CN103577543B - The ranking fraud detection method and ranking fraud detection system of application program - Google Patents
The ranking fraud detection method and ranking fraud detection system of application program Download PDFInfo
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Abstract
The invention provides the ranking fraud detection method and ranking fraud detection system of a kind of application program.Methods described includes：Active period detecting step, the active period of the application program is detected based on history ranking information；Ranking fraud detection step, is detected based at least one evidence related to any active ues credit worthiness to the active period, obtains ranking fraud detection result.The method of the present invention and system can automatically identify the ranking fraud relevant with application program, so that application user obtains real application program ranking information.
Description
Technical field
Ranking fraud detection method and ranking fraud the present invention relates to network field, more particularly to a kind of application program is examined
Examining system.
Background technology
User application, the mobile applications for especially installing and running on mobile terminal quickly grow in recent years.
User selects and installs application program for convenience, and many application program websites or application program shop can intensively provide application
Inquiry, download, evaluation of program etc. are serviced, while can also regularly, for example daily, release application program ranking list
(Application Leaderboard) is embodying some current application programs popular with users.In fact, the ranking list is
One of most important means of application program are promoted, under application program ranking very high in the ranking list would generally stimulate user a large amount of
The application program is carried, and for application developer brings huge economic well-being of workers and staff.Therefore, application developer is highly desirable to
Its application program occupies ranking higher in ranking list.
Ranking fraud (Ranking Fraud) of application program refers to that purpose is to improve application program to be arranged in application program
Ranking on row list and the deceptive practices that carry out.In fact, improving application program row different from relying on traditional market means
Name, the behavior that application developer implements ranking fraud by exaggerating its product sales volume or the false product evaluation of issue is
Through more and more universal, for example, employ " waterborne troops (human water armies) " to lift the download of application program in a short time
Amount and evaluation number of times etc..
Industry has appreciated that prevents ranking from cheating so that application user obtains real application program ranking information
Importance.In order to the ranking for preventing application program is cheated, existing method is risen according to application program ranking in a day
Degree infers the presence of ranking fraud, and is judging occur directly locking whole application program when ranking is cheated
Ranking, this mode is excessively simple and crude, it is difficult to accurately judges ranking fraud and has injured the row of normal application
Name rises.It can be seen that, this area is also very limited for the understanding and research of the ranking fraud detection problem of application program, so far also
In the absence of the correlation technique of the ranking fraud of effective detection application program.
The content of the invention
It is an object of the invention to provide the detection technique that a kind of ranking of application program is cheated, so as to automatically effectively know
Do not go out the ranking fraud relevant with application program, so that application user obtains real application program ranking information.
In order to solve the above technical problems, according to an aspect of the present invention, there is provided a kind of ranking fraud inspection of application program
Survey method, methods described includes：
Active period detecting step, the active period of the application program is detected based on history ranking information；
Ranking fraud detection step, is entered based at least one evidence related to any active ues credit worthiness to the active period
Row checking, obtains ranking fraud the result.
According to another aspect of the present invention, a kind of ranking fraud detection system of application program, the system are also provided
Including：
Active period detection unit, the active period for detecting the application program based on history ranking information；
Ranking fraud detection unit, for based at least one evidence related to any active ues credit worthiness to described active
Phase is verified, obtains ranking and cheat the result.
According to another aspect of the present invention, a kind of ranking fraud detection method of application program, methods described are also provided
Including：
The active period of application program is verified based at least one evidence related to any active ues credit worthiness, is obtained
Ranking cheats the result.
According to another aspect of the present invention, a kind of ranking fraud detection system of application program, the system are also provided
Including：
Ranking fraud detection unit, for based at least one evidence related to any active ues credit worthiness to application program
Active period verified, obtain ranking fraud the result.
The method of the present invention and equipment automatically can effectively identify the ranking fraud relevant with application program, from
And application user is obtained real application program ranking information.
Brief description of the drawings
Fig. 1 is the flow chart of the active period detection method of application program in the specific embodiment of the invention；
Fig. 2 a are an examples of the Active event in application program ranking list；
Fig. 2 b are an examples of the active period in application program ranking list；
Fig. 3 is the system construction drawing of the ranking fraud detection system of application program in the specific embodiment of the invention；
Fig. 4 is the structural representation of the ranking fraud detection system of application program in another embodiment of the present invention.
Specific embodiment
With reference to the accompanying drawings and examples, specific embodiment of the invention is described in further detail.Hereinafter implement
Example is not limited to the scope of the present invention for illustrating the present invention.
The present invention is studied for the technical problem related to application program ranking, therefore those skilled in the art are to this
" application program " in invention should be interpreted broadly, and it includes what can be published on internet and be available for user to download, evaluate, performing
Various programs or file, the i.e. Mobile solution including running on the legacy application in PC, running on mobile terminal
Program, also picture, audio, the multimedia file etc. etc. video including that can download and play.
When the ranking for detecting application program is cheated, there are several major issues for needing and solving.First, in application program
Can't always occur ranking fraud in whole life cycle, therefore the time of ranking fraud is likely to occur firstly the need of detection；The
Two, because number of applications is huge, it is difficult to manually for each application program for ranking fraud occur is demarcated, therefore need
A kind of technology of automatic detection ranking fraud is provided；3rd, uncertain can be detected in the prior art and based on which kind of foundation
The presence of ranking fraud.
Ranking fraud of the specific embodiment of the present invention to application program carried out globality analysis and
A kind of research, there is provided the technology of the ranking fraud of detectable application program, it can be believed by the history ranking to application program
The analysis of breath detects " active period " of application program, for user's prestige feature of application program in active period, based on should
The detection of ranking fraud is carried out with the related evidence of any active ues credit worthiness of program.
Analysis according to inventor finds that the application program that there is ranking fraud can't be occupied very in billboard for a long time
Ranking high, ranking situation higher is concentrated in one relatively short period only as some independent events, this
Show that ranking fraud exactly occurred within this period.In the present invention, can by application program continue ranking it is higher when
Phase is referred to as " Active event (the Leading Event) " of application program, can be referred to as into application the period that Active event frequently occurs
" active period (the Leading Session) " of program.Therefore, for ranking cheat detection firstly the need of detect each apply journey
Sequence there may exist Active event and the active period of ranking fraud.
Possess the history ranking information of application program at application program shop operator, from application program shop operator
Direct access, or the application program of lasting issue is ranked within one section of period of history more long by application program shop operator
List information is analyzed and processes, it is also possible to obtain the history ranking information of application program.Because the history of application program is arranged
Name information describes the historical information and the historical information about application user credit worthiness about application program ranking, therefore
In the specific embodiment of the invention, Active event and the work of each application program can be carried out based on the history ranking information
The detection of jump phase, and and then detection of the realization to ranking fraud.Found by the user's credit worthiness for analyzing application program, compared to
For normal application program, the application program that there is ranking fraud can be rendered into different use in Active event and active period
Family prestige feature.Therefore, it is possible to it is related to user's credit worthiness that some are extracted from the history ranking information of application program
For judging the evidence of ranking fraud, and these evidences are obtained, so as to realize the detection to ranking fraud.
Correspondingly in the present invention, will occur in the active period of an application program user behavior (including purchase, download simultaneously
Carry out grade evaluation using the application program or to the application program or carry out the comment of character property) relative users be referred to as this should
With " any active ues " of program, the corresponding creditworthiness information of any active ues in active period is referred to as " any active ues prestige
Degree ".
As shown in figure 1, there is provided a kind of ranking fraud detection of application program in the specific embodiment of the present invention
Method, methods described includes：
Active period detecting step S10, the active period of the application program is detected based on history ranking information；Ranking fraud inspection
Survey step S20, the evidence related to any active ues credit worthiness based at least one is detected to the active period, is arranged
Name fraud detection result.
Below, each step stream of above-mentioned ranking fraud detection method in the specific embodiment of the invention is illustrated with reference to accompanying drawing
Journey and function.
Because history ranking information is the data basis of the ranking fraud of detection application program in the present invention, therefore as this
One preferred embodiment of invention, the ranking fraud detection method may also include a history ranking information obtaining step, obtain
History ranking information of the application program in application program ranking list.
After an application program is published, any user can buy, download and using the application program or to this
Application program carries out grade evaluation or carries out the comment of character property.By the collection to above-mentioned these user behaviors and analysis (example
As by mobile terminal counting user using number of times, the frequency of application program downloading or buy etc.), and with reference to user its
His network behavior (such as behavior of the user in social networks, user other application program shop behavior, user once
Ranking fraud history etc.), the prestige of each application user can be graded (such as including from 1~5 this five etc.
Level, 5 represent user's prestige highest, and 1 to represent user's prestige worst), as the credit worthiness of the user.Thus, in history row
In name information, certain application program or application program seniority among brothers and sisters in historic user creditworthiness information, i.e. history each time period can be included
User's creditworthiness information of all application programs in list.
Application program ranking list can generally show the application program of K before welcome ranking, such as first 1000 etc..And
And, application program ranking list would generally be regularly updated, for example, be updated daily.Therefore, for each application program a
There is its history ranking information, the history ranking information can include being expressed as a ranking sequence corresponding with discrete-time seriesThe interval between time point in the discrete-time series fixes, i.e. application program ranking list
Update cycle.Wherein, ri aIt is application program a in time tiWhen ranking, ri a∈ { 1 ..., K ... ,+∞ } ,+∞ represents application
The row of program a K positions not before ranking list ranking；N represents the time point sum corresponding to all history ranking informations.For example,
In the case that ranking list updates daily, tiI-th day in this phase of history is meant that, n is exactly total corresponding to history ranking information
Number of days.As can be seen that ri aValue it is smaller, illustrate that i-th day ranking in ranking list of application program a is higher.
In the history ranking information obtaining step, the history ranking information can be in many ways obtained.For example, can be from
Direct access history ranking information at application program shop operator, it is also possible to from application program shop in one phase of history more long
In period the history ranking information etc. is extracted in the data of lasting issue.
S10：Active period detecting step, the active period of the application program is detected based on history ranking information.
Active period represents that application program ranking in application program ranking list is higher, that is, user's attention rate is higher
One period, therefore these active periods are only appeared in the ranking fraud that application program market can affect greatly
It is interior.So in the specific embodiment of the invention, first having to believe from the history ranking of application program for the detection that ranking is cheated
The active period of application program is detected in breath.
In a preferred embodiment of the invention, an Active event can be further included in the active period detecting step
Detecting step, the Active event of the application program is detected based on the history ranking information.
Ranking higher is occupied in ranking list because application developer is intended to its application program, therefore applies journey
Sequence developer makes its application program rank among ranking list prostatitis possible with the means that ranking is cheated.Found by analysis, applied
Program can't always occupy ranking very high in billboard, and the lasting ranking of generation period higher is " Active event ",
The example of the Active event of application program is shown in Fig. 2 a, transverse axis represents the corresponding time series of history ranking information in figure
(Date Index), the longitudinal axis represents the ranking (Ranking) of application program, event 1 (Event1) and event 2 in figure
(Event2) represent two Active events appeared in the application program placement history, its profile respectively by Active event during
Ranking point be formed by connecting.
In the specific embodiment of the invention, application program ranking standard higher in application program ranking list is that this should
It is not more than a rank threshold K* with the ranking of program.Due to application program ranking before ranking list the row of K* positions be considered as row
Name is higher, thus the time period of the lasting row in preceding K* positions of ranking of application program can be considered as an Active event, should
Active event should last till that the application program is fallen and ranking list since the row of K* positions before the application program initially enters ranking list
The row of preceding K* positions terminate.
Preferably, the step of method in embodiment of the present invention may also include setting rank threshold K*, so that really
Determine application program ranking standard higher in application program ranking list.Because the application program total quantity K in ranking list is usual
It is very big, for example, 1000 etc., therefore above-mentioned rank threshold K* is typically smaller than K values.According to application program in application program ranking list
Total quantity K and those skilled in the art the factor such as analysis demand, rank threshold K* can be whole between such as 1~500
Several values.It will be understood by those skilled in the art that the value of K* is smaller, application program is considered as ranking standard higher and gets over
It is high.In fig. 2 a, the value of the K* is 300.
According to the above-mentioned character express for Active event, the Active event e of application program a can be with equation below table
State：
A given rank threshold K* is used as ranking standard higher, wherein K* ∈ [1, K]；The Active event e of application program a
A time range including the time from the beginning a to end timeThe ranking of corresponding application program a expires
FootAndAndIt is satisfied by
Detection for Active event can be seen that according to above-mentioned statement it is only important that the ranking of detection application program is held
Between continuing at the beginning of a period of time of the row of preceding K* positions and the end time, and by between a pair of time starteds and end time
Period is defined as Active event.Therefore, in the specific embodiment of the invention, the Active event detecting step can be further included
Following steps：
Time started identification step：In this step, between being identified at the beginning of Active event from history ranking information.
Specifically, in the time started identification step, application program that can be in sequential search history ranking information on each time point
Ranking, when the ranking of current point in time is not more than rank threshold K* and the ranking at a upper time point is more than rank threshold K*, knew
Other current point in time be Active event at the beginning of between.It will be understood by those skilled in the art that due to being gone through in application program ranking
Multiple Active events are potentially included in history, therefore multiple sart point in times can be can recognize that in the time started identification step.
End time identification step：In this step, the end time of active time is identified from history ranking information.
Specifically, in the end time identification step, application program that can be in sequential search history ranking information on each time point
Ranking, when the ranking of current point in time is more than rank threshold K* and the ranking at a upper time point is not more than rank threshold K*, knew
The end time that a time point is Active event is not gone up.It will be understood by those skilled in the art that due to being gone through in application program ranking
Multiple Active events are potentially included in history, therefore multiple end time points can be can recognize that in the end time identification step.
Active event identification step：In this step by between each time started and adjacent end time after it
Time period is identified as Active event, thus detected all Active events of the application program in placement history.
What deserves to be explained is, as a kind of special circumstances, if analyze and process first of period of history when
Between on point, such as first day in historical record, the ranking of application program just before ranking list K* positions row, now described
In time started identification step, first time point is defined as a time started.Similarly, if analyzing and locating
On last time point of the period of history of reason, such as today, the row of ranking K* positions still before ranking list of application program, this
When last time point is defined as an end time in the end time identification step.
The mode of Active event in detection application program is described above, it is on this basis, preferred real in the present invention one
Apply in mode, can adjoining Active event be merged in the active period detecting step to constitute the active period.
By further study show that, some application programs can continuously occur repeatedly adjacent to each other near within one period
Active event, this period is exactly " active period " of application program in the present invention.It can be seen that, adjoining Active event is merged
Just to constitute active period.Specifically, can be using the time interval of two neighboring Active event less than an interval threshold φ as general
Two Active events merge the standard in same active period, and the time interval of two neighboring Active event refers to then adjacent two
In individual Active event at the beginning of the end time of previous Active event and latter Active event between interval.
Preferably, the step of method in embodiment of the present invention may also include setting interval threshold φ, so that really
It is fixed that two Active events are merged into the standard in same active period.The factors such as the analysis demand according to those skilled in the art,
The value of interval threshold φ can be the integer value in 2~10 times of the update cycle of application program ranking list.This area skill
Art personnel are appreciated that the value of interval threshold φ is smaller, by standard of two Active events merging in same active period just
It is higher.
The example of the active period of application program is shown in Fig. 2 b, transverse axis represents the history ranking information corresponding time in figure
Sequence (Date Index), the longitudinal axis represents the ranking (Ranking) of application program, in figure during 1 (Session1) and period 2
(Session2) two active periods appeared in the application program placement history are represented, each active period is by multiple Active events
Constitute.
According to the above-mentioned character express for active period, the active period s of application program a can be stated with equation belowization：
The active period s of application program a includes a time rangeActive event { the e adjacent with n1,…,
en, its satisfactionAnd caused in the absence of other active periods s*Additionally,HaveWherein φ is default Active event interval threshold, be for judge between Active event neighboring extent with
It is incorporated into the criterion of same active period.
Detection for active period can be seen that according to above-mentioned statement it is only important that will be using journey based on interval threshold φ
Active event adjoining in sequence placement history merges to form active period.Specifically, in the work of the specific embodiment of the invention
In jump phase detecting step, each Active event for detecting of sequential search since the initial time point in history ranking information,
When current active event is less than interval threshold φ with the time interval of a upper Active event, the two Active events are merged
In same active period, until having searched for all Active events for detecting to detect the application program in placement history
All active periods.
What deserves to be explained is, as a kind of special circumstances, if Active event not with any other Active event
Adjoining, the Active event itself is also contemplated as constituting an active period.In this case, in the active period detecting step
In, when an Active event is not less than the interval threshold φ with the time interval of a upper Active event, and the Active event is with
When the time interval of one Active event is not less than the interval threshold φ, detect the Active event from as an active period.
As previously mentioned, detected above-mentioned active period represents application program ranking in application program ranking list
It is higher, that is, one period welcome by user, the detected active period can be used as including detecting that ranking fraud exist
The data basis of interior various application program services.Therefore, after the active period for detecting application program, as the present invention one
Individual preferred embodiment, can also be sent to the active period information of detected application program application developer, answer
With program shop operator or the terminal user of application program.
For application developer, it can become according to the development of the active period information analysis correlative technology field
The demand of gesture or application user, so as to instruct the exploitation and operation of application program；For application program shop operator
Speech, it can further analyze the ranking that false ranking high in ranking list is obtained using fraudulent mean according to the active period information
Fraud etc., so as to improve the operation of application program shop；And for application terminal user, they can basis
The active period information there is a possibility that ranking fraud or selection meet the application of self-demand voluntarily judging application program
Program etc..
Additionally, as the Active event and a kind of specific implementation of active period of detection application program, following algorithm 1
Show the example for program code that active period is detected in the history ranking information of given application program a.
In above-mentioned algorithm 1, each Active event e is defined asActive period s is defined as
Wherein EsIt is the set of the Active event in active period s.Especially, first between at the beginning of history ranking information extract should
With each Active event e (the step 2-5 in algorithm 1) of program a.For the Active event e that each is extracted, detection e with it is preceding
Time interval between one Active event e* is judging whether they belong to same active period.Specifically, ifActive event e is then considered to belong to a new active period (the step 7-13 in algorithm 1).So, it is above-mentioned
Algorithm 1 can recognize Active event and active period by the single pass of the history ranking information to application program a.
Ranking fraud detection step S20, the evidence related to any active ues credit worthiness based at least one is come to the work
The jump phase detected, obtains ranking fraud detection result.
As introduction above to history ranking information, it includes historic user creditworthiness information, i.e. history each time
In section in certain application program or application program ranking list all application programs user's creditworthiness information.Meanwhile, active period is should
The period that ranking is cheated is likely to occur with program.Therefore, can be to user's letter of history ranking information in application program active period
Reputation feature is analyzed, and extracts some information related to any active ues credit worthiness as detecting the card that ranking is cheated
According to.
Specifically, user's credit worthiness of application program can be classified as in a discrete credit worthiness hierarchical system, for example, wrap
Include from 1~5 this five grades, 5 represent user's prestige highest, 1 to represent user's prestige worst.If the active period s of application program
In there is ranking fraud, the user for just necessarily having some user's credit worthinesses poor participate in it is for example false download, false evaluation or
Comment etc. is in fraud, therefore user's credit worthiness within the time period of active period s will have the use with other historical stages
The different off-note of family credit worthiness, this feature can be used to build for detecting the related to any active ues credit worthiness of ranking fraud
Evidence.
Used as a preferred embodiment of the present invention, the ranking fraud detection step can further include a proof validation
Step, is verified and is obtained fraud ginseng based at least one evidence related to any active ues credit worthiness to the active period
Number.So, after the evidence relevant with any active ues credit worthiness is extracted, fraud parameter corresponding with the evidence can be calculated,
The fraud parameter in itself can be used as the ranking fraud detection result of the ranking fraud detection method in present embodiment.Due to influence
The factor of user's prestige feature of application program is complex, only relies on one or more cards related to any active ues credit worthiness
Only obtain a detected value for reference and (take advantage of according to possibly cannot accurately judge an application program with the presence or absence of ranking fraud
Swindleness parameter), but those skilled in the art can judge that application program has ranking fraud according to the fraud parameter completely
Possibility.
For normal application program, the average credit worthiness of any active ues should be with its history within the particular active phase
The average credit worthiness of all users is consistent.Conversely, for the application program that there is ranking fraud, it is active in its active period
The average credit worthiness of user can have compared to the average credit worthiness of all users of its history and significantly decrease.As of the invention
One preferred embodiment, the evidence related to any active ues credit worthiness can be based on the average credit worthiness of any active ues of application programWith the average credit worthiness of the historic user of the application programTo constitute, and an evidence is calculated based on the evidence for being constituted
Value is used as judging the fraud parameter that ranking is cheated.
For example intuitively, the average credit worthiness of historic user of application program can be calculatedWith the active use of the application program
The average credit worthiness in familyBetween difference, or application program the average credit worthiness of historic userWith enlivening for the application program
The average credit worthiness of userBetween ratio, as the fraud parameter.
Therefore, compared to the active period of other application programs in ranking list, if the active period s of an application program is included
Significantly greater above-mentioned difference or ratio, with regard to there is a strong possibility there is ranking fraud in property to the application program.
For normal application program, the average credit worthiness of any active ues should be with application journey within the particular active phase
The average credit worthiness of the historic user of all application programs is consistent in sequence ranking list.Conversely, for there is answering for ranking fraud
With program, all application programs goes through during the average credit worthiness of any active ues is compared to application program ranking list in its active period
The average credit worthiness of history user can have and significantly decrease.As a preferred embodiment of the present invention, with any active ues prestige
Spending related evidence can be based on the average credit worthiness of any active ues of application programWith all application journeys in application program ranking list
The average credit worthiness of historic user of sequenceTo constitute, and an evidence value is calculated as judging based on the evidence for being constituted
The fraud parameter of ranking fraud.
For example intuitively, the average credit worthiness of historic user of all application programs in application program ranking list can be calculatedWith
The average credit worthiness of any active ues of application programBetween difference, or all application programs are gone through in application program ranking list
The average credit worthiness of history userWith the average credit worthiness of any active ues of application programBetween ratio, as the fraud parameter.
Therefore, compared to the active period of other application programs in ranking list, if the active period s of an application program is included
Significantly greater above-mentioned difference or ratio, with regard to there is a strong possibility there is ranking fraud in property to the application program.
In user's creditworthiness information of application program, the prestige of each user can be classified as a discrete user
In degrees of comparison system | L |, such as including from 1~5 this five grades, which represent the height of user's prestige.For one just
For normal application program a, the credit worthiness grade l of its any active ues in active period siDistribution p (li|Qs,a) should be with it
Historic user credit worthiness distribution of grades p (li|Qa) it is consistent.As a preferred embodiment of the present invention, with any active ues
The related evidence of credit worthiness can be based on the distribution of any active ues credit worthiness and the historic user prestige of the application program of application program
Degree distribution is constituted, and calculates an evidence value as judging the fraud parameter that ranking is cheated based on the evidence for being constituted.
For example, any active ues credit worthiness of the distribution of historic user credit worthiness and application program that can calculate application program is distributed
Between difference, as the fraud parameter.Specifically, can pass through firstTo calculate p (li|Qs,a)
Value, whereinIt is that user's credit worthiness grade is l in active periodiAny active ues number,It is total work in active period s
Jump number of users；P (l can be calculated using similar mode simultaneouslyi|Qa)；Then the historic user prestige of application program is calculated
Difference between degree distribution and any active ues credit worthiness distribution of application program.As a kind of specific implementation, it is possible to use p
(li|Qs,a) and p (li|Qa) between COS distance D (s) estimate the difference between them.Described by formulating, the fraud
Parameter D (s) is as follows：
It can be seen that, compared to the active period of other application programs in ranking list, if the active period s of an application program is included
Significantly greater D (s) values, with regard to there is a strong possibility there is ranking fraud in property to the application program.
For a normal application program a, the credit worthiness grade of its any active ues in active period s
liDistribution p (li|Qs,a) should be with the distribution of grades p of the historic user credit worthiness of all application programs in application program ranking list
(li| Q) it is consistent.Used as a preferred embodiment of the present invention, the evidence related to any active ues credit worthiness can be based on
The historic user credit worthiness point of all application programs in any active ues credit worthiness distribution of application program and application program ranking list
Cloth is constituted, and calculates an evidence value as judging the fraud parameter that ranking is cheated based on the evidence for being constituted.
For example, the distribution of historic user credit worthiness and the application program of all application programs in application program ranking list can be calculated
Any active ues credit worthiness distribution between difference, as the fraud parameter.Specifically, can pass through first
To calculate p (li|Qs,a) value, whereinIt is that user's credit worthiness grade is l in active periodiAny active ues number,Be
Any active ues number total in active period s；P (l can be calculated using similar mode simultaneouslyi|Q)；Then calculate and apply journey
In the historic user credit worthiness distribution of sequence and application program ranking list between the historic user credit worthiness distribution of all application programs
Difference.As a kind of specific implementation, it is possible to use p (li|Qs,a) and p (li| Q) between COS distance D (s) estimate
Difference between them.Described by formulating, fraud parameter D (s) is as follows：
It can be seen that, compared to the active period of other application programs in ranking list, if the active period s of an application program is included
Significantly greater D (s) values, with regard to there is a strong possibility there is ranking fraud in property to the application program.
Various evidences related to any active ues credit worthiness are described above, except single in above-mentioned each preferred embodiment
Solely carried out using one of which outside ranking fraud detection, in a preferred embodiment of proof validation step,
The multiple in the above-mentioned evidence related to any active ues credit worthiness can also be considered, by what is obtained based on these proof validations
Correspondence fraud parameter is weighted, so as to obtain a final fraud parameter.Tool is possible in view of above-mentioned various evidences
There are different dimensions, those skilled in the art can be according to the attention degree in actual analysis demand for each evidence, based on existing
Known method for normalizing and Weight Determination determine the weighted value of each fraud parameter in technology, will not be repeated here.
The proof validation step in ranking fraud detection step is described above, it can be based at least one with active use
Family credit worthiness it is related evidence is verified to the active period and is obtained a fraud parameter, the fraud parameter can inherently be made
It is the ranking fraud detection result of ranking fraud detection method.But in order that those skilled in the art are more easily arranged
Name fraud detection, in a preferred embodiment, ranking fraud detection step can further include a fraud parameter and sentence
Disconnected step, the fraud parameter that will be calculated according to evidence is compared with a threshold value, so as to intuitively judge to judge to answer
Cheated with the presence or absence of ranking with program.
It will be understood by those skilled in the art that based on the various cards related to any active ues credit worthiness described in above
According to those skilled in the art can be respectively provided with corresponding threshold value according to the heterogeneity of evidence and detection demand, according to set
The threshold value put carries out judgement of the application program with the presence or absence of ranking fraud, and the final result that will determine that as of the invention specific
The ranking fraud detection result of ranking fraud detection method in implementation method.For example, for described in above it is various with it is living
For the related evidence of jump user credit worthiness, when the fraud parameter for calculating exceedes set threshold value, the application is judged
There is ranking fraud phenomenon in program.
After ranking fraud detection result is obtained in ranking fraud detection step, in a preferred embodiment of the invention
In, the terminal that resulting ranking fraud detection result is sent to application program shop operator or application program can also be used
Family.For application program shop operator, it can improve application program shop according to the ranking fraud detection result
Operation；And for application terminal user, they can select to meet itself according to the ranking fraud detection result
Application program of demand etc..
As shown in figure 3, additionally providing a kind of ranking fraud detection system of application program in the specific embodiment of the invention
100, the system 100 includes：
Active period detection unit 110, the active period for detecting the application program based on history ranking information；Ranking is taken advantage of
Swindleness detection unit 120, for being detected to the active period based at least one evidence related to any active ues credit worthiness,
Obtain ranking fraud detection result.
Below, each unit function of said detecting system is illustrated with reference to accompanying drawing.
Because history ranking information is the data basis of the ranking fraud of detection application program in the present invention, therefore as this
One preferred embodiment of invention, the ranking fraud detection system 100 may also include a history ranking information acquiring unit, use
In history ranking information of the acquisition application program in application program ranking list.
The history ranking information acquiring unit can in many ways obtain the history ranking information.For example, can be from application
Direct access history ranking information at the operator of program shop, it is also possible to from application program shop in one section of period of history more long
The history ranking information etc. is extracted in the data of interior lasting issue.
Active period detection unit 110, the active period for detecting the application program based on history ranking information.
In a preferred embodiment of the invention, the active period detection unit 110 can further include an Active event
Detection module, the Active event for detecting the application program based on the history ranking information.
Preferably, the system in embodiment of the present invention may also include a rank threshold setting unit, for setting ranking
The value of threshold k *, so that it is determined that application program ranking standard higher in application program ranking list.Rank threshold K*'s takes
Value can be the integer between 1~500.
In the specific embodiment of the invention, the Active event detection module is further included：
Time started identification module, between being identified at the beginning of Active event from history ranking information.Specifically,
The time started identification module can be in sequential search history ranking information on each time point application program ranking, when current
Between the ranking put when being not more than rank threshold K* and the ranking at a upper time point and being more than rank threshold K*, identification current point in time is
Between at the beginning of Active event.
End time identification module, the end time for identifying active time from history ranking information.Specifically,
The end time identification module can be in sequential search history ranking information on each time point application program ranking, when current
Between the ranking put when being not more than rank threshold K* more than rank threshold K* and the ranking at a upper time point, recognized that a upper time point was
The end time of Active event.
Active event identification module, for by the time period between each time started and adjacent end time after it
Active event is identified as, all Active events of the application program in placement history are thus detected.
What deserves to be explained is, as a kind of special circumstances, if analyze and process first of period of history when
Between on point, such as first day in historical record, the ranking of application program just before ranking list K* positions row, now this start
First time point is defined as a time started by time identification module.Similarly, if analyze and process go through
On last time point in history period, such as today, the row of ranking K* positions still before ranking list of application program, the now knot
Last time point is defined as an end time by beam time identification module.
In a preferred embodiment of the invention, the active period detection unit 110 is used to merging adjoining enlivens thing
Part is constituting the active period of the application program.
Preferably, the ranking fraud detection system 100 in embodiment of the present invention may also include an interval threshold and set single
Unit, the value for setting interval threshold φ, so that it is determined that two Active events are merged into the standard in same active period.Should
The value of interval threshold φ can be the integer value in 2~10 times of the update cycle of application program ranking list.
In the specific embodiment of the invention, active period detection unit 110 is from the initial time point in history ranking information
Start sequential search each Active event for detecting, when the time interval of current active event and a upper Active event was less than should
During interval threshold φ, by the two Active events merge in same active period, until searched for it is all detect enliven thing
Part is detecting all active periods of the application program in placement history.
What deserves to be explained is, as a kind of special circumstances, if Active event not with any other Active event
Adjoining, the Active event itself is also contemplated as constituting an active period.In this case, the active period detection unit 110
For being not less than the interval threshold φ with the time interval of a upper Active event when an Active event, and the Active event is with
When the time interval of one Active event is not less than the interval threshold φ, detect the Active event from as an active period.
Used as a preferred embodiment of the invention, ranking fraud detection system 100 can also send including an active period
Unit, application developer, application program shop operator are sent to by the active period information of detected application program
Or application user.
Ranking fraud detection unit 120, for the evidence related to any active ues credit worthiness based at least one come to institute
State active period to be detected, obtain ranking fraud detection result.
Used as a preferred embodiment of the present invention, the ranking fraud detection unit 120 can further include an evidence
Authentication module, for the active period to be verified and obtained based at least one evidence related to any active ues credit worthiness
One fraud parameter.
In a preferred embodiment, the evidence related to any active ues credit worthiness can be based on the active use of application program
The average credit worthiness in familyWith the average credit worthiness of the historic user of application programTo constitute, and based on the evidence meter for being constituted
An evidence value is calculated as judging the fraud parameter that ranking is cheated.In another preferred embodiment, with any active ues
The related evidence of credit worthiness can be based on the average credit worthiness of any active ues of application programIt is all with application program ranking list to answer
With the average credit worthiness of the historic user of programConstitute, and an evidence value is calculated as sentencing based on the evidence for being constituted
The fraud parameter of disconnected ranking fraud.In another preferred embodiment, the evidence related to any active ues credit worthiness can be based on
Any active ues credit worthiness distribution of application program and the historic user credit worthiness of the application program are distributed to constitute, and based on institute's structure
Into the evidence calculate an evidence value as judge ranking cheat fraud parameter.In another preferred embodiment
In, the evidence related to any active ues credit worthiness can be based on the distribution of any active ues credit worthiness and the application program seniority among brothers and sisters of application program
The historic user credit worthiness distribution of all application programs is constituted in list, and calculates evidence value work based on the evidence for being constituted
It is the fraud parameter for judging ranking fraud.
In addition to one of which is used alone in above-mentioned each preferred embodiment to carry out ranking fraud detection,
Proof validation module can also consider the multiple in the above-mentioned evidence related to any active ues credit worthiness, will be based on these cards
The correspondence fraud parameter obtained according to checking is weighted, so as to obtain a final fraud parameter.
In order that those skilled in the art more easily carry out ranking fraud detection, in a preferred embodiment,
Ranking fraud detection unit 120 can further include a fraud parameter judge module, by what is be calculated according to evidence
Fraud parameter is compared with a threshold value, so as to intuitively judge to judge that application program is cheated with the presence or absence of ranking.
After ranking fraud detection result is obtained in ranking fraud detection step, in a preferred embodiment of the invention
In, ranking fraud detection system 100 also includes a ranking fraud detection result transmitting element, by resulting ranking fraud detection
Result is sent to the terminal user of application program shop operator or application program.
It will be understood by those skilled in the art that when application program Active event and active period information known in the case of,
Those skilled in the art can directly implement above-mentioned ranking fraud detection step according to above-mentioned Active event and active period information,
So as to realize the detection of application program ranking fraud.Therefore, one is additionally provided in another embodiment of the invention
The ranking fraud detection method of application program is planted, methods described includes：It is related to any active ues credit worthiness based at least one
Evidence detects come the active period to application program, obtains ranking fraud detection result.In the application of the specific embodiment
In program ranking fraud detection method, the technology contents implemented and ranking fraud detection step phase in specific embodiment before
Together, here is omitted.
Accordingly, a kind of ranking fraud inspection of application program is additionally provided in another embodiment of the present invention simultaneously
Examining system, the system includes：Ranking fraud detection unit, for based at least one card related to any active ues credit worthiness
Detected according to the active period, obtain ranking fraud detection result.Taken advantage of in the application program ranking of the specific embodiment
In swindleness detecting system, the technology contents implemented are identical with ranking fraud detection unit in specific embodiment before, herein not
Repeat again.
Fig. 4 is a kind of structural representation of the ranking fraud detection system 400 of application program provided in an embodiment of the present invention,
The specific embodiment of the invention is not limited implementing for ranking fraud detection system 400.As shown in figure 4, the ranking is taken advantage of
Swindleness detecting system 400 can include：
Processor (processor) 410, communication interface (Communications Interface) 420, memory
(memory) 430 and communication bus 440.Wherein：
Processor 410, communication interface 420 and memory 430 complete mutual communication by communication bus 440.
Communication interface 420, communicates for the network element with such as client etc..
Processor 410, for configuration processor 432, can specifically realize ranking fraud detection in embodiment described in above-mentioned Fig. 3
The correlation function of system.
Specifically, program 432 can include program code, and described program code includes computer-managed instruction.
Processor 410 is probably a central processor CPU, or specific integrated circuit ASIC (Application
Specific Integrated Circuit), or it is arranged to implement one or more integrated electricity of the embodiment of the present invention
Road.
Memory 430, for depositing program 432.Memory 430 may include high-speed RAM memory, it is also possible to also include
Nonvolatile memory (non-volatile memory), for example, at least one magnetic disk storage.Program 432 can specifically be wrapped
Include：
Active period detection unit, the active period for detecting the application program based on history ranking information；
Ranking fraud detection unit, for based at least one evidence related to any active ues credit worthiness to described active
Phase detected, obtains ranking fraud detection result.
Program 432 can also specifically include：
Ranking fraud detection unit, for being entered to active period based at least one evidence related to any active ues credit worthiness
Row detection, obtains ranking fraud detection result.
Each unit implements the corresponding units that may refer in above embodiment in program 432, will not be described here.
It is apparent to those skilled in the art that, for convenience and simplicity of description, the equipment of foregoing description
With the specific work process of module, the correspondence description in aforementioned means embodiment is may be referred to, will not be repeated here.
Those of ordinary skill in the art are it is to be appreciated that the list of each example described with reference to the embodiments described herein
Unit and method and step, can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually
Performed with hardware or software mode, depending on the application-specific and design constraint of technical scheme.Professional and technical personnel
Described function, but this realization can be realized it is not considered that exceeding using distinct methods to each specific application
The scope of the present invention.
If the function is to realize in the form of SFU software functional unit and as independent production marketing or when using, can be with
Storage is in a computer read/write memory medium.Based on such understanding, technical scheme is substantially in other words
The part contributed to original technology or the part of the technical scheme can be embodied in the form of software product, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used to so that a computer equipment (can be individual
People's computer, server, or network equipment etc.) perform all or part of step of each embodiment methods described of the invention.
And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only storage (ROM, Read-Only Memory), arbitrary access are deposited
Reservoir (RAM, Random Access Memory), magnetic disc or CD etc. are various can be with the medium of store program codes.
Embodiment of above is merely to illustrate the present invention, and not limitation of the present invention, about the common of technical field
Technical staff, without departing from the spirit and scope of the present invention, can also make a variety of changes and modification, therefore all
Equivalent technical scheme falls within scope of the invention, and scope of patent protection of the invention should be defined by the claims.
Claims (59)
1. the ranking fraud detection method of a kind of application program, it is characterised in that methods described includes：
Active period detecting step, the Active event of the application program is detected based on history ranking information, based on interval threshold φ
Active event adjoining in application program placement history is merged to form active period, and/or when an Active event and upper
The time interval of Active event is not less than the interval threshold φ, and the Active event and next Active event time interval not
During less than the interval threshold φ, the Active event is detected certainly as an active period, wherein, the Active event is the application
Program continues the ranking time period higher in application program ranking list, and ranking standard higher is the application program in application
Ranking in program ranking list is not more than a rank threshold K*, and the time interval of two neighboring Active event refers to then two neighboring
In Active event at the beginning of the end time of previous Active event and latter Active event between interval；
Ranking fraud detection step, is tested the active period based at least one evidence related to any active ues credit worthiness
Card, obtains ranking fraud the result.
2. method according to claim 1, it is characterised in that the ranking fraud detection step is further included：
Proof validation step, is verified simultaneously based at least one evidence related to any active ues credit worthiness to the active period
Obtain a fraud parameter.
3. method according to claim 2, it is characterised in that the evidence related to any active ues credit worthiness is based on institute
The average credit worthiness of the historic user of the average credit worthiness of any active ues and the application program for stating application program is constituted.
4. method according to claim 3, it is characterised in that
The fraud parameter is that the average credit worthiness of historic user of the application program and any active ues of the application program are put down
The difference or ratio of equal credit worthiness.
5. method according to claim 2, it is characterised in that the evidence related to any active ues credit worthiness is based on institute
The historic user for stating all application programs in the average credit worthiness of any active ues and application program ranking list of application program is averagely believed
Reputation degree is constituted.
6. method according to claim 5, it is characterised in that
The fraud parameter is the average credit worthiness of historic user of all application programs in application program ranking list and the application
The difference or ratio of the average credit worthiness of any active ues of program.
7. method according to claim 2, it is characterised in that
Any active ues credit worthiness that the evidence related to any active ues credit worthiness is based on the application program is distributed and described
The historic user credit worthiness distribution of application program is constituted.
8. method according to claim 7, it is characterised in that
The fraud parameter is that the historic user credit worthiness distribution of the application program and any active ues of the application program are believed
Difference between the distribution of reputation degree.
9. method according to claim 8, it is characterised in that by the historic user credit worthiness for calculating the application program
COS distance between any active ues credit worthiness distribution of distribution and the application program calculates the difference between them.
10. method according to claim 2, it is characterised in that
The evidence related to any active ues credit worthiness is based on any active ues credit worthiness distribution of the application program and applies
The historic user credit worthiness distribution of all application programs is constituted in program ranking list.
11. methods according to claim 10, it is characterised in that
The fraud parameter is the distribution of historic user credit worthiness and the application of all application programs in application program ranking list
Difference between any active ues credit worthiness distribution of program.
12. methods according to claim 11, it is characterised in that by calculating all application journeys in application program ranking list
COS distance between the historic user credit worthiness distribution of sequence and any active ues credit worthinesses distribution of the application program is calculated
Difference between them.
13. methods according to claim 2, it is characterised in that in the proof validation step, consider it is described extremely
A few evidence related to any active ues credit worthiness, will be based on described at least one evidence related to any active ues credit worthiness
The correspondence fraud parameter that checking is obtained is weighted, so as to obtain a final fraud parameter.
14. method according to any one of claim 2-12, it is characterised in that the ranking fraud detection step enters one
Step includes：
Whether fraud parameter judges step, the fraud parameter is compared with a threshold value, so as to judge the application program
There is ranking fraud.
15. methods according to claim 13, it is characterised in that the ranking fraud detection step is further included：
Fraud parameter judges step, the final fraud parameter is compared with a threshold value, so as to judge the application program
With the presence or absence of ranking fraud.
16. methods according to claim 1, it is characterised in that methods described also includes：
History ranking information obtaining step, obtains history ranking letter of the application program in application program ranking list
Breath.
17. methods according to claim 16, it is characterised in that in the history ranking information obtaining step, from should
The history ranking information is obtained with program shop operator, or the history is extracted from the data of application program shop issue
Ranking information.
18. methods according to claim 1, it is characterised in that the history ranking information was included in history each time period
The user of all application programs believes in application program ranking list in user's credit worthiness or history each time period of the application program
Reputation degree.
19. methods according to claim 1, it is characterised in that methods described also includes：By the detected application
The active period of program is sent in application developer, application program shop operator, application user at least
One.
20. methods according to claim 1, it is characterised in that methods described also includes：By the detected ranking
Fraud detection result is sent at least one of application program shop operator, application user.
The ranking fraud detection system of 21. a kind of application programs, it is characterised in that the system includes：
Active period detection unit, the Active event for detecting the application program based on history ranking information, based on interval threshold
Value φ merges Active event adjoining in application program placement history to form active period, and/or when an Active event with
The time interval of a upper Active event is not less than the interval threshold φ, and between the time of the Active event and next Active event
When not less than the interval threshold φ, the Active event is detected certainly as an active period, wherein, the Active event is described
Application program continues the ranking time period higher in application program ranking list, and ranking standard higher is that the application program exists
Ranking in application program ranking list is not more than a rank threshold K*, and the time interval of two neighboring Active event refers to then adjacent
In two Active events at the beginning of the end time of previous Active event and latter Active event between interval；
Ranking fraud detection unit, for being entered to the active period based at least one evidence related to any active ues credit worthiness
Row checking, obtains ranking fraud the result.
22. systems according to claim 21, it is characterised in that the ranking fraud detection unit is further included：
Proof validation module, for being tested the active period based at least one evidence related to any active ues credit worthiness
Demonstrate,prove and obtain a fraud parameter.
23. systems according to claim 22, it is characterised in that the evidence related to any active ues credit worthiness is based on
The average credit worthiness of any active ues of the application program and the average credit worthiness of the historic user of the application program are constituted.
24. systems according to claim 22, it is characterised in that the evidence related to any active ues credit worthiness is based on
The historic user of all application programs is average in the average credit worthiness of any active ues and application program ranking list of the application program
Credit worthiness is constituted.
25. systems according to claim 22, it is characterised in that the evidence related to any active ues credit worthiness is based on
Any active ues credit worthiness distribution of the application program and the historic user credit worthiness distribution of the application program are constituted.
26. systems according to claim 22, it is characterised in that the evidence related to any active ues credit worthiness is based on
The historic user prestige of all application programs in any active ues credit worthiness distribution of the application program and application program ranking list
Degree distribution is constituted.
27. systems according to claim 22, it is characterised in that the proof validation module, it is described for considering
At least one evidence related to any active ues credit worthiness, will be based on described at least one card related to any active ues credit worthiness
The correspondence fraud parameter obtained according to checking is weighted, so as to obtain a final fraud parameter.
28. system according to any one of claim 22-26, it is characterised in that the ranking fraud detection unit enters
One step includes：
Fraud parameter judge module, for the fraud parameter to be compared with a threshold value, so as to judge the application program
With the presence or absence of ranking fraud.
29. systems according to claim 27, it is characterised in that the ranking fraud detection unit is further included：
Fraud parameter judge module, for the final fraud parameter to be compared with a threshold value, so as to judge the application
Program is cheated with the presence or absence of ranking.
30. systems according to claim 21, it is characterised in that the system also includes：
History ranking information acquiring unit, for obtaining the history ranking of the application program in application program ranking list
Information.
31. systems according to claim 30, it is characterised in that the history ranking information acquiring unit, for from should
The history ranking information is obtained with program shop operator, or the history is extracted from the data of application program shop issue
Ranking information.
32. systems according to claim 21, it is characterised in that the system also includes an active period transmitting element, use
In the active period of the detected application program is sent into application developer, application program shop operation
At least one of business, application user.
33. systems according to claim 21, it is characterised in that the system is also sent out including a ranking fraud detection result
Unit is sent, for the detected ranking fraud detection result to be sent into application program shop operator, application program
At least one of user.
The ranking fraud detection method of 34. a kind of application programs, it is characterised in that methods described includes：
The active period of application program is verified based at least one evidence related to any active ues credit worthiness, obtains ranking
Fraud the result, the active period is to be closed Active event adjoining in application program placement history based on interval threshold φ
And formed, and/or when an Active event is not less than the interval threshold φ, and the work with the time interval of a upper Active event
When jump event is not less than the interval threshold φ with the time interval of next Active event, detect the Active event from as a work
The jump phase, wherein, the Active event is that the application program continues ranking time period higher, row in application program ranking list
Name standard higher is that ranking of the application program in application program ranking list is not more than a rank threshold K*, described active
Event detects that the application program is obtained based on history ranking information, and the time interval of two neighboring Active event refers to then
In two neighboring Active event at the beginning of the end time of previous Active event and latter Active event between interval.
35. methods according to claim 34, it is characterised in that methods described is further included：
Proof validation step, is verified simultaneously based at least one evidence related to any active ues credit worthiness to the active period
Obtain a fraud parameter.
36. methods according to claim 35, it is characterised in that the evidence related to any active ues credit worthiness is based on
The average credit worthiness of any active ues of the application program and the average credit worthiness of the historic user of the application program are constituted.
37. methods according to claim 36, it is characterised in that
The fraud parameter is that the average credit worthiness of historic user of the application program and any active ues of the application program are put down
The difference or ratio of equal credit worthiness.
38. methods according to claim 35, it is characterised in that the evidence related to any active ues credit worthiness is based on
The historic user of all application programs is average in the average credit worthiness of any active ues and application program ranking list of the application program
Credit worthiness is constituted.
39. method according to claim 38, it is characterised in that
The fraud parameter is the average credit worthiness of historic user of all application programs in application program ranking list and the application
The difference or ratio of the average credit worthiness of any active ues of program.
40. methods according to claim 35, it is characterised in that
Any active ues credit worthiness that the evidence related to any active ues credit worthiness is based on the application program is distributed and described
The historic user credit worthiness distribution of application program is constituted.
41. methods according to claim 40, it is characterised in that
The fraud parameter is that the historic user credit worthiness distribution of the application program and any active ues of the application program are believed
Difference between the distribution of reputation degree.
42. methods according to claim 41, it is characterised in that by the historic user prestige for calculating the application program
COS distance between degree distribution and any active ues credit worthiness distribution of the application program calculates the difference between them.
43. methods according to claim 35, it is characterised in that
The evidence related to any active ues credit worthiness is based on any active ues credit worthiness distribution of the application program and applies
The historic user credit worthiness distribution of all application programs is constituted in program ranking list.
44. methods according to claim 43, it is characterised in that
The fraud parameter is the distribution of historic user credit worthiness and the application of all application programs in application program ranking list
Difference between any active ues credit worthiness distribution of program.
45. methods according to claim 44, it is characterised in that by calculating all application journeys in application program ranking list
COS distance between the historic user credit worthiness distribution of sequence and any active ues credit worthinesses distribution of the application program is calculated
Difference between them.
46. methods according to claim 35, it is characterised in that in the proof validation step, consider described
At least one evidence related to any active ues credit worthiness, will be based on described at least one card related to any active ues credit worthiness
The correspondence fraud parameter obtained according to checking is weighted, so as to obtain a final fraud parameter.
47. method according to any one of claim 35-45, it is characterised in that the ranking fraud detection step is entered
One step includes：
Whether fraud parameter judges step, the fraud parameter is compared with a threshold value, so as to judge the application program
There is ranking fraud.
48. methods according to claim 46, it is characterised in that the ranking fraud detection step is further included：
Fraud parameter judges step, the final fraud parameter is compared with a threshold value, so as to judge the application program
With the presence or absence of ranking fraud.
49. methods according to claim 34, it is characterised in that methods described also includes：By the detected row
Name fraud detection result is sent at least one of application program shop operator, application user.
The ranking fraud detection system of 50. a kind of application programs, it is characterised in that the system includes：
Ranking fraud detection unit, for the work based at least one evidence related to any active ues credit worthiness to application program
The jump phase is verified that obtain ranking fraud the result, the active period is to go through application program ranking based on interval threshold φ
Adjoining Active event merges what is formed in history, and/or when an Active event is not small with the time interval of a upper Active event
When the interval threshold φ, and the Active event are not less than the interval threshold φ with the time interval of next Active event,
The Active event is detected certainly as an active period, wherein, the Active event is the application program in application program ranking list
It is upper to continue the ranking time period higher, ranking standard higher be ranking of the application program in application program ranking list not
More than a rank threshold K*, the Active event detects that the application program is obtained based on history ranking information, adjacent two
The time interval of individual Active event refers to then end time of previous Active event in two neighboring Active event and latter active
Interval between at the beginning of event.
51. systems according to claim 50, it is characterised in that the ranking fraud detection unit is further included：
Proof validation module, for being tested the active period based at least one evidence related to any active ues credit worthiness
Demonstrate,prove and obtain a fraud parameter.
52. systems according to claim 51, it is characterised in that the evidence related to any active ues credit worthiness is based on
The average credit worthiness of any active ues of the application program and the average credit worthiness of the historic user of the application program are constituted.
53. systems according to claim 51, it is characterised in that the evidence related to any active ues credit worthiness is based on
The historic user of all application programs is average in the average credit worthiness of any active ues and application program ranking list of the application program
Credit worthiness is constituted.
54. systems according to claim 51, it is characterised in that the evidence related to any active ues credit worthiness is based on
Any active ues credit worthiness distribution of the application program and the historic user credit worthiness distribution of the application program are constituted.
55. systems according to claim 51, it is characterised in that the evidence related to any active ues credit worthiness is based on
The historic user prestige of all application programs in any active ues credit worthiness distribution of the application program and application program ranking list
Degree distribution is constituted.
56. systems according to claim 51, it is characterised in that the proof validation module, it is described for considering
At least one evidence related to any active ues credit worthiness, will be based on described at least one card related to any active ues credit worthiness
The correspondence fraud parameter obtained according to checking is weighted, so as to obtain a final fraud parameter.
57. system according to any one of claim 51-55, it is characterised in that the ranking fraud detection unit enters
One step includes：
Fraud parameter judge module, for the fraud parameter to be compared with a threshold value, so as to judge the application program
With the presence or absence of ranking fraud.
58. systems according to claim 56, it is characterised in that the ranking fraud detection unit is further included：
Fraud parameter judge module, for the final fraud parameter to be compared with a threshold value, so as to judge the application
Program is cheated with the presence or absence of ranking.
59. systems according to claim 50, it is characterised in that the system is also sent out including a ranking fraud detection result
Unit is sent, for the detected ranking fraud detection result to be sent into application program shop operator, application program
At least one of user.
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| CN103530796B (en) | 2013-10-10 | 2016-06-01 | 北京智谷睿拓技术服务有限公司 | The active period detection method of application program and active period detection system |
| US10715544B2 (en) * | 2016-02-05 | 2020-07-14 | Sony Corporation | Method, apparatus and system for calculating a risk score of a user request by a user on a web application |
| CN107346367B (en) * | 2016-05-04 | 2020-09-18 | 阿里巴巴集团控股有限公司 | Method and device for segmenting numerical value of business variable |
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