CN109714636A - A kind of user identification method, device, equipment and medium - Google Patents
A kind of user identification method, device, equipment and medium Download PDFInfo
- Publication number
- CN109714636A CN109714636A CN201811573563.4A CN201811573563A CN109714636A CN 109714636 A CN109714636 A CN 109714636A CN 201811573563 A CN201811573563 A CN 201811573563A CN 109714636 A CN109714636 A CN 109714636A
- Authority
- CN
- China
- Prior art keywords
- user
- internet behavior
- behavior data
- probability
- standard value
- 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.)
- Granted
Links
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention discloses a kind of user identification method, device, equipment and media, which comprises determines the internet behavior data of user；Determine that the user is the probability of target user based on the internet behavior data and preset identification model；Wherein, the target user includes the user for carrying out specific internet behavior, the internet behavior data of the determining user, comprising: counts the user and logs in the used IP address number of live streaming platform when institute in the set time period；Alternatively, count the user logs in the used number of devices of live streaming platform when institute in the set time period.By using above-mentioned technical proposal, the recognition accuracy of target user can be improved.
Description
Technical field
The present embodiments relate to computer field more particularly to a kind of user identification method, device, equipment and media.
Background technique
On live streaming platform, in order to acquire an advantage, in the prevalence of the work of the brushes popularities such as some false brush barrages, brush concern
Disadvantage behavior.
The problems such as above-mentioned cheating will cause network blockage, live streaming Platform Server pressure is excessive, the live streaming to platform
Ecological environment causes strong influence.Therefore in order to reduce above-mentioned cheating bring negative effect, using reasonable method
Finding has the user of cheating suspicion significant.The existing recognition methods to cheating user generallys use strong rule, and described
Strong rule is set generally according to the experience of business personnel, and there is no reasonable principles for the threshold value setting of some indexs, is existed
Biggish randomness；For example, if experience thinks that the IP address number that user uses is more than or equal to 10, it is determined that the user is cheating
Suspicion user, if the IP address number that some user uses is 9, it is determined that some user is not cheating suspicion user.Undoubtedly
The rule of above-mentioned identification cheating user is unreasonable.
Summary of the invention
The present invention provides a kind of user identification method, device, equipment and medium, can be improved by the method to target
The recognition accuracy of user.
To achieve the above object, the embodiment of the present invention adopts the following technical scheme that
In a first aspect, the embodiment of the invention provides a kind of user identification methods, which comprises
Determine the internet behavior data of user；
Determine that the user is the probability of target user based on the internet behavior data and preset identification model；
Wherein, the target user includes the user for carrying out specific internet behavior；
The internet behavior data of the determining user, comprising:
It counts the user and logs in the used IP address number of live streaming platform when institute in the set time period；Alternatively,
It counts the user and logs in the used number of devices of live streaming platform when institute in the set time period.
Second aspect, the embodiment of the invention provides a kind of customer identification device, described device includes:
Determining module, for determining the internet behavior data of user；
Identification module, for determining that the user is target based on the internet behavior data and preset identification model
The probability of user；
Wherein, the target user includes the user for carrying out specific internet behavior；
The determining module is specifically used for: counting when the user logs in live streaming platform in the set time period and used
IP address number；Alternatively,
It counts the user and logs in the used number of devices of live streaming platform when institute in the set time period.
The third aspect, the embodiment of the invention provides a kind of electronic equipment, which includes:
One or more processors；
Storage device, for storing multiple programs；
When at least one of the multiple program by one or more of processors execute when so that it is one or
Multiple processors realize user identification method described in above-mentioned first aspect.
Fourth aspect, the embodiment of the invention also provides a kind of computer readable storage mediums, are stored thereon with computer
Program, the program realize user identification method described in above-mentioned first aspect when being executed by processor.
A kind of user identification method provided in an embodiment of the present invention, by determining the internet behavior data of user, specifically:
It counts the user and logs in the used IP address number of live streaming platform when institute in the set time period；Alternatively, counting the user
The used number of devices of institute and based on internet behavior data and preset when logging in live streaming platform in the set time period
Identification model determines that the user is the probability of target user；Wherein, the target user includes carrying out specific internet behavior
The technological means of user improves the recognition accuracy to target user.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, institute in being described below to the embodiment of the present invention
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without creative efforts, can also implement according to the present invention
The content of example and these attached drawings obtain other attached drawings.
Fig. 1 is a kind of user identification method flow diagram that the embodiment of the present invention one provides；
Fig. 2 is the method flow schematic diagram of a kind of determining constant a and b value that the embodiment of the present invention one provides；
Fig. 3 is a kind of linear partition schematic diagram of the log probability that provides of the embodiment of the present invention one than curve；
Fig. 4 is linear partition schematic diagram of another log probability that provides of the embodiment of the present invention one than curve；
Fig. 5 is a kind of customer identification device structural schematic diagram provided by Embodiment 2 of the present invention；
Fig. 6 is the structural schematic diagram for a kind of electronic equipment that the embodiment of the present invention three provides.
Specific embodiment
To keep the technical problems solved, the adopted technical scheme and the technical effect achieved by the invention clearer, below
It will the technical scheme of the embodiment of the invention will be described in further detail in conjunction with attached drawing, it is clear that described embodiment is only
It is a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those skilled in the art exist
Every other embodiment obtained under the premise of creative work is not made, shall fall within the protection scope of the present invention.
Embodiment one
Fig. 1 is a kind of user identification method flow diagram that the embodiment of the present invention one provides.The present embodiment is disclosed to be used
Family recognition methods is applicable to identify the user for being engaged in certain internet behavior, for example, to direct broadcasting room carry out brush barrage,
The user of brush concern behavior identifies, can be executed by customer identification device, wherein the device can be by software and/or hardware
It realizes, and is typically integrated in terminal, such as smart phone or computer etc..Referring specifically to shown in Fig. 1, this method may include
Following steps:
Step 110, counting user log in the used IP address number of live streaming platform when institute in the set time period；Alternatively,
Counting user logs in the used number of devices of live streaming platform when institute in the set time period.
The i.e. described internet behavior data include the IP address number or the number of devices.Wherein, the set period of time
It can be specific some day, a certain week or some moon.Determine that the internet behavior data of user can specifically pass through behavior
Get acquisition User action log ready, the behavior get ready be the place that is needed in engineering for counting user behavior to bury a little (such as
Click event, page jump) it is inserted into and buries point code, the internet behavior of user will be recorded in User action log later, be led to
The user for carrying out specific internet behavior can be determined by crossing acquisition User action log and inquiring user behavior, described specific to surf the net
For for example specially which user has sent barrage information for main broadcaster A.User is also recorded in User action log simultaneously
Carrying out network environment information (such as IP address) and used terminal device information used in internet behavior, (such as terminal is set
Standby ID).The User action log can directly can be obtained by a data acquisition interface at mobile terminal (such as smart phone)
It takes.
Step 120 determines that the user is target user based on the internet behavior data and preset identification model
Probability.
Wherein, the target user includes the user for carrying out specific internet behavior.
Wherein, the specific internet behavior can be the behavior of positive worth promotion, such as online contribution, can also be
The behavior that passive needs resist, such as the behavior of brush barrage is carried out for identical main broadcaster by live streaming platform or passes through live streaming
Platform carries out brush concern behavior for identical main broadcaster.The behavior that usually passive needs resist often brings some negative shadows
It rings, it is as escribed above that the behavior of brush barrage is carried out or by live streaming platform for identical for identical main broadcaster by live streaming platform
Main broadcaster carry out brush concern behavior, it will usually cause network blockage, live streaming Platform Server pressure it is excessive the problems such as.Therefore in order to
It reduces negative effect brought by the behavior of brush barrage or brush concern behavior or is engaged in certain beneficial to behavior, originally to actively advocate
A kind of user identification method disclosed in embodiment, the target for going out to be engaged in the behavior of brush barrage or brush concern behavior for identification are used
Family, with alerted perhaps take other measures rectified or identified be engaged in contribution etc. public goods behavior group, to give
To praise, healthy tendency in society etc. is built, the present embodiment is engaged in the net of the behavior of brush barrage or brush concern behavior etc. to identify
It is illustrated for upper cheating user.
Further, determine that the user is target user based on the internet behavior data and preset identification model
Probability, comprising:
Determine that the user is the probability of target user according to following preset recognition function:
Wherein, A (x) indicates that the user is the probability of target user, and x indicates the internet behavior data of the user, a with
B is setting constant.
Identify that the accuracy of target user depends on the setting value of constant a and b by the recognition function, in view of this, this
Embodiment provides the algorithm of a kind of determining constant a and b value, and shown in Figure 2, the algorithm specifically comprises the following steps:
Step 210 determines cheating suspicion user set and non-cheating suspicion user set.
Specifically, can determine that cheating suspicion user gathers, i.e., be by the user that spectators complain by way of receiving and complaining
Cheating suspicion user, the user not complained by spectators are non-cheating suspicion user.It can also be stepped on by auditor to each
The behavior of record direct broadcasting room user carries out auditing determining cheating suspicion user and non-cheating suspicion user, if specifically, for example examining
Core finds that logging in direct broadcasting room by 10 different equipment in certain user one day pays close attention to different main broadcasters, can recognize at this time
It is cheating suspicion user for the user.Cheating suspicion user and non-cheating suspicion user can be also obtained by expertise.
Each user is in setting in step 220, statistics cheating suspicion user set and non-cheating suspicion user set
Between internet behavior data in section.
Wherein, the internet behavior data can include: log in the live streaming platform used IP of when institute in the set time period
Number of addresses；Alternatively, logging in the used number of devices of live streaming platform when institute in the set time period.
Step 230, for each standard value in each standard value, calculate separately each user in cheating suspicion user set
Internet behavior data be greater than present standard values the first combined chance and non-cheating suspicion user set in each user it is online
Behavioral data is greater than the second combined chance of present standard values.
Wherein, each standard value includes the possibility value of the internet behavior data of user, such as surfing the net as user
It is used IP address number when logging in live streaming platform in the set time period for data, then possible value is 1,2,3,
4 ... ... n, i.e., each standard value are as follows: the value of 1,2,3,4 ... ... n, n can be set according to business experience, be typically set at
30.When the internet behavior data of user is log in live streaming platform in the set time period, used IP address number, then right
The possibility value of used IP address number when each standard value answered is logs in live streaming platform in the set time period；Work as user
Internet behavior data to log in live streaming platform in the set time period when used number of devices, then corresponding each standard value
The possibility value of used number of devices when to log in live streaming platform in the set time period.
Specifically, being greater than according to the internet behavior data that following formula (1) calculates each user in cheating suspicion user set
First combined chance of present standard values:
Wherein, p (S, vi) indicate that the internet behavior data of each user in cheating suspicion user's set S are greater than present standard values
viThe first combined chance, N (S, v > vi) indicate that internet behavior data v is greater than present standard values in cheating suspicion user's set S
viNumber of users, N (S) indicate cheating suspicion user's set S in total number of users.
The internet behavior data of each user are greater than the second combined chance of present standard values in non-cheating suspicion user set
Calculation, be greater than the first comprehensive of present standard values with the internet behavior data of each user in above-mentioned cheating suspicion user set
The calculation for closing probability is identical.
Step 240, the log probability ratio for calculating first combined chance and second combined chance obtain corresponding every
The log probability ratio of a standard value.
Specifically, calculating the log probability ratio of corresponding each standard value according to following formula (2):
Wherein, r (vi) indicate corresponding standard value viLog probability ratio, p (S, vi) indicate in cheating suspicion user's set S
The internet behavior data of each user are greater than present standard values viThe first combined chance, p (N, vi) indicate non-cheating suspicion user collection
The internet behavior data for closing each user in N are greater than present standard values viThe second combined chance.
Step 250, the log probability based on each standard value of correspondence compare curve than construction log probability, wherein horizontal axis table
Show each standard value, the longitudinal axis indicates the log probability ratio of corresponding each standard value.
Each standard value viCorrespond to a log probability ratio r (vi), therefore available vi~r (vi) log probability
Than curve, horizontal axis indicates each standard value vi, the log probability ratio r (v of the corresponding each standard value of longitudinal axis expressioni)。
Step 260 carries out linear fit than curve to the log probability, is fitted corresponding linear partition when loss reduction
The endpoint value in section respectively corresponds the constant a and b.
Specifically, determining that the log probability compares point of inflexion on a curve using polygometry, it is contemplated that the problem of computation complexity,
It can set and the log probability is divided into three sections according to inflection point than curve, every section uses linear fit, and digital simulation loses；
By different division modes, and fitting under every kind of division mode loss is solved, finally solves a kind of fitting loss reduction
Optimal dividing mode, the endpoint value in corresponding linear partition section respectively corresponds the constant a and b under optimal dividing mode.Into
Linear partition schematic diagram of one step referring to Fig. 3 and log probability shown in Fig. 4 than curve, corresponding two inflection points difference in Fig. 3
For inflection point g1 and inflection point g2, log probability has been partitioned into three sections, respectively curved section 31, curved section 32 and curved section than curve
33；Corresponding two inflection points are respectively inflection point g3 and inflection point g4 in Fig. 4, and log probability has been partitioned into three sections than curve, respectively
For curved section 34, curved section 35 and curved section 36；If fitting loss reduction under division mode shown in Fig. 4, then constant a
Value is the corresponding horizontal axis data of inflection point g3, and the value of constant b is the corresponding horizontal axis data of inflection point g4, reference can be made to shown in Fig. 4.
Further, the loss of the fitting under certain division mode is calculated according to following formula (3):
Wherein, S represents a kind of division mode of the log probability than curve, and Q (S) indicates the fitting damage at division mode S
It loses, rmThe corresponding horizontal axis region of m sections of curves divided is indicated, for example, it is assumed that the first segment curve 31 divided shown in Fig. 3 is corresponding
Horizontal axis region r1=[0,1], the corresponding horizontal axis region r2=[1,3] of second segment curve 32, the corresponding cross of third section curve 33
Axis region r3=[3,4]；xiIndicate region rmOn value, yiIndicate xiThe corresponding ordinate of orthogonal axes on curve；M indicates to divide
Curved section sum, the present embodiment for log probability is divided into 3 sections than curve, l (x) indicate fitting loss function,
Chi square function, i.e. l (x)=x are selected herein2, αmAnd βmIt indicates the parameter that m sections of curve negotiating least square methods are estimated, has
Body calculation method are as follows:
All possible division mode is traversed, the minimum value of Q (S), the corresponding linear partition section of Q (S) minimum value are acquired
Endpoint value respectively correspond the numerical value of the constant a and b.
A kind of user identification method provided in this embodiment, by combining cheating suspicion user set and non-cheating suspicion
The internet behavior data of each user in the set time period in user's set, determine preset identification letter using specific algorithm
The setting value of constant a and b in number improve recognition accuracy of the recognition function to cheating user.
Embodiment two
Fig. 5 is a kind of customer identification device structural schematic diagram provided by Embodiment 2 of the present invention.It is shown in Figure 5, it is described
Device comprises determining that module 510 and identification module 520；
Wherein it is determined that module 510, for determining the internet behavior data of user；Identification module 520, for based on described
Internet behavior data and preset identification model determine that the user is the probability of target user；Wherein, the target user
User including carrying out specific internet behavior.
Further, determining module 510 is specifically used for: count the user log in the set time period live streaming platform when
The used IP address number of institute；It used is set alternatively, counting institute when the user logs in live streaming platform in the set time period
Standby number.
Further, identification module 520 is specifically used for: determining that the user is target according to following preset recognition function
The probability of user:
Wherein, A (x) indicates that the user is the probability of target user, and x indicates the internet behavior data of the user, a with
B is setting constant.
Further, described device further includes computing module, for determine the constant a in the preset recognition function with
The numerical value of b.
Further, the computing module includes:
Determination unit, for determining cheating suspicion user set and non-cheating suspicion user set；
Statistic unit is being set for counting each user in cheating suspicion user set and non-cheating suspicion user set
The internet behavior data fixed time in section；
First computing unit, for calculating separately cheating suspicion user set for each standard value in each standard value
In each user internet behavior data be greater than it is each in the first combined chance and non-cheating suspicion user set of present standard values
The internet behavior data of user are greater than the second combined chance of present standard values；
Second computing unit, for calculating the log probability ratio of first combined chance Yu second combined chance,
Obtain corresponding to the log probability ratio of each standard value；
Structural unit compares curve than construction log probability for the log probability based on each standard value of correspondence, wherein horizontal
Axis indicates each standard value, and the longitudinal axis indicates the log probability ratio of corresponding each standard value；
Fitting unit is fitted corresponding line when loss reduction for carrying out linear fit than curve to the log probability
The endpoint value of property demarcation interval respectively corresponds the constant a and b；
Wherein, each standard value includes the possibility value of the internet behavior data of user.
Further, the first computing unit is specifically used for: calculating in cheating suspicion user set according to following formula and respectively uses
The internet behavior data at family are greater than the first combined chance of present standard values:
Wherein, p (S, vi) indicate that the internet behavior data of each user in cheating suspicion user's set S are greater than present standard values
viThe first combined chance, N (S, v > vi) indicate that internet behavior data v is greater than present standard values in cheating suspicion user's set S
viNumber of users, N (S) indicate cheating suspicion user's set S in total number of users.
Further, the second computing unit is specifically used for: the logarithm for calculating corresponding each standard value according to following formula is general
Rate ratio:
Wherein, r (vi) indicate corresponding standard value viLog probability ratio, p (S, vi) indicate in cheating suspicion user's set S
The internet behavior data of each user are greater than present standard values viThe first combined chance, p (N, vi) indicate non-cheating suspicion user collection
The internet behavior data for closing each user in N are greater than present standard values viThe second combined chance.
Customer identification device provided in this embodiment, by combining cheating suspicion user set and non-cheating suspicion user
The internet behavior data of each user in the set time period, are determined in preset recognition function using specific algorithm in set
Constant a and b setting value, improve the recognition function to cheating user recognition accuracy.
Embodiment three
Fig. 6 is the structural schematic diagram for a kind of electronic equipment that the embodiment of the present invention three provides.Fig. 6, which is shown, to be suitable for being used in fact
The block diagram of the example electronic device 12 of existing embodiment of the present invention.The electronic equipment 12 that Fig. 6 is shown is only an example, no
The function and use scope for coping with the embodiment of the present invention bring any restrictions.
As shown in fig. 6, electronic equipment 12 is showed in the form of universal computing device.The component of electronic equipment 12 may include
But be not limited to: one or more processor or processing unit 16, system storage 28, connect different system components (including
System storage 28 and processing unit 16) bus 18.
Bus 18 indicates one of a few class bus structures or a variety of, including memory bus or Memory Controller,
Peripheral bus, graphics acceleration port, processor or the local bus using any bus structures in a variety of bus structures.It lifts
For example, these architectures include but is not limited to industry standard architecture (ISA) bus, microchannel architecture (MAC)
Bus, enhanced isa bus, Video Electronics Standards Association (VESA) local bus and peripheral component interconnection (PCI) bus.
Electronic equipment 12 typically comprises a variety of computer system readable media.These media can be it is any can be electric
The usable medium that sub- equipment 12 accesses, including volatile and non-volatile media, moveable and immovable medium.
System storage 28 may include the computer system readable media of form of volatile memory, such as arbitrary access
Memory (RAM) 30 and/or cache memory 32.Electronic equipment 12 may further include other removable/not removable
Dynamic, volatile/non-volatile computer system storage medium.Only as an example, storage system 34 can be used for read and write can not
Mobile, non-volatile magnetic media (Fig. 6 do not show, commonly referred to as " hard disk drive ").Although being not shown in Fig. 6, Ke Yiti
For the disc driver for being read and write to removable non-volatile magnetic disk (such as " floppy disk "), and to moving non-volatile light
The CD drive of disk (such as CD-ROM, DVD-ROM or other optical mediums) read-write.In these cases, each driver
It can be connected by one or more data media interfaces with bus 18.Memory 28 may include that at least one program produces
Product, the program product have one group of (such as determining module 510 and identification module 520 of customer identification device) program module, this
A little program modules are configured to perform the function of various embodiments of the present invention.
Program with one group of (such as determining module 510 and identification module 520 of customer identification device) program module 42/
Utility 40 can store in such as memory 28, and such program module 42 includes but is not limited to operating system, one
Or multiple application programs, other program modules and program data, each of these examples or certain combination in may
Realization including network environment.Program module 42 usually executes function and/or method in embodiment described in the invention.
Electronic equipment 12 can also be with one or more external equipments 14 (such as keyboard, sensing equipment, display 24 etc.)
Communication, can also be enabled a user to one or more equipment interact with the electronic equipment 12 communicate, and/or with make the electricity
Any equipment (such as network interface card, modem etc.) that sub- equipment 12 can be communicated with one or more of the other calculating equipment
Communication.This communication can be carried out by input/output (I/O) interface 22.Also, electronic equipment 12 can also be suitable by network
Orchestration 20 and one or more network (such as local area network (LAN), wide area network (WAN) and/or public network, such as internet)
Communication.As shown, network adapter 20 is communicated by bus 18 with other modules of electronic equipment 12.Although should be understood that
It is not shown in the figure, other hardware and/or software module can be used in conjunction with electronic equipment 12, including but not limited to: microcode is set
Standby driver, redundant processing unit, external disk drive array, RAID system, tape drive and data backup storage system
System etc..
Processing unit 16 by the program that is stored in system storage 28 of operation, thereby executing various function application and
Data processing, such as realize user identification method provided by the embodiment of the present invention, this method comprises:
Determine the internet behavior data of user；
Determine that the user is the probability of target user based on the internet behavior data and preset identification model；
Wherein, the target user includes the user for carrying out specific internet behavior；
The internet behavior data of the determining user, comprising:
It counts the user and logs in the used IP address number of live streaming platform when institute in the set time period；Alternatively,
It counts the user and logs in the used number of devices of live streaming platform when institute in the set time period.
Processing unit 16 by the program that is stored in system storage 28 of operation, thereby executing various function application and
Data processing, such as realize user identification method provided by the embodiment of the present invention.
Certainly, it will be understood by those skilled in the art that processor can also realize it is provided by any embodiment of the invention
The technical solution of user identification method.
Example IV
The embodiment of the present invention four additionally provides a kind of computer readable storage medium, is stored thereon with computer program, should
The user identification method as provided by the embodiment of the present invention is realized when program is executed by processor, this method comprises:
Determine the internet behavior data of user；
Determine that the user is the probability of target user based on the internet behavior data and preset identification model；
Wherein, the target user includes the user for carrying out specific internet behavior；
The internet behavior data of the determining user, comprising:
It counts the user and logs in the used IP address number of live streaming platform when institute in the set time period；Alternatively,
It counts the user and logs in the used number of devices of live streaming platform when institute in the set time period.
Certainly, a kind of computer readable storage medium provided by the embodiment of the present invention, the computer program stored thereon
The method operation being not limited to the described above, can also be performed the phase in user identification method provided by any embodiment of the invention
Close operation.
The computer storage medium of the embodiment of the present invention, can be using any of one or more computer-readable media
Combination.Computer-readable medium can be computer-readable signal media or computer readable storage medium.It is computer-readable
Storage medium for example may be-but not limited to-the system of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, device or
Device, or any above combination.The more specific example (non exhaustive list) of computer readable storage medium includes: tool
There are electrical connection, the portable computer diskette, hard disk, random access memory (RAM), read-only memory of one or more conducting wires
(ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-
ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer-readable storage
Medium can be any tangible medium for including or store program, which can be commanded execution system, device or device
Using or it is in connection.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium other than storage medium is read, which can send, propagates or transmit and be used for
By the use of instruction execution system, device or device or program in connection.
The program code for including on computer-readable medium can transmit with any suitable medium, including --- but it is unlimited
In wireless, electric wire, optical cable, RF etc. or above-mentioned any appropriate combination.
The computer for executing operation of the present invention can be write with one or more programming languages or combinations thereof
Program code, described program design language include object oriented program language-such as Java, Smalltalk, C++,
Further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with
It fully executes, partly execute on the user computer on the user computer, being executed as an independent software package, portion
Divide and partially executes or executed on a remote computer or server completely on the remote computer on the user computer.?
Be related in the situation of remote computer, remote computer can pass through the network of any kind --- including local area network (LAN) or
Wide area network (WAN)-be connected to subscriber computer, or, it may be connected to outer computer (such as mentioned using Internet service
It is connected for quotient by internet).
Note that the above is only a better embodiment of the present invention and the applied technical principle.It will be appreciated by those skilled in the art that
The invention is not limited to the specific embodiments described herein, be able to carry out for a person skilled in the art it is various it is apparent variation,
It readjusts and substitutes without departing from protection scope of the present invention.Therefore, although being carried out by above embodiments to the present invention
It is described in further detail, but the present invention is not limited to the above embodiments only, without departing from the inventive concept, also
It may include more other equivalent embodiments, and the scope of the invention is determined by the scope of the appended claims.
Claims (10)
1. a kind of user identification method characterized by comprising
Determine the internet behavior data of user；
Determine that the user is the probability of target user based on the internet behavior data and preset identification model；
Wherein, the target user includes the user for carrying out specific internet behavior；The internet behavior data of the determining user, packet
It includes:
It counts the user and logs in the used IP address number of live streaming platform when institute in the set time period；Alternatively,
It counts the user and logs in the used number of devices of live streaming platform when institute in the set time period.
2. the method according to claim 1, wherein being based on the internet behavior data and preset identification mould
Type determines that the user is the probability of target user, comprising:
Determine that the user is the probability of target user according to following preset recognition function:
Wherein, A (x) indicates that the user is the probability of target user, and x indicates the internet behavior data of the user, and a is with b
Set constant.
3. according to the method described in claim 2, it is characterized in that, the method also includes: determine the preset identification letter
The numerical value of constant a and b in number.
4. according to the method described in claim 3, it is characterized in that, determining the constant a's and b in the preset recognition function
Numerical value, comprising:
Determine cheating suspicion user set and non-cheating suspicion user set；
Count each user in the set time period online in cheating suspicion user set and non-cheating suspicion user set
Behavioral data；
For each standard value in each standard value, the internet behavior data of each user in cheating suspicion user set are calculated separately
Internet behavior data greater than each user in the first combined chance of present standard values and non-cheating suspicion user set are greater than
Second combined chance of present standard values；
The log probability ratio for calculating first combined chance Yu second combined chance, obtains pair for corresponding to each standard value
Number likelihood ratio；
Log probability based on each standard value of correspondence compares curve than construction log probability, wherein horizontal axis indicates each standard value, indulges
Axis indicates the log probability ratio of corresponding each standard value；
Linear fit is carried out than curve to the log probability, is fitted the endpoint value in corresponding linear partition section when loss reduction
Respectively correspond the constant a and b；
Wherein, each standard value includes the possibility value of the internet behavior data of user.
5. according to the method described in claim 4, it is characterized in that, being calculated separately for each standard value in each standard value
The internet behavior data of each user are greater than the first combined chance of present standard values in cheating suspicion user set, comprising:
It is greater than the of present standard values according to the internet behavior data that following formula calculates each user in cheating suspicion user set
One combined chance:
Wherein, p (S, vi) indicate that the internet behavior data of each user in cheating suspicion user's set S are greater than present standard values vi's
First combined chance, N (S, v > vi) indicate that internet behavior data v is greater than present standard values v in cheating suspicion user's set Si's
Number of users, N (S) indicate the total number of users in cheating suspicion user's set S.
6. according to the method described in claim 5, it is characterized in that, calculating first combined chance and second synthesis generally
The log probability ratio of rate obtains the log probability ratio for corresponding to each standard value, comprising:
The log probability ratio of corresponding each standard value is calculated according to following formula:
Wherein, r (vi) indicate corresponding standard value viLog probability ratio, p (S, vi) indicate respectively to use in cheating suspicion user's set S
The internet behavior data at family are greater than present standard values viThe first combined chance, p (N, vi) indicate non-cheating suspicion user's set N
In each user internet behavior data be greater than present standard values viThe second combined chance.
7. a kind of customer identification device, which is characterized in that described device includes:
Determining module, for determining the internet behavior data of user；
Identification module, for determining that the user is target user based on the internet behavior data and preset identification model
Probability；
Wherein, the target user includes the user for carrying out specific internet behavior；
The determining module is specifically used for: counting the user and logs in the live streaming platform used IP of when institute in the set time period
Number of addresses；Alternatively,
It counts the user and logs in the used number of devices of live streaming platform when institute in the set time period.
8. device according to claim 7, which is characterized in that the identification module is specifically used for:
Determine that the user is the probability of target user according to following preset recognition function:
Wherein, A (x) indicates that the user is the probability of target user, and x indicates the internet behavior data of the user, and a is with b
Set constant.
9. a kind of electronic equipment, which is characterized in that the electronic equipment further include:
One or more processors；
Storage device, for storing one or more programs；
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
Now such as user identification method of any of claims 1-6.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
Such as user identification method of any of claims 1-6 is realized when execution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811573563.4A CN109714636B (en) | 2018-12-21 | 2018-12-21 | User identification method, device, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811573563.4A CN109714636B (en) | 2018-12-21 | 2018-12-21 | User identification method, device, equipment and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109714636A true CN109714636A (en) | 2019-05-03 |
| CN109714636B CN109714636B (en) | 2021-04-23 |
Family
ID=66256003
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811573563.4A Active CN109714636B (en) | 2018-12-21 | 2018-12-21 | User identification method, device, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109714636B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110990729A (en) * | 2019-12-05 | 2020-04-10 | 秒针信息技术有限公司 | Job and live analysis method and device, electronic equipment and readable storage medium |
| CN112788351A (en) * | 2019-11-01 | 2021-05-11 | 武汉斗鱼鱼乐网络科技有限公司 | Target live broadcast room identification method, device, equipment and storage medium |
| CN112995686A (en) * | 2021-02-03 | 2021-06-18 | 上海哔哩哔哩科技有限公司 | Data processing method, live broadcast method, authentication server and live broadcast data server |
| CN113347497A (en) * | 2021-08-02 | 2021-09-03 | 武汉斗鱼鱼乐网络科技有限公司 | Target user identification method and device, electronic equipment and storage medium |
| CN114071196A (en) * | 2020-08-03 | 2022-02-18 | 武汉斗鱼鱼乐网络科技有限公司 | Method, system, medium and equipment for identifying target live broadcast room |
| WO2023179162A1 (en) * | 2022-03-24 | 2023-09-28 | 上海哔哩哔哩科技有限公司 | Data processing method and apparatus |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102523126A (en) * | 2011-12-29 | 2012-06-27 | 深圳市同洲视讯传媒有限公司 | Method and device for sending alarm event |
| CN103793484A (en) * | 2014-01-17 | 2014-05-14 | 五八同城信息技术有限公司 | Fraudulent conduct identification system based on machine learning in classified information website |
| CN104424433A (en) * | 2013-08-22 | 2015-03-18 | 腾讯科技（深圳）有限公司 | Anti-cheating method and anti-cheating system of application program |
| CN104852886A (en) * | 2014-02-14 | 2015-08-19 | 腾讯科技（深圳）有限公司 | Protection method and device for user account |
| CN105100032A (en) * | 2014-05-23 | 2015-11-25 | 腾讯科技（北京）有限公司 | Method and apparatus for preventing resource steal |
| CN105808639A (en) * | 2016-02-24 | 2016-07-27 | 平安科技（深圳）有限公司 | Network access behavior recognizing method and device |
| CN106209862A (en) * | 2016-07-14 | 2016-12-07 | 微梦创科网络科技（中国）有限公司 | A kind of steal-number defence implementation method and device |
| WO2017027003A1 (en) * | 2015-08-10 | 2017-02-16 | Hewlett Packard Enterprise Development Lp | Evaluating system behaviour |
| CN108055281A (en) * | 2017-12-27 | 2018-05-18 | 百度在线网络技术（北京）有限公司 | Account method for detecting abnormality, device, server and storage medium |
| CN108763359A (en) * | 2018-05-16 | 2018-11-06 | 武汉斗鱼网络科技有限公司 | A kind of usage mining method, apparatus and electronic equipment with incidence relation |
| US20180337937A1 (en) * | 2017-05-19 | 2018-11-22 | Salesforce.Com, Inc. | Feature-Agnostic Behavior Profile Based Anomaly Detection |
-
2018
- 2018-12-21 CN CN201811573563.4A patent/CN109714636B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102523126A (en) * | 2011-12-29 | 2012-06-27 | 深圳市同洲视讯传媒有限公司 | Method and device for sending alarm event |
| CN104424433A (en) * | 2013-08-22 | 2015-03-18 | 腾讯科技（深圳）有限公司 | Anti-cheating method and anti-cheating system of application program |
| CN103793484A (en) * | 2014-01-17 | 2014-05-14 | 五八同城信息技术有限公司 | Fraudulent conduct identification system based on machine learning in classified information website |
| CN104852886A (en) * | 2014-02-14 | 2015-08-19 | 腾讯科技（深圳）有限公司 | Protection method and device for user account |
| CN105100032A (en) * | 2014-05-23 | 2015-11-25 | 腾讯科技（北京）有限公司 | Method and apparatus for preventing resource steal |
| WO2017027003A1 (en) * | 2015-08-10 | 2017-02-16 | Hewlett Packard Enterprise Development Lp | Evaluating system behaviour |
| CN105808639A (en) * | 2016-02-24 | 2016-07-27 | 平安科技（深圳）有限公司 | Network access behavior recognizing method and device |
| CN106209862A (en) * | 2016-07-14 | 2016-12-07 | 微梦创科网络科技（中国）有限公司 | A kind of steal-number defence implementation method and device |
| US20180337937A1 (en) * | 2017-05-19 | 2018-11-22 | Salesforce.Com, Inc. | Feature-Agnostic Behavior Profile Based Anomaly Detection |
| CN108055281A (en) * | 2017-12-27 | 2018-05-18 | 百度在线网络技术（北京）有限公司 | Account method for detecting abnormality, device, server and storage medium |
| CN108763359A (en) * | 2018-05-16 | 2018-11-06 | 武汉斗鱼网络科技有限公司 | A kind of usage mining method, apparatus and electronic equipment with incidence relation |
Non-Patent Citations (1)
| Title |
|---|
| 姚伟: "业务系统异常行为检测", 《邮电设计技术》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112788351A (en) * | 2019-11-01 | 2021-05-11 | 武汉斗鱼鱼乐网络科技有限公司 | Target live broadcast room identification method, device, equipment and storage medium |
| CN112788351B (en) * | 2019-11-01 | 2022-08-05 | 武汉斗鱼鱼乐网络科技有限公司 | Target live broadcast room identification method, device, equipment and storage medium |
| CN110990729A (en) * | 2019-12-05 | 2020-04-10 | 秒针信息技术有限公司 | Job and live analysis method and device, electronic equipment and readable storage medium |
| CN110990729B (en) * | 2019-12-05 | 2023-11-03 | 秒针信息技术有限公司 | Job analysis method, device, electronic equipment and readable storage medium |
| CN114071196A (en) * | 2020-08-03 | 2022-02-18 | 武汉斗鱼鱼乐网络科技有限公司 | Method, system, medium and equipment for identifying target live broadcast room |
| CN112995686A (en) * | 2021-02-03 | 2021-06-18 | 上海哔哩哔哩科技有限公司 | Data processing method, live broadcast method, authentication server and live broadcast data server |
| CN112995686B (en) * | 2021-02-03 | 2022-04-19 | 上海哔哩哔哩科技有限公司 | Data processing method, live broadcast method, authentication server and live broadcast data server |
| CN113347497A (en) * | 2021-08-02 | 2021-09-03 | 武汉斗鱼鱼乐网络科技有限公司 | Target user identification method and device, electronic equipment and storage medium |
| WO2023179162A1 (en) * | 2022-03-24 | 2023-09-28 | 上海哔哩哔哩科技有限公司 | Data processing method and apparatus |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109714636B (en) | 2021-04-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109714636A (en) | A kind of user identification method, device, equipment and medium | |
| CN110188262B (en) | A kind of abnormal object determines method, apparatus, equipment and medium | |
| CN108197570A (en) | A kind of demographic method, device, electronic equipment and storage medium | |
| CN109246483A (en) | A kind of direct broadcasting room recommended method, device, equipment and storage medium | |
| CN110111811A (en) | Audio signal detection method, device and storage medium | |
| CN111090729A (en) | Method, device, server and storage medium for identifying fraudulent group | |
| CN112566228A (en) | Method and system for reducing equipment power consumption in network video conference | |
| CN109670078A (en) | A kind of method, apparatus, equipment and the storage medium of determining video display sequence | |
| CN110889010A (en) | Audio matching method, device, medium and electronic equipment | |
| CN112788351B (en) | Target live broadcast room identification method, device, equipment and storage medium | |
| CN108765503B (en) | Skin color detection method, device and terminal | |
| CN109637540B (en) | Bluetooth evaluation method, device, equipment and medium for intelligent voice equipment | |
| CN116805012A (en) | Quality assessment method and device for multi-mode knowledge graph, storage medium and equipment | |
| CN109376064B (en) | Method and equipment for generating interface test report | |
| CN113347497B (en) | Target user identification method and device, electronic equipment and storage medium | |
| CN109840182A (en) | A kind of resource monitoring method, device and electronic equipment | |
| CN110675009A (en) | Method and device for evaluating convenience of cell | |
| CN115016890A (en) | Virtual machine resource allocation method and device, electronic equipment and storage medium | |
| CN110738509A (en) | Method and device for quantifying association degree between users | |
| CN115333964A (en) | Data acquisition method, device and equipment and readable storage medium | |
| CN106446821A (en) | Method and device for identifying gender of user and electronic equipment | |
| CN103561022A (en) | Voice service access method, related device and voice service system | |
| CN109348236B (en) | Storage capacity evaluation method and device, server and storage medium | |
| CN110189763B (en) | Sound wave configuration method and device and terminal equipment | |
| CN113849471A (en) | Data compression method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |