characteristic vector of each described non-setting identity user that behavioral data is concentrated, the identity that training is preset processes model,
Obtain described identity processor.
Method the most according to claim 5, it is characterised in that described default identity processes model and includes logistic regression mould
Type.
7. according to the arbitrary described method of claim 1-6, it is characterised in that described according to identity processor to described pending
Whether the identity characteristic vector of user processes, be to set identity to determine the identity of described pending user, specifically include:
Described pending user corresponding to identity characteristic vector calculating described pending user according to described identity processor is
The probit of the user of described setting identity；
Judge that whether described probit is more than or equal to predetermined probabilities threshold value；If so, determine that described pending user is for described
Set the user of identity.
8. the processing means of a user identity, it is characterised in that described device includes:
Acquisition module, for obtaining the behavioral data of pending user；
Conversion module, for the behavioral data of described pending user being converted into identity characteristic vector according to identity converter,
Described identity characteristic vector is for unique behavioral data identifying described pending user；
Processing module, for processing the identity characteristic vector of described pending user according to identity processor, to determine
Whether the identity of described pending user is to set identity.
Device the most according to claim 8, it is characterised in that described device also includes:
Identity converter generation module, for according to setting the first behavior data set of identity user and non-setting identity user
Second behavioral data collection, generates described identity converter.
Device the most according to claim 9, it is characterised in that described identity converter generation module, specifically for using
Behavioral data and described second behavioral data of each the described setting identity user in described first behavior data set are concentrated
The behavioral data of each described non-setting identity user, the identity transformation model that training is preset, obtain described identity and convert
Device.
11. devices according to claim 9, it is characterised in that described device also includes:
Identity processor generation module, for according to described first behavior data set, described second behavioral data collection and described body
Part converter, generates described identity processor.
12. devices according to claim 11, it is characterised in that described identity processor generation module, specifically for:
By behavioral data and the described second behavior number of described for each in described first behavior data set setting identity user
According to the behavioral data of each the described non-setting identity user concentrated, it is separately input in described identity converter, obtains institute
State the second behavioral data collection described in the identity characteristic vector sum of the described setting identity user of each in the first behavior data set
In each described non-setting identity user identity characteristic vector；
Use described in the identity characteristic vector sum of the described setting identity user of each in described first behavior data set second
The identity characteristic vector of each described non-setting identity user that behavioral data is concentrated, the identity that training is preset processes model,
Obtain described identity processor.
13. devices according to claim 12, it is characterised in that described default identity processes model and includes logistic regression
Model.
14.-13 arbitrary described devices according to Claim 8, it is characterised in that described processing module, specifically include:
Computing unit, for according to described identity processor calculate described pending user identity characteristic vector corresponding described in
Pending user is the probit of the user of described setting identity；
Judging unit, is used for judging that whether described probit is more than or equal to predetermined probabilities threshold value；If so, wait to locate described in determining
Reason user is the user of described setting identity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610405268.2A CN106095915A (en) | 2016-06-08 | 2016-06-08 | The processing method and processing device of user identity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610405268.2A CN106095915A (en) | 2016-06-08 | 2016-06-08 | The processing method and processing device of user identity |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106095915A true CN106095915A (en) | 2016-11-09 |
Family
ID=57227723
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610405268.2A Pending CN106095915A (en) | 2016-06-08 | 2016-06-08 | The processing method and processing device of user identity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106095915A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107330445A (en) * | 2017-05-31 | 2017-11-07 | 北京京东尚科信息技术有限公司 | The Forecasting Methodology and device of user property |
| CN107688865A (en) * | 2017-07-31 | 2018-02-13 | 上海恺英网络科技有限公司 | Identify the method and apparatus of potential high consumption user in online game |
| CN111753266A (en) * | 2019-03-29 | 2020-10-09 | 阿里巴巴集团控股有限公司 | User authentication method, multimedia content pushing method and device |
| US10904724B2 (en) | 2017-08-04 | 2021-01-26 | Beijing Didi Infinity Technology And Development Co., Ltd. | Methods and systems for naming a pick up location |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103530540A (en) * | 2013-09-27 | 2014-01-22 | 西安交通大学 | User identity attribute detection method based on man-machine interaction behavior characteristics |
| CN104462320A (en) * | 2014-12-01 | 2015-03-25 | 中国联合网络通信集团有限公司 | Method and device for realizing classification of network users |
| CN104778283A (en) * | 2015-05-11 | 2015-07-15 | 苏州大学 | User occupation classification method and system based on microblog |
| CN105095324A (en) * | 2014-05-23 | 2015-11-25 | 富士通株式会社 | User classification apparatus, user classification method and electronic device |
| CN105224681A (en) * | 2015-10-23 | 2016-01-06 | 武汉大学 | Based on the customer requirement retrieval method and system of family's place of working context environmental |
| CN105335390A (en) * | 2014-07-09 | 2016-02-17 | 阿里巴巴集团控股有限公司 | Object classification method, business pushing method and server |
| CN105378717A (en) * | 2013-03-12 | 2016-03-02 | 国际商业机器公司 | Method for user categorization in social media, computer program, and computer |
| CN105512938A (en) * | 2016-02-03 | 2016-04-20 | 宜人恒业科技发展（北京）有限公司 | Online credit risk assessment method based on long-term using behavior of user |
-
2016
- 2016-06-08 CN CN201610405268.2A patent/CN106095915A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105378717A (en) * | 2013-03-12 | 2016-03-02 | 国际商业机器公司 | Method for user categorization in social media, computer program, and computer |
| CN103530540A (en) * | 2013-09-27 | 2014-01-22 | 西安交通大学 | User identity attribute detection method based on man-machine interaction behavior characteristics |
| CN105095324A (en) * | 2014-05-23 | 2015-11-25 | 富士通株式会社 | User classification apparatus, user classification method and electronic device |
| CN105335390A (en) * | 2014-07-09 | 2016-02-17 | 阿里巴巴集团控股有限公司 | Object classification method, business pushing method and server |
| CN104462320A (en) * | 2014-12-01 | 2015-03-25 | 中国联合网络通信集团有限公司 | Method and device for realizing classification of network users |
| CN104778283A (en) * | 2015-05-11 | 2015-07-15 | 苏州大学 | User occupation classification method and system based on microblog |
| CN105224681A (en) * | 2015-10-23 | 2016-01-06 | 武汉大学 | Based on the customer requirement retrieval method and system of family's place of working context environmental |
| CN105512938A (en) * | 2016-02-03 | 2016-04-20 | 宜人恒业科技发展（北京）有限公司 | Online credit risk assessment method based on long-term using behavior of user |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107330445A (en) * | 2017-05-31 | 2017-11-07 | 北京京东尚科信息技术有限公司 | The Forecasting Methodology and device of user property |
| CN107330445B (en) * | 2017-05-31 | 2020-06-05 | 北京京东尚科信息技术有限公司 | User attribute prediction method and device |
| CN107688865A (en) * | 2017-07-31 | 2018-02-13 | 上海恺英网络科技有限公司 | Identify the method and apparatus of potential high consumption user in online game |
| US10904724B2 (en) | 2017-08-04 | 2021-01-26 | Beijing Didi Infinity Technology And Development Co., Ltd. | Methods and systems for naming a pick up location |
| CN111753266A (en) * | 2019-03-29 | 2020-10-09 | 阿里巴巴集团控股有限公司 | User authentication method, multimedia content pushing method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6568240B2 (en) | Method and system for determining transportation service charges | |
| CN107038478B (en) | Road condition prediction method and device, computer equipment and readable medium | |
| CN106095915A (en) | The processing method and processing device of user identity | |
| Wang et al. | Detecting transportation modes based on LightGBM classifier from GPS trajectory data | |
| CN108537326A (en) | For the method for automatic driving vehicle, medium and system | |
| US20190179312A1 (en) | Method and Apparatus for Generating Information | |
| CN107316048A (en) | Point cloud classifications method and device | |
| CN111442778A (en) | Travel scheme recommendation method, device and equipment and computer readable storage medium | |
| CN110443288B (en) | Trajectory similarity calculation method based on sequencing learning | |
| CN114450557B (en) | Route deviation quantification and vehicle route learning based thereon | |
| CN104599002B (en) | Method and equipment for predicting order value | |
| WO2019047645A1 (en) | Information acquisition method and device | |
| CN107341166A (en) | A kind of vehicle-mounted APP recommendation method and system | |
| CN106767835A (en) | Localization method and device | |
| CN111104585B (en) | Question recommending method and device | |
| CN110879960A (en) | Method and computing device for generating image data set for convolutional neural network learning | |
| CN111415024A (en) | Arrival time estimation method and estimation device | |
| CN115018039A (en) | Neural network distillation method, target detection method and device | |
| Yang et al. | Feature selection in conditional random fields for map matching of GPS trajectories | |
| Dalumpines et al. | Making mode detection transferable: extracting activity and travel episodes from GPS data using the multinomial logit model and Python | |
| CN112380918A (en) | Road vehicle state identification method and device, electronic equipment and storage medium | |
| CN112101156A (en) | Target identification method and device and electronic equipment | |
| CN113888867B (en) | Parking space recommendation method and system based on LSTM (least squares) position prediction | |
| WO2019049141A1 (en) | A system and method for using knowledge gathered by a vehicle | |
| CN106204404A (en) | The method of adjustment of a kind of public transport transport power and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20161109 |