Method for determining same user, medium and equipment

Abstract

The invention provides a method, medium and device for determining the same user, and relates to the technical field of data processing, and the method comprises the steps: obtaining a target associated data list corresponding to a target service platform, obtaining a first target data list according to the target associated data list, determining the same user according to the first target data list, and determining the same user according to the target associated data list. Obtaining a second target data list, according to the first target data list and the second target data list, when determining the same user and knowing and judging the similarity between the users, endowing different weights to data of a plurality of features corresponding to the users, and meanwhile, determining the similarity between the users based on the difference of the number of the features corresponding to the users. The weight corresponding to each feature is obtained by adopting different modes, so that the accuracy of obtaining the feature vector corresponding to the data end user is improved, and the accuracy of obtaining the same user is relatively high.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, medium and device for determining a same user. Background Art

[0002] With the rapid development of mobile Internet, users' digital life has crossed the boundary of a single device and a single service platform and extended to multiple devices and multiple service platforms, making users' activities on multiple service platforms increasingly frequent. Among them, each service platform has multiple data ports to meet users' login and usage needs in different scenarios. In order to ensure the user's experience continuity and data consistency on different data ports, cross-port technical identification of the same user has become an indispensable key technology in the mobile Internet era. It can not only ensure the user's experience continuity and data consistency between different data ports based on the same service platform, but also provide service providers with comprehensive user portraits and behavior analysis data. Therefore, confirming the same user based on data from different data terminals has become an urgent problem that needs to be solved. Summary of the invention

[0003] In view of the above technical problem, the technical solution adopted by the present invention is a method for determining the same user, and the method comprises the following steps: A target-associated data list corresponding to the target service platform is obtained, wherein the target-associated data list includes a plurality of target-associated data, and the target-associated data is data uploaded through a plurality of data ports corresponding to the target service platform.

[0004] According to the target associated data list, a first target data list is obtained, wherein the first target data list includes a plurality of first target data, and the first target data is target associated data with a specified identifier obtained from the target associated data list.

[0005] Based on the first target data list, target users corresponding to all first target data with consistent designated identifiers in the first target data list are associated to determine associated users, where the associated users are used to indicate that the associated users are the same user.

[0006] According to the target associated data list, a second target data list is obtained, wherein the second target data list includes a plurality of second target data, and the second target data is target associated data obtained from the target associated data list and having no designated identifier.

[0007] Based on the first target data list and the second target data list, if several feature data included in the second target data and several corresponding feature data included in the first target data meet the preset comparison rules, the user corresponding to the second target data and the user corresponding to the first target data are determined as associated users.

[0008] The present invention also provides a non-transitory computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above-mentioned method for determining the same user.

[0009] The present invention also provides an electronic device, comprising a processor and the above-mentioned non-transitory computer-readable storage medium.

[0010] The present invention has at least the following beneficial effects: obtaining a target associated data list corresponding to a target service platform, obtaining a first target data list according to the target associated data list, and based on the first target data list, associating target users corresponding to all first target data with consistent designated identifiers in the first target data list to determine associated users, wherein associated users are used to indicate that associated users are the same user, obtaining a second target data list according to the target associated data list, and based on the first target data list and the second target data list, if several feature data included in the second target data and several corresponding feature data included in a first target data meet a preset comparison rule, then the user corresponding to the second target data and the user corresponding to the first target data are determined as associated users. It can be seen that when judging the similarity between users, different weights are assigned to the data of several features corresponding to the users. At the same time, based on the different numbers of features corresponding to the users, different methods are used to obtain the weight corresponding to each feature, thereby improving the accuracy of obtaining the feature vector corresponding to the data end user, so that the accuracy of obtaining the same user is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A flow chart of a method for determining the same user provided in Embodiment 1 of the present invention; Figure 2 This is a flow chart of S500 provided in the first embodiment of the present invention; Figure 3This is a flow chart of S501 provided in the first embodiment of the present invention; Figure 4 This is a flowchart of S3 provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0014] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0015] Embodiment 1 This embodiment provides a method for determining the same user, the method comprising the following steps: Figure 1 As shown: S100, obtaining a target-related data list corresponding to a target service platform, wherein the target-related data list includes a plurality of target-related data, and the target-related data is data uploaded through a plurality of data ports corresponding to the target service platform.

[0016] Specifically, the target service platform is an Internet application developed based on Internet technology that can realize several functions.

[0017] Specifically, each target-related data corresponds to a data port, wherein the data port is a port for users to log in and use, such as data ports such as APP, mini-programs, and web pages.

[0018] S200, obtaining a first target data list according to the target associated data list, wherein the first target data list includes a plurality of first target data, and the first target data is target associated data with a specified identifier obtained from the target associated data list.

[0019] Specifically, the designated identifier is a unique identifier that represents the identity of the target user, for example, a designated identifier such as a login account.

[0020] Furthermore, the target user is a user who uses the data port corresponding to the target associated data to connect to the target service platform.

[0021] S300: Based on the first target data list, target users corresponding to all first target data with consistent designated identifiers in the first target data list are associated to determine associated users, where the associated users are used to indicate that the associated users are the same user.

[0022] Specifically, when the similarity between the specified identifiers is 1, it is determined that the specified identifiers are consistent. Those skilled in the art know that any method for obtaining identifier similarity in the prior art falls within the protection scope of the present invention and will not be described in detail herein.

[0023] S400, obtaining a second target data list according to the target associated data list, wherein the second target data list includes a plurality of second target data, and the second target data is target associated data obtained from the target associated data list and having no designated identifier.

[0024] S500, based on the first target data list and the second target data list, if a number of feature data included in the second target data and a number of corresponding feature data included in the first target data meet the preset comparison rules, the user corresponding to the second target data and the user corresponding to the first target data are determined as associated users.

[0025] Specifically, S500 also includes the following steps: Figure 2 As shown: S501, obtaining a target feature vector list corresponding to a target associated data list, wherein the target feature vector list includes a plurality of target feature vectors, each target associated data corresponds to a target feature vector, and the target feature vector is a vector generated based on data corresponding to a plurality of target features in each target associated data.

[0026] Specifically, in S501, the target feature vector is obtained by the following steps: Figure 3 As shown: S1, obtaining a target feature list according to a target associated data list, wherein the target feature list includes a plurality of target features, and the target features are initial features screened from an initial feature list corresponding to the target data list.

[0027] Specifically, S1 also includes the following steps: S11, obtain the initial feature data list set B corresponding to the target associated data list = {B1 , ..., B i , ..., B n}, B i ={B i1 , ..., B ij , ..., B im}, B ij is the data under the jth initial feature in the initial feature data corresponding to the i-th target associated data, the value of j is 1 to m, m is the number of initial features, the value of i is 1 to n, and n is the number of target associated data in the target associated data list.

[0028] Specifically, the initial features are network features characterized when the target user uses the data port corresponding to the target associated data to connect to the target service platform, such as: device model, operating system, IP data, max address, network card and other initial features.

[0029] Furthermore, when the target associated data does not include data under a certain initial feature, the data under this initial feature is 0.

[0030] S12, when B in B ij When all are not 0, the jth initial feature is obtained as the target feature.

[0031] S2, obtaining the number of target features, wherein the number of target features is the number of target features in the target feature list.

[0032] S3, according to the number of target features, obtain the target feature vector, wherein the target feature vector is obtained in S3 through the following steps, such as Figure 4 As shown: S31, when the number of target features is less than 2 or the number of target features is not less than 20, a target feature vector is obtained, wherein the target feature vector is a vector generated by concatenating the vectors generated based on each target associated data.

[0033] Specifically, for example, when the number of target features is 1, the target feature vector is a vector generated based on one target associated data. When the number of target features is not less than 20, the target feature vector is obtained as L=(L 1 , ..., L v , ..., L b ), L v The vector generated for the vth target association data, L v =(L v1 , ..., L vx , ..., L vp ), L vx For L vThe bit value of the xth position in , x ranges from 1 to p, p is the dimension of the vector generated for each target associated data, and v ranges from 1 to b, b is the number of target features.

[0034] Specifically, those skilled in the art know that any method of generating a vector from text in the prior art falls within the protection scope of the present invention and will not be described in detail herein.

[0035] S32, when the number of target features is not less than 2 and the number of target features is less than 20, obtain a specified priority, wherein the specified priority is a weight corresponding to a specified feature, and the specified feature is any target feature other than IP data obtained from the target feature list.

[0036] Specifically, the value range of the designated priority is 0 to 0.5, wherein those skilled in the art know that the designated priority can be selected according to actual needs, all of which fall within the protection scope of the present invention and will not be described in detail here.

[0037] S33, based on the number of target features and the specified priority, confirm the target feature vector, wherein in S33, the target feature vector is obtained by the following steps: S331, when the number of target features is not less than 2 and the number of target features is less than the target value, obtain the target feature vector F=(F 1 , ..., F v , ..., F b ), F v =η 1 v ×β v , β v is the vector generated based on the target association data corresponding to the vth target feature, η 1 v β v The corresponding first weight, η 1 v It is the ratio of 2 to v minus the specified priority, where the target value is the reciprocal of the specified priority, the bth target feature is IP data, the value of v is 1 to b, and b is the number of target features.

[0038] S332: When the number of target features is not less than the target value and the number of target features is less than 20, obtain the target feature vector E=(E 1 ,……,E v ,……,E b ), E v =η 2 v ×β v , β v is the vector generated based on the target association data corresponding to the vth target feature, η 2v β v The corresponding second weight, η 2 v It is 10 multiplied by the ratio of the value of the specified priority to v, where the target value is the reciprocal of the specified priority, the bth target feature is IP data, and the value of v is 1 to b, where b is the number of target features.

[0039] S502, obtaining a target similarity list corresponding to each target feature vector, wherein the target similarity list includes a plurality of target similarities, wherein the target similarity is the similarity between the target feature vector and a feature vector to be selected, and the feature vector to be selected is any target feature vector in the target feature vector list except the target feature vector.

[0040] S503: When the target similarity satisfies a preset condition, target users corresponding to a number of target feature vectors corresponding to the target similarity are determined as associated users.

[0041] Specifically, the preset condition is that the similarity is not less than a preset similarity threshold, wherein the preset similarity threshold has a value range of 0.7 to 0.8. Those skilled in the art know that the preset similarity threshold can be selected according to actual needs, which falls within the protection scope of the present invention and will not be described in detail here.

[0042] From the above content, we can see that when judging the similarity between users, different weights are assigned to the data of several features corresponding to the users. At the same time, based on the different numbers of features corresponding to the users, different methods are used to obtain the weight corresponding to each feature, which improves the accuracy of obtaining the feature vector corresponding to the user on the data end, making the accuracy of obtaining the same user higher.

[0043] This embodiment can obtain a target associated data list corresponding to a target service platform, obtain a first target data list based on the target associated data list, and based on the first target data list, associate target users corresponding to all first target data with consistent specified identifiers in the first target data list to determine associated users, where associated users are used to indicate that associated users are the same user. According to the target associated data list, obtain a second target data list, and on the basis of the first target data list and the second target data list, if several feature data included in the second target data and several corresponding feature data included in a first target data meet a preset comparison rule, then the user corresponding to the second target data and the user corresponding to the first target data are determined as associated users. It can be seen that when judging the similarity between users, different weights are assigned to the data of several features corresponding to the users. At the same time, based on the different numbers of features corresponding to the users, different methods are used to obtain the weight corresponding to each feature, thereby improving the accuracy of obtaining the feature vector corresponding to the data end user, so that the accuracy of obtaining the same user is higher.

[0044] Embodiment 2 An embodiment of the present invention provides a non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the non-transitory computer-readable storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the steps: A target-associated data list corresponding to the target service platform is obtained, wherein the target-associated data list includes a plurality of target-associated data, and the target-associated data is data uploaded through a plurality of data ports corresponding to the target service platform.

[0045] According to the target associated data list, a first target data list is obtained, wherein the first target data list includes a plurality of first target data, and the first target data is target associated data with a specified identifier obtained from the target associated data list.

[0046] Based on the first target data list, target users corresponding to all first target data with consistent designated identifiers in the first target data list are associated to determine associated users, where the associated users are used to indicate that the associated users are the same user.

[0047] According to the target associated data list, a second target data list is obtained, wherein the second target data list includes a plurality of second target data, and the second target data is target associated data obtained from the target associated data list and having no designated identifier.

[0048] Based on the first target data list and the second target data list, if several feature data included in the second target data and several corresponding feature data included in the first target data meet the preset comparison rules, the user corresponding to the second target data and the user corresponding to the first target data are determined as associated users.

[0049] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0050] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0051] Embodiment 3 An embodiment of the present invention provides an electronic device, which includes a processor and the non-transitory computer-readable storage medium in the second embodiment of the present invention.

[0052] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for determining the same user, characterized in that: The method comprises the following steps: Obtaining a target-related data list corresponding to the target service platform, wherein the target-related data list includes a plurality of target-related data, and the target-related data is data uploaded through a plurality of data ports corresponding to the target service platform; According to the target associated data list, a first target data list is acquired, wherein the first target data list includes a plurality of first target data, and the first target data is target associated data with a specified identifier acquired from the target associated data list; Based on the first target data list, target users corresponding to all first target data with the same designated identifier in the first target data list are associated to determine associated users, where the associated users are used to indicate that the associated users are the same user; According to the target associated data list, a second target data list is acquired, wherein the second target data list includes a plurality of second target data, and the second target data is target associated data acquired from the target associated data list and does not have a designated identifier; Based on the first target data list and the second target data list, if several feature data included in the second target data and several corresponding feature data included in the first target data meet the preset comparison rules, the user corresponding to the second target data and the user corresponding to the first target data are determined as associated users.

2. The method for determining the same user according to claim 1, characterized in that: Each target-related data corresponds to a data port, wherein the data port is a port for users to log in and use.

3. The method for determining the same user according to claim 1, characterized in that: The designated identifier is a unique identifier that characterizes the identity of the target user, wherein the target user is a user who uses the data port corresponding to the target associated data to connect to the target service platform.

4. The method for determining the same user according to claim 1, characterized in that: If a plurality of feature data included in the second target data and a plurality of corresponding feature data included in the first target data meet a preset comparison rule, determining a user corresponding to the second target data and a user corresponding to the first target data as associated users comprises the following steps: Obtain a target feature vector list corresponding to the target associated data list, wherein the target feature vector list includes a plurality of target feature vectors, each target associated data corresponds to a target feature vector, and the target feature vector is a vector generated based on data corresponding to a plurality of target features in each target associated data; Obtain a target similarity list corresponding to each target feature vector, wherein the target similarity list includes a plurality of target similarities, wherein the target similarity is the similarity between the target feature vector and a feature vector to be selected, and the feature vector to be selected is any target feature vector in the target feature vector list except the target feature vector; When the target similarity satisfies a preset condition, target users corresponding to a number of target feature vectors corresponding to the target similarity are determined as associated users.

5. The method for determining the same user according to claim 4, characterized in that: Obtaining the target feature vector list corresponding to the target associated data list includes the following steps: According to the target associated data list, a target feature list is obtained, wherein the target feature list includes a plurality of target features, and the target features are initial features screened from an initial feature list corresponding to the target data list; Obtaining the number of target features, wherein the number of target features is the number of target features in the target feature list; According to the number of target features, obtain the target feature vector; Wherein, obtaining the target feature vector includes the following steps: When the number of target features is less than 2 or the number of target features is not less than 20, a target feature vector is obtained, wherein the target feature vector is a vector generated by concatenating the vectors generated based on each target associated data; When the number of target features is not less than 2 and the number of target features is less than 20, a designated priority is obtained, wherein the designated priority is a weight corresponding to a designated feature, and the designated feature is any target feature other than IP data obtained from the target feature list; Based on the number of target features and the assigned priorities, a target feature vector is identified.

6. The method for determining the same user according to claim 4, characterized in that: The preset condition is that the similarity is not less than a preset similarity threshold, wherein the preset similarity threshold has a value range of 0.7 to 0.

8.

7. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the non-transitory computer-readable storage medium, characterized in that: The at least one instruction or the at least one program is loaded and executed by the processor to implement the method for determining the same user as described in any one of claims 1-6.

8. An electronic device, characterized in that: The invention comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 7.

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