CN110060053A - A kind of recognition methods, equipment and computer-readable medium - Google Patents

A kind of recognition methods, equipment and computer-readable medium Download PDF

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CN110060053A
CN110060053A CN201910093177.3A CN201910093177A CN110060053A CN 110060053 A CN110060053 A CN 110060053A CN 201910093177 A CN201910093177 A CN 201910093177A CN 110060053 A CN110060053 A CN 110060053A
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trade company
user
association
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similarity
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CN110060053B (en
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石翼
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Advanced New Technologies Co Ltd
Advantageous New Technologies Co Ltd
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Alibaba Group Holding Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
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Abstract

This application provides a kind of identifying schemes, the user that transaction occurred with sample trade company in first time window is obtained first, as association user, then according to the transaction count of the association user, the more central user of transaction count is determined in the association user, and then obtain the trade company that transaction occurred with the central user in the second time window, as association trade company, it is possible thereby to the trading activity based on user, it is determining may have like attribute with sample trade company be associated with trade company, such as sample trade company is risk trade company, it is then associated with trade company and is likely to be association trade company.On this basis, the similarity between the association trade company and the sample trade company is further determined according to the incidence relation between trade company and user, and is denoised based on similarity, the lower association trade company of similarity is excluded, to promote the accuracy of identification.In addition, the program can be extended to other behaviors with user, there are the identifications of associated other data objects.

Description

A kind of recognition methods, equipment and computer-readable medium
Technical field
This application involves information technology field more particularly to a kind of recognition methods, equipment and computer-readable medium.
Background technique
With the development of internet technology, more and more users are paid the bill by online payment platform to trade company, to purchase Buy service or product.Some people may realize some violations or illegal purpose using online payment platform, such as By opening up the account number of trade company in online payment platform, come the transaction such as swindled, gambled.
Such risk trade company can have potential wind for the user of network payment platform and use payment platform Danger, such as user's monetary losses or public sentiment risk etc. can be caused, it is therefore desirable to such risk trade company is identified in time.It adopts at present Mode mainly identifies such risk trade company by way of cluster, but the accuracy of recognition result not can guarantee.If There is mistake in the result of identification, it is likely that normal trade company can be identified as risk trade company, will greatly affect normal quotient in this way The usage experience at family.
Apply for content
The purpose of the application is to provide a kind of identifying schemes, to solve the accuracy of recognition result in existing scheme Not high problem.
The embodiment of the present application provides a kind of trade company's recognition methods, this method comprises:
The user that transaction occurred with sample trade company in first time window is obtained, as association user；
According to the transaction count of the association user, determine that the more center of transaction count is used in the association user Family；
The trade company that transaction occurred with the central user in the second time window is obtained, as association trade company；
According to the incidence relation between trade company and user, determine similar between the association trade company and the sample trade company Degree；
Determine that similarity meets the target trade company of preset condition in the association trade company.
The embodiment of the present application also provides a kind of data object recognition methods, this method comprises:
The user that correlating event occurred with sample data object in first time window is obtained, as association user；
According to the correlating event frequency of the association user, correlating event generation time is determined in the association user The more central user of number；
The data object that correlating event occurred with the central user in the second time window is obtained, as incidence number According to object；
According to the incidence relation between data object and user, the associated data object and the sample data pair are determined Similarity as between；
Determine that similarity meets the target data objects of preset condition in the associated data object.
The embodiment of the present application provides a kind of trade company's identification equipment, which includes:
First relating module, for obtaining the user that transaction occurred with sample trade company in first time window, as Association user；
Screening module determines transaction count for the transaction count according to the association user in the association user More central user；
Second relating module, for obtaining the trade company that transaction occurred with the central user in the second time window, As association trade company；
Module is denoised, for determining the association trade company and the sample according to the incidence relation between trade company and user Similarity between trade company, and determine that similarity meets the target trade company of preset condition in the association trade company.
The embodiment of the present application also provides a kind of data objects to identify that equipment, the equipment include:
First relating module, for obtaining the use that correlating event occurred with sample data object in first time window Family, as association user；
Screening module determines in the association user for the correlating event frequency according to the association user The more central user of correlating event frequency；
Second relating module, for obtaining the number that correlating event occurred with the central user in the second time window According to object, as associated data object；
Denoise module, for according to the incidence relation between data object and user, determine the associated data object with Similarity between the sample data object, and determine that similarity meets the mesh of preset condition in the associated data object Mark data object.
In addition, some embodiments of the present application additionally provide a kind of calculating equipment, which includes for storing computer The memory of program instruction and processor for executing computer program instructions, wherein when the computer program instructions are by this When processor executes, triggers the equipment and execute the recognition methods.
Other embodiments of the application additionally provide a kind of computer-readable medium, are stored thereon with computer program and refer to It enables, the computer-readable instruction can be executed by processor to realize the recognition methods.
In trade company's identifying schemes provided by the embodiments of the present application, obtains sent out in first time window with sample trade company first The user for giving birth to transaction, as association user, then according to the transaction count of the association user, in the association user really Determine the more central user of transaction count, and then obtains the quotient that transaction occurred with the central user in the second time window Family, as association trade company, it is possible thereby to the trading activity based on user, the determining pass that may have like attribute with sample trade company Join trade company, such as sample trade company is risk trade company, is then associated with trade company and is likely to be association trade company.On this basis, into one Step determines the similarity between the association trade company and the sample trade company according to the incidence relation between trade company and user, and It is denoised based on similarity, the lower association trade company of similarity is excluded, to promote the accuracy of identification.
In addition, the program can extend to arbitrarily, there are associated data objects with certain behaviors of user, to identify It accurately identifies the data object with special characteristic, such as is commented by webpage that a large number of users malice thumbs up, by user's malice Commodity of valence etc..
Detailed description of the invention
By reading a detailed description of non-restrictive embodiments in the light of the attached drawings below, the application's is other Feature, objects and advantages will become more apparent upon:
Fig. 1 is a kind of process flow diagram of trade company's recognition methods provided by the embodiments of the present application；
Fig. 2 is the schematic diagram for successively obtaining central user by sample trade company in the embodiment of the present application, being associated with trade company；
Fig. 3 is the process flow diagram that the similarity between association trade company and sample trade company is determined in the embodiment of the present application；
Fig. 4 is the process flow diagram of another trade company recognition methods provided by the embodiments of the present application；
Fig. 5 is the schematic diagram that processing is iterated in the embodiment of the present application；
Fig. 6 is the process flow diagram of data object recognition methods provided by the embodiments of the present application；
Fig. 7 is the structural schematic diagram that a kind of trade company provided by the embodiments of the present application identifies equipment；
Fig. 8 is a kind of structural schematic diagram for calculating equipment provided by the embodiments of the present application；
The same or similar appended drawing reference represents the same or similar component in attached drawing.
Specific embodiment
The application is described in further detail with reference to the accompanying drawing.
In a typical configuration of this application, terminal, the equipment of service network include one or more processors (CPU), input/output interface, network interface and memory.
Memory may include the non-volatile memory in computer-readable medium, random access memory (RAM) and/or The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable medium Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media, can be by any side Method or technology realize that information stores.Information can be the device or other numbers of computer readable instructions, data structure, program According to.The example of the storage medium of computer includes, but are not limited to phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory techniques, CD-ROM (CD- ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storages Equipment or any other non-transmission medium, can be used for storage can be accessed by a computing device information.
The embodiment of the present application provides trade company's recognition methods, and this method can be based on the trading activity of user, determining and sample This trade company may have the association trade company of like attribute, such as sample trade company is risk trade company, then is associated with trade company and is likely to It is association trade company.On this basis, the association trade company and institute are further determined according to the incidence relation between trade company and user The similarity between sample trade company is stated, and is denoised based on similarity, the lower association trade company of similarity is excluded, to be promoted The accuracy of trade company's identification.
In actual scene, the identification equipment for executing this method can be user equipment, the network equipment or user equipment Constituted equipment is integrated by network with the network equipment.Wherein, the user equipment include but is not limited to personal computer, All kinds of terminal devices such as smart phone, tablet computer, the network equipment include but is not limited to that such as network host, single network take Device, multiple network server collection or the set of computers based on cloud computing etc. of being engaged in are realized, can be used to implement when alarm clock is arranged Part processing function.Here, cloud is made of a large amount of hosts or network server for being based on cloud computing (Cloud Computing), Wherein, cloud computing is one kind of distributed computing, a virtual machine consisting of a loosely coupled set of computers.
Fig. 1 shows a kind of process flow of trade company's recognition methods provided by the embodiments of the present application, includes at least following place Manage step:
Step S101 obtains the user that transaction occurred with sample trade company in first time window, as association user. Wherein, the sample trade company is the sample according to selected by the demand of identification scene, these samples can have certain specific Attribute, in order to identify the target trade company for obtaining and there is like attribute.For example, the attribute, which can be, is accused of gambling, then may be used It, can be with if user needs to identify the trade company for being accused of fraudulent act using the gambling trade company that will have been acknowledged as sample trade company Using the swindle trade company having been acknowledged as sample trade company.
In actual scene, the set of trade company can be denoted as merchant cluster, and the set of user can be denoted as user collection Group, at this point, the trade company in the merchant cluster is sample trade company, determining association user can be put into user cluster. The first time window refers to a preset period, and the length of the period can be according to the demand of actual scene Setting, for example, it can be set to being 1 hour, 90 minutes, 2 hours etc..
If in the embodiment of the present application, the first time window that sets as the period of 20 points to 21 points of hour, Merchant cluster includes 2 sample trade company m1 and m2, the two sample trade companies are gambling trade company.By transferring the two samples The transaction data of payment platform where this trade company can count and all friendship occur between 20 points to 21 points with m1 or m2 Easy user, such as the result of association user obtained in embodiment is to be denoted as u1-10, i.e. user cluster comprising 10 users In include tetra- elements of u1-10.
Step S102 determines that transaction count is more in the association user according to the transaction count of the association user Central user.
In actual scene, for certain a kind of trade company with like attribute, generally all can some frequently trade User group, such as trade company of gambling, the user of transaction may include some users to gamble once in a while, it also can be comprising frequent The user to gamble.Wherein, the user to gamble once in a while participates in gambling at other gambling trade companies since the number of gambling is few Probability is also relatively small, conversely, the user frequently to gamble is more likely to since the number for participating in gambling is more in different gamblings Bo Shanghuchu participates in gambling.It therefore, is frequency by the more central user of transaction count under the scene of gambling trade company identification A possibility that numerous user to gamble, the other gambling trade companies that can be found by these users, can be bigger, needs according to transaction time Number determines central user in association user.
In actual scene, it can determine that the more center of transaction count is used in the association user in the following way Family.For example, can be ranked up according to the transaction count of association user, then chosen from sequence according to the result of sequence it is N number of, Centered on user.By taking the user in aforementioned user cluster as an example, the transaction count of each user can be as shown in the table:
u1 u2 u3 u4 u5 u6 u7 u8 u9 u10
2 3 21 105 99 6 1 5 63 1
It follows that according to the ranking results of transaction count from greatly to it is small successively are as follows: u4, u5, u9, u3, u6, u8, u2, U1, u7 and u10.Wherein, N is positive integer, and a fixed value can be redefined for according to the empirical value of application scenarios, can also be with It is that an identified dynamic value is adjusted according to the real trade number dynamic of user each in sequence.For example, N can be set as Fixed value 5, thus the result of central user is u4, u5, u9, u3 and u6.In the present embodiment, since the setting of fixed value is general Depending on the empirical value under the scene, it is thus possible to appearance and situation unmatched in certain processing, such as aforementioned N is to fix The case where when value 5, the transaction count of central user u6 and the gap of non-central user are smaller, and the gap with other central users It is larger instead, it is thus possible to the association user weaker with sample trade company relevance to be determined as central user.
In order to enable correlation degree when determining central user between more more enough reflections and sample trade company, it can be based on each The dynamic value that the order of magnitude of a association user determines.Firstly, determining transaction count according to the transaction count of the association user The order of magnitude.For example, being indicated with the metric order of magnitude, wherein 2=2 × 100, its order of magnitude be 0,21=2.1 × 101, its The order of magnitude is 1,105=1.05 × 102, its order of magnitude be 2, and so on, can get the number of each association user transaction count Magnitude, as shown in the table:
u1 u2 u3 u4 u5 u6 u7 u8 u9 u10
0 0 1 2 1 0 0 0 1 0
Then, according to the order of magnitude of the transaction count, the higher center of quantification grade is used in the association user Association user of the order of magnitude 1 or more can be chosen in family, such as the present embodiment, the N thereby determined that is 4, corresponding center User is u3, u4, u5, u9.Compared to the mode of fixed value, when determining central user, can be obtained more based on the order of magnitude Representational data, the correlation degree between the central user determined and sample trade company can be significantly hotter than other non-central use Family, so that subsequent processing knot is more accurate.Here, it will be appreciated by those skilled in the art that above-mentioned according to the order of magnitude Determine that concrete mode when central user is only for example, the existing or other deformations or expansion based on similar principles that occur from now on If exhibition mode can be suitable for the application, also should include comprising within the scope of protection of this application, and in the form of reference In this.For example, the threshold value of transaction count can be set, such as 20,80, if the transaction count of association user is more than the threshold value, Determine it as central user.2 × 10 be can be understood as due to 201, 80 can be understood as=8 × 101, therefore, such mode It can be understood as the extension of the aforementioned mode based on the order of magnitude, thus include within the scope of protection of this application.
After determining central user, user cluster can be updated with this, such as in the user cluster include at this time User can be updated to u3, u4, u5, u9.
Step S103 obtains the trade company that transaction occurred with the central user in the second time window, as association Trade company.Wherein, second time window is a period for being different from the first time window, can be by selecting again It takes the mode of a period to determine, can also be determined by the way of sliding first time window.For example, when described first Between window be 20 points to 21 points, window can be slided backward into half an hour, thereby determine that the second time window be 20 thirty extremely 21 thirty.
Since identified central user is that there are the stronger users of correlation degree with sample trade company, with these centers User occurred have the biggish trade companies may comprising other and sample trade company with like attribute in the trade company of transaction.Example Such as, when sample trade company is gambling trade company, in the association user based on determined by respective center user, having very big may include Others gambling trade company, therefore new gambling trade company can be found in this manner.But due to the user of related to gambling activities in addition into It bribes except winning, it is also possible to other normal trading activities, such as purchase daily life articles etc. are had, it is identified at this time It equally can include that normal trade company needs to lead to avoid for normal trade company being erroneously identified as gambling trade company in association user Later continuous step is accurately identified.
Step S104 determines the association trade company and the sample trade company according to the incidence relation between trade company and user Between similarity.The quantity of identified association trade company is 3 in the embodiment of the present application, and respectively m3, m4 and m5, Fig. 2 is The schematic diagram for successively obtaining central user by sample trade company, being associated with trade company, node therein is trade company or user, between node Line indicate that trading activity occurred between trade company and user.Trading activity is able to reflect out being associated between trade company and user Relationship, such as multiple central users all only be associated withs trade company with one trading activity have occurred, it may be considered that the association trade company and Meeting similarity with higher, logic are between sample of users: central user is higher with sample of users correlation degree User, the incidence relation that trading activity is shown can reflect the attribute of trade company, such as gambling quotient to a certain extent The corresponding central user in family is some users frequently to gamble, these users are likely to also join at other gambling trade companies Gambling, if multiple central users are all only traded with a trade company in a period of time, then this trade company is gambling trade company Probability will be high, i.e. the similarity of the trade company and sample trade company is higher.
When judging similarity, can be according to following principle: the quantity of multiple central users be more, then is associated with trade company and sample Trade company has the probability of like attribute also can be higher, and the similarity for being associated with trade company and sample trade company is higher.In addition, if multiple centers Also with other users transaction occurred for part or all users in user, then other than transaction occurs with the trade company Probability can reduce, and the similarity for being associated with trade company and sample trade company is higher.It as a result, can be according to trade company and use based on above principle Incidence relation between family determines the similarity between the association trade company and the sample trade company.
It, can be between the expression trade company that formalized based on Swing structure and user in some embodiments of the present application Incidence relation.Friendship occurred to being associated with trade company with one for the user of two central users of Swing representation composition Easily, the structure that Swing structure shows as the node of two users in Fig. 2, the node of trade company and its line are constituted, example As u1, u2 and m3 and its between the structure that is constituted of line.
Based on the Swing structure, in some embodiments of the present application, the phase between association trade company and sample trade company is being determined When seemingly spending, processing step as shown in Figure 3 can be used, comprising:
Step S301 obtains the first quantity of Swing structure relevant to the first association trade company.Due to calculating association quotient Similarity between family and the sample trade company needs to calculate the similarity between each association trade company and sample trade company one by one, First association trade company is currently calculated trade company, such as when calculating the similarity between m3 and sample trade company, should M3 is the first association trade company.By taking scene shown in Fig. 2 as an example, wherein the quantity of Swing structure relevant to m3 is 1, i.e., The Swing structure being made of u1, u2 and m3.
Step S302 is obtained to the first user to the second quantity of relevant other Swing structures.Wherein, described first User is to for user couple corresponding to the first relevant Swing structure of association trade company, such as the first association trade company m3, with it Relevant Swing structure is the Swing structure being made of u1, u2 and m3, in the Swing structure corresponding user to for u1 and U2. it follows that corresponding first user is to as u1 and u2 for the first association trade company m3.
To the first user in this present embodiment to u1 and u2, relevant other Swing structures are by u1, u2 and m5 institute The Swing structure of composition, it is possible thereby to determine that the second quantity of other Swing structures relevant to u1 and u2 to the first user is 1。
Step S303, according to first quantity and the second quantity, determine the first association trade company and the sample trade company it Between similarity.Based on principle when judging similarity it was determined that first is associated with the phase between trade company and the sample trade company It is positively correlated like degree and first quantity, and negatively correlated with the second quantity.Therefore, first can be calculated based on following mode The similarity being associated between trade company and the sample trade company: S=nX-mY, wherein n is the first quantity, and m is the second quantity, X is positively related parameter, for indicating each positive influences of the Swing structure relevant to the first association trade company for similarity Degree, Y are negative relevant parameter, for indicate each to the first user to relevant other Swing structures for similarity Negative effect degree.X and Y can be based on specific application scenarios, in conjunction with the difference in scene because usually determining.
Here, those skilled in the art are it should be understood that the mode of above-mentioned calculating similarity is only for example, it is existing or modern If the other deformations based on similar principles occurred afterwards or extended mode can be suitable for the application, it should also be included in this In the protection scope of application, and it is incorporated herein in the form of reference.For example, Swing structure is for similarity in actual scene Influence may be more complicated, therefore can be in conjunction with the other information of user and/or trade company in related Swing structure, such as Evaluation information, other transaction records, credit value in transaction platform etc. individually assess each Swing structure for similarity Positive influences or negative effect etc., similarity final is determined with this.
By taking scene shown in Fig. 2 as an example, for being associated with trade company m3, corresponding first quantity is that the 1, second quantity is 1；It is right In association trade company m4, corresponding first quantity is that the 1, second quantity is 0；For being associated with trade company m5, corresponding first quantity It is 1 for the 3, second quantity.It is possible thereby to which the similarity calculated between each association trade company and sample trade company is as shown in the table:
Trade company m3 m4 m5
First quantity 1 1 3
Second quantity 1 0 1
Similarity X-Y X 3X-Y
Step S105 determines that similarity meets the target trade company of preset condition in the association trade company.Wherein, described pre- If condition can be set according to the demand of practical application scene, such as can be ranked up according to the specific value of similarity, choosing It takes the forward association trade company that sorts as target trade company, if in the present embodiment, the value for setting the X is greater than Y, then can determine Sequence after similarity S1, S2 and S3 sequence corresponding to three associations trade company m3, m4 and m5 are as follows: S3 > S2 > S1, if choosing The first two data for wherein sorting forward, then identified target trade company is m4 and m5.
Alternatively, a threshold value can also be set, the association trade company that similarity is more than or equal to the threshold value is determined as target quotient Family, such as given threshold Z, then can be by being respectively compared the size of S1, S2, S3 and Z, so that it is determined that target trade company.Here, this Field technical staff it should be understood that the concrete form of above-mentioned preset condition is only for example, it is existing or occur from now on based on If other deformations of similar principles or extended mode can be suitable for the application, it should also be included in the protection model of the application In enclosing, and it is incorporated herein in the form of reference.
In some embodiments of the present application, according to the incidence relation between trade company and user, the association trade company is determined When similarity between the sample trade company, can also in conjunction with the aforementioned mode for filtering out central user from association user, First from association trade company in filter out center trade company, then with center trade company substitution script association trade company, calculate similarity and from In identify target trade company.Fig. 4 shows another trade company recognition methods provided by the embodiments of the present application, including at least following Processing step:
Step S401 obtains the user that transaction occurred with sample trade company in first time window, as association user.
Step S402 determines that transaction count is more in the association user according to the transaction count of the association user Central user.
Step S403 obtains the trade company that transaction occurred with the central user in the second time window, as association Trade company.
Step S404 determines that transaction count is more in the association trade company according to the transaction count of the association trade company Center trade company.
Step S405 determines the center trade company and the sample trade company according to the incidence relation between trade company and user Between similarity.
Step S406 determines that similarity meets the target trade company of preset condition in the center trade company.
Wherein, described when determining the center trade company, can according to association trade company transaction count use in determination Mode as heart user class.For example, first determining the order of magnitude of transaction count, then according to the transaction count of the association trade company According to the order of magnitude of the transaction count, the higher center trade company of quantification grade in the association trade company, so that quotient The recognition result at family is more accurate.
In order to enable recognition result is more accurate, in some embodiments of the present application, can be based on above-mentioned any one Kind scheme is iterated processing, i.e., the sample that will execute target trade company identifying processing as after determined by an identifying processing This trade company, and slide after the first time window makes time window change, it is iterated processing, until processing obtains The target trade company convergence obtained, it is possible thereby to which the error rate of identification is substantially reduced.Fig. 5 is the schematic diagram of iterative processing.Wherein, When judging whether target trade company restrains, can be compared based on the result of this identification with result before at least once, If these results are consistent or difference is within the scope of setting, it may be considered that the target trade company convergence that processing obtains.
For example, 50 target trade companies are identified after the 10th iterative processing, 50 target trade companies and preceding primary knowledge The target trade company (i.e. this sample trade company) not obtained is identical, it is possible thereby to which the target trade company for thinking that this processing obtains receives It holds back, as final recognition result.Also such as, after the 7th iterative processing, 100 target trade companies are identified, with the 6th The recognition result of secondary processing all only has 1 target trade company difference, has 2 target trade companies different from the recognition result of the 5th processing, Judge that convergent condition is the difference with recognition result twice before within 3% if setting, it may be considered that the 7th iteration The target trade company that processing obtains has restrained, and can be used as final recognition result.Here, those skilled in the art should can manage Solution, it is above-mentioned to judge that the whether convergent mode of target trade company is only for example, it is existing or occur from now on based on the other of similar principles It, also should be comprising within the scope of protection of this application, and with reference if deformation or extended mode can be suitable for the application Form be incorporated herein.
In addition, can be used in the row of identification with user the embodiment of the present application also provides a kind of data object recognition methods For there are associated data objects, such as can be the trade company with user there are trading activity, be furthermore also possible to can by with Webpage that family thumbs up, can be by commodity of user's evaluation etc..Similar with the scene that trade company identifies, other behaviors with user, which exist, closes The data object of connection can also be based on sample data object, find central user, and then find there is class with sample data object Like the associated data object of attribute, and according between user and data object be based on user behavior caused by incidence relation, The similarity between associated data object and sample data object is determined, to complete to identify.
Fig. 6 shows the process flow of data object recognition methods provided by the embodiments of the present application, may include following place Manage step:
Step S601 obtains the user that correlating event occurred with sample data object in first time window, as Association user.
Step S602 determines association according to the correlating event frequency of the association user in the association user The more central user of event frequency.
Step S603 obtains the data object that correlating event occurred with the central user in the second time window, As associated data object.
Step S604, according to the incidence relation between data object and user, determine the associated data object with it is described Similarity between sample data object.
Step S605 determines that similarity meets the target data objects of preset condition in the associated data object.
The program can extend to other certain behaviors with user there are associated data objects as a result, to identify The data object with particular community is accurately identified, so that application scenarios are more extensive.Such as it is disliked for identification by user Anticipate the webpage thumbed up, by commodity etc. of user's malice evaluation, carry out wind so as to the behavior to malice brush temperature, brush evaluation Dangerous prevention and control.It is similar with the scheme that trade company identifies, it can be used in improving the extension side of identification accuracy in aforementioned trade company's identification scene The schemes such as case, such as iterative processing, extraction center trade company also can be applied in the identifying schemes of data object, since it is related to Principle it is similar, details are not described herein again.
Based on the same inventive concept, a kind of trade company's identification equipment is additionally provided in the embodiment of the present application, the equipment is corresponding Method be trade company's recognition methods in previous embodiment, and its principle solved the problems, such as is similar to this method.
Trade company's identification equipment provided by the embodiments of the present application can be based on the trading activity of user, and determining and sample trade company can There can be the association trade company of like attribute, such as sample trade company is risk trade company, then be associated with trade company and be likely to be association quotient Family.On this basis, the association trade company and the sample quotient are further determined according to the incidence relation between trade company and user Similarity between family, and denoised based on similarity, the lower association trade company of similarity is excluded, to promote trade company's identification Accuracy.
In actual scene, trade company identification equipment can be user equipment, the network equipment or user equipment and network Equipment is integrated constituted equipment by network.Wherein, the user equipment includes but is not limited to personal computer, intelligent hand All kinds of terminal devices such as machine, tablet computer, the network equipment include but is not limited to as network host, single network server, Multiple network server collection or the set of computers based on cloud computing etc. are realized, at part when can be used to implement setting alarm clock Manage function.Here, cloud is made of a large amount of hosts or network server for being based on cloud computing (Cloud Computing), wherein cloud Calculating is one kind of distributed computing, a virtual machine consisting of a loosely coupled set of computers.
Fig. 7 shows a kind of structure of trade company's identification equipment provided by the embodiments of the present application, includes at least: the first association mould Block 710, screening module 720, the second relating module 730 and denoising module 740.
First relating module 710 is used to obtain the use that transaction occurred with sample trade company in first time window Family, as association user.Wherein, the sample trade company is the sample according to selected by the demand of identification scene, these samples can To obtain the target trade company with like attribute in order to identify with certain specific attributes.For example, the attribute can be and relate to Dislike gambling, then it can be using the gambling trade company having been acknowledged as sample trade company, if user, which needs to identify, is accused of swindle row For trade company, then can be using the swindle trade company having been acknowledged as sample trade company.
In actual scene, the set of trade company can be denoted as merchant cluster, and the set of user can be denoted as user collection Group, at this point, the trade company in the merchant cluster is sample trade company, determining association user can be put into user cluster. The first time window refers to a preset period, and the length of the period can be according to the demand of actual scene Setting, for example, it can be set to being 1 hour, 90 minutes, 2 hours etc..
If in the embodiment of the present application, the first time window that sets as the period of 20 points to 21 points of hour, Merchant cluster includes 2 sample trade company m1 and m2, the two sample trade companies are gambling trade company.By transferring the two samples The transaction data of payment platform where this trade company can count and all friendship occur between 20 points to 21 points with m1 or m2 Easy user, such as the result of association user obtained in embodiment is to be denoted as u1-10, i.e. user cluster comprising 10 users In include tetra- elements of u1-10.
The screening module 720 is used for the transaction count according to the association user, determines and hands in the association user The more central user of easy number.
In actual scene, for certain a kind of trade company with like attribute, generally all can some frequently trade User group, such as trade company of gambling, the user of transaction may include some users to gamble once in a while, it also can be comprising frequent The user to gamble.Wherein, the user to gamble once in a while participates in gambling at other gambling trade companies since the number of gambling is few Probability is also relatively small, conversely, the user frequently to gamble is more likely to since the number for participating in gambling is more in different gamblings Bo Shanghuchu participates in gambling.It therefore, is frequency by the more central user of transaction count under the scene of gambling trade company identification A possibility that numerous user to gamble, the other gambling trade companies that can be found by these users, can be bigger, needs according to transaction time Number determines central user in association user.
In actual scene, screening module 720 can determine transaction count in the association user in the following way More central user.For example, can be ranked up according to the transaction count of association user, then according to the result of sequence from sequence Chosen in column it is N number of, centered on user.By taking the user in aforementioned user cluster as an example, the transaction count of each user can be as Shown in following table:
u1 u2 u3 u4 u5 u6 u7 u8 u9 u10
2 3 21 105 99 6 1 5 63 1
It follows that according to the ranking results of transaction count from greatly to it is small successively are as follows: u4, u5, u9, u3, u6, u8, u2, U1, u7 and u10.Wherein, N is positive integer, and a fixed value can be redefined for according to the empirical value of application scenarios, can also be with It is that an identified dynamic value is adjusted according to the real trade number dynamic of user each in sequence.For example, N can be set as Fixed value 5, thus the result of central user is u4, u5, u9, u3 and u6.In the present embodiment, since the setting of fixed value is general Depending on the empirical value under the scene, it is thus possible to appearance and situation unmatched in certain processing, such as aforementioned N is to fix The case where when value 5, the transaction count of central user u6 and the gap of non-central user are smaller, and the gap with other central users It is larger instead, it is thus possible to the association user weaker with sample trade company relevance to be determined as central user.
In order to enable correlation degree when determining central user between more more enough reflections and sample trade company, it can be based on each The dynamic value that the order of magnitude of a association user determines.Firstly, screening module can be according to the transaction count of the association user, really Determine the order of magnitude of transaction count.For example, being indicated with the metric order of magnitude, wherein 2=2 × 100, its order of magnitude be 0,21 =2.1 × 101, its order of magnitude be 1,105=1.05 × 102, its order of magnitude be 2, and so on, can get each association user The order of magnitude of transaction count, as shown in the table:
u1 u2 u3 u4 u5 u6 u7 u8 u9 u10
0 0 1 2 1 0 0 0 1 0
Then, screening module can be according to the order of magnitude of the transaction count, the quantification grade in the association user Higher central user, such as association user of the order of magnitude 1 or more can be chosen in the present embodiment, the N thereby determined that is 4, corresponding central user is u3, u4, u5, u9.Compared to the mode of fixed value, based on the order of magnitude come when determining central user, More representational data can be obtained, the correlation degree between the central user determined and sample trade company can be significantly hotter than Other non-central users, so that subsequent processing knot is more accurate.Here, it will be appreciated by those skilled in the art that above-mentioned Determine that concrete mode when central user is only for example according to the order of magnitude, existing or its based on similar principles that occurs from now on It, also should be comprising within the scope of protection of this application, and to draw if it is deformed or extended mode can be suitable for the application Form is incorporated herein.For example, the threshold value of transaction count can be set, such as 20,80, if the transaction count of association user is super The threshold value is crossed, then determines it as central user.2 × 10 be can be understood as due to 201, 80 can be understood as=8 × 101, because This, such mode is it can be appreciated that be the extension of the aforementioned mode based on the order of magnitude, thus be included in the protection model of the application In enclosing.
After determining central user, user cluster can be updated with this, such as in the user cluster include at this time User can be updated to u3, u4, u5, u9.
With the central user transaction occurred for second relating module 730 in the second time window for obtaining Trade company, as association trade company.Wherein, second time window is a period for being different from the first time window, It can be determined, can also be determined by the way of sliding first time window by way of choosing a period again. For example, the first time window is 20 points to 21 points, window can be slided backward into half an hour, thereby determine that for the second time Window is 20 thirty to 21 thirty.
Since identified central user is that there are the stronger users of correlation degree with sample trade company, with these centers User occurred have the biggish trade companies may comprising other and sample trade company with like attribute in the trade company of transaction.Example Such as, when sample trade company is gambling trade company, in the association user based on determined by respective center user, having very big may include Others gambling trade company, therefore new gambling trade company can be found in this manner.But due to the user of related to gambling activities in addition into It bribes except winning, it is also possible to other normal trading activities, such as purchase daily life articles etc. are had, it is identified at this time It equally can include that normal trade company needs to lead to avoid for normal trade company being erroneously identified as gambling trade company in association user Later continuous step is accurately identified.
The denoising module 740 is used to determine the association trade company and institute according to the incidence relation between trade company and user State the similarity between sample trade company.The quantity of identified association trade company is 3, respectively m3, m4 in the embodiment of the present application And m5, Fig. 2 are the schematic diagram for successively obtaining central user by sample trade company, being associated with trade company, node therein is trade company or use Family, the line between node indicate that trading activity occurred between trade company and user.Trading activity is able to reflect out trade company and uses Incidence relation between family, such as all only be associated with trade company with one has occurred trading activity to multiple central users, it may be considered that Meeting similarity with higher, logic are between the association trade company and sample of users: central user is and sample of users is closed The higher user of connection degree, the incidence relation that trading activity is shown can reflect the category of trade company to a certain extent Property, such as the corresponding central user of gambling trade company is some users frequently to gamble, these users are likely in other gamblings Bo Shanghuchu also gambles, if multiple central users are all only traded with a trade company in a period of time, then this quotient Family is that the probability of gambling trade company will be high, i.e. the similarity of the trade company and sample trade company is higher.
When judging similarity, can be according to following principle: the quantity of multiple central users be more, then is associated with trade company and sample Trade company has the probability of like attribute also can be higher, and the similarity for being associated with trade company and sample trade company is higher.In addition, if multiple centers Also with other users transaction occurred for part or all users in user, then other than transaction occurs with the trade company Probability can reduce, and the similarity for being associated with trade company and sample trade company is higher.It as a result, can be according to trade company and use based on above principle Incidence relation between family determines the similarity between the association trade company and the sample trade company.
It, can be between the expression trade company that formalized based on Swing structure and user in some embodiments of the present application Incidence relation.Friendship occurred to being associated with trade company with one for the user of two central users of Swing representation composition Easily, the structure that Swing structure shows as the node of two users in Fig. 2, the node of trade company and its line are constituted, example As u1, u2 and m3 and its between the structure that is constituted of line.
Based on the Swing structure, in some embodiments of the present application, denoising module is determining association trade company and sample trade company Between similarity when, processing step as shown in Figure 3 can be used, comprising:
Step S301 obtains the first quantity of Swing structure relevant to the first association trade company.Due to calculating association quotient Similarity between family and the sample trade company needs to calculate the similarity between each association trade company and sample trade company one by one, First association trade company is currently calculated trade company, such as when calculating the similarity between m3 and sample trade company, should M3 is the first association trade company.By taking scene shown in Fig. 2 as an example, wherein the quantity of Swing structure relevant to m3 is 1, i.e., The Swing structure being made of u1, u2 and m3.
Step S302 is obtained to the first user to the second quantity of relevant other Swing structures.Wherein, described first User is to for user couple corresponding to the first relevant Swing structure of association trade company, such as the first association trade company m3, with it Relevant Swing structure is the Swing structure being made of u1, u2 and m3, in the Swing structure corresponding user to for u1 and U2. it follows that corresponding first user is to as u1 and u2 for the first association trade company m3.
To the first user in this present embodiment to u1 and u2, relevant other Swing structures are by u1, u2 and m5 institute The Swing structure of composition, it is possible thereby to determine that the second quantity of other Swing structures relevant to u1 and u2 to the first user is 1。
Step S303, according to first quantity and the second quantity, determine the first association trade company and the sample trade company it Between similarity.Based on principle when judging similarity it was determined that first is associated with the phase between trade company and the sample trade company It is positively correlated like degree and first quantity, and negatively correlated with the second quantity.Therefore, first can be calculated based on following mode The similarity being associated between trade company and the sample trade company: S=nX-mY, wherein n is the first quantity, and m is the second quantity, X is positively related parameter, for indicating each positive influences of the Swing structure relevant to the first association trade company for similarity Degree, Y are negative relevant parameter, for indicate each to the first user to relevant other Swing structures for similarity Negative effect degree.X and Y can be based on specific application scenarios, in conjunction with the difference in scene because usually determining.
Here, those skilled in the art are it should be understood that the mode of above-mentioned calculating similarity is only for example, it is existing or modern If the other deformations based on similar principles occurred afterwards or extended mode can be suitable for the application, it should also be included in this In the protection scope of application, and it is incorporated herein in the form of reference.For example, Swing structure is for similarity in actual scene Influence may be more complicated, therefore can be in conjunction with the other information of user and/or trade company in related Swing structure, such as Evaluation information, other transaction records, credit value in transaction platform etc. individually assess each Swing structure for similarity Positive influences or negative effect etc., similarity final is determined with this.
By taking scene shown in Fig. 2 as an example, for being associated with trade company m3, corresponding first quantity is that the 1, second quantity is 1；It is right In association trade company m4, corresponding first quantity is that the 1, second quantity is 0；For being associated with trade company m5, corresponding first quantity It is 1 for the 3, second quantity.It is possible thereby to which the similarity calculated between each association trade company and sample trade company is as shown in the table:
Trade company m3 m4 m5
First quantity 1 1 3
Second quantity 1 0 1
Similarity X-Y X 3X-Y
In addition, denoising module 740 is also used to determine that similarity meets the target quotient of preset condition in the association trade company Family.Wherein, the preset condition can be set according to the demand of practical application scene, such as can be according to the specific number of similarity Value is ranked up, and chooses the forward association trade company that sorts as target trade company, if in the present embodiment, the value for setting the X is big Sequence in Y, then after can determining similarity S1, S2 and S3 sequence corresponding to three associations trade company m3, m4 and m5 are as follows: S3 > S2 > S1, if choosing the first two data for wherein sorting forward, identified target trade company is m4 and m5.
Alternatively, a threshold value can also be set, the association trade company that similarity is more than or equal to the threshold value is determined as target quotient Family, such as given threshold Z, then can be by being respectively compared the size of S1, S2, S3 and Z, so that it is determined that target trade company.Here, this Field technical staff it should be understood that the concrete form of above-mentioned preset condition is only for example, it is existing or occur from now on based on If other deformations of similar principles or extended mode can be suitable for the application, it should also be included in the protection model of the application In enclosing, and it is incorporated herein in the form of reference.
In some embodiments of the present application, the second relating module is determined according to the incidence relation between trade company and user When the similarity being associated between trade company and the sample trade company, center can also be filtered out from association user in conjunction with aforementioned The mode of user first filters out center trade company from association trade company, then with the association trade company of center trade company substitution script, calculates Similarity simultaneously therefrom identifies target trade company.As a result, in another trade company's identification equipment provided by the embodiments of the present application, described the Two relating modules be used for according to it is described association trade company transaction count, in the association trade company determination transaction count it is more in Heart trade company, and according to the incidence relation between trade company and user, determine the phase between the center trade company and the sample trade company Determine that similarity meets the target trade company of preset condition like degree, and in the center trade company.
Wherein, described when determining the center trade company, can according to association trade company transaction count use in determination Mode as heart user class.For example, first determining the order of magnitude of transaction count, then according to the transaction count of the association trade company According to the order of magnitude of the transaction count, the higher center trade company of quantification grade in the association trade company, so that quotient The recognition result at family is more accurate.
In order to enable recognition result is more accurate, in the trade company's identification equipment provided in some embodiments of the present application, It can also include iteration control module, be used for using the target trade company as sample trade company, and slide the first time window, It controls first relating module, screening module, the second relating module and denoising module is iterated processing until processing obtains Target trade company convergence.
By iteration control module, it can will execute target trade company determined by an identifying processing and once be identified as after The sample trade company of processing, and slide after the first time window makes time window change, it is iterated processing, directly The target trade company convergence obtained to processing, it is possible thereby to which the error rate of identification is substantially reduced.Fig. 5 is the signal of iterative processing Figure.Wherein, when judging whether target trade company restrains, can based on this identification result with before at least once result into Row compares, if these results are consistent or difference is within the scope of setting, it may be considered that the target trade company that processing obtains receives It holds back.
For example, 50 target trade companies are identified after the 10th iterative processing, 50 target trade companies and preceding primary knowledge The target trade company (i.e. this sample trade company) not obtained is identical, it is possible thereby to which the target trade company for thinking that this processing obtains receives It holds back, as final recognition result.Also such as, after the 7th iterative processing, 100 target trade companies are identified, with the 6th The recognition result of secondary processing all only has 1 target trade company difference, has 2 target trade companies different from the recognition result of the 5th processing, Judge that convergent condition is the difference with recognition result twice before within 3% if setting, it may be considered that the 7th iteration The target trade company that processing obtains has restrained, and can be used as final recognition result.Here, those skilled in the art should can manage Solution, it is above-mentioned to judge that the whether convergent mode of target trade company is only for example, it is existing or occur from now on based on the other of similar principles It, also should be comprising within the scope of protection of this application, and with reference if deformation or extended mode can be suitable for the application Form be incorporated herein.
In addition, the embodiment of the present application also provides a kind of data objects to identify equipment, it can be used in row of the identification with user For there are associated data objects, such as can be the trade company with user there are trading activity, be furthermore also possible to can by with Webpage that family thumbs up, can be by commodity of user's evaluation etc..Similar with the scene that trade company identifies, other behaviors with user, which exist, closes The data object of connection can also be based on sample data object, find central user, and then find there is class with sample data object Like the associated data object of attribute, and according between user and data object be based on user behavior caused by incidence relation, The similarity between associated data object and sample data object is determined, to complete to identify.
The structure of the data object identification equipment is similar with trade company above-mentioned identification equipment, may include the first association mould Block, screening module, the second relating module and denoising module.Wherein, the first relating module, for obtaining in first time window The user that correlating event occurred with sample data object, as association user；Screening module is used for according to the association user Correlating event frequency, the more central user of correlating event frequency is determined in the association user；Second closes Gang mould block is for obtaining the data object that correlating event occurred with the central user in the second time window, as association Data object；Module is denoised to be used for according to the incidence relation between data object and user, determine the associated data object with Similarity between the sample data object, and determine that similarity meets the mesh of preset condition in the associated data object Mark data object.
The program can extend to other certain behaviors with user there are associated data objects as a result, to identify The data object with particular community is accurately identified, so that application scenarios are more extensive.Such as it is disliked for identification by user Anticipate the webpage thumbed up, by commodity etc. of user's malice evaluation, carry out wind so as to the behavior to malice brush temperature, brush evaluation Dangerous prevention and control.It is similar with the scheme that trade company identifies, it can be used in improving the extension side of identification accuracy in aforementioned trade company's identification scene The schemes such as case, such as iterative processing, extraction center trade company also can be applied in the identifying schemes of data object, since it is related to Principle it is similar, details are not described herein again.
In addition, a part of the application can be applied to computer program product, such as computer program instructions, when its quilt When computer executes, by the operation of the computer, it can call or provide according to the present processes and/or technical solution. And the program instruction of the present processes is called, it is possibly stored in fixed or moveable recording medium, and/or pass through Broadcast or the data flow in other signal-bearing mediums and transmitted, and/or be stored according to program instruction run calculating In the working storage of machine equipment.Here, include a calculating equipment as shown in Figure 8 according to some embodiments of the present application, The equipment includes being stored with one or more memories 810 of computer-readable instruction and for executing computer-readable instruction Processor 820, wherein when the computer-readable instruction is executed by the processor, so that the equipment, which executes, is based on aforementioned The method and/or technology scheme of multiple embodiments of application.
In addition, some embodiments of the present application additionally provide a kind of computer-readable medium, it is stored thereon with computer journey Sequence instruction, the computer-readable instruction can be executed by processor with the method for realizing multiple embodiments of aforementioned the application and/ Or technical solution.
It should be noted that the application can be carried out in the assembly of software and/or software and hardware, for example, can adopt With specific integrated circuit (ASIC), general purpose computer or any other realized similar to hardware device.In some embodiments In, the software program of the application can be executed by processor to realize above step or function.Similarly, the software of the application Program (including relevant data structure) can be stored in computer readable recording medium, for example, RAM memory, magnetic or CD-ROM driver or floppy disc and similar devices.In addition, hardware can be used to realize in some steps or function of the application, for example, As the circuit cooperated with processor thereby executing each step or function.
It is obvious to a person skilled in the art that the application is not limited to the details of above-mentioned exemplary embodiment, Er Qie In the case where without departing substantially from spirit herein or essential characteristic, the application can be realized in other specific forms.Therefore, no matter From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and scope of the present application is by appended power Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims Variation is included in the application.Any reference signs in the claims should not be construed as limiting the involved claims.This Outside, it is clear that one word of " comprising " does not exclude other units or steps, and odd number is not excluded for plural number.That states in device claim is multiple Unit or device can also be implemented through software or hardware by a unit or device.The first, the second equal words are used to table Show title, and does not indicate any particular order.

Claims (14)

1. a kind of trade company's recognition methods, wherein this method comprises:
The user that transaction occurred with sample trade company in first time window is obtained, as association user；
According to the transaction count of the association user, the more central user of transaction count is determined in the association user；
The trade company that transaction occurred with the central user in the second time window is obtained, as association trade company；
According to the incidence relation between trade company and user, the similarity between the association trade company and the sample trade company is determined；
Determine that similarity meets the target trade company of preset condition in the association trade company.
2. according to the method described in claim 1, wherein, according to the incidence relation between the trade company and user, determine described in The similarity being associated between trade company and the sample trade company, comprising:
Obtain the first quantity of Swing structure relevant to the first association trade company, wherein in the Swing representation two To trade company is associated with one transaction occurred for the user of heart user composition, between the first association trade company and the sample trade company Similarity and first quantity are positively correlated；
It obtains to the first user to the second quantity of relevant other Swing structures, wherein first user is to for first It is associated with the corresponding user couple of the relevant Swing structure of trade company, it is similar between the first association trade company and the sample trade company Degree is negatively correlated with the second quantity；
According to first quantity and the second quantity, the similarity between the first association trade company and the sample trade company is determined.
3. according to the method described in claim 1, wherein, according to the transaction count of the association user, in the association user The more central user of middle determining transaction count, comprising:
According to the transaction count of the association user, the order of magnitude of transaction count is determined；
According to the order of magnitude of the transaction count, the higher central user of quantification grade in the association user.
4. according to the method described in claim 1, wherein, according to the incidence relation between trade company and user, determining the association Similarity between trade company and the sample trade company, comprising:
According to the transaction count of the association trade company, the more center trade company of transaction count is determined in the association trade company, and According to the incidence relation between trade company and user, the similarity between the center trade company and the sample trade company is determined；
Determine that similarity meets the target trade company of preset condition in the association trade company, comprising:
Determine that similarity meets the target trade company of preset condition in the center trade company.
5. according to the method described in claim 4, wherein, according to the transaction count of the association trade company, in the association trade company The more center trade company of middle determining transaction count, comprising:
According to the transaction count of the association trade company, the order of magnitude of transaction count is determined；
According to the order of magnitude of the transaction count, the higher center trade company of quantification grade in the association trade company.
6. the method according to any one of claims 1 to 5, wherein this method further include:
Using the target trade company as sample trade company, and the first time window is slided, is iterated processing until processing obtains The target trade company convergence obtained.
7. a kind of data object recognition methods, wherein this method comprises:
The user that correlating event occurred with sample data object in first time window is obtained, as association user；
According to the correlating event frequency of the association user, determined in the association user correlating event frequency compared with More central users；
The data object that correlating event occurred with the central user in the second time window is obtained, as associated data pair As；
According to the incidence relation between data object and user, determine the associated data object and the sample data object it Between similarity；
Determine that similarity meets the target data objects of preset condition in the associated data object.
8. a kind of trade company identifies equipment, wherein the equipment includes:
First relating module, for obtaining the user that transaction occurred with sample trade company in first time window, as association User；
Screening module determines that transaction count is more in the association user for the transaction count according to the association user Central user；
Second relating module, for obtaining the trade company that transaction occurred with the central user in the second time window, as It is associated with trade company；
Module is denoised, for determining the association trade company and the sample trade company according to the incidence relation between trade company and user Between similarity, and determine that similarity meets the target trade company of preset condition in the association trade company.
9. equipment according to claim 8, wherein the denoising module, it is relevant to the first association trade company for obtaining First quantity of Swing structure, wherein the user of two central users of Swing representation composition with one to closing Transaction occurred for connection trade company, and the similarity and first quantity between the first association trade company and the sample trade company are positively correlated； It obtains to the first user to the second quantity of relevant other Swing structures, wherein first user with first to be associated with The corresponding user couple of the relevant Swing structure of trade company, it is described first association trade company and the sample trade company between similarity with Second quantity is negatively correlated；And according to first quantity and the second quantity, determine the first association trade company and the sample trade company Between similarity.
10. equipment according to claim 8, wherein the denoising module, for the transaction time according to the association trade company Number, determines the more center trade company of transaction count in the association trade company, and according to the incidence relation between trade company and user, It determines the similarity between the center trade company and the sample trade company, and determines that similarity meets in the center trade company The target trade company of preset condition.
11. the equipment according to any one of claim 8 to 10, wherein the equipment further include:
Iteration control module is used for using the target trade company as sample trade company, and slides the first time window, controls institute It states the first relating module, screening module, the second relating module and denoising module is iterated processing until the target that processing obtains Trade company's convergence.
12. a kind of data object identifies equipment, wherein the equipment includes:
First relating module, for obtaining the user that correlating event occurred with sample data object in first time window, As association user；
Screening module determines association for the correlating event frequency according to the association user in the association user The more central user of event frequency；
Second relating module, for obtaining the data pair that correlating event occurred with the central user in the second time window As associated data object；
Denoise module, for according to the incidence relation between data object and user, determine the associated data object with it is described Similarity between sample data object, and determine that similarity meets the number of targets of preset condition in the associated data object According to object.
13. a kind of calculating equipment, wherein the equipment includes memory by storing computer program instructions and based on executing The processor of calculation machine program instruction, wherein when the computer program instructions are executed by the processor, trigger the equipment and execute Method described in any one of claims 1 to 7.
14. a kind of computer-readable medium, is stored thereon with computer program instructions, the computer-readable instruction can be processed Device is executed to realize the method as described in any one of claims 1 to 7.
CN201910093177.3A 2019-01-30 2019-01-30 Identification method, equipment and computer readable medium Active CN110060053B (en)

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