Target user acquisition method and device, electronic equipment and medium

Abstract

The embodiment of the invention provides a target user acquisition method and device, electronic equipment and a medium, and belongs to the field of artificial intelligence. The method comprises the following steps: acquiring user information of a plurality of first users, wherein each piece of user information comprises personal information and behavior information; screening the user information of each first user according to a target service scene to obtain multiple pieces of sub-information of the user information of each first user; for each piece of sub-information, determining a score corresponding to the sub-information according to a scoring mode configured for the sub-information in advance; for the user information of each first user, performing calculation according to the score corresponding to each piece of sub-information of the user information of the first user to obtain a total score of the user information of the first user; and taking the first user corresponding to the user information of which the total score is greater than a preset threshold as a target user. According to the embodiment of the invention, the target user of the target application scene can be quickly obtained.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to a method, device, electronic device and medium for acquiring a target user. Background Art

[0002] In today's business environment, accurate positioning of target users is crucial to the business development of enterprises. The existing methods for acquiring target users mainly adopt traditional market research methods, that is, collecting user information mainly through questionnaires, interviews, etc., but this manual collection method is time-consuming and costly, and there may be subjectivity in the data analysis and processing process. In the context of diversified business scenarios, manual collection methods are difficult to quickly screen out target users for specific business scenarios. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a target user acquisition method, device, electronic device and medium, aiming to solve the problem that existing target user acquisition methods are difficult to quickly determine the target users required by the target business scenario.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for acquiring a target user, the method comprising:

[0005] Acquire user information of multiple first users, each of the user information includes personal information and behavior information;

[0006] According to the target business scenario, the user information of each of the first users is screened to obtain a plurality of sub-information of the user information of each of the first users, each of the sub-information including one item of the user information;

[0007] For each of the sub-information, executing: determining a score corresponding to the sub-information according to a scoring method pre-configured for the sub-information;

[0008] For each user information of the first user, executing: calculating according to the score corresponding to each sub-information of the user information of the first user to obtain a total score of the user information of the first user;

[0009] The first user corresponding to the user information whose total score is greater than a preset threshold is taken as the target user.

[0010] In some implementations, the user information of each of the first users is screened according to the target business scenario to obtain multiple sub-information of the user information of each of the first users, including:

[0011] Acquire the preset core indicators in the target business scenario and the user information of the historical personnel of the target business scenario, where the historical personnel are personnel associated with the target business scenario within a preset first historical time period;

[0012] For each of the user information of the historical personnel, executing: filtering the user information of each of the historical personnel according to the target business scenario to obtain a plurality of sub-information of each of the historical personnel;

[0013] For each of the multiple sub-information of the historical personnel, executing: calculating the correlation between the same sub-information of all the historical personnel and the core indicator by the control variable method, and obtaining the prediction value corresponding to each sub-information;

[0014] According to the prediction values ​​corresponding to the plurality of sub-information, the prediction values ​​are sorted from large to small, and the sub-information corresponding to the top N prediction values ​​are obtained as the N target sub-information corresponding to the target business scenario, where N is a positive integer greater than 1;

[0015] The user information of each of the first users is screened according to the N target sub-information to obtain a plurality of sub-information of the user information of each of the first users.

[0016] In some implementations, after filtering the user information of each of the first users according to the target business scenario to obtain a plurality of sub-information of the user information of each of the first users, and before determining the score corresponding to the sub-information according to the scoring method pre-configured for the sub-information, the method further includes:

[0017] Analyze the correlation between the target sub-information and the core indicator to obtain the correlation result;

[0018] Determining a scoring method for the target sub-information according to the correlation result, wherein the correlation result indicates whether the target sub-information is positively correlated or negatively correlated with the core indicator;

[0019] The step of determining the score corresponding to the sub-information according to the scoring method pre-configured for the sub-information includes:

[0020] The sub-information of the first user is scored in the same manner as the target sub-information to obtain a score corresponding to the sub-information of the first user.

[0021] In some implementations, calculating according to the score corresponding to each sub-information of the user information of the first user to obtain the total score of the user information of the first user includes:

[0022] Obtaining a weight corresponding to each sub-information of the first user;

[0023] According to the score corresponding to each sub-information of the first user and the weight corresponding to the sub-information of the first user, a weighted sum is performed on the multiple sub-information of the first user to obtain the total score of the user information of the first user.

[0024] In some implementations, obtaining the weight corresponding to each sub-information of the first user includes:

[0025] Determining a weight of the target sub-information according to the predicted value of the target sub-information;

[0026] The weight of each of the sub-information of the first user is determined according to the weight of the target sub-information.

[0027] In some implementations, after taking the user corresponding to the user information whose total score is greater than a preset threshold as the target user, the method further includes:

[0028] In response to the feedback operation of the second user, displaying a scoring method for each sub-information and a weight of the sub-information;

[0029] According to the feedback operation, the scoring method of the sub-information and the weight of the sub-information are adjusted.

[0030] The scoring method includes at least one of a ratio scoring method, a quantitative decreasing scoring method, a veto scoring method, a graded scoring method, and a combing decreasing scoring method.

[0031] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a target user acquisition device, the device comprising:

[0032] An information acquisition module, used to acquire user information of a plurality of first users, each of the user information including personal information and behavior information;

[0033] an information determination module, configured to filter the user information of each of the first users according to a target business scenario to obtain a plurality of sub-information of the user information of each of the first users, each of the sub-information including one item of the user information;

[0034] An information scoring module, configured to execute, for each of the sub-information, the following steps: determining a score corresponding to the sub-information according to a scoring method pre-configured for the sub-information;

[0035] a total score calculation module, configured to perform, for each user information of the first user: calculating according to the score corresponding to each sub-information of the user information of the first user, to obtain a total score of the user information of the first user;

[0036] The user determination module is configured to take the first user corresponding to the user information whose total score is greater than a preset threshold as a target user.

[0037] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the target user acquisition method described in the first aspect when executing the computer program.

[0038] To achieve the above objectives, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the target user acquisition method described in the first aspect above.

[0039] The target user acquisition method, device, electronic device and medium proposed in the present application screen user information according to the requirements of the target business scenario to obtain multiple sub-information, and pre-configure a scoring method for each sub-information, so that the scoring results of different sub-information are more in line with the actual needs of the target business scenario. By comprehensively analyzing the scores of all sub-information, the total score of the corresponding user information is obtained, and the user information can be scored more comprehensively. The corresponding users whose user information scores exceed the set threshold are taken as target users, so as to obtain the target users of the target application scenario more quickly and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flowchart of a method for acquiring a target user provided in an embodiment of the present application;

[0041] Figure 2 yes Figure 1 A schematic flow chart of step S102 in FIG.

[0042] Figure 3 yes Figure 1 Schematic diagram of the process of step S103 in ;

[0043] Figure 4 is another flow chart of the target user acquisition method provided in an embodiment of the present application;

[0044] Figure 5 is a schematic diagram of the structure of a target user acquisition device provided in an embodiment of the present application;

[0045] Figure 6 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0049] First, some nouns involved in this application are analyzed:

[0050] Business scenarios are specific situations or challenges encountered by an enterprise during its operations, involving multiple aspects such as the enterprise's goals, strategies, customer needs, and market bargaining.

[0051] The Control Variable Method is a scientific research method used to control certain variables in experiments or studies in order to accurately observe and measure the relationship between other variables. It usually includes independent variables (independent variables) that are manipulated or changed in the experiment, dependent variables (dependent variables) that are measured or observed in the experiment, and their changes are considered to be the result of changes in the independent variables, and controlled variables: variables that remain constant in the experiment to eliminate their influence on the experimental results. The control variable method allows researchers to determine the correlation between independent and dependent variables and the degree of influence of independent variables on dependent variables.

[0052] In today's business environment, accurate positioning of target users is crucial to the business development of enterprises. The existing methods for acquiring target users mainly adopt traditional market research methods, that is, collecting user information mainly through questionnaires, interviews, etc., but this manual collection method is time-consuming and costly, and there may be subjectivity in the data analysis and processing process. In the context of diversified business scenarios, manual collection methods are difficult to quickly screen out target users for specific business scenarios.

[0053] Based on this, the embodiments of the present application provide a target user acquisition method, device, electronic device and medium, aiming to solve the problem that the existing target user acquisition method is difficult to quickly determine the target users required by the target business scenario.

[0054] The target user acquisition method, device, electronic device and medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the target user acquisition method in the embodiments of the present application is described.

[0055] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0056] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0057] The target user acquisition method provided in the embodiment of the present application relates to the field of artificial intelligence. The target user acquisition method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the target user acquisition method, etc., but is not limited to the above forms.

[0058] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0059] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0060] Figure 1 This is a flow chart of the target user acquisition method provided in the embodiment of the present application, please refer to Figure 1 The target user acquisition method provided in the embodiment of the present application may include but is not limited to steps S101 to S105.

[0061] Step S101: Obtain user information of multiple first users, each of which includes personal information and behavior information.

[0062] In this step, personal information includes but is not limited to the user's name, age, gender, place of residence, current location, educational background, work experience, etc., and behavioral information includes but is not limited to user delivery information, purchase information, browsing information, etc.

[0063] Step S102: Filter the user information of each of the first users according to the target business scenario to obtain a plurality of sub-information of the user information of each of the first users, each of the sub-information including one item of the user information.

[0064] In this step, the business scenario can be a specific situation or challenge encountered by the business during operation, such as recruitment, sales, product development, etc. According to the needs of the target business scenario, the user information of each first user is screened, and then the screened information of the first user is divided into multiple sub-information, each sub-information includes one item of information in the user information, that is, representing a certain attribute of the user information, such as gender, education level, and skills.

[0065] For example, the requirement of the recruitment business scenario is to recruit employees for a certain position, then the sub-information of the target user required for the sales business scenario is the user's age, education, work experience, etc. The requirement of the sales business scenario is sales volume, then the sub-information of the target user required for the sales business scenario is the user's age, browsing history, purchase history, income, etc.

[0066] For example, the user information of user A is 24 years old, female, bachelor's degree, has been engaged in computer-related positions for five years, and parents are self-employed. The current target business scenario is recruitment. According to the needs of the target business scenario, the user information of user A is first filtered, and the two types of information in the user information of user A, namely, female and parents are self-employed, are filtered out. Then, the filtered user information of user A is divided into first sub-information, second sub-information, third sub-information and fourth sub-information, wherein the first sub-information includes age, the second sub-information includes education level, and the third sub-information includes work experience.

[0067] Step S103: for each sub-information, executing: determining a score corresponding to the sub-information according to a scoring method pre-configured for the sub-information.

[0068] In this step, for different sub-information, a scoring method corresponding to each sub-information is pre-configured, wherein the scoring method can be a quantitative scoring (such as a scoring based on a numerical value) or a qualitative scoring (such as a scoring based on a category or level), which is not limited here; according to the pre-configured scoring method for the sub-information, the score of the sub-information corresponding to each user is calculated.

[0069] For example, the target business scenario is recruitment. For the age of the first sub-information of user A, the pre-configured scoring method is the grade scoring method, and the specific scoring rules are: 4 points for 18 to 25 years old, 6 points for 26 to 35 years old, 10 points for 36 to 45 years old, 4 points for 46 to 55 years old, 1 point for 56 years old and above, and no points for those under 18 years old; thus, the score of the first sub-information of user A is 4 points. The scoring method for the second sub-information of education is the veto scoring method, and the specific scoring rules are: 5 points for undergraduate to postgraduate education, 10 points for postgraduate education and above, and no points for education below undergraduate education. Thus, the score of the second sub-information of user A is 5 points. The scoring method for the third sub-information of work experience is the quantitative decreasing scoring method, and the specific scoring rules are: 10 years of computer-related work experience is a full score of 10 points, and the score decreases by 1 point for each year of reduction, and the minimum score is 0 points; thus, the score of the third sub-information of user A is 5 points.

[0070] Step S104: for each user information of the first user, executing: calculating according to the score corresponding to each sub-information of the user information of the first user to obtain a total score of the user information of the first user.

[0071] In this step, the score of each sub-information of the first user is first calculated, and then the scores of all sub-information of user A are calculated using a preset total score calculation method to obtain the total score of the user information of the first user, wherein the total score calculation method can be to add the scores of all sub-information or to perform a weighted summation of the scores of all sub-information, which is not limited here.

[0072] Exemplarily, the user information of user A is filtered into the first sub-information, the second sub-information and the third sub-information. The score of the first sub-information is 4 points, the score of the second sub-information is 5 points, and the score of the third sub-information is 5 points. The score of the first sub-information, the score of the second sub-information and the score of the third sub-information of user A can be added to obtain a total score of 14 points for the user information of user A. Different weights can also be assigned to each sub-information. For example, the weight of the first sub-information is 0.1, the weight of the second sub-information is 0.4, and the weight of the third sub-information is 0.5. The sub-information is weighted and summed according to the weight corresponding to each sub-information, and the total score of user A is 4.9.

[0073] Step S105: The first user corresponding to the user information whose total score is greater than a preset threshold is taken as a target user.

[0074] In this step, a total score threshold of the required target users is set in advance according to the requirements of the target business scenario, the total score of each first user is compared with the total score threshold, and the first users exceeding the total score threshold are taken as target users of the target business scenario.

[0075] Exemplarily, the total score threshold of the target user of the recruitment business scenario is set to 11, and the multiple first users include user A, user B, and user C, among which user A has a total score of 14 points, user B has a total score of 10 points, and user C has a total score of 16 points. Then user A and user C are selected as the target users of the recruitment business scenario.

[0076] Through the above steps S101 to S105, the electronic device screens the user information according to the needs of the target business scenario, obtains multiple sub-information, and pre-configures a scoring method for each sub-information, so that the scoring results of different sub-information are more in line with the actual needs of the target business scenario. By comprehensively evaluating the scores of all sub-information, the total score of the corresponding user information is obtained, and the user information can be scored more comprehensively. The corresponding users whose user information scores exceed the set threshold are taken as target users, so as to obtain the target users of the target application scenario more quickly and accurately.

[0077] In some embodiments, Figure 2 As shown, in step S102, the user information of each of the first users is screened according to the target business scenario to obtain multiple sub-information of the user information of each of the first users, which may include but is not limited to steps S1021 to S1025.

[0078] Step S1021, obtaining the core indicators preset in the target business scenario and the user information of the historical personnel of the target business scenario, where the historical personnel are personnel associated with the target business scenario within a preset first historical time period.

[0079] Step S1022: for each of the historical personnel's user information, perform: according to the target business scenario, filter the user information of each of the historical personnel to obtain a plurality of sub-information of each of the historical personnel.

[0080] Step S1023, for each of the multiple sub-information of the historical personnel, execute: by using the control variable method, calculate the correlation degree between the same sub-information of all the historical personnel and the core indicator, and obtain the prediction value corresponding to each sub-information.

[0081] Step S1024: Sort the prediction values ​​corresponding to the multiple sub-information from large to small, and obtain the sub-information corresponding to the top N prediction values ​​as the N target sub-information corresponding to the target business scenario, where N is a positive integer greater than 1.

[0082] Step S1025: Filter the user information of each of the first users according to the N target sub-information to obtain a plurality of sub-information of the user information of each of the first users.

[0083] In this implementation, by obtaining the core indicators of the target business scenario and the user information of historical personnel engaged in the target business scenario within the first historical time period, the user number information of the historical personnel is first screened according to the core indicators to screen out multiple sub-information that may be related to the core indicators, and then the control variable method is used to calculate the degree of correlation between the same type of sub-information of all historical personnel and the core indicators to obtain the predicted value of this type of sub-information, and the predicted values ​​corresponding to the multiple types of sub-information are sorted from large to small, and the sub-information ranked in the top N is used as the target sub-information of the current target business scenario, and the user information of the first user is screened according to the information type in the target sub-information to obtain multiple sub-information of the first user, and the user attributes contained in each of the multiple sub-information of the first user correspond one-to-one to the user attributes contained in the target sub-information.

[0084] It should be noted that the core indicators of the target business scenario can be set according to the actual situation of the target business scenario. For example, the core indicator of the marketing business scenario can be set to increase sales, and the core indicator of the recruitment business scenario can be set to new employee performance scores, etc., which are not limited here; the first historical time period can be set according to actual conditions. For example, the first historical time period of the marketing business scenario is the same quarter of the previous two years, and the first historical time period of the recruitment business scenario is the last 12 months, etc., which are not limited here; N can also be set according to actual needs, and can be the top 2 sub-information or the top 4 sub-information, which is not limited here.

[0085] Exemplarily, the target business scenario is a recruitment business scenario, and the core indicator is set as the new employee performance score. The past 12 months are set as the time range for data collection, i.e., the first historical time period. User information of all new employees who joined the company in the past 12 months, such as education background, work experience, skills, age, gender, family background, etc., is collected. According to the core indicator, the new employee performance score, sub-information related to the indicator is screened out, such as the first sub-information of working years, the second sub-information of age, the third sub-information of education background, and the fourth sub-information of skills. The influence of different sub-information on the core indicator is compared by the control variable method. According to the degree of influence, different prediction values ​​of each sub-information are calculated. For example, the prediction value of the first sub-information is 80, the prediction value of the second sub-information is 40, the prediction value of the third sub-information is 60, and the prediction value of the fourth sub-information is 20. The sub-information is sorted from large to small according to the prediction value, i.e., the first sub-information, the third sub-information, the second sub-information, and the fourth sub-information. The first three sub-information are taken as the target sub-information of the current recruitment business scenario, i.e., the first sub-information of working years, the third sub-information of education background, and the second sub-information of age are the target sub-information of the current recruitment business scenario. Based on these sub-information, corresponding sub-information is filtered out from the user information of the first user. For example, the user information of the first user includes 24 years old, female, bachelor's degree, working in computer-related positions for five years, and parents are self-employed. Then the first sub-information of the first user is 5 years of work, the third sub-information is bachelor's degree, and the third sub-information is age 24.

[0086] In this embodiment, user information of historical personnel engaged in the target business scenario is obtained, the user information of the historical personnel is screened to obtain multiple sub-information, and the predicted value of each type of sub-information of the historical personnel and the core indicators of the target business scenario is calculated. Multiple target sub-information of the target application scenario is obtained according to the predicted value to identify the sub-information that has a significant impact on the target business scenario, and the user information of the first user is screened according to the target sub-information to obtain the sub-information of the first user, so as to obtain the sub-information of the first user that is most relevant to the target application scenario, thereby improving the accuracy of the user information scoring of the first user.

[0087] In some implementations, after the user information of each of the first users is screened according to the target business scenario to obtain a plurality of sub-information of the user information of each of the first users in step S102, before the score corresponding to the sub-information is determined according to the scoring method pre-configured for the sub-information in step S103, the following may be included but not limited to:

[0088] Analyze the correlation between the target sub-information and the core indicator to obtain the correlation result;

[0089] A scoring method for the target sub-information is determined based on the correlation result, and the correlation result indicates whether the target sub-information is positively correlated or negatively correlated with the core indicator.

[0090] The step S103 of determining the score corresponding to the sub-information according to the scoring method pre-configured for the sub-information may include, but is not limited to, the following contents:

[0091] The sub-information of the first user is scored in the same manner as the target sub-information to obtain a score corresponding to the sub-information of the first user.

[0092] In this implementation, statistical methods are used to analyze whether the target sub-information is positively correlated or negatively correlated with the core indicators, that is, the correlation results between the target sub-information and the core indicators are obtained through statistical methods. The statistical method can be the Pearson correlation coefficient method, the Spearman rank correlation coefficient method, or any correlation coefficient method that can quantify the relationship between the target sub-information and the core indicators, which is not limited here; according to the correlation results between each target sub-information and the core indicators, and the specific content of each target sub-information, a corresponding scoring method is configured for each target sub-information, and the scoring methods include but are not limited to ratio scoring method, quantitative decreasing scoring method, veto scoring method, graded scoring method, combing decreasing scoring method, etc.

[0093] Since the sub-information of the first user is obtained by filtering based on the target sub-information, the user attributes contained in the sub-information of the first user are the same as the user attributes contained in the target sub-information. Therefore, the scoring method of each sub-information of the first user is the same as the scoring method of the target sub-information corresponding to the sub-information; according to the scoring method of the target sub-information, the score of the corresponding sub-information in the multiple sub-information of the first user is calculated.

[0094] Exemplarily, the target business scenario is the recruitment business scenario, the core indicator is the new employee performance indicator, and the target sub-information is the years of work experience, user age, and educational background. Through historical data analysis, the years of work experience are positively correlated with the new employee performance indicator, that is, the longer the years of work experience, the better the new employee performance is generally. The user age and the new employee performance indicator show an inverted U-shaped relationship, that is, middle-aged employees have the best performance. The educational background is positively correlated with the new employee performance indicator, that is, the higher the education level, the better the new employee performance is generally.

[0095] The scoring method for setting years of work is to increase one point for each additional year of work experience, with a full score of 10 points; the scoring method for setting user age is to set 4 points for 18 to 25 years old, 6 points for 26 to 35 years old, 10 points for 36 to 45 years old, 4 points for 46 to 55 years old, 1 point for 56 and above, and no points for under 18 years old; the scoring method for setting educational background is to set 5 points for undergraduate, 7 points for master, and 10 points for doctorate; the sub-information of the first user is 5 years of work, 24 years old, and bachelor's degree, so the first user's working experience score is 5 points, the user age score is 4 points, and the educational background score is 5 points.

[0096] In this embodiment, by analyzing the correlation between multiple target sub-information and core indicators, a scoring method for the target sub-information is obtained, and the score of the sub-information corresponding to the first user is calculated by the scoring method of the target sub-information. This can accurately predict the performance of the first user in the target business scenario, thereby accurately scoring the sub-information of the first user.

[0097] In some embodiments, Figure 3 As shown, in step S103, the score corresponding to each sub-information of the user information of the first user is calculated to obtain the total score of the user information of the first user, which may include but is not limited to steps S1031 to S1032.

[0098] Step S1031: Obtain the weight corresponding to each sub-information of the first user.

[0099] Step S1032: performing weighted summation on multiple sub-information of the first user according to the score corresponding to each sub-information of the first user and the weight corresponding to the sub-information of the first user, to obtain a total score of the user information of the first user.

[0100] In this implementation, the importance of each sub-information to the overall user information in the target business scenario, that is, the weight, is determined; the weight corresponding to each sub-information of the first user and the score corresponding to the sub-information are used to perform a weighted sum on multiple sub-information of the first user to obtain the total score of the user information of the first user. The weight of the sub-information can be determined based on the predicted value of the sub-information and the core indicator, and can also be set based on actual conditions, which is not limited here.

[0101] Exemplarily, the sub-information of the first user includes first sub-information, second sub-information and third sub-information. The score of the first sub-information is 5 points and the weight of the first sub-information is 0.44. The score of the second sub-information is 4 points and the weight of the second sub-information is 0.22. The score of the third sub-information is 5 points and the weight of the third sub-information is 0.34. The total score of the first user is 2.6.

[0102] In this embodiment, by assigning a corresponding weight to each sub-information, the influence of different sub-information on the user's overall score can be more accurately reflected, thereby eliminating the deviation that may be caused by a single scoring standard.

[0103] In some implementations, obtaining the weight corresponding to each sub-information of the first user in step S1031 may include, but is not limited to, the following contents:

[0104] Determining a weight of the target sub-information according to the predicted value of the target sub-information;

[0105] The weight of each of the sub-information of the first user is determined according to the weight of the target sub-information.

[0106] In this implementation, the predicted value of the target sub-information indicates the degree of influence of the target sub-information on the core indicator. Therefore, the weight of each target sub-information is determined based on the predicted value of each target sub-information, and the weight of the corresponding sub-information in the first user is determined based on the weight of the target sub-information.

[0107] Exemplarily, the predicted value of the first target sub-information is 80, the predicted value of the second target sub-information is 40, and the predicted value of the third target sub-information is 60. It can be determined that the weight of the first target sub-information is 0.44, the weight of the second target sub-information is 0.22, and the predicted value of the third target sub-information is 0.34. Therefore, the weight of the first sub-information of the first user is 0.44, the weight of the second sub-information is 0.22, and the weight of the third sub-information is 0.34.

[0108] In this embodiment, by determining the weight based on the predicted value of the target sub-information, it is possible to ensure that information that has a greater impact on the core indicators receives a higher weight. By assigning appropriate weights to each user's sub-information, the user information of each user can be scored more accurately to screen target users that are more in line with the target business scenario.

[0109] Figure 4 This is another flow chart of the target user acquisition method provided in the embodiment of the present application, see Figure 4 The target user acquisition method provided in the embodiment of the present application may include but is not limited to steps S101 to S107.

[0110] Step S101: Obtain user information of multiple first users, each of which includes personal information and behavior information.

[0111] Step S102: Filter the user information of each of the first users according to the target business scenario to obtain a plurality of sub-information of the user information of each of the first users, each of the sub-information including one item of the user information.

[0112] Step S103: for each sub-information, executing: determining a score corresponding to the sub-information according to a scoring method pre-configured for the sub-information.

[0113] Step S104: for each user information of the first user, executing: calculating according to the score corresponding to each sub-information of the user information of the first user to obtain a total score of the user information of the first user.

[0114] Step S105: The first user corresponding to the user information whose total score is greater than a preset threshold is taken as a target user.

[0115] Step S106: In response to the feedback operation of the second user, display the scoring method of each sub-information and the weight of the sub-information.

[0116] Step S107: According to the feedback operation, the scoring method of the sub-information and the weight of the sub-information are adjusted.

[0117] In this implementation, after acquiring the target user of the target scenario, feedback from the second user on the target user is received. The second user is the manager of the target application scenario. The feedback can be expressed as a score for the screened target user, and can be expressed as satisfaction or dissatisfaction with the target user. Based on the feedback operation of the second user, the scoring method and the corresponding weight of each sub-information are displayed to the second user. The second user can adjust the scoring method and the corresponding weight of each sub-information according to the situation of the target person to better meet the needs of the target business scenario.

[0118] Figure 5 This is a schematic diagram of the structure of the target user acquisition device provided in the embodiment of the present application. Figure 5 The embodiment of the present application further provides a target user acquisition device 800, which can implement the above target user acquisition method. The target user acquisition device 800 includes:

[0119] The information acquisition module 801 is used to acquire user information of multiple first users, each of which includes personal information and behavior information;

[0120] An information determination module 802 is used to filter the user information of each of the first users according to a target business scenario to obtain a plurality of sub-information of the user information of each of the first users, each of the sub-information including one item of the user information;

[0121] The information scoring module 803 is used to perform, for each of the sub-information, the following steps: determining a score corresponding to the sub-information according to a scoring method pre-configured for the sub-information;

[0122] A total score calculation module 804 is used to perform, for each piece of user information of the first user: calculating according to the score corresponding to each piece of the sub-information of the user information of the first user to obtain a total score of the user information of the first user;

[0123] The user determination module 805 is configured to take the first user corresponding to the user information whose total score is greater than a preset threshold as a target user.

[0124] In some implementations, the information determination module 802 includes:

[0125] A data acquisition submodule, used to acquire the preset core indicators in the target business scenario and the user information of the historical personnel of the target business scenario, wherein the historical personnel are personnel associated with the target business scenario within a preset first historical time period;

[0126] The first screening submodule is used to perform, for each of the user information of the historical personnel: screening the user information of each of the historical personnel according to the target business scenario to obtain a plurality of sub-information of each of the historical personnel;

[0127] The prediction value calculation submodule is used to perform, for each of the plurality of sub-information of the historical personnel, the following operations: calculating the correlation between the same sub-information of all the historical personnel and the core indicator by the control variable method, and obtaining the prediction value corresponding to each sub-information;

[0128] A sorting submodule, used to sort the prediction values ​​corresponding to the plurality of sub-information from large to small, and obtain the sub-information corresponding to the top N prediction values ​​as the N target sub-information corresponding to the target business scenario, where N is a positive integer greater than 1;

[0129] The second screening module is used to screen the user information of each of the first users according to the N target sub-information to obtain a plurality of sub-information of the user information of each of the first users.

[0130] In some implementations, the target user acquisition device 800 further includes:

[0131] An analysis module, used to analyze the correlation between the target sub-information and the core indicator to obtain the correlation result;

[0132] A scoring method determination module, used to determine a scoring method for the target sub-information according to the correlation result, wherein the correlation result indicates whether the target sub-information is positively correlated or negatively correlated with the core indicator;

[0133] The information scoring module 803 further includes:

[0134] The scoring submodule is used to score the sub-information of the first user in the same way as the scoring of the target sub-information, so as to obtain a score corresponding to the sub-information of the first user.

[0135] In some implementations, the total score calculation module 804 includes:

[0136] A weight acquisition submodule, used to acquire a weight corresponding to each sub-information of the first user;

[0137] The total score calculation submodule is used to perform weighted summation on multiple sub-information of the first user according to the score corresponding to each sub-information of the first user and the weight corresponding to the sub-information of the first user, so as to obtain the total score of the user information of the first user.

[0138] In some implementations, the weight acquisition submodule includes:

[0139] A first weight determination unit, configured to determine a weight of the target sub-information according to a predicted value of the target sub-information;

[0140] The second weight determination unit is configured to determine a weight of each of the sub-information of the first user according to the weight of the target sub-information.

[0141] In some implementations, the target user acquisition device 800 further includes:

[0142] A response module, configured to display a scoring method for each sub-information and a weight of the sub-information in response to a feedback operation of the second user;

[0143] An adjustment module is used to adjust the scoring method of the sub-information and the weight of the sub-information according to the feedback operation.

[0144] In some embodiments, the scoring method includes at least one of a ratio scoring method, a quantitative decreasing scoring method, a veto scoring method, an equal scoring method, and a combing decreasing scoring method.

[0145] The specific implementation of the target user acquisition device 800 is substantially the same as the specific implementation of the target user acquisition method described above, and will not be described in detail herein.

[0146] When the electronic device is started, the target user acquisition device 800 filters the user information according to the requirements of the target business scenario, obtains multiple sub-information, and pre-configures a scoring method for each sub-information, so that the scoring results of different sub-information are more in line with the actual needs of the target business scenario. By comprehensively analyzing the scores of all sub-information, the total score of the corresponding user information is obtained, which can score the user information more comprehensively, and take the corresponding users whose user information scores exceed the set threshold as target users, so as to obtain the target users of the target application scenario more quickly and accurately.

[0147] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned target user acquisition method when executing the computer program. The electronic device can be any intelligent terminal including a desktop computer, a tablet computer, a mobile phone, and a car computer.

[0148] See also Figure 6 , Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application, wherein the electronic device includes:

[0149] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0150] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the target user acquisition method of the embodiment of this application;

[0151] Input / output interface 903, used to implement information input and output;

[0152] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0153] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0154] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0155] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned target user acquisition method is implemented.

[0156] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0157] The target user acquisition method, device, electronic device and medium provided in the embodiments of the present application screen user information according to the requirements of the target business scenario to obtain multiple sub-information, and pre-configure a scoring method for each sub-information, so that the scoring results of different sub-information are more in line with the actual needs of the target business scenario. By comprehensively analyzing the scores of all sub-information, the total score of the corresponding user information is obtained, and the user information can be scored more comprehensively. The corresponding users whose user information scores exceed the set threshold are taken as target users, so as to obtain the target users of the target application scenario more quickly and accurately.

[0158] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0159] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0160] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0161] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0162] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application 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 application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising 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.

[0163] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0164] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0165] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0167] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0168] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for acquiring target users, characterized in that: The method comprises: Acquire user information of multiple first users, each of the user information includes personal information and behavior information; According to the target business scenario, the user information of each of the first users is screened to obtain a plurality of sub-information of the user information of each of the first users, each of the sub-information including one item of the user information; For each of the sub-information, executing: determining a score corresponding to the sub-information according to a scoring method pre-configured for the sub-information; For each user information of the first user, executing: calculating according to the score corresponding to each sub-information of the user information of the first user to obtain a total score of the user information of the first user; The first user corresponding to the user information whose total score is greater than a preset threshold is taken as the target user.

2. The method according to claim 1, characterized in that The user information of each of the first users is screened according to the target business scenario to obtain multiple sub-information of the user information of each of the first users, including: Acquire the preset core indicators in the target business scenario and the user information of the historical personnel of the target business scenario, where the historical personnel are personnel associated with the target business scenario within a preset first historical time period; For each of the user information of the historical personnel, executing: filtering the user information of each of the historical personnel according to the target business scenario to obtain a plurality of sub-information of each of the historical personnel; For each of the multiple sub-information of the historical personnel, executing: calculating the correlation between the same sub-information of all the historical personnel and the core indicator by the control variable method, and obtaining the prediction value corresponding to each sub-information; According to the prediction values ​​corresponding to the plurality of sub-information, the prediction values ​​are sorted from large to small, and the sub-information corresponding to the top N prediction values ​​are obtained as the N target sub-information corresponding to the target business scenario, where N is a positive integer greater than 1; The user information of each of the first users is screened according to the N target sub-information to obtain a plurality of sub-information of the user information of each of the first users.

3. The method according to claim 2, characterized in that After filtering the user information of each of the first users according to the target business scenario to obtain a plurality of sub-information of the user information of each of the first users, and before determining the score corresponding to the sub-information according to the scoring method pre-configured for the sub-information, the method further includes: Analyze the correlation between the target sub-information and the core indicator to obtain the correlation result; Determining a scoring method for the target sub-information according to the correlation result, wherein the correlation result indicates whether the target sub-information is positively correlated or negatively correlated with the core indicator; The step of determining the score corresponding to the sub-information according to the scoring method pre-configured for the sub-information includes: The sub-information of the first user is scored in the same manner as the target sub-information to obtain a score corresponding to the sub-information of the first user.

4. The method according to claim 2, characterized in that: The calculating according to the score corresponding to each sub-information of the user information of the first user to obtain the total score of the user information of the first user includes: Obtaining a weight corresponding to each sub-information of the first user; According to the score corresponding to each sub-information of the first user and the weight corresponding to the sub-information of the first user, a weighted sum is performed on the multiple sub-information of the first user to obtain the total score of the user information of the first user.

5. The method according to claim 4, characterized in that The obtaining the weight corresponding to each sub-information of the first user includes: Determining a weight of the target sub-information according to the predicted value of the target sub-information; The weight of each of the sub-information of the first user is determined according to the weight of the target sub-information.

6. The method according to any one of claims 1 to 5, characterized in that: After taking the user corresponding to the user information whose total score is greater than the preset threshold as the target user, the method further includes: In response to the feedback operation of the second user, displaying a scoring method for each sub-information and a weight of the sub-information; According to the feedback operation, the scoring method of the sub-information and the weight of the sub-information are adjusted.

7. The method according to claim 1, characterized in that The scoring method includes at least one of a ratio scoring method, a quantitative decreasing scoring method, a veto scoring method, a graded scoring method, and a combing decreasing scoring method.

8. A target user acquisition device, characterized in that: The device comprises: An information acquisition module, used to acquire user information of a plurality of first users, each of the user information including personal information and behavior information; an information determination module, configured to filter the user information of each of the first users according to a target business scenario to obtain a plurality of sub-information of the user information of each of the first users, each of the sub-information including one item of the user information; An information scoring module, configured to execute, for each of the sub-information, the following steps: determining a score corresponding to the sub-information according to a scoring method pre-configured for the sub-information; a total score calculation module, configured to perform, for each user information of the first user: calculating according to the score corresponding to each sub-information of the user information of the first user, to obtain a total score of the user information of the first user; The user determination module is configured to take the first user corresponding to the user information whose total score is greater than a preset threshold as a target user.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the target user acquisition method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the target user acquisition method according to any one of claims 1 to 7 is implemented.

Images