User information generation method, device, electronic device and computer readable medium

Abstract

The embodiments of the present disclosure disclose a method, device, electronic device, and computer-readable medium for generating user information. A specific implementation of the method includes: generating information characterizing whether the first user data related to the target user is reasonable; in response to the information that the first user data is reasonable, generating a prediction score corresponding to the target user based on the first user data, a first weight corresponding to the first user data, second user data related to the target user, and a second weight corresponding to the second user data; based on the prediction score, generating information characterizing whether the target user is a real user participating in the target activity displayed in the target application. This implementation can accurately and effectively determine the authenticity of users participating in the target activity displayed in the target application.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, device, electronic device, and computer-readable medium for generating user information. Background Art

[0002] At present, there are a large number of fake users who use a large number of mobile phone numbers, ID card information, and bank card information to directly participate in the target activities displayed in the target application. In addition, there are also robots that create fake clicks. This causes a lot of economic costs, opportunity costs, and time costs for marketers. Existing methods often use manual retrieval methods to determine whether the users participating in the target activities displayed in the target application are real users.

[0003] However, when using the above method to determine the real user, the following technical problems often occur:

[0004] First, the importance of each type of user data is not fully considered.

[0005] Second, the search is too cumbersome and cannot accurately and effectively determine the authenticity of the user. Summary of the invention

[0006] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0007] Some embodiments of the present disclosure propose user information generation methods, devices, equipment, and computer-readable media to solve the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide a method for generating user information, the method comprising: generating information characterizing whether first user data related to a target user is reasonable; in response to the above information being that the above first user data is reasonable, generating a prediction score corresponding to the above target user based on the above first user data, a first weight corresponding to the above first user data, and a second weight related to the above target user and corresponding to the above second user data, wherein the above prediction score characterizes the probability that the above target user is a real user participating in a target activity in a target application, and the above first weight and the above second weight are constantly changing; based on the above prediction score, generating information characterizing whether the above target user is a real user participating in the above target activity displayed in the above target application.

[0009] In a second aspect, some embodiments of the present disclosure provide a user information generating device, the device comprising: a first generating unit, configured to generate information characterizing whether first user data related to a target user is reasonable; a second generating unit, configured to generate a prediction score corresponding to the target user in response to the above information being that the above first user data is reasonable, based on the above first user data, a first weight corresponding to the above first user data, second user data related to the target user and a second weight corresponding to the above second user data, wherein the above prediction score characterizes the probability that the above target user is a real user participating in a target activity in a target application, and the above first weight and the above second weight are constantly changing; a third generating unit, configured to generate information characterizing whether the above target user is a real user participating in the above target activity displayed in the above target application based on the above prediction score.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any method as described in the first aspect.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, any method in the first aspect is implemented.

[0012] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: the user information generation method of some embodiments of the present disclosure can accurately determine the authenticity of users participating in the target activity displayed in the target application. Specifically, the inventors found that the reason for the inaccurate determination of the authenticity of users participating in the target activity displayed in the target application is that the importance between the user data (first user data and second user data) is not fully considered. As an example, the importance of the target user's identity information is greater than the device information used by the target user. Based on this, the user information generation method of some embodiments of the present disclosure assigns corresponding weights to the first user data and the second user data to show the importance of the first user data and the second user data. Furthermore, this implementation can accurately and effectively determine the authenticity of users participating in the target activity displayed in the target application. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0014] Figure 1 is a schematic diagram of an application scenario diagram of a method for generating user information in some embodiments of the present disclosure;

[0015] Figure 2 is a flow chart of some embodiments of the method for generating user information according to the present disclosure;

[0016] Figure 3 are flow charts of other embodiments of the method for generating user information according to the present disclosure;

[0017] Figure 4 is a schematic diagram of generating a prediction score and a second score set corresponding to a user information generating method of some embodiments of the present disclosure;

[0018] Figure 5 is a schematic diagram of the structure of some embodiments of the user information generating device according to the present disclosure;

[0019] Figure 6 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0020] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0021] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0022] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0023] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0024] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0025] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0026] Figure 1 It is a schematic diagram of an application scenario diagram of a method for generating user information in some embodiments of the present disclosure.

[0027] like Figure 1 As shown, the electronic device 101 may first generate information 102 that characterizes whether the first user data related to the target user is reasonable. As an example, the information 102 that characterizes whether the first user data related to the target user is reasonable may be that the first user data is reasonable. Then, in response to the information 102 that the first user data is reasonable, a prediction score 107 corresponding to the target user is generated according to the first user data 103, the first weight 104 corresponding to the first user data 103, the second user data 105 related to the target user, and the second weight 106 corresponding to the second user data 105. Among them, the prediction score 107 characterizes the probability that the target user is a real user participating in the target activity displayed in the target application, and the first weight 104 and the second weight 106 are constantly changing. As an example, the first user data 103 may be "name: Sun**, mobile phone number: 154******148, ID number: 154*********562". The first weight may be "0.6". The second user data 105 may be: "Time spent using the target application: 145 hours, time spent participating in the target activity: 11 hours, device information used: Android operating system". The second weight may be 0.4. The prediction score 107 may be "85 points". Finally, based on the prediction score 107, information 108 is generated to characterize whether the target user is a real user who participates in the target activity displayed in the target application. As an example, the information 108 characterizing whether the target user is a real user who participates in the target activity displayed in the target application may be "the target user is a real user who participates in the target activity displayed in the target application".

[0028] It should be noted that the user information generation method can be executed by the electronic device 101. The above-mentioned electronic device 101 can be hardware or software. When the electronic device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the electronic device 101 is embodied as software, it can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0029] It should be understood that Figure 1 The number of electronic devices in the embodiment is only for illustration. Any number of electronic devices may be provided according to implementation requirements.

[0030] Continue to refer Figure 2 , shows a process 200 of some embodiments of the method for generating user information according to the present disclosure. The method for generating user information comprises the following steps:

[0031] Step 201: Generate information indicating whether first user data related to a target user is reasonable.

[0032] In some embodiments, the execution entity (e.g. Figure 1 The electronic device 101 shown in the figure can generate information characterizing whether the first user data related to the target user is reasonable. The above-mentioned first user data can be a part of the data related to the target user. Determining whether the data is reasonable can be understood as whether the corresponding data related to the above-mentioned target user is one-to-one corresponding. The above-mentioned target user is a user whose authenticity has been determined to be determined. As an example, the above-mentioned execution subject can receive the first user data analysis information transmitted from the relevant technical personnel as information characterizing whether the first user data related to the target user is reasonable.

[0033] In some optional implementations of some embodiments, the first user data is the identity information of the target user. As an example, the first user data may include but is not limited to at least one of the following: the name information of the target user, the ID card number information of the target user, and the mobile phone number information of the target user. And the generation of the information representing whether the first user data related to the target user is reasonable may include the following steps:

[0034] In the first step, the first user data is processed for data dimension to obtain a first vector. As an example, the first user data may be processed for data dimension using a principal component analysis method (PCA).

[0035] The second step is to input the first vector into a pre-trained user information confirmation network to obtain information characterizing whether the first user data related to the target user is reasonable. The user information confirmation network can be one of the following: linear regression network, convolutional neural network (CNN), fully connected network, support vector machine (SVM).

[0036] Optionally, performing data dimension processing on the first user data to obtain the first vector may include the following steps:

[0037] In the first step, word embedding is performed on the discrete data in the first user data to obtain a discrete processing result. The word embedding can be to embed a high-dimensional space with a dimension equal to the number of all words into a continuous vector space with a much lower dimension, and each word or phrase is mapped to a vector in the real number field.

[0038] The second step is to input the continuous data in the first user data into a pre-trained convolutional neural network to obtain a continuous data vector.

[0039] The third step is to input the above continuous data vector into the data flattening layer to obtain the continuous processing result.

[0040] The fourth step is to concatenate the above continuous processing result and the above discrete processing result to obtain the above first vector.

[0041] Step 202, in response to the above information that the above first user data is reasonable, based on the above first user data, the first weight corresponding to the above first user data, the second user data related to the above target user and the second weight corresponding to the above second user data, generate a prediction score corresponding to the above target user.

[0042] In some embodiments, in response to the above information that the above first user data is reasonable, the above execution subject can generate a prediction score corresponding to the above target user based on the above first user data, the first weight corresponding to the above first user data, the second user data related to the above target user and the second weight corresponding to the above second user data. Among them, the above prediction score represents the probability that the above target user is a real user participating in the target activity displayed in the target application, and the above first weight and the above second weight are constantly changing. The above second user data can be a part of the data related to the above target user. The above first weight and the above second weight represent the importance of the above first user data and the above second user data. The value of the initial first weight and the value of the initial second weight are estimated by relevant technical personnel based on past data and experience. The above first weight and the above second weight are constantly changing as the amount of user data (i.e., the first user data and the second user data) continues to increase.

[0043] As an example, the execution entity may input the product of the first user data and the first weight and the product of the second user data and the second weight into a regression number to obtain the prediction score.

[0044] In some optional implementations of some embodiments, the second user data includes: device information used by the target user, relevant information of the target application used by the target user, and relevant information of the target user's participation in the target activity. As an example, the device information used by the target user may be the device model used by the target and the corresponding operating system information. The relevant information of the target application used by the target user may include but is not limited to at least one of the following: the registration time of the target user in the target application, the usage time of the target user in the target application, the online time of the target user in the target application, and the offline time of the target user in the target application. The relevant information of the target user's participation in the target activity may include but is not limited to at least one of the following: the duration of the target user's participation in the target activity, and the name identification of the target user's participation in the target activity. And in response to the above information being reasonable for the first user data, generating a prediction score corresponding to the target user based on the first user data, the first weight corresponding to the first user data, the second user data related to the target user, and the second weight corresponding to the second user data may include the following steps:

[0045] In the first step, in response to the information that the first user data is reasonable, the second user data is processed for data dimension to generate a second vector, wherein the data dimension of the second vector is the same as the data dimension of the first vector.

[0046] The second step is to determine a first multiplication result of the first vector and the first weight and a second multiplication result of the second vector and the second weight.

[0047] The third step is to input the first multiplication result and the second multiplication result into a pre-trained first score prediction network to obtain a first score as the predicted score. The first score prediction network represents the corresponding relationship between the first multiplication result, the second multiplication result and the predicted score. The first score prediction network can be a machine learning network or a deep learning network. Optionally, the first weight and the second weight are continuously changed as parameter variables during the training of the first score prediction network.

[0048] Step 203: Based on the prediction score, generate information indicating whether the target user is a real user who participates in the target activity displayed in the target application.

[0049] In some embodiments, the execution subject may generate information indicating whether the target user is a real user participating in the target activity displayed in the target application based on the prediction score. As an example, in response to the prediction score being greater than or equal to a preset threshold, information indicating that the target user is a real user participating in the target activity displayed in the target application is generated. In response to the prediction score being less than a preset threshold, information indicating that the target user is not a real user participating in the target activity displayed in the target application is generated.

[0050] It can be seen from some of the above embodiments that the user information generation method of some embodiments of the present disclosure can accurately determine the authenticity of users participating in the target activity displayed in the target application. Specifically, the inventors found that the reason for the inaccurate determination of the authenticity of users participating in the target activity displayed in the target application is that the importance between the user data (first user data and second user data) is not fully considered. As an example, the importance of the target user's identity information is greater than the device information used by the target user. Based on this, the user information generation method of some embodiments of the present disclosure assigns corresponding weights to the first user data and the second user data to show the importance of the first user data and the second user data. Furthermore, this implementation can accurately and effectively determine the authenticity of users participating in the target activity displayed in the target application.

[0051] Continue to refer Figure 3 , shows a process 300 of another embodiment of the method for generating user information according to the present disclosure. The method for generating user information includes the following steps:

[0052] Step 301: Generate information indicating whether first user data related to a target user is reasonable.

[0053] Step 302, in response to the above information that the above first user data is reasonable, based on the above first user data, the first weight corresponding to the above first user data, the second user data related to the above target user and the second weight corresponding to the above second user data, generate a prediction score corresponding to the above target user.

[0054] In some embodiments, the specific implementation of steps 301-302 and the technical effects brought about can be referred to Figure 2 The corresponding steps 201-202 in the embodiments are not described in detail here.

[0055] Step 303: Input the first multiplication result and the second multiplication result into at least one pre-trained second score prediction network to obtain a second score set.

[0056] In some embodiments, the execution entity may input the first multiplication result and the second multiplication result into at least one pre-trained second score prediction network to obtain a second score set. Each second score prediction network in the at least one second score prediction network may be a neural network or a machine learning network.

[0057] Step 304: in response to the prediction score being greater than or equal to a preset threshold, assigning corresponding weights to the prediction score and each second score in the second score set.

[0058] In some embodiments, in response to the prediction score being greater than or equal to a preset threshold, the execution entity may assign corresponding weights to the prediction score and each second score in the second score set.

[0059] Step 305: determine a weighted sum of the prediction score, the second score set and the corresponding weights to obtain a sum value.

[0060] In some embodiments, the execution entity may determine a weighted sum of the prediction score, the second score set, and the corresponding weights to obtain a summed value.

[0061] Step 306: Based on the added value, generate the information indicating whether the target user is a real user who participates in the target activity displayed in the target application.

[0062] In some embodiments, the execution subject may generate the information indicating whether the target user is a real user participating in the target activity displayed in the target application based on the added value. As an example, in response to the added value being greater than or equal to a preset threshold, information indicating that the target user is a real user participating in the target activity displayed in the target application is generated. In response to the added value being less than a preset threshold, information indicating that the target user is not a real user participating in the target activity displayed in the target application is generated.

[0063] In some optional implementations of some embodiments, the first score prediction network and the at least one second score prediction network are retrained according to the information characterizing whether the target user is a real user participating in the target activity displayed in the target application, to obtain the retrained first score prediction network and the at least one second score prediction network. As an example, the target user data and the information characterizing whether the target user is a real user participating in the target activity displayed in the target application are used as training data samples of the first score prediction network and the at least one second score prediction network to retrain, to obtain the retrained first score prediction network and the at least one second score prediction network.

[0064] As an example, Figure 4 As shown, first, the first user data 401 is processed by data dimension to obtain a first vector 404. Similarly, the second user data 402 can be processed by data dimension to obtain a second vector 405. Then, the first weight 403 is multiplied by the first vector 404 to obtain a first multiplication result 407. Similarly, the second vector 405 is multiplied by the second weight 406 to obtain a second multiplication result 408. Finally, the first multiplication result 407 and the second multiplication result 408 are input into the pre-trained first score prediction network 409 to obtain a first score 411 as a prediction score. Similarly, the first multiplication result 407 and the second multiplication result 408 are input into at least one second score prediction network 410 to obtain a second score set 412. Among them, the at least one second score prediction network 410 includes: a second score prediction network 4101, a second score prediction network 4102, and a second score prediction network 4103. The above-mentioned second score set 412 includes: a second score 4121, a second score 4122, and a second score 4123.

[0065] The embodiments of the present disclosure solve the second technical problem mentioned in the background technology: "The search is too cumbersome and cannot accurately and effectively determine the authenticity of the user." The reasons for not being able to accurately and effectively determine the authenticity of the user are often as follows: Existing methods often use manual retrieval methods to determine whether the users participating in the target activities displayed in the target application are real users, resulting in inaccurate manual analysis. In order to achieve this effect, the present disclosure introduces a first score prediction network and at least one second score prediction network. Furthermore, the corresponding trade-off between the output of the first score prediction network (prediction score) and the output of at least one second score prediction network (second score set) is used to accurately and effectively determine the authenticity of the target user.

[0066] Continue to refer Figure 5 As an implementation of the above methods in the above figures, the present disclosure provides some embodiments of a user information generating device, and these device embodiments are Figure 2 Corresponding to the above method embodiments, the device can be specifically applied to various electronic devices.

[0067] like Figure 5 As shown, the user information generating device 500 of some embodiments includes: a first generating unit 501, a second generating unit 502 and a third generating unit 503. The first generating unit 501 is configured to generate information indicating whether the first user data related to the target user is reasonable. The second generating unit 502 is configured to generate a prediction score corresponding to the target user based on the first user data, the first weight corresponding to the first user data, the second user data related to the target user and the second weight corresponding to the second user data in response to the above information indicating that the first user data is reasonable, wherein the above prediction score indicates the probability that the target user is a real user participating in the target activity in the target application, and the above first weight and the above second weight are constantly changing. The third generating unit 503 is configured to generate information indicating whether the target user is a real user participating in the target activity displayed in the target application based on the above prediction score.

[0068] In some optional implementations of some embodiments, the first user data is the identity information of the target user. The first generating unit 501 may be further configured to: perform data dimension processing on the first user data to obtain a first vector; input the first vector into a pre-trained user information confirmation network to obtain information characterizing whether the first user data related to the target user is reasonable.

[0069] In some optional implementations of some embodiments, the first generation unit 501 can be further configured to: perform word embedding on the discrete data in the above-mentioned first user data to obtain a discrete processing result; input the continuous data in the above-mentioned first user data into a pre-trained convolutional neural network to obtain a continuous data vector; input the above-mentioned continuous data vector into a data flattening layer to obtain a continuous processing result; splice the above-mentioned continuous processing result and the above-mentioned discrete processing result to obtain the above-mentioned first vector.

[0070] In some optional implementations of some embodiments, the second user data includes: device information used by the target user, relevant information of the target application used by the target user, and relevant information of the target user participating in the target activity. The second generation unit 502 can be further configured to: in response to the information that the first user data is reasonable, perform data dimension processing on the second user data to generate a second vector, wherein the data dimension of the second vector is the same as the data dimension of the first vector; determine a first multiplication result of the first vector and the first weight and a second multiplication result of the second vector and the second weight; input the first multiplication result and the second multiplication result into a pre-trained first score prediction network to obtain a first score as the prediction score.

[0071] In some optional implementations of some embodiments, the third generation unit 503 can be further configured to: input the above-mentioned first multiplication result and the above-mentioned second multiplication result into at least one pre-trained second score prediction network to obtain a second score set; based on the above-mentioned predicted score and the above-mentioned second score set, generate information characterizing whether the above-mentioned target user is a real user participating in the above-mentioned target activity displayed in the above-mentioned target application.

[0072] In some optional implementations of some embodiments, the third generation unit 503 can be further configured to: in response to the above-mentioned prediction score being greater than or equal to a preset threshold, assign corresponding weights to the above-mentioned prediction score and each second score in the above-mentioned second score set; determine the weighted sum of the above-mentioned prediction score, the above-mentioned second score set and the above-mentioned corresponding weights to obtain an added value; based on the above-mentioned added value, generate the above-mentioned information characterizing whether the above-mentioned target user is a real user participating in the above-mentioned target activity displayed in the above-mentioned target application.

[0073] In some optional implementations of some embodiments, the apparatus further includes a training unit. The training unit may be configured to retrain the first score prediction network and the at least one second score prediction network based on the information characterizing whether the target user is a real user participating in the target activity displayed in the target application, to obtain the retrained first score prediction network and the at least one second score prediction network.

[0074] In some optional implementations of some embodiments, the first weight and the second weight are parameter variables that are continuously changed as the first score prediction network is trained.

[0075] It is understood that the units described in the device 500 are similar to those described in the reference Figure 2Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 500 and the units included therein, and will not be described in detail here.

[0076] Reference below Figure 6 , which shows an electronic device (eg, Figure 1 A schematic diagram of the structure of an electronic device) 600. Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0077] like Figure 6 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0078] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 6 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0079] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0080] It should be noted that the computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-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 computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer 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. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0081] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0082] The computer-readable medium may be included in the device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: generates information indicating whether the first user data related to the target user is reasonable; in response to the information indicating that the first user data is reasonable, generates a prediction score corresponding to the target user based on the first user data, the first weight corresponding to the first user data, the second user data related to the target user, and the second weight corresponding to the second user data, wherein the prediction score indicates the probability that the target user is a real user participating in the target activity in the target application, and the first weight and the second weight are constantly changing; based on the prediction score, generates information indicating whether the target user is a real user participating in the target activity displayed in the target application.

[0083] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0084] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0085] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor includes a first generation unit, a second generation unit, and a third generation unit. The names of these units do not constitute limitations on the units themselves in certain circumstances, for example, the first generation unit may also be described as "a unit that generates information characterizing whether the first user data related to the target user is reasonable".

[0086] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0087] According to one or more embodiments of the present disclosure, a user information generation method is provided, including: generating information characterizing whether first user data related to a target user is reasonable; in response to the above information being reasonable, generating a prediction score corresponding to the above target user based on the above first user data, a first weight corresponding to the above first user data, second user data related to the above target user, and a second weight corresponding to the above second user data, wherein the above prediction score characterizes the probability that the above target user is a real user participating in a target activity in a target application, and the above first weight and the above second weight are constantly changing; based on the above prediction score, generating information characterizing whether the above target user is a real user participating in the above target activity displayed in the above target application.

[0088] According to one or more embodiments of the present disclosure, the first user data is the identity identification information of the target user; and the information generated to characterize whether the first user data related to the target user is reasonable includes: performing data dimension processing on the first user data to obtain a first vector; and inputting the first vector into a pre-trained user information confirmation network to obtain information to characterize whether the first user data related to the target user is reasonable.

[0089] According to one or more embodiments of the present disclosure, the data dimension processing of the first user data to obtain the first vector includes: word embedding of discrete data in the first user data to obtain a discrete processing result; inputting the continuous data in the first user data into a pre-trained convolutional neural network to obtain a continuous data vector; inputting the continuous data vector into a data flattening layer to obtain a continuous processing result; and concatenating the continuous processing result and the discrete processing result to obtain the first vector.

[0090] According to one or more embodiments of the present disclosure, the second user data includes: device information used by the target user, relevant information of the target application used by the target user, and relevant information of the target user participating in the target activity; and in response to the information that the first user data is reasonable, based on the first user data, the first weight corresponding to the first user data, the second user data related to the target user and the second weight corresponding to the second user data, generating a prediction score corresponding to the target user, including: in response to the information that the first user data is reasonable, performing data dimension processing on the second user data to generate a second vector, wherein the data dimension of the second vector is the same as the data dimension of the first vector; determining a first multiplication result of the first vector and the first weight and a second multiplication result of the second vector and the second weight; inputting the first multiplication result and the second multiplication result into a pre-trained first score prediction network to obtain a first score as the prediction score.

[0091] According to one or more embodiments of the present disclosure, the above-mentioned generating, based on the above-mentioned prediction score, information characterizing whether the above-mentioned target user is a real user participating in the above-mentioned target activity displayed in the above-mentioned target application, includes: inputting the above-mentioned first multiplication result and the above-mentioned second multiplication result into at least one pre-trained second score prediction network to obtain a second score set; based on the above-mentioned prediction score and the above-mentioned second score set, generating information characterizing whether the above-mentioned target user is a real user participating in the above-mentioned target activity displayed in the above-mentioned target application.

[0092] According to one or more embodiments of the present disclosure, the above-mentioned generating, based on the above-mentioned prediction score and the above-mentioned second score set, information characterizing whether the above-mentioned target user is a real user participating in the above-mentioned target activity displayed in the above-mentioned target application includes: in response to the above-mentioned prediction score being greater than or equal to a preset threshold, assigning corresponding weights to the above-mentioned prediction score and each second score in the above-mentioned second score set; determining a weighted sum of the above-mentioned prediction score, the above-mentioned second score set and the above-mentioned corresponding weights to obtain an added value; and generating, based on the above-mentioned added value, the above-mentioned information characterizing whether the above-mentioned target user is a real user participating in the above-mentioned target activity displayed in the above-mentioned target application.

[0093] According to one or more embodiments of the present disclosure, the method further includes: based on the information characterizing whether the target user is a real user participating in the target activity displayed in the target application, retraining the first score prediction network and the at least one second score prediction network to obtain the retrained first score prediction network and the at least one second score prediction network.

[0094] According to one or more embodiments of the present disclosure, the first weight and the second weight are continuously changed as parameter variables along with the training of the first score prediction network.

[0095] According to one or more embodiments of the present disclosure, a user information generating device is provided, comprising: a first generating unit, configured to generate information characterizing whether first user data related to a target user is reasonable; a second generating unit, configured to generate a prediction score corresponding to the target user based on the first user data, a first weight corresponding to the first user data, second user data related to the target user and a second weight corresponding to the second user data in response to the information that the first user data is reasonable, wherein the prediction score characterizes the probability that the target user is a real user participating in a target activity in a target application, and the first weight and the second weight are continuously changing; and a third generating unit, configured to generate information characterizing whether the target user is a real user participating in the target activity displayed in the target application based on the prediction score.

[0096] According to one or more embodiments of the present disclosure, the first user data is the identity information of the target user. The first generation unit can be further configured to: perform data dimension processing on the first user data to obtain a first vector; input the first vector into a pre-trained user information confirmation network to obtain information representing whether the first user data related to the target user is reasonable.

[0097] According to one or more embodiments of the present disclosure, the first generation unit can be further configured to: perform word embedding on the discrete data in the above-mentioned first user data to obtain a discrete processing result; input the continuous data in the above-mentioned first user data into a pre-trained convolutional neural network to obtain a continuous data vector; input the above-mentioned continuous data vector into a data flattening layer to obtain a continuous processing result; splice the above-mentioned continuous processing result and the above-mentioned discrete processing result to obtain the above-mentioned first vector.

[0098] According to one or more embodiments of the present disclosure, the second user data includes: device information used by the target user, relevant information of the target application used by the target user, and relevant information of the target user's participation in the target activity. The second generation unit may be further configured to: in response to the information that the first user data is reasonable, perform data dimension processing on the second user data to generate a second vector, wherein the data dimension of the second vector is the same as the data dimension of the first vector; determine a first multiplication result of the first vector and the first weight and a second multiplication result of the second vector and the second weight; input the first multiplication result and the second multiplication result into a pre-trained first score prediction network to obtain a first score as the prediction score.

[0099] According to one or more embodiments of the present disclosure, the third generation unit can be further configured to: input the above-mentioned first multiplication result and the above-mentioned second multiplication result into at least one pre-trained second score prediction network to obtain a second score set; based on the above-mentioned prediction score and the above-mentioned second score set, generate information characterizing whether the above-mentioned target user is a real user participating in the above-mentioned target activity displayed in the above-mentioned target application.

[0100] According to one or more embodiments of the present disclosure, the third generation unit may be further configured to: in response to the prediction score being greater than or equal to a preset threshold, assign corresponding weights to the prediction score and each second score in the second score set; determine the weighted sum of the prediction score, the second score set and the corresponding weights to obtain an added value; and based on the added value, generate the information characterizing whether the target user is a real user participating in the target activity displayed in the target application.

[0101] According to one or more embodiments of the present disclosure, the apparatus further includes a training unit. The training unit may be configured to retrain the first score prediction network and the at least one second score prediction network based on the information characterizing whether the target user is a real user participating in the target activity displayed in the target application, to obtain the retrained first score prediction network and the at least one second score prediction network.

[0102] According to one or more embodiments of the present disclosure, the first weight and the second weight are continuously changed as parameter variables along with the training of the first score prediction network.

[0103] According to one or more embodiments of the present disclosure, an electronic device is provided, including: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any of the above embodiments.

[0104] According to one or more embodiments of the present disclosure, a computer-readable medium is provided, on which a computer program is stored, wherein when the program is executed by a processor, the method described in any of the above embodiments is implemented.

[0105] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for generating user information, comprising: Generate information indicating whether the first user data related to the target user is reasonable; In response to the information that the first user data is reasonable, a prediction score corresponding to the target user is generated based on the first user data, a first weight corresponding to the first user data, second user data related to the target user, and a second weight corresponding to the second user data, wherein the prediction score represents the probability that the target user is a real user participating in the target activity displayed in the target application, the first weight and the second weight are constantly changing, the first user data is the identity information of the target user, and the second user data includes: device information used by the target user, relevant information of the target application, and relevant information of the target user participating in the target activity; Based on the prediction score, information is generated indicating whether the target user is a real user who participates in the target activity displayed in the target application.

2. The method according to claim 1, wherein: The generating of information indicating whether the first user data related to the target user is reasonable includes: Performing data dimension processing on the first user data to obtain a first vector; The first vector is input into a pre-trained user information confirmation network to obtain information representing whether the first user data related to the target user is reasonable.

3. The method according to claim 2, wherein: The performing data dimension processing on the first user data to obtain a first vector includes: Performing word embedding on discrete data in the first user data to obtain a discrete processing result; Inputting the continuous data in the first user data into a pre-trained convolutional neural network to obtain a continuous data vector; Inputting the continuous data vector into a data flattening layer to obtain a continuous processing result; The continuous processing result and the discrete processing result are concatenated to obtain the first vector.

4. The method according to claim 2, wherein: In response to the information that the first user data is reasonable, generating a prediction score corresponding to the target user based on the first user data, a first weight corresponding to the first user data, second user data related to the target user, and a second weight corresponding to the second user data, including: In response to the information indicating that the first user data is reasonable, performing data dimension processing on the second user data to generate a second vector, wherein the data dimension of the second vector is the same as the data dimension of the first vector; Determine a first multiplication result of the first vector and the first weight and a second multiplication result of the second vector and the second weight; The first multiplication result and the second multiplication result are input into a pre-trained first score prediction network to obtain a first score as the predicted score.

5. The method according to claim 4, wherein: The generating, based on the prediction score, information indicating whether the target user is a real user who participates in the target activity displayed in the target application includes: Inputting the first multiplication result and the second multiplication result into at least one pre-trained second score prediction network to obtain a second score set; Based on the predicted score and the second score set, information characterizing whether the target user is a real user participating in the target activity displayed in the target application is generated.

6. The method according to claim 5, wherein: The generating, based on the predicted score and the second score set, information indicating whether the target user is a real user who participates in the target activity displayed in the target application includes: In response to the prediction score being greater than or equal to a preset threshold, assigning corresponding weights to the prediction score and each second score in the second score set; Determine a weighted sum of the prediction score, the second score set, and the corresponding weights to obtain a sum value; Based on the added value, the information representing whether the target user is a real user who participates in the target activity displayed in the target application is generated.

7. The method according to claim 5, wherein: The method further comprises: Based on the information characterizing whether the target user is a real user participating in the target activity displayed in the target application, the first score prediction network and the at least one second score prediction network are retrained to obtain the retrained first score prediction network and the at least one second score prediction network.

8. The method according to claim 4, wherein: The first weight and the second weight are continuously changed as parameter variables along with the training of the first score prediction network.

9. A user information generating device, comprising: A first generating unit is configured to generate information indicating whether the first user data related to the target user is reasonable; A second generating unit is configured to generate a prediction score corresponding to the target user based on the first user data, a first weight corresponding to the first user data, second user data related to the target user, and a second weight corresponding to the second user data in response to the information being reasonable for the first user data, wherein the prediction score represents the probability that the target user is a real user participating in a target activity displayed in a target application, the first weight and the second weight are constantly changing, the first user data is the identity information of the target user, and the second user data includes: device information used by the target user, relevant information of the target application, and relevant information of the target user participating in the target activity; A third generating unit is configured to generate information indicating whether the target user is a real user who participates in the target activity displayed in the target application based on the prediction score.

10. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

11. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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