Method, system, device and computer-readable storage medium for acquiring user portrait groups based on group behavior

Abstract

The present invention provides a method, system, device and computer-readable storage medium for obtaining user portrait groups based on group behavior. This user portrait group acquisition method obtains similar groups corresponding to each user based on the user's operation behavior portrait, and then obtains corresponding user portrait groups according to different accuracy thresholds based on the similar groups. Through the technical solution proposed in this application, it is possible to accurately profile user behavior and obtain portrait groups through group behavior. This technical solution establishes a similar user group for each user, and then realizes automatic classification and acquisition of user portrait groups by setting different aggregation accuracy thresholds. It has the advantages of accurate portraits and adjustable aggregation accuracy. It can obtain various types of user portraits with different accuracies according to different accuracy requirements, which meets the user portrait requirements in product design, content recommendation and other fields, and has promotional value.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular, discloses a method, system, device and computer-readable storage medium for acquiring a user portrait group based on group behavior. Background Art

[0002] User portrait is a crucial link in product development. With accurate user portrait, targeted services can be provided to users. User portrait of traditional software products is that product managers build some user types by themselves, and classify users into a user type according to the information filled in by users and their behaviors. The types of user portraits built by traditional product managers are often too rough. Therefore, user portraits with thousands of faces for each person are developed, which use user behavior data and model reasoning to achieve personalized content recommendation. It is often difficult to design corresponding products for a single new user with completely personalized user portraits with thousands of faces. It can only find relevant content and recommend it to users based on the user behavior that has already occurred. The content recommended to users is often too homogeneous and of uneven quality, leaving users trapped in information cocoons. Summary of the invention

[0003] In order to solve the above problems existing in the prior art, the present invention provides a method, system, device and computer-readable storage medium for acquiring a user portrait group based on group behavior.

[0004] In a first aspect of the present application, a method for obtaining a user portrait group based on group behavior is provided, which specifically comprises the following steps:

[0005] Obtain the user's operation behavior profile based on the user's historical operation behavior log;

[0006] Based on the operation behavior portraits of all users, obtain the similar groups corresponding to each user;

[0007] Sort the similar groups in order from most to least according to the number of users included in the similar groups;

[0008] The similar groups are cyclically calculated according to the sorting to obtain several user portrait groups corresponding to all users, where:

[0009] Assume that the similar groups corresponding to the group containing n users are G1, G2, ..., Gn, and the corresponding user portrait groups are R1, R2, ..., Rm, where n and m are both non-zero natural numbers and m is less than or equal to n. Then the process of obtaining the user portrait groups R1, R2, ..., Rm specifically includes:

[0010] Step S1, set constant values ​​i=1, j=2;

[0011] Step S2, let R1 = G1;

[0012] Step S3, obtaining the behavior deviations D1, D2, ..., Di between R1, R2, ..., Ri and Gj;

[0013] Step S4, obtaining the minimum value of the behavior deviation and setting the behavior deviation corresponding to the minimum value as Dx;

[0014] Step S5, obtaining the user portrait group Rx corresponding to the behavior deviation degree Dx, and determining whether the number of users in the user portrait group Rx is greater than a first preset threshold: if yes, proceed to step S6, if no, proceed to step S7;

[0015] Step S6, when j is less than or equal to n, set i=i+1, j=j+1, Ri=Gj, and then return to step S3;

[0016] Step S7, when j is less than or equal to n, let the user portrait group Rx be the union of the user portrait group Rx and the similar group Gj, and let j=j+1, and then return to step S3.

[0017] In a possible implementation of the first aspect, in the process of obtaining the operation behavior portrait according to the historical operation behavior log, the following is further included:

[0018] Several function points are pre-set, each function point corresponds to a functional operation;

[0019] Get the number of user operations on function points;

[0020] The user behavior vector composed of the number of operations corresponding to all functional points is used as the operation behavior portrait.

[0021] In a possible implementation of the first aspect, in the process of obtaining a similar group corresponding to each user according to the operation behavior portraits of all users, the process further includes:

[0022] For the selected user, obtain the standard deviation of the user behavior vector of the selected user and the remaining users in all users;

[0023] Obtain the remaining users whose standard deviation is less than the second preset threshold as the similar group corresponding to the selected user;

[0024] Repeat the above steps until all users are traversed.

[0025] In a possible implementation of the first aspect, in the process of sequentially sorting the similar groups according to the number of users included in the similar groups, if there are several similar groups with the same number of users, the several similar groups are randomly sorted.

[0026] In a possible implementation of the first aspect above, the first preset threshold is positively correlated with the fuzziness of the user portrait group;

[0027] The larger the value of the first preset threshold is, the more users a single user portrait group includes, and the more blurred the user portrait corresponding to the user portrait group is.

[0028] The second aspect of the present application provides a user portrait group acquisition system based on group behavior, which is applied to the user portrait group acquisition method based on group behavior provided in the first aspect, including:

[0029] A behavior collection unit is used to obtain a user's operation behavior profile based on the user's historical operation behavior log;

[0030] The clustering unit is used to obtain similar groups corresponding to each user based on the operation behavior profiles of all users;

[0031] A sorting unit, used to sort the similar groups in order from most to least according to the number of users included in the similar groups;

[0032] The user portrait unit is used to perform a cyclic operation on similar groups according to the sorting to obtain several user portrait groups corresponding to all users, where:

[0033] Assume that the similar groups corresponding to the group containing n users are G1, G2, ..., Gn, and the corresponding user portrait groups are R1, R2, ..., Rm, where n and m are both non-zero natural numbers and m is less than or equal to n. Then the process of obtaining the user portrait groups R1, R2, ..., Rm specifically includes:

[0034] Step S1, set constant values ​​i=1, j=2;

[0035] Step S2, let R1 = G1;

[0036] Step S3, obtaining the behavior deviations D1, D2, ..., Di between R1, R2, ..., Ri and Gj;

[0037] Step S4, obtaining the minimum value of the behavior deviation and setting the behavior deviation corresponding to the minimum value as Dx;

[0038] Step S5, obtaining the user portrait group Rx corresponding to the behavior deviation degree Dx, and determining whether the number of users in the user portrait group Rx is greater than a first preset threshold: if yes, proceed to step S6, if no, proceed to step S7;

[0039] Step S6, when j is less than or equal to n, set i=i+1, j=j+1, Ri=Gj, and then return to step S3;

[0040] Step S7, when j is less than or equal to n, let the user portrait group Rx be the union of the user portrait group Rx and the similar group Gj, and let j=j+1, and then return to step S3.

[0041] In a possible implementation of the second aspect, the behavior collection unit further includes:

[0042] The setting module is used to pre-set several function points, each of which corresponds to a functional operation;

[0043] The collection module is used to obtain the number of operations performed by users on function points;

[0044] A generation module is used to use the user behavior vector composed of the number of operations corresponding to all function points as the operation behavior profile

[0045] In a possible implementation of the second aspect, the clustering unit further includes:

[0046] A calculation module, used for obtaining, for a selected user, a standard deviation of a user behavior vector of the selected user and the remaining users in all users;

[0047] The judgment module is used to obtain the remaining users whose standard deviation is less than the second preset threshold as the similar group corresponding to the selected user.

[0048] A third aspect of the present application provides a user portrait group acquisition device based on group behavior, including:

[0049] Memory for storing computer programs;

[0050] A processor is used to implement the method for obtaining a user portrait group based on group behavior provided in the first aspect when executing a computer program.

[0051] The fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for obtaining a user portrait group based on group behavior provided in the first aspect is implemented.

[0052] Compared with the prior art, this application has the following beneficial effects:

[0053] Through the technical solution proposed in this application, it is possible to accurately profile user behavior and obtain portrait groups through group behavior. This technical solution establishes a similar user group for each user, and then realizes automatic classification and acquisition of user portrait groups by setting different aggregation accuracy thresholds. It has the advantages of accurate portraits and adjustable aggregation accuracy. It can obtain various types of user portraits with different accuracy requirements according to different accuracy requirements. It meets the user portrait requirements in product design, content recommendation and other fields, and has promotional value. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0055] Figure 1 According to an embodiment of the present application, a flow chart of a method for acquiring a user portrait group based on group behavior is shown;

[0056] Figure 2 According to an embodiment of the present application, a schematic diagram of a process for acquiring a user portrait group is shown;

[0057] Figure 3 According to an embodiment of the present application, a schematic diagram of a process of obtaining an operation behavior portrait based on a historical operation behavior log is shown;

[0058] Figure 4 According to an embodiment of the present application, a schematic diagram of a process of obtaining a similar group corresponding to each user based on the operation behavior portraits of all users is shown;

[0059] Figure 5 According to an embodiment of the present application, a structural schematic diagram of a user portrait group acquisition system based on group behavior is shown;

[0060] Figure 6 According to an embodiment of the present application, a schematic diagram of the structure of a user portrait group acquisition device based on group behavior is shown;

[0061] Figure 7 According to an embodiment of the present application, a schematic diagram of the structure of a computer-readable storage medium is shown. DETAILED DESCRIPTION

[0062] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0063] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0064] In view of the practical problem that the accuracy of user portraits in the prior art cannot be freely adjusted and the accuracy is low, the present application provides a method, system, device and computer-readable storage medium for obtaining user portraits based on group behavior. Through the technical solution provided by the present application, various user portraits with different accuracies are obtained according to different accuracy requirements, which meets the user portrait requirements in the fields of product design, content recommendation, etc. and has a promotional value. The technical solution provided by the present application will be explained and illustrated in combination with the embodiments below.

[0065] In some embodiments of the present application, Figure 1 The figure shows a flow chart of a method for obtaining a user portrait group based on group behavior. Specifically, the method includes the following steps:

[0066] Step 101: Obtain the user's operation behavior profile based on the user's historical operation behavior log.

[0067] Step 102: Based on the operation behavior portraits of all users, obtain the similar groups corresponding to each user.

[0068] Step 103: Sort the similar groups in order from most to least according to the number of users included in the similar groups.

[0069] Step 104: Perform a loop operation on the similar groups according to the ranking to obtain a number of user portrait groups corresponding to all users.

[0070] Specifically, Figure 2 A schematic diagram of a process for acquiring a user portrait group is shown, wherein the similar groups corresponding to the group containing n users are set to be G1, G2, ..., Gn, and the corresponding user portrait groups are R1, R2, ..., Rm, wherein n and m are both non-zero natural numbers and m is less than or equal to n. The process of acquiring the user portrait groups R1, R2, ..., Rm is as follows: Figure 2 The shown may include:

[0071] Step S104a, setting constant values ​​i=1, j=2.

[0072] Step S104b, set R1=G1.

[0073] Step S104c, obtaining the behavior deviations D1, D2, ..., Di of R1, R2, ..., Ri and Gj.

[0074] Step S104d, obtaining the minimum value of the behavior deviations and setting the behavior deviation corresponding to the minimum value as Dx.

[0075] Step S104e, obtaining the user portrait group Rx corresponding to the behavior deviation degree Dx, and determining whether the number of users in the user portrait group Rx is greater than a first preset threshold: if so, proceed to step S6, if not, proceed to step S7.

[0076] Step S104f, when j is less than or equal to n, set i=i+1, j=j+1, Ri=Gj, and then return to step S104c.

[0077] Step S104g, when j is less than or equal to n, let the user portrait group Rx be the union of the user portrait group Rx and the similar group Gj, and let j=j+1, and then return to step S104c.

[0078] Through the above steps 101 to 104, it is possible to accurately obtain the user portrait group.

[0079] In some embodiments of the present application, Figure 3 A schematic diagram of a process of obtaining an operation behavior profile based on a historical operation behavior log is shown, which may specifically include:

[0080] Step 301: Pre-set a number of function points, each function point corresponding to a functional operation.

[0081] Step 302: Obtain the number of operations performed by the user on the function point.

[0082] Step 303: The user behavior vector composed of the number of operations corresponding to all function points is used as an operation behavior profile.

[0083] In the above embodiment, various behaviors of the user using the software can be collected to serve as the user's operation behavior profile. Specifically, some function points can be set in the software product. When the user operates a certain function of the software product, the background will collect the corresponding data information, and then obtain the user behavior vector by counting the number of operations of each user on each function point. For example, for three statistical function points, a user operates on the first function point 100 times, on the second function point 50 times, and on the third function point 200 times, then the user behavior vector of the user can be (100, 50, 200).

[0084] In some embodiments of the present application, Figure 4 A schematic diagram of a process for obtaining a similar group corresponding to each user based on the operation behavior portraits of all users is shown, which may specifically include:

[0085] Step 401: For a selected user, obtain the standard deviation of the user behavior vector of the selected user and the remaining users in all users.

[0086] Step 402: Obtain the remaining users whose standard deviation is less than the second preset threshold as the similarity group corresponding to the selected user.

[0087] Step 403: Repeat the above steps until the traversal of all users is completed.

[0088] In some embodiments of the present application, based on the setting of user behavior vectors, for a similar group, a weighted average method can be used to obtain the average behavior vector corresponding to the similar group. For example, for a similar group G = {user A, user B, user C}, where the behavior vector of user A is (101, 201, 301), the behavior vector of user B is (100, 200, 300), and the behavior vector of user C is (99, 199, 299), then the average behavior vector of the similar group can be expressed as ((101+100+99) / 3, (201+200+199) / 3, (301+300+299) / 3) = (100, 200, 300).

[0089] In the aforementioned step S3, the acquisition of the behavioral deviations D1, D2, ..., Di can also be represented according to the standard deviation value associated with the average behavior vector between the similar group and the user portrait group to be confirmed, thereby achieving the acquisition of the behavioral deviations D1, D2, ..., Di.

[0090] In some embodiments of the present application, in the process of sequentially sorting similar groups according to the number of users included in the similar groups, if there are several similar groups with the same number of users, the several similar groups are randomly sorted.

[0091] For example, in some specific embodiments of the present application, all users may include user A, user B, and user C, where user A's similarity group G1 = {user B}, user B's similarity group is G2 = {user A, user C}, and user C's similarity group is G3 = {user B}. Since similarity group G2 contains 2 users and similarity groups G1 and G3 each contain 1 user, the final ordering can be G2\G1\G3 or G2\G3\G1.

[0092] In some embodiments of the present application, in the aforementioned step 104, the first preset threshold may be dynamically adjustable, and the value of the first preset threshold is positively correlated with the degree of blurriness of the user portrait group. In other words, the larger the value of the first preset threshold, the more users a single user portrait group contains, and the more blurry the user portrait corresponding to the user portrait group.

[0093] It is understandable that the user portrait groups R1, R2, ..., Rm are constructed based on the first preset threshold, and the accuracy of the user portrait group can be adjusted by adjusting the value of the first preset threshold. When the first preset threshold is larger, the number of users included in the obtained user portrait group is more, and the corresponding user portrait is more vague; when the first preset threshold is large enough, all users can be included in one user portrait group, at this time m = 1, that is, there is only one user portrait group; and when the first preset threshold is small enough, m = n can be obtained, that is, one independent user corresponds to one user portrait.

[0094] In some embodiments of the present application, Figure 5 A user portrait group acquisition system based on group behavior is provided, which is applied to the user portrait group acquisition method based on group behavior provided in the above embodiment. Specifically, Figure 5 As shown, the user portrait group acquisition system may include:

[0095] The behavior collection unit 001 is used to obtain the user's operation behavior profile based on the user's historical operation behavior log;

[0096] Clustering unit 002, used to obtain similar groups corresponding to each user based on the operation behavior portraits of all users;

[0097] A sorting unit 003, used to sort the similar groups in order from most to least according to the number of users included in the similar groups;

[0098] The user portrait unit 004 is used to perform a cyclic operation on the similar groups according to the sorting to obtain a number of user portrait groups corresponding to all users, wherein: the similar groups corresponding to the groups containing n users are set to be G1, G2, ..., Gn, and the corresponding user portrait groups are R1, R2, ..., Rm, wherein n and m are both non-zero natural numbers and m is less than or equal to n, then the acquisition process of the user portrait groups R1, R2, ..., Rm is as follows Figure 2 As shown, no further elaboration is given here.

[0099] In some embodiments of the present application, further, in the above-mentioned behavior collection unit 001, it also includes:

[0100] The setting module is used to pre-set several function points, each of which corresponds to a functional operation;

[0101] The collection module is used to obtain the number of operations performed by users on function points;

[0102] A generation module is used to use the user behavior vector composed of the number of operations corresponding to all function points as the operation behavior profile

[0103] In some embodiments of the present application, further, in the above clustering unit 002, it also includes:

[0104] A calculation module, used for obtaining, for a selected user, a standard deviation of a user behavior vector of the selected user and the remaining users in all users;

[0105] The judgment module is used to obtain the remaining users whose standard deviation is less than the second preset threshold as the similar group corresponding to the selected user.

[0106] It can be understood that in the above embodiment, the functions performed by the behavior collection unit 001 to the user portrait unit 004 are consistent with the actions performed by steps 101 to 104 in the above embodiment, and will not be repeated here.

[0107] In some embodiments of the present application, a user portrait group acquisition device based on group behavior is also provided, and the device includes:

[0108] Memory for storing computer programs;

[0109] A processor is used to implement the steps of the image straightening method described in the technical solution of this application when executing a computer program.

[0110] It is understandable that various aspects of the technical solution of the present application can be implemented as a system, method or program product. Therefore, various aspects of the technical solution of the present application can be specifically implemented in the following forms, namely, a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software, which can be collectively referred to as "circuit", "module" or "platform" here.

[0111] Figure 6 According to some embodiments of the present application, a schematic diagram of a structure of a user portrait group acquisition device based on group behavior is shown. Figure 6 The electronic device 600 implemented according to the implementation in this embodiment is described in detail. Figure 6 The electronic device 600 shown is merely an example and should not impose any limitations on the functions and scope of use of any embodiment of the technical solution of the present application.

[0112] like Figure 6As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0113] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 executes the implementation steps described in the above-mentioned image stitching method area according to the present embodiment. For example, the processing unit 610 can execute the following steps: Figures 1 to 5 Follow the steps shown in .

[0114] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access unit (RAM) 6201 and / or a cache storage unit 6202 , and may further include a read-only storage unit (ROM) 6203 .

[0115] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0116] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0117] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 through the bus 630. It should be understood that although Figure 6 Not shown, other hardware and / or software modules may be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0118] In some embodiments of the present application, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the relevant steps of the method for obtaining a user portrait group based on group behavior provided in the above embodiments can be implemented.

[0119] Although this embodiment does not list other specific implementation methods in detail, in some possible implementation methods, various aspects of the technical solution described in the present application can also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the image stitching method area of ​​the technical solution of the present application according to the implementation methods in various embodiments of the technical solution of the present application.

[0120] Figure 7 According to some embodiments of the present application, a schematic diagram of the structure of a computer-readable storage medium is shown. Figure 7 As shown, a program product 800 for implementing the above method in an embodiment of the technical solution of the present application is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. Of course, the program product produced according to this embodiment is not limited to this. In the technical solution of the present application, the readable storage medium can be any tangible medium containing or storing a program, and the program can be used by or in combination with an instruction execution system, an apparatus or a device.

[0121] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0122] Computer readable storage media may include data signals propagated in baseband or as a carrier-area, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0123] The program code for performing the operation of the technical solution of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as C language or similar programming languages. The program code can be executed entirely on the user computing device, locally on the user device, as an independent software package, locally on the user computing device, locally on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0124] To sum up, through the technical solution proposed in this application, it is possible to accurately profile user behavior and obtain portrait groups through group behavior. This technical solution establishes a similar user group for each user, and then realizes automatic classification and acquisition of user portrait groups by setting different aggregation accuracy thresholds. It has the advantages of accurate portraits and adjustable aggregation accuracy. It can obtain various types of user portraits with different accuracy requirements according to different accuracy requirements. It meets the user portrait requirements in product design, content recommendation and other fields, and has promotional value.

[0125] The above description is only a description of the preferred embodiment of the technical solution of the present application, and is not any limitation on the scope of the technical solution of the present application. Any changes and modifications made by ordinary technicians in the field of the technical solution of the present application based on the above disclosure shall fall within the scope of protection of the claims.

Claims

1. A method for obtaining user portrait groups based on group behavior, characterized in that: include: Obtain the user's operation behavior profile based on the user's historical operation behavior log; According to the operation behavior portraits of all users, obtain the similar groups corresponding to each user; Sorting the similar groups in order of number of users included in the similar groups; According to the ranking, the similar groups are cyclically calculated to obtain a number of user portrait groups corresponding to all users, wherein: Suppose that the similar groups corresponding to the group containing n users are G1, G2, ..., Gn, and the corresponding user portrait groups are R1, R2, ..., Rm, where n and m are both non-zero natural numbers and m is less than or equal to n. Then the acquisition process of the user portrait groups R1, R2, ..., Rm specifically includes: Step S1, set constant values ​​i=1, j=2; Step S2, set R1=G1; Step S3, obtaining the behavior deviations D1, D2, ..., Di between R1, R2, ..., Ri and Gj; Step S4, obtaining the minimum value of the behavior deviations and setting the behavior deviation corresponding to the minimum value as Dx; Step S5, obtaining the user portrait group Rx corresponding to the behavior deviation degree Dx, and determining whether the number of users in the user portrait group Rx is greater than a first preset threshold: if yes, proceed to step S6, if no, proceed to step S7; Step S6, when j is less than or equal to n, set i=i+1, j=j+1, Ri=Gj, and then return to step S3; Step S7, when j is less than or equal to n, let the user portrait group Rx be the union of the user portrait group Rx and the similar group Gj, and let j=j+1, and then return to step S3.

2. The method for obtaining a user portrait group based on group behavior according to claim 1, characterized in that: The process of obtaining the operation behavior portrait according to the historical operation behavior log also includes: A number of function points are pre-set, each of which corresponds to a functional operation; Obtaining the number of operations performed by the user on the function point; The user behavior vector composed of the number of operations corresponding to all the function points is used as the operation behavior portrait.

3. The method for acquiring user portrait groups based on group behavior according to claim 2, characterized in that: In the process of obtaining the similar groups corresponding to each user according to the operation behavior portraits of all users, it also includes: For a selected user, obtaining a standard deviation of the user behavior vector of the selected user and the remaining users in all users; Acquire the remaining users whose standard deviation is less than a second preset threshold as the similar group corresponding to the selected user; Repeat the above steps until the traversal of all the users is completed.

4. The method for acquiring user portrait groups based on group behavior according to claim 1, characterized in that: In the process of sequentially sorting the similar groups according to the number of users included in the similar groups, if there are several similar groups including the same number of users, the several similar groups are randomly sorted.

5. The method for acquiring user portrait groups based on group behavior according to claim 1, characterized in that: The first preset threshold is positively correlated with the fuzziness of the user portrait group; The larger the value of the first preset threshold is, the more users a single user portrait group includes, and the more blurred the user portrait corresponding to the user portrait group is.

6. A user portrait group acquisition system based on group behavior, characterized in that: Applied to the method for acquiring a user portrait group based on group behavior as claimed in any one of claims 1 to 5, comprising: A behavior collection unit is used to obtain a user's operation behavior profile based on the user's historical operation behavior log; A clustering unit, used to obtain a similar group corresponding to each user based on the operation behavior portraits of all users; A sorting unit, configured to sort the similar groups in order of number of users included in the similar groups; The user portrait unit is used to perform a cyclic operation on the similar groups according to the ranking to obtain a plurality of user portrait groups corresponding to all users, wherein: Suppose that the similar groups corresponding to the group containing n users are G1, G2, ..., Gn, and the corresponding user portrait groups are R1, R2, ..., Rm, where n and m are both non-zero natural numbers and m is less than or equal to n. Then the acquisition process of the user portrait groups R1, R2, ..., Rm specifically includes: Step S1, set constant values ​​i=1, j=2; Step S2, set R1=G1; Step S3, obtaining the behavior deviations D1, D2, ..., Di between R1, R2, ..., Ri and Gj; Step S4, obtaining the minimum value of the behavior deviations and setting the behavior deviation corresponding to the minimum value as Dx; Step S5, obtaining the user portrait group Rx corresponding to the behavior deviation degree Dx, and determining whether the number of users in the user portrait group Rx is greater than a first preset threshold: if yes, proceed to step S6, if no, proceed to step S7; Step S6, when j is less than or equal to n, set i=i+1, j=j+1, Ri=Gj, and then return to step S3; Step S7, when j is less than or equal to n, let the user portrait group Rx be the union of the user portrait group Rx and the similar group Gj, and let j=j+1, and then return to step S3.

7. The user portrait group acquisition system based on group behavior according to claim 6, characterized in that: The behavior collection unit further includes: A setting module, used to pre-set a number of function points, each of which corresponds to a functional operation; A collection module, used to obtain the number of operations performed by the user on the function point; A generation module is used to use a user behavior vector composed of the number of operations corresponding to all the function points as the operation behavior portrait.

8. The user portrait group acquisition system based on group behavior according to claim 7, characterized in that: The clustering unit also includes: A calculation module, configured to obtain, for a selected user, a standard deviation of the user behavior vector of the selected user and the remaining users in the total number of users; The judgment module is used to obtain the remaining users corresponding to the standard deviation being less than a second preset threshold as the similar group corresponding to the selected user.

9. A user portrait group acquisition device based on group behavior, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the method for acquiring a user portrait group based on group behavior as described in any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for obtaining a user portrait group based on group behavior according to any one of claims 1 to 5 is implemented.

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