CN110633994A - Identification method and device for single swiping behavior - Google Patents

Identification method and device for single swiping behavior Download PDF

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CN110633994A
CN110633994A CN201910631176.XA CN201910631176A CN110633994A CN 110633994 A CN110633994 A CN 110633994A CN 201910631176 A CN201910631176 A CN 201910631176A CN 110633994 A CN110633994 A CN 110633994A
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contact
transaction data
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许翠
郭佳敏
梁思维
董一帆
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Agricultural Bank of China
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    • 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
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
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    • G—PHYSICS
    • 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
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
    • G06Q30/0635—Processing of requisition or of purchase orders

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Abstract

The application discloses a method and a device for identifying a single-swiping behavior.

Description

Identification method and device for single swiping behavior
Technical Field
The application relates to the technical field of electronic commerce, in particular to a method and a device for identifying a bill swiping behavior.
Background
With the rapid development of e-commerce technology, online shopping brings great convenience to people's lives, and more consumers buy commodities on online shopping platforms. When a consumer browses commodities on line, a plurality of online shopping platforms provide sorting and screening functions according to multiple dimensions such as sales volume and user scores, meanwhile, unfair competition among merchants is also endless, and the form brushing is a common means, so that not only are merchants hired or purchase robot accounts brush forms for stores of the merchants, but also sales volume and commodity scores are rapidly improved, and the consumer is attracted to purchase commodities.
In order to counteract the merchant's behavior of swiping a bill and maintain good platform reputation and operational order, online shopping platforms often detect the merchant's behavior of swiping a bill. At present, the online shopping platform detects the bank-swiping behavior of a merchant based on the bank-swiping behavior detection model.
However, a current detection model for checking the form of the online shopping platform is a classifier using a classification algorithm such as a support vector machine and a decision tree, and when the classifier is constructed, a consumer and a merchant are regarded as independent individuals, and the connection between the individuals is ignored, so that when the classifier is used for detecting the form-checking behavior of the merchant, the detection result may be inaccurate, and the hidden abnormal transaction relationship between the consumer and the merchant cannot be found.
Disclosure of Invention
In view of the above, the present application is proposed in order to provide a method and apparatus for identification of a swiping behavior overcoming the above mentioned problems or at least partially solving the above mentioned problems. The specific scheme is as follows:
a method of identifying a swiping behavior, the method comprising:
acquiring target transaction data;
generating a social network model based on shopping behaviors according to the target transaction data;
determining a conglomerate subgroup involving a swipe behavior in the shopping behavior-based social networking model;
analyzing the aggregate subgroup related to the swipe action to determine merchants and users related to the swipe action.
Optionally, the generating a social networking model based on shopping behavior according to the target transaction data includes:
determining an actor according to the target transaction data;
determining connections between the actors and attributes of the connections based on the target transaction data;
determining a contact value between the actors according to the contact attribute;
and determining a social network model based on shopping behaviors according to the contact values among the actors.
Optionally, the determining an actor from the target transaction data comprises:
and determining users and merchants of the online shopping platform with shopping behaviors according to the target transaction data.
Optionally, the determining the contact between the actors and the attributes of the contact according to the target transaction data includes:
determining historical order data existing between the actors based on the target transaction data;
and determining the attributes of the commodities, the attributes of the trading behaviors and the attributes of the user evaluation information after the trading is finished according to the historical order data.
Optionally, the determining a contact value between the actors according to the property of the contact includes:
determining an attribute of a connection between a first actor and a second actor;
for each attribute, determining a final contact value of the attribute according to the weight coefficient of the attribute and the initial contact value of the attribute;
and calculating the contact value between the first actor and the second actor by superposing the final contact values of the attributes.
An apparatus for identifying a swiping behavior, the apparatus comprising:
an acquisition unit for acquiring target transaction data;
the social network model generating unit is used for generating a social network model based on shopping behaviors according to the target transaction data;
a condensation subgroup determination unit for determining a condensation subgroup related to the order-brushing behavior in the shopping behavior-based social network model;
and the condensation subgroup analysis unit is used for analyzing the condensation subgroup related to the brushing behavior to determine the merchant and the user related to the brushing behavior.
Optionally, the social network model generating unit includes:
the actor determining subunit is used for determining actors according to the target transaction data;
a contact determining subunit, configured to determine, according to the target transaction data, a contact between the actors and an attribute of the contact;
a contact value determination subunit, configured to determine a contact value between the actors according to the contact attribute;
and the social network model determining subunit is used for determining the social network model based on the shopping behaviors according to the contact values among the actors.
Optionally, the contact value determining subunit is specifically configured to:
determining an attribute of a connection between a first actor and a second actor;
for each attribute, determining a final contact value of the attribute according to the weight coefficient of the attribute and the initial contact value of the attribute;
and calculating the contact value between the first actor and the second actor by superposing the final contact values of the attributes.
A storage medium on which a program is stored, which when executed by a processor implements the method of recognition of a swipe action as described above.
A terminal, comprising:
a processor and a memory;
wherein the processor is configured to execute a program stored in the memory;
the memory is to store a program to at least:
acquiring target transaction data;
generating a social network model based on shopping behaviors according to the target transaction data;
determining a conglomerate subgroup involving a swipe behavior in the shopping behavior-based social networking model;
analyzing the aggregate subgroup related to the swipe action to determine merchants and users related to the swipe action.
By means of the technical scheme, the application discloses a method and a device for identifying a single-swiping behavior.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical solutions of the present application more clearly understood, and the following detailed description of the present application is provided in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a schematic flow chart illustrating a method for identifying a brush line according to the present disclosure;
FIG. 2 is a schematic flow chart diagram illustrating a method for generating a social networking model based on shopping behaviors from the target transaction data according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a device for identifying a single row by brushing according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a server of an online shopping platform according to an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It can be known from the background art that a detection model for a single-file behavior commonly used in an online shopping platform at present is a classifier using a classification algorithm such as a support vector machine and a decision tree, and when the classifier is constructed, consumers and merchants are regarded as independent individuals, and the connection among the individuals is ignored.
The embodiment of the application provides a corresponding improvement scheme aiming at the situation. The scheme can be provided for an online shopping platform server to use, and particularly provides a method for identifying the behavior of a single-swiping bank, which is based on social networks and condensation subgroups to identify the behavior of the single-swiping bank.
The social network is a point-like network topology structure formed by the actors and the connections between the actors. The actor is an actor in the social network, has subjective initiative and initiative, and is often represented by nodes in the social network. The relationships between actors are connections, which are often represented by edges in a social network.
The social network analysis is a computable analysis method based on the fusion theory and method of multiple disciplines such as informatics, mathematics, sociology, management, psychology and the like, and is provided for understanding the formation of various human social relationships, behavior characteristic analysis and information propagation rules, and can be used for analyzing the social network.
Actors with relatively strong, direct, close, frequent, or positive relationships throughout the social network can be found based on social network analysis, and these actors constitute small groups throughout the social network, such small groups being referred to as agglomerative subgroups. The cohesive subgroup has a much more interesting value than other loose network structures in social networks.
According to the method and the device, the social network model based on the shopping behaviors is established through analysis of transaction data, the recognition of the single-swiping behaviors is realized through recognition of the coacervate subgroup in the social network model based on the shopping behaviors, and the efficiency and the accuracy of single-swiping behavior recognition are improved.
The following describes the brush row identification method provided in the present application in detail.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for identifying a brushing bank as an identification line according to an embodiment of the present application, where an execution subject of the method is a server of an online shopping platform, and the method specifically includes the following steps:
s101: target transaction data is obtained.
The transaction data comprises the time of the user browsing the commodity on the online shopping platform, the duration of the transaction, the order information of the user to a certain commodity, comment data after the order is completed and the like.
In the present application, all transaction data of the online shopping platform may be used as target transaction data. However, if the amount of users of the online shopping platform is too large, the amount of generated transaction data is too large, and if all transaction data of the online shopping platform are used as target transaction data, the identification efficiency of the order swiping behavior will be affected, so that in the application, a sampling mode can be adopted to randomly obtain part of transaction data of the online shopping platform as target transaction data.
S102: and generating a social network model based on shopping behaviors according to the target transaction data.
In the present application, the elements of the social network model based on the shopping behavior may be determined according to the target transaction data, and then the social network model based on the shopping behavior may be generated according to the elements. The specific implementation will be illustrated in detail by the following examples.
S103: determining a subgroup of agglomerations in the shopping behavior-based social networking model that involves a swipe behavior.
In the application, the aggregate subgroup related to the single-swiping behavior in the social network model based on the shopping behavior can be identified by analyzing the social network model based on the shopping behavior.
Examples are as follows: the relationship between the actors with the behavior of the waybill is uniform and compact, and presents high similarity, and the normal transaction behaviors often have large difference, and the coagulation subgroup with the characteristics in the social network model based on the shopping behaviors is identified as the coagulation subgroup related to the behavior of the waybill.
S104: analyzing the condensed subgroup related to the brushing behavior to determine merchants and users related to the brushing behavior.
In the application, the aggregate subgroup related to the order-swiping behavior can be analyzed to determine suspicious transaction behaviors, and then merchants and users related to the order-swiping behavior can be found.
Referring to fig. 2, fig. 2 is a schematic flow chart of a method for generating a social network model based on shopping behavior according to the target transaction data according to an embodiment of the present application, where the method specifically includes the following steps:
s201: determining an actor according to the target transaction data;
the core goal of the swiping bank for identification is to identify the merchant and the consumer involved in the swiping bank in the network transaction, so in the application, the actor refers to the user and the merchant of the online shopping platform with the shopping behavior, and can be represented by using the unique identifier such as the user identifier id and the merchant identifier id. These actors constitute nodes in a social network based on shopping behavior.
S202: determining connections between the actors and attributes of the connections according to the target transaction data;
in the application, the contact refers to historical order data existing between a user and a merchant, and the attribute of the contact, namely the characteristic of the transaction, can be extracted through information such as commodity information, transaction time, transaction times, comment information and the like.
The properties of the contact mainly comprise the following parts:
the self attributes of the commodities comprise the browsing times of the commodities, the historical orders of the commodities, the volume of deals in a period of time and the like;
the attributes of the transaction behavior comprise the stay time of the user on the commodity page, the duration of the transaction, the time difference between commodity browsing and payment of the user, the transaction times of the user and the merchant and the like;
and after the transaction is finished, the attributes of the user evaluation information comprise the user evaluation content, the length of the evaluation text, the commodity and merchant scores of the user, the user evaluation times and the like.
S203: determining a contact value between the actors according to the contact attribute;
in the present application, different weight coefficients may be set for various attributes of the contact between actors, and for any attribute, the final contact value of the attribute may be determined from the weight coefficient of the attribute and the initial contact value of the attribute, and the contact value between actors may be calculated by superimposing the final contact values of the attributes.
Examples are as follows: contact S between actor i and actor j(i,j)The calculation formula of (2) is as follows:
Figure BDA0002128705140000071
wherein K represents the number of attributes of the transaction between actor i and actor j, wkWeight coefficients representing the respective attributes are
Figure BDA0002128705140000072
Fk(i,j)Contact values representing different contact attributes between actor i and actor j.
S204: and determining a social network model based on shopping behaviors according to the contact values among the actors.
In a social network based on shopping behavior, the connection between N actors can be represented by an N symmetric relation matrix, and the element S in the matrix(i,j)And S(j,i)Represents the value of the association between actor i and actor j.
In order to simplify calculation and improve analysis efficiency, a relation matrix can be preprocessed in a threshold processing mode, a relation value lower than a threshold is ignored, and the structure of a social network model based on shopping behaviors is simplified.
Examples are as follows: assuming that the threshold of the relation value is set as t, the conversion formula of the initial relation matrix preprocessing is as follows:
Figure BDA0002128705140000081
through the above conversion formula, the relation value less than t in the relation matrix is set to 0, and the relation matrix is changed into a sparse matrix.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a device for identifying a brush row as an identification line according to an embodiment of the present application, where the device includes the following units:
an acquisition unit 31 for acquiring target transaction data;
a social network model generating unit 32, configured to generate a social network model based on the shopping behavior according to the target transaction data;
a coagulation subgroup determination unit 33 for determining a coagulation subgroup relating to a swipe behavior in the shopping behavior-based social network model;
and the condensation subgroup analysis unit 34 is used for analyzing the condensation subgroup related to the brushing behavior to determine the merchants and users related to the brushing behavior.
Optionally, the social network model generating unit includes:
the actor determining subunit is used for determining actors according to the target transaction data;
a contact determining subunit, configured to determine, according to the target transaction data, a contact between the actors and an attribute of the contact;
a contact value determination subunit, configured to determine a contact value between the actors according to the contact attribute;
and the social network model determining subunit is used for determining the social network model based on the shopping behaviors according to the contact values among the actors.
Optionally, the contact value determining subunit is specifically configured to:
determining an attribute of a connection between a first actor and a second actor;
for each attribute, determining a final contact value of the attribute according to the weight coefficient of the attribute and the initial contact value of the attribute;
and calculating the contact value between the first actor and the second actor by superposing the final contact values of the attributes.
The device for identifying the brush row direction comprises a processor and a memory, wherein all the units are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more, and the identification of the brushing behavior is realized by adjusting the kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
The embodiment of the application provides a storage medium, wherein a program is stored on the storage medium, and the program realizes the identification method of the single swiping behavior when being executed by a processor.
The embodiment of the application provides a processor, wherein the processor is used for running a program, and the identification method of the list swiping behavior is executed when the program runs.
On the other hand, the present application also provides a server of an online shopping platform, as shown in fig. 4, which shows a schematic structural diagram of a component of the server of the online shopping platform of the present application, and the server 1100 of the online shopping platform of the present embodiment may include: a processor 1101 and a memory 1102.
Optionally, the server of the online shopping platform may further include a communication interface 1103, an input unit 1104, and a display 1105 and a communication bus 1106.
The processor 1101, the memory 1102, the communication interface 1103, the input unit 1104, and the display 1105 all communicate with each other via a communication bus 1106.
In this embodiment, the processor 1101 may be a Central Processing Unit (CPU), an application specific integrated circuit, a digital signal processor, an off-the-shelf programmable gate array or other programmable logic device.
The processor may call a program stored in the memory 1102. Specifically, the processor may perform operations performed by a server of the online shopping platform in an embodiment of the identification method of the billing behavior.
The memory 1102 is used for storing one or more programs, which may include program codes including computer operation instructions, and in this embodiment, at least the programs for implementing the following functions are stored in the memory.
In one possible implementation, the memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as an image playing function, etc.), and the like; the storage data area may store data created during use of the computer, such as user data, user access data, and audio, video, image data, etc.
Further, the memory 1102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device or other volatile solid-state storage device.
The communication interface 1103 may be an interface of a communication module, such as an interface of a GSM module.
The present application may also include a display 1104 and an input unit 1105, and the like.
Of course, the structure of the server of the online shopping platform shown in fig. 4 does not constitute a limitation to the server of the online shopping platform in the embodiment of the present application, and the server of the online shopping platform may include more or less components than those shown in fig. 4 or some components in combination in practical applications.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (10)

1. A method for identifying a swiping behavior, the method comprising:
acquiring target transaction data;
generating a social network model based on shopping behaviors according to the target transaction data;
determining a conglomerate subgroup involving a swipe behavior in the shopping behavior-based social networking model;
analyzing the aggregate subgroup related to the swiping behavior to determine the merchant and the user related to the swiping behavior.
2. The method of claim 1, wherein generating a social networking model based on shopping behavior from the targeted transaction data comprises:
determining an actor according to the target transaction data;
determining connections between the actors and attributes of the connections according to the target transaction data;
determining a contact value between the actors according to the contact attribute;
and determining a social network model based on shopping behaviors according to the contact values among the actors.
3. The method of claim 2, wherein determining actors based on the target transaction data comprises:
and determining users and merchants of the online shopping platform with shopping behaviors according to the target transaction data.
4. The method of claim 2, wherein the determining of the contact between the actors and the attributes of the contact from the targeted transaction data comprises:
determining historical order data existing between the actors based on the target transaction data;
and determining the attributes of the commodities, the attributes of the trading behaviors and the attributes of the user evaluation information after the trading is finished according to the historical order data.
5. The method of claim 2, wherein determining a contact value between the actors based on the attributes of the contact comprises:
determining an attribute of a connection between a first actor and a second actor;
for each attribute, determining a final contact value of the attribute according to the weight coefficient of the attribute and the initial contact value of the attribute;
and calculating the contact value between the first actor and the second actor by superposing the final contact values of the attributes.
6. An apparatus for recognizing a swiping behavior, the apparatus comprising:
an acquisition unit for acquiring target transaction data;
the social network model generating unit is used for generating a social network model based on shopping behaviors according to the target transaction data;
a condensation subgroup determination unit for determining a condensation subgroup related to a swipe behavior in the shopping behavior-based social network model;
and the condensation subgroup analysis unit is used for analyzing the condensation subgroup related to the brushing behavior to determine the merchant and the user related to the brushing behavior.
7. The apparatus of claim 6, wherein the social network model generating unit comprises:
the actor determining subunit is used for determining actors according to the target transaction data;
a contact determining subunit, configured to determine, according to the target transaction data, a contact between the actors and an attribute of the contact;
a contact value determination subunit, configured to determine a contact value between the actors according to the contact attribute;
and the social network model determining subunit is used for determining the social network model based on the shopping behaviors according to the contact values among the actors.
8. The apparatus according to claim 7, wherein the contact value determining subunit is specifically configured to:
determining an attribute of a connection between a first actor and a second actor;
for each attribute, determining a final contact value of the attribute according to the weight coefficient of the attribute and the initial contact value of the attribute;
and calculating the contact value between the first actor and the second actor by superposing the final contact values of the attributes.
9. A storage medium on which a program is stored, the program implementing the identification method of a swiping behavior according to any one of claims 1 to 5 when executed by a processor.
10. A terminal, comprising:
a processor and a memory;
wherein the processor is configured to execute a program stored in the memory;
the memory is to store a program to at least:
acquiring target transaction data;
generating a social network model based on shopping behaviors according to the target transaction data;
determining a conglomerate subgroup involving a swipe behavior in the shopping behavior-based social networking model;
analyzing the aggregate subgroup related to the swiping behavior to determine the merchant and the user related to the swiping behavior.
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