Target user determination method and device, computer equipment, storage medium and computer program product

Abstract

The invention relates to a target user determination method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring historical work information of each user in a plurality of service platforms in a historical time period and historical activity parameters of historical activities provided by each service platform, and acquiring target activity parameters and target activity requirements of target activities provided by each service platform; for a target service platform in the service platforms, target work information of target activities provided by the users for the target service platform is determined according to the historical work information and the historical activity parameters, and the target service platform is any service platform in the service platforms; and determining a target user from the users according to the target work information, the target activity parameter and the target activity demand, so that the target service platform promotes a target activity provided by the target service platform to the target user. By adopting the method, the problem of resource waste can be avoided.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a target user determination method, device, computer equipment, storage medium and computer program product. Background Art

[0002] Aggregation platforms refer to platforms that aggregate the transportation resources provided by multiple third-party online ride-hailing service providers to provide online ride-hailing services to users. Due to the complex organizational structure of aggregation platforms, online ride-hailing service providers within the aggregation platform face greater competition pressure within the same platform and across platforms.

[0003] In the prior art, each online ride-hailing service provider selects some drivers from multiple drivers based on causal inference and provides them with marketing activities to encourage the drivers to provide more transportation resources for the online ride-hailing service provider.

[0004] However, since this method selects drivers with poor accuracy, the marketing campaign is less effective, resulting in a waste of resources. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product with high accuracy for determining target users in response to the above technical problems, which can improve the effectiveness of marketing activities and avoid the occurrence of resource waste.

[0006] In a first aspect, the present application provides a method for determining a target user, comprising:

[0007] Obtain historical work information of each user in multiple service platforms within a historical time period and historical activity parameters of historical activities provided by each service platform, and obtain target activity parameters and target activity requirements of target activities provided by each service platform;

[0008] For a target service platform in each of the service platforms, determining target work information of a target activity provided by each user for the target service platform according to the historical work information and the historical activity parameters, the target service platform being any service platform in each of the service platforms;

[0009] A target user is determined from among the users according to the target work information, the target activity parameters and the target activity requirements, so that the target service platform promotes the target activity provided by the target service platform to the target user.

[0010] In one of the embodiments, the target work information of the target activity provided by each user for the target service platform is determined based on the historical work information and the historical activity parameters, including: clustering each user based on the historical work information to obtain a clustering result for each user; determining the login probability information of each user logging into the target service platform based on the clustering result, the historical work information and the historical activity parameters; and determining the target work information based on the clustering result, the login probability information and the historical work information.

[0011] In one of the embodiments, each user is clustered according to the historical work information to obtain a clustering result for each user, including: determining the work status information of each user on each service platform according to the historical work information; clustering each user according to the work status information to obtain a clustering result for each user; wherein the clustering result includes single-dwelling and multi-dwelling, the single-dwelling is used to indicate that the user only provides services on the target service platform, and the multi-dwelling is used to indicate that the user provides services on the target service platform and on service platforms other than the target service platform.

[0012] In one of the embodiments, the login probability information of each user logging into the target service platform is determined based on the clustering result, the historical work information and the historical activity parameters, including: for each of the users, determining a target login probability information determination model from a plurality of pre-trained candidate login probability information determination models based on the clustering result of the user; and inputting the historical work information and the historical activity parameters into the target login probability information determination model to obtain the login probability information of the user.

[0013] In one of the embodiments, the target work information is determined based on the clustering result, the login probability information and the historical work information, including: when the clustering result is uni-habitat, the target work information is determined based on the login probability information and the historical work information; when the clustering result is multi-habitat, whether the user chooses to provide services on the target service platform is determined based on the login probability information and the historical work information, and if so, the target work information is determined based on the historical work information.

[0014] In one embodiment, the target activity requirement includes a first requirement and a second requirement, and determining the target user from each user based on the target work information, the target activity parameter and the target activity requirement includes: when the target activity requirement is the first requirement, inputting the target work information and the target activity parameter into a first model to obtain the target user output by the first model; when the target activity requirement is the second requirement, inputting the target work information and the target activity parameter into a second model to obtain the target user output by the second model; wherein the first model contains a first parameter, the second model contains a second parameter, the first parameter is determined based on the first requirement, and the second parameter is determined based on the second requirement.

[0015] In a second aspect, the present application further provides a target user determination device, comprising:

[0016] An acquisition module, used to acquire historical work information of each user in multiple service platforms within a historical time period and historical activity parameters of historical activities provided by each service platform, and to acquire target activity parameters and target activity requirements of target activities provided by each service platform;

[0017] A determination module, for determining, for a target service platform in each of the service platforms, target work information of a target activity provided by each of the users for the target service platform according to the historical work information and the historical activity parameters, the target service platform being any service platform in each of the service platforms;

[0018] An execution module is used to determine a target user from each of the users according to the target work information, the target activity parameters and the target activity requirements, so that the target service platform promotes the target activity provided by the target service platform to the target user.

[0019] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in any embodiment of the first aspect when executing the computer program.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any embodiment of the first aspect above.

[0021] In a fifth aspect, the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method described in any embodiment of the first aspect above.

[0022] The above-mentioned target user determination method, device, computer equipment, storage medium and computer program product obtain the historical work information of each user in multiple service platforms within a historical time period and the historical activity parameters of the historical activities provided by each service platform, and obtain the target activity parameters and target activity requirements of the target activities provided by each service platform; for the target service platform in each of the service platforms, determine the target work information of each user for the target activity provided by the target service platform according to the historical work information and the historical activity parameters, and the target service platform is any service platform in each of the service platforms; determine the target user from each of the users according to the target work information, the target activity parameters and the target activity requirements, so that the target service platform promotes the target activity provided by the target service platform to the target user. The target user determination method provided in the present application determines the target users of each platform from multiple users based on the historical work information of each user on each platform and the historical activity parameters of each service platform, so as to promote the target activities provided by each service platform to the target users corresponding to each service platform. Since the historical work information of the users on each platform and the historical activity parameters of each service platform are taken into consideration, the target users of each service platform determined are more accurate, and the effect of the promoted target activities is better, thereby effectively avoiding the occurrence of resource waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A schematic diagram of a flow chart of a method for determining a target user in one embodiment;

[0025] Figure 2 A flowchart of a method for determining target work information of a target activity provided by each user for a target service platform in one embodiment;

[0026] Figure 3 A schematic diagram of a flow chart of a method for obtaining clustering results of each user in one embodiment;

[0027] Figure 4 A flowchart of a method for determining the login probability information of each user logging into a target service platform in one embodiment;

[0028] Figure 5 A flowchart of a method for determining target work information according to clustering results, login probability information and historical work information in one embodiment;

[0029] Figure 6 A schematic diagram of a flow chart of a method for determining a target user in one embodiment;

[0030] Figure 7 A schematic flow chart of a target user determination method in another embodiment;

[0031] Figure 8 is a structural block diagram of a target user determination device in one embodiment;

[0032] Fig. 9 is an internal structure diagram of a computer device in one embodiment;

[0033] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in another embodiment. DETAILED DESCRIPTION

[0034] 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.

[0035] Aggregation platforms refer to platforms that aggregate the transportation resources provided by multiple third-party online ride-hailing service providers to provide online ride-hailing services to users. Due to the complex organizational structure of aggregation platforms, online ride-hailing service providers within the aggregation platform face greater competition pressure within the same platform and across platforms.

[0036] In the prior art, each online ride-hailing service provider selects some drivers from multiple drivers based on causal inference and provides them with marketing activities to encourage the drivers to provide more transportation resources for the online ride-hailing service provider.

[0037] However, since this method selects drivers with poor accuracy, the marketing campaign is less effective, resulting in a waste of resources.

[0038] In view of this, the present application provides a target user determination method, which can effectively improve the accuracy of the target users determined for each service platform, thereby making the target activities promoted more effective and effectively avoiding the problem of waste of resources.

[0039] The target user determination method provided in the present application can be implemented by a computer device, which can be a terminal or a server. The terminal can be, but is not limited to, various personal computers, laptops, tablet computers, and Internet of Things devices, and the server can be implemented by an independent server or a server cluster composed of multiple servers.

[0040] In an exemplary embodiment, Figure 1As shown, a method for determining a target user is provided, the method comprising the following steps:

[0041] Step 101: Obtain historical work information of each user in multiple service platforms within a historical time period and historical activity parameters of historical activities provided by each service platform, and obtain target activity parameters and target activity requirements of target activities provided by each service platform.

[0042] Optionally, the historical time period may be a time period pre-set by a technician based on actual needs. The historical time period may be one month or one year, and this application does not limit this.

[0043] The user may be an online car-hailing driver, and the historical work information may be used to represent the user's daily order completion information. Specifically, it may include the user's daily driving time, number of completed orders, completed order revenue, and activity rewards obtained.

[0044] The historical activities may include reward activities and guarantee activities. The reward activities refer to activities that issue fixed rewards to users when the users meet the preset conditions of the activity settings. Guarantee activities refer to activities that issue rewards to users to make up the user's order revenue to the preset revenue value if the user's order revenue is less than the preset revenue value when the user meets the preset conditions of the activity settings.

[0045] The historical activity parameters include the activity's target user promotion list, activity time, activity order completion requirements, and activity award indicators.

[0046] The target activity also includes reward activities and guarantee activities. The target activity parameters may include the relevant content of the above historical activity parameters, and may also include reward activity parameters and guarantee activity parameters. The reward activity parameter is the ratio of the activity reward to the number of orders completed by the preset activity, and the guarantee activity parameter is the difference between the preset benefit value of the activity and the product of the number of orders completed by the preset activity and the average income of the number of orders completed in the actual historical period of the user. The target activity requirements are also the preset conditions that the user needs to meet.

[0047] In some exemplary embodiments, the historical work information of each user in multiple service platforms during the historical time period, the historical activity parameters of historical activities provided by each service platform, and the target activity parameters and target activity requirements of the target activities provided by each service platform can be obtained through the service information database.

[0048] Step 102: For the target service platform in each of the service platforms, determine the target work information of the target activity provided by each user for the target service platform according to the historical work information and the historical activity parameters.

[0049] The target service platform is any one of the service platforms.

[0050] Optionally, the target work information may be used to characterize the completion status of each user's target activity provided by the target service platform.

[0051] In some exemplary embodiments, for the target service platform in each service platform, the historical work information of each user, the historical activity parameters and the target activity parameters of the target activity can be respectively input into a pre-trained target work information determination model to obtain the target work information of each user output by the target work information determination model.

[0052] Step 103: determining a target user from among the users according to the target work information, the target activity parameters and the target activity requirements, so that the target service platform promotes the target activity provided by the target service platform to the target user.

[0053] In some exemplary embodiments, the target work information, target activity parameters, and target activity requirements of each user may be input into a pre-trained target user determination model to obtain a target user output by the target user determination model.

[0054] The matching degree between each user and the target activity may also be determined based on the work information, target activity parameters and target activity requirements of each user, and a user whose matching degree is greater than a preset matching degree threshold may be determined as the target user.

[0055] Furthermore, after determining the target users, the target service platform can promote the target activities provided by the target service platform to the target users. Since the promoted target users are accurately screened, the effect of the target activities can be effectively improved, thereby avoiding the problem of waste of resources.

[0056] The above-mentioned target user determination method obtains the historical work information of each user in multiple service platforms within the historical time period and the historical activity parameters of the historical activities provided by each service platform, and obtains the target activity parameters and target activity requirements of the target activities provided by each service platform; for the target service platform in each service platform, the target work information of the target activity provided by each user for the target service platform is determined according to the historical work information and the historical activity parameters, and the target service platform is any service platform in each service platform; the target user is determined from each user according to the target work information, the target activity parameters and the target activity requirements, so that the target service platform promotes the target activity provided by the target service platform to the target user. The target user determination method provided in the present application determines the target user of each platform from multiple users according to the historical work information of each user in each platform and the historical activity parameters of each service platform, so as to promote the target activities provided by each service platform to the target users corresponding to each service platform. Since the historical work information of the user on each platform and the historical activity parameters of each service platform are taken into consideration, the target users of each service platform determined are more accurate, and the effect of the promoted target activities is better, thereby effectively avoiding the occurrence of resource waste problems.

[0057] In an exemplary embodiment, Figure 2 As shown, the process of determining the target work information of the target activity provided by each user for the target service platform according to the historical work information and the historical activity parameters includes the following steps:

[0058] Step 201: cluster each user according to the historical work information to obtain a clustering result for each user.

[0059] Optionally, the clustering result can be used to characterize whether each user is a single user. As mentioned above, the aggregation platform includes multiple online car-hailing service providers, so the user may only provide capacity resources to one online car-hailing service provider, or may provide capacity resources to multiple online car-hailing service providers.

[0060] Optionally, the historical work information of each user may be input into a pre-trained clustering result determination model to obtain a clustering result for each user.

[0061] Step 202: Determine the login probability information of each user logging into the target service platform according to the clustering result, the historical work information and the historical activity parameters.

[0062] In some exemplary embodiments, the clustering result, the historical work information and the historical activity parameters may be input into a pre-trained login probability information determination model to obtain the login probability information output by the login probability information determination model.

[0063] Exemplarily, different service platforms may correspond to different login probability information determination models, or only one login probability information determination model may be trained, but when inputting, the identifier corresponding to the target service platform also needs to be input.

[0064] Step 203: Determine the target work information according to the clustering result, the login probability information and the historical work information.

[0065] In some exemplary embodiments, the clustering result, the login probability information and the historical work information may be input into a pre-trained target work information determination model to obtain the target work information output by the target work information determination model.

[0066] In an exemplary embodiment, Figure 3 As shown, clustering the users according to the historical work information to obtain clustering results for the users includes the following steps:

[0067] Step 301, determining the work status information of each user on each service platform according to the historical work information;

[0068] Step 302: cluster the users according to the work status information to obtain clustering results for the users.

[0069] The clustering result includes single-dwelling and multi-dwelling. The single-dwelling is used to indicate that the user only provides services on the target service platform, and the multi-dwelling is used to indicate that the user provides services on the target service platform and on service platforms other than the target service platform.

[0070] In some exemplary embodiments, the user's work status information on each service platform can be determined based on the user's historical work information. For example, the aggregation platform includes three service platforms corresponding to online car-hailing service providers, namely A1, A2 and A3. If it is determined based on the user's historical work information that the user only provides transportation resources on the A1 platform, then the user's work status information can be determined to be A1. If it is determined based on the user's historical work information that the user provides transportation resources on both the A1 and A3 platforms, then the user's work status information can be determined to be A1 and A3.

[0071] Further, if the target service platform is A1 and the user's historical work information is A1, it can be determined that the clustering result of the user is single-dwelling; if the target service platform is A1 and the user's historical work information is A1 and A3, it can be determined that the clustering result of the user is multi-dwelling.

[0072] In an optional embodiment of the present application, the silhouette coefficient method can also be used to determine the second clustering result of each user based on the historical work information. The second clustering result is used to characterize the completed order volume of each user. The second clustering result can be used to determine the differences in the completed order volume among different types of users.

[0073] In an exemplary embodiment, Figure 4 As shown, the method of determining the login probability information of each user logging into the target service platform according to the clustering result, the historical work information and the historical activity parameter includes the following steps:

[0074] Step 401: for each of the users, determine a target login probability information determination model from a plurality of pre-trained candidate login probability information determination models according to the clustering result of the user.

[0075] Step 402: Input the historical work information and the historical activity parameters into the target login probability information determination model to obtain the login probability information of the user.

[0076] Optionally, the login probability information is used to characterize the probability of a user logging into the target service platform.

[0077] Exemplarily, different clustering results correspond to different login probability information determination models.

[0078] In some exemplary embodiments, for each of the users, a candidate login probability information determination model corresponding to the clustering result can be first determined from multiple pre-trained candidate login probability information determination models based on the clustering result as the target login probability information determination model.

[0079] Furthermore, the user's historical work information, the historical activity parameters and the user's historical feature data may be input into the target login probability information determination model to obtain the user's login probability information.

[0080] The target login probability information determination model may be a logistic regression model. The historical feature data may characterize the user's enthusiasm and fatigue status in providing transportation resources. The historical feature data may include the average number of orders completed by the user within a preset time period, the duration of logging into the target service platform, the number of days the user continuously provides transportation resources, and the number of days the user does not provide transportation resources.

[0081] In the above method, for each of the users, a target login probability information determination model is determined from a plurality of pre-trained candidate login probability information determination models according to the clustering results of the user, and the historical work information and the historical activity parameters are input into the target login probability information determination model to obtain the login probability information of the user. Since the clustering results include unimodal and bimodal users, the probability of unimodal users and bimodal users logging into the target service platform is different. Therefore, using different models for prediction according to different clustering results can effectively improve the accuracy of the prediction results.

[0082] In an exemplary embodiment, Figure 5 As shown, determining the target work information according to the clustering result, the login probability information and the historical work information includes the following steps:

[0083] Step 501: When the clustering result is single, determine the target work information according to the login probability information and the historical work information.

[0084] The target information may specifically be the user's predicted completed order quantity.

[0085] In some exemplary embodiments, when the clustering result is single and the login probability indicated by the login probability information is greater than a preset login probability threshold, the user's historical work information and historical feature data can be input into a pre-trained target work information determination model to obtain the target work information output by the target work information determination model. The target work information determination model can be a fitted fixed effect regression model.

[0086] Step 502: When the clustering result is multi-habitat, determine whether the user chooses to provide services on the target service platform based on the login probability information and the historical work information. If yes, determine the target work information based on the historical work information.

[0087] In some exemplary embodiments, when the clustering result is multi-habitat, it is first determined whether the user chooses to provide services on the target service platform based on the login probability information and the historical work information.

[0088] Furthermore, when it is determined that the user provides services on the target service platform, the target work information is determined based on the historical work information.

[0089] In an exemplary embodiment, Figure 6 As shown, the target activity requirement includes a first requirement and a second requirement, and determining a target user from each user according to the target work information, the target activity parameter and the target activity requirement includes the following steps:

[0090] Step 601: when the target activity requirement is the first requirement, input the target work information and the target activity parameter into the first model to obtain the target user output by the first model;

[0091] Step 602: when the target activity requirement is the second requirement, input the target work information and the target activity parameter into the second model to obtain the target user output by the second model;

[0092] The first model includes a first parameter, the second model includes a second parameter, the first parameter is determined according to the first requirement, and the second parameter is determined according to the second requirement.

[0093] Optionally, the first requirement may be to maximize benefits, that is, to maximize the revenue of the target service platform. The second requirement may be to maximize completed orders, that is, to maximize the number of completed orders for each target user.

[0094] The first parameter and the second parameter can be understood as the weight ratio of each information and each parameter in the target work information and the target activity parameter, and can be pre-set by the technician according to actual needs.

[0095] In some exemplary embodiments, when the target activity demand is the first demand, the initial parameters in the initial model can be adjusted to the first parameters to obtain the first model, and the target work information and the target activity parameters can be input into the first model to obtain the target user output by the first model. The target activity can be promoted to the target user, so that the target service platform can maximize its revenue.

[0096] Furthermore, in the case where the target activity demand is the second demand, the initial parameters in the initial model can be adjusted to the second parameters to obtain the second model, and the target work information and the target activity parameters can be input into the second model to obtain the target user output by the second model, and the target activity can be promoted to the target user, so that the target service platform can maximize the number of user completed orders.

[0097] In an exemplary embodiment, Figure 7 As shown, another method for determining a target user is provided, the method comprising the following steps:

[0098] Step 701: Obtain historical work information of each user in multiple service platforms within a historical time period and historical activity parameters of historical activities provided by each service platform, and obtain target activity parameters and target activity requirements of target activities provided by each service platform;

[0099] Step 702: for a target service platform in each of the service platforms, determine the work status information of each user in each of the service platforms according to the historical work information; perform clustering processing on each of the users according to the work status information to obtain a clustering result of each of the users; wherein the clustering result includes single-dwelling and multi-dwelling, the single-dwelling is used to indicate that the user only provides services on the target service platform, and the multi-dwelling is used to indicate that the user provides services on the target service platform and also provides services on service platforms other than the target service platform; the target service platform is any service platform in each of the service platforms;

[0100] Step 703: for each of the users, determine a target login probability information determination model from a plurality of pre-trained candidate login probability information determination models according to the clustering result of the user; input the historical work information and the historical activity parameter into the target login probability information determination model to obtain the login probability information of the user;

[0101] Step 704: if the clustering result is single-dwelling, determine the target work information according to the login probability information and the historical work information; if the clustering result is multiple-dwelling, determine whether the user chooses to provide services on the target service platform according to the login probability information and the historical work information; if yes, determine the target work information according to the historical work information;

[0102] Step 705: When the target activity requirement is the first requirement, the target work information and the target activity parameters are input into the first model to obtain the target user output by the first model; when the target activity requirement is the second requirement, the target work information and the target activity parameters are input into the second model to obtain the target user output by the second model; so that the target service platform promotes the target activity provided by the target service platform to the target user; wherein the first model contains the first parameter, the second model contains the second parameter, the first parameter is determined according to the first requirement, and the second parameter is determined according to the second requirement.

[0103] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0104] Based on the same inventive concept, the embodiment of the present application also provides a target user determination device for implementing the target user determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more target user determination device embodiments provided below can refer to the limitations of the target user determination method above, and will not be repeated here.

[0105] In an exemplary embodiment, Figure 8 As shown, a target user determination device 800 is provided, comprising: an acquisition module 801, a determination module 802 and an execution module 803, wherein:

[0106] The acquisition module 801 is used to acquire historical work information of each user in multiple service platforms within a historical time period and historical activity parameters of historical activities provided by each service platform, and to acquire target activity parameters and target activity requirements of target activities provided by each service platform;

[0107] A determination module 802 is used to determine, for a target service platform in each of the service platforms, target work information of a target activity provided by each user for the target service platform according to the historical work information and the historical activity parameter, and the target service platform is any service platform in each of the service platforms;

[0108] The execution module 803 is used to determine the target user from each of the users according to the target work information, the target activity parameters and the target activity requirements, so that the target service platform promotes the target activity provided by the target service platform to the target user.

[0109] In one embodiment, the determination module 802 is specifically used to cluster each user according to the historical work information to obtain a clustering result for each user; determine the login probability information of each user logging into the target service platform according to the clustering result, the historical work information and the historical activity parameters; and determine the target work information according to the clustering result, the login probability information and the historical work information.

[0110] In one embodiment, the determination module 802 is specifically used to determine the work status information of each user on each service platform based on the historical work information; cluster each user based on the work status information to obtain a clustering result for each user; wherein the clustering result includes single-dwelling and multi-dwelling, the single-dwelling is used to indicate that the user only provides services on the target service platform, and the multi-dwelling is used to indicate that the user provides services on the target service platform and also provides services on service platforms other than the target service platform.

[0111] In one embodiment, the determination module 802 is specifically used to determine, for each of the users, a target login probability information determination model from a plurality of pre-trained candidate login probability information determination models according to the clustering results of the users; and input the historical work information and the historical activity parameters into the target login probability information determination model to obtain the login probability information of the user.

[0112] In one embodiment, the determination module 802 is specifically used to determine the target work information based on the login probability information and the historical work information when the clustering result is uni-inhabited; and to determine whether the user chooses to provide services on the target service platform based on the login probability information and the historical work information when the clustering result is multi-inhabited, and if so, determine the target work information based on the historical work information.

[0113] In one embodiment, the target activity requirement includes a first requirement and a second requirement. The execution module 803 is specifically used to, when the target activity requirement is the first requirement, input the target work information and the target activity parameters into the first model to obtain the target user output by the first model; when the target activity requirement is the second requirement, input the target work information and the target activity parameters into the second model to obtain the target user output by the second model; wherein the first model contains a first parameter, the second model contains a second parameter, the first parameter is determined according to the first requirement, and the second parameter is determined according to the second requirement.

[0114] Each module in the above-mentioned target user determination device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0115] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining a target user is implemented.

[0116] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Fig.10As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for determining a target user is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0117] Those skilled in the art will understand that Fig. 9 and Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0118] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0119] Obtain historical work information of each user in multiple service platforms within a historical time period and historical activity parameters of historical activities provided by each service platform, and obtain target activity parameters and target activity requirements of target activities provided by each service platform;

[0120] For a target service platform in each of the service platforms, determining target work information of a target activity provided by each user for the target service platform according to the historical work information and the historical activity parameters, the target service platform being any service platform in each of the service platforms;

[0121] A target user is determined from among the users according to the target work information, the target activity parameters and the target activity requirements, so that the target service platform promotes the target activity provided by the target service platform to the target user.

[0122] In one embodiment, when the processor executes the computer program, the following steps are also implemented: clustering the users according to the historical work information to obtain clustering results for the users; determining the login probability information of the users logging into the target service platform according to the clustering results, the historical work information and the historical activity parameters; and determining the target work information according to the clustering results, the login probability information and the historical work information.

[0123] In one embodiment, when the processor executes the computer program, the following steps are also implemented: determining the work status information of each user on each service platform based on the historical work information; clustering each user based on the work status information to obtain a clustering result for each user; wherein the clustering result includes single-dwelling and multi-dwelling, the single-dwelling is used to indicate that the user only provides services on the target service platform, and the multi-dwelling is used to indicate that the user provides services on the target service platform and also provides services on service platforms other than the target service platform.

[0124] In one embodiment, when the processor executes the computer program, the following steps are also implemented: for each of the users, a target login probability information determination model is determined from a plurality of pre-trained candidate login probability information determination models according to the clustering result of the user; the historical work information and the historical activity parameters are input into the target login probability information determination model to obtain the login probability information of the user.

[0125] In one embodiment, when the processor executes the computer program, the following steps are also implemented: when the clustering result is uni-habitat, the target work information is determined based on the login probability information and the historical work information; when the clustering result is multi-habitat, it is determined based on the login probability information and the historical work information whether the user chooses to provide services on the target service platform, and if so, the target work information is determined based on the historical work information.

[0126] In one embodiment, when the processor executes the computer program, the following steps are also implemented: when the target activity requirement is the first requirement, the target work information and the target activity parameters are input into the first model to obtain the target user output by the first model; when the target activity requirement is the second requirement, the target work information and the target activity parameters are input into the second model to obtain the target user output by the second model; wherein the first model contains a first parameter, the second model contains a second parameter, the first parameter is determined according to the first requirement, and the second parameter is determined according to the second requirement.

[0127] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0128] Obtain historical work information of each user in multiple service platforms within a historical time period and historical activity parameters of historical activities provided by each service platform, and obtain target activity parameters and target activity requirements of target activities provided by each service platform;

[0129] For a target service platform in each of the service platforms, determining target work information of a target activity provided by each user for the target service platform according to the historical work information and the historical activity parameters, the target service platform being any service platform in each of the service platforms;

[0130] A target user is determined from among the users according to the target work information, the target activity parameters and the target activity requirements, so that the target service platform promotes the target activity provided by the target service platform to the target user.

[0131] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: clustering the users according to the historical work information to obtain clustering results for the users; determining the login probability information of the users logging into the target service platform according to the clustering results, the historical work information and the historical activity parameters; and determining the target work information according to the clustering results, the login probability information and the historical work information.

[0132] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining the work status information of each user on each service platform based on the historical work information; clustering each user based on the work status information to obtain a clustering result for each user; wherein the clustering result includes single-dwelling and multi-dwelling, the single-dwelling is used to indicate that the user only provides services on the target service platform, and the multi-dwelling is used to indicate that the user provides services on the target service platform and also provides services on service platforms other than the target service platform.

[0133] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: for each of the users, a target login probability information determination model is determined from multiple pre-trained candidate login probability information determination models based on the clustering results of the user; the historical work information and the historical activity parameters are input into the target login probability information determination model to obtain the login probability information of the user.

[0134] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the clustering result is uni-habitat, the target work information is determined based on the login probability information and the historical work information; when the clustering result is multi-habitat, whether the user chooses to provide services on the target service platform is determined based on the login probability information and the historical work information, and if so, the target work information is determined based on the historical work information.

[0135] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: when the target activity requirement is the first requirement, the target work information and the target activity parameters are input into the first model to obtain the target user output by the first model; when the target activity requirement is the second requirement, the target work information and the target activity parameters are input into the second model to obtain the target user output by the second model; wherein the first model contains a first parameter, the second model contains a second parameter, the first parameter is determined according to the first requirement, and the second parameter is determined according to the second requirement.

[0136] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0137] Obtain historical work information of each user in multiple service platforms within a historical time period and historical activity parameters of historical activities provided by each service platform, and obtain target activity parameters and target activity requirements of target activities provided by each service platform;

[0138] For a target service platform in each of the service platforms, determining target work information of a target activity provided by each user for the target service platform according to the historical work information and the historical activity parameters, the target service platform being any service platform in each of the service platforms;

[0139] A target user is determined from among the users according to the target work information, the target activity parameters and the target activity requirements, so that the target service platform promotes the target activity provided by the target service platform to the target user.

[0140] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: clustering the users according to the historical work information to obtain clustering results for the users; determining the login probability information of the users logging into the target service platform according to the clustering results, the historical work information and the historical activity parameters; and determining the target work information according to the clustering results, the login probability information and the historical work information.

[0141] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining the work status information of each user on each service platform based on the historical work information; clustering each user based on the work status information to obtain a clustering result for each user; wherein the clustering result includes single-dwelling and multi-dwelling, the single-dwelling is used to indicate that the user only provides services on the target service platform, and the multi-dwelling is used to indicate that the user provides services on the target service platform and also provides services on service platforms other than the target service platform.

[0142] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: for each of the users, a target login probability information determination model is determined from multiple pre-trained candidate login probability information determination models based on the clustering results of the user; the historical work information and the historical activity parameters are input into the target login probability information determination model to obtain the login probability information of the user.

[0143] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the clustering result is uni-habitat, the target work information is determined based on the login probability information and the historical work information; when the clustering result is multi-habitat, whether the user chooses to provide services on the target service platform is determined based on the login probability information and the historical work information, and if so, the target work information is determined based on the historical work information.

[0144] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented: when the target activity requirement is the first requirement, the target work information and the target activity parameters are input into the first model to obtain the target user output by the first model; when the target activity requirement is the second requirement, the target work information and the target activity parameters are input into the second model to obtain the target user output by the second model; wherein the first model contains a first parameter, the second model contains a second parameter, the first parameter is determined according to the first requirement, and the second parameter is determined according to the second requirement.

[0145] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0146] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods 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 the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0147] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining a target user, characterized in that: The method comprises: Obtaining historical work information of each user in multiple service platforms within a historical time period and historical activity parameters of historical activities provided by each of the service platforms, and obtaining target activity parameters and target activity requirements of target activities provided by each of the service platforms; For a target service platform among the service platforms, determining target work information of a target activity provided by each user for the target service platform according to the historical work information and the historical activity parameters, the target service platform being any service platform among the service platforms; A target user is determined from among the users according to the target work information, the target activity parameters and the target activity requirements, so that the target service platform promotes the target activity provided by the target service platform to the target user.

2. The method according to claim 1, characterized in that The determining, according to the historical work information and the historical activity parameters, the target work information of the target activity provided by each user for the target service platform includes: Performing clustering processing on each of the users according to the historical work information to obtain a clustering result for each of the users; Determining the login probability information of each of the users logging into the target service platform according to the clustering result, the historical work information and the historical activity parameters; The target work information is determined according to the clustering result, the login probability information and the historical work information.

3. The method according to claim 2, characterized in that The clustering of the users according to the historical work information to obtain clustering results of the users includes: Determine the work status information of each user on each service platform according to the historical work information; Performing clustering processing on each of the users according to the work status information to obtain a clustering result for each of the users; The clustering results include single-dwelling and multi-dwelling, wherein the single-dwelling is used to indicate that the user only provides services on the target service platform, and the multi-dwelling is used to indicate that the user provides services on the target service platform and on service platforms other than the target service platform.

4. The method according to claim 2, characterized in that: The determining, according to the clustering result, the historical work information and the historical activity parameter, the login probability information of each user logging into the target service platform includes: For each of the users, determining a target login probability information determination model from a plurality of pre-trained candidate login probability information determination models according to the clustering result of the user; The historical work information and the historical activity parameters are input into the target login probability information determination model to obtain the login probability information of the user.

5. The method according to claim 3, characterized in that: The determining the target work information according to the clustering result, the login probability information and the historical work information includes: In the case where the clustering result is single-dwelling, determining the target work information according to the login probability information and the historical work information; In the case that the clustering result is multi-habitat, whether the user chooses to provide services on the target service platform is determined based on the login probability information and the historical work information. If yes, the target work information is determined based on the historical work information.

6. The method according to claim 1, characterized in that The target activity requirement includes a first requirement and a second requirement, and determining a target user from each of the users according to the target work information, the target activity parameter and the target activity requirement includes: When the target activity requirement is the first requirement, inputting the target work information and the target activity parameter into the first model to obtain the target user output by the first model; When the target activity requirement is the second requirement, inputting the target work information and the target activity parameter into the second model to obtain the target user output by the second model; The first model includes a first parameter, the second model includes a second parameter, the first parameter is determined according to the first requirement, and the second parameter is determined according to the second requirement.

7. A target user determination device, characterized in that: The device comprises: An acquisition module, used to acquire historical work information of each user in multiple service platforms within a historical time period and historical activity parameters of historical activities provided by each of the service platforms, and to acquire target activity parameters and target activity requirements of target activities provided by each of the service platforms; a determination module, for determining, for a target service platform among the service platforms, target work information of a target activity provided by each user for the target service platform according to the historical work information and the historical activity parameters, wherein the target service platform is any service platform among the service platforms; An execution module is used to determine a target user from each of the users according to the target work information, the target activity parameters and the target activity requirements, so that the target service platform promotes the target activity provided by the target service platform to the target user.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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