CN113485900A - User behavior analysis method, system, equipment and computer medium - Google Patents
User behavior analysis method, system, equipment and computer medium Download PDFInfo
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Abstract
The invention discloses a user behavior analysis method, a system, equipment and a computer medium, wherein the method comprises the following steps: constructing a user relationship network based on the user behavior data set; extracting target information from the user relationship network according to a preset rule; and analyzing the target information by utilizing a graph computing technology to obtain a user behavior analysis result. The method can quickly acquire the user behavior characteristics through the relational network structure, and more intuitively display the change trend of the user behavior.
Description
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to a method, a system, a device, and a computer medium for user behavior analysis.
Background
In the field of banking, user behaviors mainly include logging in an online bank APP, inquiring, transferring accounts, withdrawing money and the like, user behavior preference can be effectively mined and marketing recommendation can be carried out by analyzing the user behaviors, and information such as user accounts, equipment and operation risks can be monitored.
In the prior art, an analysis method for user behaviors comprises rule matching, machine learning and the like, but is limited by huge user behavior data volume in the bank field and close relation between user behaviors and occurrence time, the data analysis operation amount by using the prior method is high, the related information of the user behaviors is difficult to obtain quickly, and the complex relation among data cannot be intuitively reflected by the user behavior result obtained by analysis.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a user behavior analysis method, system, device and computer medium, which can quickly obtain user behavior characteristics from a large data set and dynamically and intuitively display the behavior change trend of a user.
In a first aspect, an embodiment of the present invention provides a user behavior analysis method, including:
constructing a user relationship network based on the user behavior data set;
extracting target information from the user relationship network according to a preset rule;
and analyzing the target information by utilizing a graph computing technology to obtain a user behavior analysis result.
In a certain embodiment, the building a user relationship network based on the user behavior data set specifically includes: the user behavior data set comprises node data and edge data; and constructing a user relationship basic network according to the incidence relation between the node data and the edge data.
In one embodiment, the node data includes a subject element and an event element; wherein the event element further comprises a time element.
In one embodiment, a sliding time window is determined based on the time element; and sequentially analyzing the target information according to the sliding time windows to obtain user behavior analysis results under each sliding time window.
In one embodiment, the graph computation technique includes a clustering algorithm or a graph convolution neural network algorithm.
In one embodiment, the user behavior analysis result is visually displayed.
In a second aspect, an embodiment of the present invention further provides a user behavior analysis system, including:
the building unit is used for building a user relationship network based on the user behavior data set;
the extraction unit is used for extracting target information from the user relationship network according to a preset rule;
and the analysis unit is used for analyzing the target information by utilizing a graph calculation technology to obtain a user behavior analysis result.
Preferably, the user behavior analysis system further includes: and the visualization unit is used for visually displaying the user behavior analysis result.
In a third aspect, the present invention provides a data processing apparatus comprising a processor, the processor being coupled to a memory, the memory storing a program, the program being executable by the processor to cause the data processing apparatus to perform the user behavior analysis method of the first aspect.
In a fourth aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the user behavior analysis method according to the first aspect.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
according to the invention, a user relationship network is established, a user behavior result under a preset rule is quickly calculated by using a graph calculation method based on a data network structure (node-edge), the operation efficiency is improved, the user behavior mode change in different time ranges is dynamically obtained by combining a sliding time window, and the user behavior mode is visually displayed in a visual manner, so that technicians are helped to clearly and effectively understand the association relationship between user behavior data.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a user behavior analysis method according to an embodiment of the present invention;
FIG. 2 is a partial relationship diagram of elements in a bank user behavior data set according to an embodiment of the present invention;
fig. 3 is a schematic application diagram of a bank user behavior analysis method model according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a user behavior analysis system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not used as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, an embodiment of the present invention provides a user behavior analysis method, which specifically includes the following steps:
and S11, constructing a user relationship network based on the user behavior data set.
Specifically, the user behavior data set comprises node data and edge data; constructing a user relationship basic network according to the incidence relation between the node data and the edge data,
the node data includes a main element including a main body of a user behavior and an event element including elements such as a behavior of a user and a device. For example, in user behavior data in banking, user behaviors include login, transfer, and query; the device elements contain device MAC addresses, IMEI numbers, and device fingerprint information.
Specifically, the main body element may be associated with other relationships, such as a relationship between a user and a user, a relationship between a user and a personal information element, and the like, in addition to the event element and the device element.
It can be understood that, for the user data stored in the database, a complete user relationship network may be constructed by taking the user, the behavior event of the user, the used terminal device, and the like as nodes, and taking the relationship between the user and the event, the relationship between the user and the device, and the like as edges.
And S12, extracting target information from the user relationship network according to a preset rule.
To illustrate S12, the following usage scenarios are provided as examples:
scene 1: setting the 'user who logs in the device in the last month' as a rule, and extracting relevant data by using a rule engine, namely: randomly selecting a user, finding out the login event element node in the last month directly connected with the user, checking the number of equipment element nodes associated with the part of nodes, if the number of equipment exceeds 10, defining the user as a risk user, and extracting the user account; according to the logic, traversing the user relationship network, acquiring a full risk user account and related associated data, and defining the acquired data as target information.
Scene 2: setting 'equipment with log-in records in the last three months' as a rule, and extracting relevant data by using a rule engine, namely: randomly selecting one device, finding out the log-in event element nodes which are directly connected with the device in the last three months, checking the number of user main body element nodes which are related to the part of nodes, if the number of the user main bodies exceeds 10, defining the device as a risk device, and extracting the device number; according to the logic, traversing the user relationship network, acquiring the total risk equipment number and the related associated data, and defining the acquired data as the target information.
Scene 3: setting a 'user with a behavior event record in the last month' as a rule, and extracting relevant data by using a rule engine, namely: randomly selecting a user, finding out the total event element nodes which are directly connected with the user in the last six months, summarizing the nodes according to the types of the event elements, and setting the event type with the largest number of nodes as a user behavior type; according to the logic, traversing the user relationship network, acquiring the full amount of user behavior types and related associated data, and defining the acquired data as target information.
And S13, analyzing the target information by using a graph calculation technology to obtain a user behavior analysis result.
In particular, graph computation techniques include clustering algorithms or graph convolution neural network algorithms.
By utilizing the graph calculation method, the user behavior model can be identified, and the specific elements in the user relationship network can be extracted quickly and conveniently so as to be applied to the field of customer management in the following process.
In one embodiment, the event elements of the user behavior data set further include a time element, namely: the event occurrence time can be determined according to the time element or preset with a sliding time window.
And sequentially executing and acquiring user behavior analysis results corresponding to the event occurrence time according to the sliding time windows, so that the user behavior analysis results under each sliding time window can be obtained, and behavior mode change results of the user at different time periods can be further obtained by using the user behavior analysis results.
By the method, the behavior result of the user in the continuous time period can be obtained, the behavior patterns of the client in different time periods are observed by modifying the time window, and the dynamic change of the behavior patterns of the user is analyzed to obtain richer user behavior information.
Specifically, in this embodiment, any time period may be selected by modifying the sliding time window, the user behavior analysis result in the time period is obtained by using the user behavior analysis method, and the user behavior analysis result is displayed according to the time sequence.
In a specific embodiment, a user relationship basic network is used as a basis for behavior data analysis, a graph calculation method is used for rapidly acquiring a user behavior pattern, finally, related data are visually displayed, the visual display can show the association relationship of the data in a multi-dimensional mode in the form of nodes and edges, so that analysts can conveniently and visually explore and research the user behavior analysis result, and the user behavior pattern is mined.
Another embodiment of the present invention, as shown in fig. 2 and 3, provides a banking customer behavior analysis model.
Fig. 2 shows a schematic diagram of partial relationship of elements in a bank customer behavior data set in a business system, wherein event elements include event attributes, occurrence time, and personal information elements such as mobile phones and addresses, and according to the association relationship among the event elements, user subject elements, device elements, and other elements, the event elements can be connected in pairs to form a user relationship network.
Referring to fig. 3, a basic data platform is connected to the business system, and the platform is built by using Hadoop or other data formats, can collect and store data from the source system, can further summarize the data, and processes the summarized data; the basic data platform is connected to the relational network platform, and the relational network platform is constructed by a graph calculation database or other databases, can store relational data and provides basic functions of inquiry, analysis, graph-related calculation and the like.
In the embodiment, the target information of customer behavior analysis is extracted from the user relationship network by using a rule engine in a relationship network platform, and the behavior of the bank customer is calculated, inquired and analyzed by using a graph calculation method on the basis of the target information, so that a customer behavior pattern is mined.
It should be noted that the application model provided in fig. 3 can also calculate and extract specific data elements to provide marketing or wind control scenario usage.
According to the invention, a user relationship network is established, a user behavior result under a preset rule is quickly calculated by using a graph calculation method based on a data network structure (node-edge), the operation efficiency is improved, the user behavior mode change in different time ranges is dynamically obtained by combining a sliding time window, and the user behavior mode is visually displayed in a visual manner, so that technicians are helped to clearly and effectively understand the association relationship between user behavior data.
In a second aspect, an embodiment of the present invention further provides a user behavior analysis system, which includes a construction unit 101, an extraction unit 102, and an analysis unit 103.
The construction unit 101 is configured to construct a user relationship network based on the user behavior data set.
The extracting unit 102 is configured to extract target information from the user relationship network according to a preset rule.
The analysis unit 103 is configured to analyze the target information by using a graph computation technique to obtain a user behavior analysis result.
Specifically, the user behavior analysis system further includes a visualization unit 104, and the visualization unit 104 is configured to visually display the user behavior analysis result.
In a third aspect, the present invention provides a data processing apparatus comprising a processor, the processor being coupled to a memory, the memory storing a program, the program being executable by the processor to cause the data processing apparatus to perform the user behavior analysis method of the first aspect.
In a fourth aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the user behavior analysis method according to the first aspect.
It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium, and may include the processes of the embodiments of the methods when executed. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A user behavior analysis method is characterized by comprising the following steps:
constructing a user relationship network based on the user behavior data set;
extracting target information from the user relationship network according to a preset rule;
and analyzing the target information by utilizing a graph computing technology to obtain a user behavior analysis result.
2. The user behavior analysis method according to claim 1, wherein the user relationship network is constructed based on the user behavior data set, specifically:
the user behavior data set comprises node data and edge data;
and constructing a user relationship basic network according to the incidence relation between the node data and the edge data.
3. The user behavior analysis method according to claim 2, wherein the node data includes a subject element and an event element; wherein the event element further comprises a time element.
4. The user behavior analysis method according to claim 3, further comprising:
determining a sliding time window according to the time element;
and sequentially analyzing the target information according to the sliding time windows to obtain user behavior analysis results under each sliding time window.
5. The method of user behavior analysis according to claim 1, wherein the graph computation technique comprises a clustering algorithm or a graph convolution neural network algorithm.
6. The user behavior analysis method according to claim 1, further comprising:
and visually displaying the user behavior analysis result.
7. A user behavior analysis system, comprising:
the building unit is used for building a user relationship network based on the user behavior data set;
the extraction unit is used for extracting target information from the user relationship network according to a preset rule;
and the analysis unit is used for analyzing the target information by utilizing a graph calculation technology to obtain a user behavior analysis result.
8. The user behavior analysis method according to claim 7, further comprising:
and the visualization unit is used for visually displaying the user behavior analysis result.
9. A data processing apparatus, characterized by comprising:
a processor coupled to a memory, the memory storing a program for execution by the processor to cause the data processing apparatus to perform the user behavior analysis method of any of claims 1 to 5.
10. A computer storage medium storing computer instructions for performing the method of any one of claims 1 to 5.
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| CN111046237A (en) * | 2018-10-10 | 2020-04-21 | 北京京东金融科技控股有限公司 | User behavior data processing method and device, electronic equipment and readable medium |
| CN112528166A (en) * | 2020-12-16 | 2021-03-19 | 平安养老保险股份有限公司 | User relationship analysis method and device, computer equipment and storage medium |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107153847A (en) * | 2017-05-31 | 2017-09-12 | 北京知道创宇信息技术有限公司 | Predict method and computing device of the user with the presence or absence of malicious act |
| CN109146707A (en) * | 2018-08-27 | 2019-01-04 | 罗孚电气（厦门）有限公司 | Power consumer analysis method, device and electronic equipment based on big data analysis |
| CN111046237A (en) * | 2018-10-10 | 2020-04-21 | 北京京东金融科技控股有限公司 | User behavior data processing method and device, electronic equipment and readable medium |
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