Service resource allocation method

Abstract

The invention discloses a service resource allocation method, and relates to the technical field of data processing, and the method comprises the steps: obtaining the user behavior data of each user in a plurality of users in a service scene; grouping the plurality of users according to the user behavior data of each user in the plurality of users in the service scene to obtain at least two reference groups; according to the behavior difference degree of every two first users in the at least two first users, determining the contribution degree of each first user in the reference group; and according to the contribution degree of each first user in the reference group, allocating a service resource corresponding to the service scene to a target user in the reference group. The method can improve the accuracy of service resource allocation.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular, relates to a business resource allocation method. Background Art

[0002] User grouping refers to a method of matching corresponding user groups through rules, which aims to help the business end match the corresponding user groups and understand the behavioral characteristics of the user groups, so that the business end can allocate business resources that meet the needs of the user groups according to the behavioral characteristics of the user groups. However, when allocating resources to users in a user group, it is difficult to accurately distinguish the needs of different users in the same user group, which reduces the accuracy of allocating business resources to different users in the user group. Summary of the invention

[0003] The embodiment of the present application provides a service resource allocation method, which can improve the accuracy of resource allocation.

[0004] In a first aspect, an embodiment of the present application provides a service resource allocation method, the method comprising:

[0005] Obtain user behavior data of each user in a business scenario among multiple users;

[0006] According to user behavior data of each user in the business scenario, the multiple users are grouped to obtain at least two reference groups, where the reference groups include at least two first users, and the user behavior data of users in any two reference groups in the at least two reference groups show different behavior differences in the business scenario;

[0007] Determine the contribution of each first user in the reference group according to the behavior difference between each two first users among the at least two first users, where the contribution is used to characterize the importance of the first user in the reference group for the business scenario;

[0008] According to the contribution of each first user in the reference group, business resources corresponding to the business scenario are allocated to the target user in the reference group.

[0009] In a second aspect, an embodiment of the present application provides a service resource allocation device, the device comprising:

[0010] An acquisition module, used to acquire user behavior data of each user among multiple users in a business scenario;

[0011] A grouping module, for grouping multiple users according to user behavior data of each user in a business scenario, to obtain at least two reference groups, wherein the reference groups include at least two first users, and the user behavior data of users in any two reference groups in the at least two reference groups exhibit different behavior differences in the business scenario;

[0012] A first determination module, configured to determine a contribution of each first user in a reference group according to a difference in behavior between each two first users among at least two first users, wherein the contribution is used to characterize an importance of the first user in the reference group for a business scenario;

[0013] The allocation module is used to allocate business resources corresponding to the business scenario to the target user in the reference group according to the contribution of each first user in the reference group.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, and when the program or instruction is executed by the processor, the steps of the business resource allocation method shown in the first aspect are implemented.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon; when the computer program instructions are executed by a processor, the steps of the business resource allocation method shown in the first aspect are implemented.

[0016] In a fifth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the steps of the business resource allocation method shown in the first aspect.

[0017] The business resource allocation method, device, equipment, storage medium and program product of the embodiments of the present application can group multiple users through the user behavior data of multiple users in business scenarios, and can identify user groups with similar behavioral characteristics and needs, that is, at least two reference groups. Then, for each reference group, the importance of each first user in the reference group relative to the entire reference group, that is, the contribution, is calculated. Then, based on the contribution of each first user, business resources corresponding to the business scenario are allocated to the target user in the reference group. In this way, by allocating resources to the target user based on the contribution of the first user in the reference group, accurate services can be provided to the users in the reference group, which helps to optimize the configuration of business resources and improve the accuracy of business resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 A schematic diagram showing a flow chart of a service resource allocation method provided in some embodiments of the present application;

[0020] Figure 2 A flowchart showing a specific implementation of step 120 provided in some embodiments of the present application is shown;

[0021] Figure 3 A flowchart showing a specific implementation of step 121 provided in some embodiments of the present application is shown;

[0022] Figure 4 A schematic flow chart of a method for processing data on original tag information provided by some embodiments of the present application is shown;

[0023] Figure 5 A flowchart showing a specific implementation of step 122 provided in some embodiments of the present application is shown;

[0024] Figure 6 A schematic flow chart of an exemplary grouping method provided in some embodiments of the present application is shown;

[0025] Figure 7 A flowchart showing a specific implementation of step 1225 provided in some embodiments of the present application is shown;

[0026] Figure 8 A flowchart showing a specific implementation of step 130 provided in some embodiments of the present application is shown;

[0027] Fig. 9 A flowchart showing a specific implementation of step 133 provided in some embodiments of the present application is shown;

[0028] Fig.10 A flowchart showing a specific implementation of step 140 provided in some embodiments of the present application is shown;

[0029] Fig.11 A flowchart showing a specific implementation of step 140 provided in other embodiments of the present application is shown;

[0030] Fig.12 A schematic diagram showing the structure of a service resource allocation device provided in some embodiments of the present application is shown;

[0031] Fig.13 A schematic diagram of the structure of an electronic device provided in some embodiments of the present application is shown. DETAILED DESCRIPTION

[0032] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0033] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0034] It should be noted that the acquisition, storage, use and processing of data in the embodiments of the present application are in compliance with the relevant provisions of national laws and regulations.

[0035] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0036] In order to solve the problems in the related art, the embodiments of the present application provide a business resource allocation method, apparatus, device, storage medium and program product.

[0037] The following is combined with Figures 1 to 11 , the business resource allocation method provided in the embodiment of the present application is described in detail through specific embodiments and their application scenarios.

[0038] First, combine Figure 1 A service resource allocation method provided in an embodiment of the present application is described in detail.

[0039] Figure 1 A flow chart of a service resource allocation method provided in some embodiments of the present application is shown.

[0040] like Figure 1 As shown, the service resource allocation method provided in the embodiment of the present application may include steps 110 to 140.

[0041] Step 110, obtaining user behavior data of each user in a business scenario among multiple users.

[0042] Step 120, based on the user behavior data of each user in the multiple users in the business scenario, the multiple users are grouped to obtain at least two reference groups, the reference groups include at least two first users, and the user behavior data of users in any two reference groups in the at least two reference groups show different behavioral differences in the business scenario.

[0043] Step 130, determining the contribution of each first user in the reference group according to the behavior difference between each two first users among the at least two first users, where the contribution is used to characterize the importance of the first user in the reference group for the business scenario.

[0044] Step 140 : Allocate business resources corresponding to the business scenario to the target user in the reference group according to the contribution of each first user in the reference group.

[0045] In an embodiment of the present application, multiple users are grouped by using their user behavior data in business scenarios, and user groups with similar behavioral characteristics and needs, i.e., at least two reference groups, can be identified. Next, for each reference group, the importance, i.e., contribution, of each first user in the reference group relative to the entire reference group is calculated. Then, based on the contribution of each first user, business resources corresponding to the business scenario are allocated to the target user in the reference group. In this way, by allocating resources to the target user based on the contribution of the first user in the reference group, accurate services can be provided to the users in the reference group, which helps to optimize business resource allocation and improve the accuracy of business resource allocation.

[0046] The above steps are described in detail below, as shown below.

[0047] First, step 110 is involved. In some embodiments of the present application, in order to more accurately understand the user's behavior pattern, obtaining the user behavior data of each user in a business scenario among multiple users may include collecting the behavior data of multiple users in a specific business scenario. Exemplarily, the specific business scenario may include shopping, social networking, education, medical health, etc. User behavior data refers to the records of various operations and activities generated by users in specific business scenarios, such as browsing pages, clicking buttons, purchasing products, posting comments, etc. These user behavior data can be obtained through various channels, including but not limited to log records on user devices, application interface calls, server-side monitoring data, etc.

[0048] It is worth mentioning here that when obtaining user behavior data, it is first necessary to clarify the observation time period related to the business scenario in order to focus on the behavior patterns that have a direct impact on business results. After determining the observation time period related to the business scenario, obtain the user behavior data of multiple users within the observation time period. These user behavior data should cover various interactive behaviors related to the business scenario for multiple users, including but not limited to browsing, clicking, purchasing, commenting, sharing, etc., to ensure the comprehensiveness and accuracy of the user behavior data.

[0049] Secondly, regarding step 120, in some embodiments of the present application, in order to process the user behavior data obtained in step 110 to classify users with similar behavior patterns into the same reference group, thereby forming different reference groups, based on this, Figure 2 As shown, step 120 may specifically include steps 121 to 122.

[0050] Step 121 , calculating the behavior difference between every two users in the business scenario according to the user behavior data of each user in the business scenario.

[0051] Exemplarily, the behavioral difference is used to measure the degree of difference in the behavioral performance of two users in a business scenario, and can be calculated by comparing the behavioral data characteristics of the two users, such as differences in operation frequency, operation type, operation time, etc. Specifically, the behavioral difference can be calculated by comparing the frequency difference between the two users logging into the APP. If user A logs in 5 times a week, each time for 30 minutes, and user B logs in 2 times a week, each time for 15 minutes, then there is a difference in the business type of login behavior. For another example, user A mainly consumes traffic, and user B mainly consumes call duration, which also has differences in the business type of consumption history data.

[0052] Step 122: grouping the multiple users according to the behavioral differences between every two users in the multiple users in the business scenario to obtain at least two reference groups.

[0053] Exemplarily, a reference group refers to a set of users divided according to the similarity or difference of user behaviors. Users with similar behavioral characteristics are grouped into a reference group for targeted analysis and processing.

[0054] In the embodiment of the present application, by calculating the behavioral difference between two users in a specific business scenario based on the user behavior data of multiple users in a specific business scenario, the degree of behavioral similarity between two users can be understood more accurately. Then, based on the behavioral difference, multiple users are grouped, and multiple users can be divided into reference groups with different behavioral characteristics, which helps the business end to better understand user needs and optimize business resource allocation strategies.

[0055] In some embodiments of the present application, in order to achieve accuracy and comprehensiveness in calculating the difference in behavior between every two users in a business scenario, the user behavior data of each user in a business scenario includes a behavior metric value corresponding to each of the J business types in the business scenario, where J is an integer greater than 1. The business type refers to different business operations or service types distinguished in a specific business scenario. The behavior metric value refers to a numerical value used to quantify the behavior performance of a user in a specific business type.

[0056] Exemplarily, in the digital 5G messaging service, the service type may include the frequency and time pattern of user login to the application (Application, APP), the manufacturer and model of the mobile phone used, the amount and frequency of consumption, and the types of services consumed such as call duration, traffic usage, and purchase of value-added services. Specifically, it can be determined by collecting and analyzing the user's operation records, consumption records, and mobile terminal information in the digital 5G messaging service. For login behavior, the behavior measurement value can be the number of times the user visits the APP per week, the duration of each visit to the APP, etc.; for mobile terminal usage preferences, the behavior measurement value can be the number of months a certain brand of terminal is used, the proportion of time a specific model of terminal is used, etc.; for consumption history data, the behavior measurement value can be the monthly consumption amount, the annual total consumption, the consumption frequency of a certain service, etc.

[0057] Based on this, Figure 3 As shown, the above step 121 may specifically include steps 1211 to 1212.

[0058] Step 1211, according to every two users among the multiple users, according to the behavior measurement value of the first target user under the j-th business type and the behavior measurement value of the second target user under the j-th business type, calculate the behavior difference between every two users under the j-th business type, every two users include the first target user and the second target user, j∈[1,2,3,…,J].

[0059] Step 1212: Determine the behavior difference between every two users in a business scenario according to the behavior difference between every two users in each of the J business types.

[0060] Exemplarily, for every two users, the behavior metrics of the two users under the jth business type are obtained respectively, and the behavior difference of the two users under the business type is calculated. This process is repeated for all business types, i.e., j∈[1,2,3,...,J]. Next, the behavior difference under each business type obtained in step 1211 is integrated, such as performing weighted average processing or maximum difference processing on the obtained multiple behavior differences, to obtain the behavior difference of every two users in the entire business scenario.

[0061] Specifically, the behavior difference L between the first target user and the second target user in every two users can be calculated using the following formula (1):

[0062]

[0063] In formula (1), x j represents the characteristic value of the first target user under the jth service type, y j Indicates the feature value of the second target user under the j-th service type.

[0064] It is worth noting that before calculating the behavioral difference between the first target user and the second target user among every two users, it is necessary to perform data preprocessing on the user behavior data to improve the accuracy and efficiency of data analysis, which specifically includes the following steps.

[0065] Step 1: Feature classification and business confirmation. According to the actual business scenario, the features in the user behavior data are clearly divided into continuous variable features and discrete variable features. In actual business, some features expressed in numerical form may actually be discrete, for example, representing different categories or states rather than continuously changing quantities.

[0066] Step 2: Filling missing values ​​and digitizing discrete variables. Among them, different strategies are used to fill missing values ​​for classified features. Specifically, for continuous variable features, since their values ​​are continuous, the average value of the variable feature can be used to fill missing values ​​to maintain the overall distribution characteristics of the data. For discrete variable features, since the values ​​represent different categories or states, the mode, that is, the value with the highest frequency, can be used to fill missing values ​​to ensure the consistency and representativeness of the data. In addition, discrete variables can be converted to numerical variables through multiple random partitions. Specifically, a five-fold partitioning algorithm is used to count the frequency values ​​of each discrete variable in each fold of the evaluation results, and then the average value of these frequency values ​​is calculated, and this is used as the basis for converting discrete variables into numerical variables.

[0067] Step three, normalization and feature matrix construction. Among them, after completing the missing value filling and discrete variable digitization, all feature data can also be normalized. The purpose of normalization is to eliminate the differences between different features due to different dimensions and value ranges, so that all features are comparable in value. Specifically, the minimum-maximum normalization method can be used to linearly map the eigenvalues ​​to the [0, 1] interval. Then, after normalization, all features are converted into a two-dimensional feature matrix. Each row of this two-dimensional feature matrix represents a user, and each column represents a business type. Each element in the matrix is ​​the value of the corresponding user on the corresponding business type.

[0068] Exemplarily, the normalized eigenvalue x_scale of the original eigenvalue x may be calculated using the following formula (2):

[0069]

[0070] In formula (2), x represents the original eigenvalue, min(x) represents the minimum value of the original eigenvalue, and max(x) represents the maximum value of the original eigenvalue.

[0071] Step 4: Distance calculation and determination of initial threshold for clustering. The distance between every two users is calculated based on the constructed feature matrix. Specifically, the distance can be Euclidean distance, Manhattan distance, etc.

[0072] In the embodiment of the present application, by subdividing into J business types under the business scenario, the behavioral differences between users can be more finely identified, so as to perform more effective user grouping in step 122. Such grouping results can help the business end to understand user behavior patterns more accurately, discover behavioral characteristics between different user groups, and provide data support for subsequent user personalized services, market strategy adjustments and product optimization. In addition, this difference calculation and grouping method can also improve the efficiency of data analysis and decision-making, and reduce operating costs.

[0073] For example, Figure 4 As shown in the figure, in the data preprocessing stage, the original label information of multiple users, assuming that the original label information of 10 users with IDs from U1 to U10, is extracted and converted. These original labels specifically include the price of the most commonly used mobile phone of each user, the average number of times the mobile phone is used, the price of the most recently purchased mobile phone, and the number of times the new mobile phone is used. Based on this, a feature matrix is ​​constructed, which systematically organizes and reflects the user mobile terminal preferences and usage characteristics of the above four business types. Specifically, the columns in the matrix are named user_prefer_price, user_avg_use, last_ter_price, and last_ter_use, which represent the price of the most commonly used mobile phones of multiple users, the average number of times they are used, the price of the most recent mobile phone, and the number of times the most recent mobile phone is used. Table 1 shows the content of this feature matrix, where each row corresponds to the feature data of a user, and each column is accurately mapped to the four business types mentioned above.

[0074]

[0075] Table 1

[0076] In Table 1 above, each row represents a user, and each column represents a service type.

[0077] Next, the above formula (1) is used to calculate the behavior difference L between every two users. Some results are shown in Table 2 below.

[0078] L 0.322587603329861 1.215479987951037 1.4440695225087663 0.6548416977696344 1.188041287613741 0.5258547844255081 0.5903950824469726 0.7299290938166308 1.4565878150965825 1.1068666207208395 ……

[0079] Table 2

[0080] In some embodiments of the present application, in order to achieve refined grouping of multiple users, the at least two reference groups may include a first reference group and a second reference group. Figure 5 As shown, the above step 122 may specifically include steps 1221 to 1225.

[0081] Step 1221 , sorting the behavior difference between every two users in the business scenario among the multiple users in ascending order of the behavior difference to obtain a first sorting sequence.

[0082] For example, the behavior differences between all users are quantified and sorted in ascending order, thereby obtaining a first sorting sequence of behavior differences. This first sorting sequence reflects the degree of behavior similarity between users, and the smaller the behavior difference, the more similar the behavior. In this way, the behavior sequence after orderly sorting of the behavior difference can reflect the distribution of behavior similarity between users.

[0083] Step 1222: Filter reference behavior differences from the plurality of behavior differences in the first sorting sequence, wherein the order of the reference behavior differences in the first sorting sequence satisfies a preset condition.

[0084] For example, a behavior difference at a certain percentage position in the first sorting sequence can be selected as a reference behavior difference, or a specific value can be set as a reference behavior difference based on business experience. For example, the lower quartile of the first sorting sequence is calculated and the lower quartile is used as the reference behavior difference. Referring to Table 2 above, the reference behavior difference is determined to be 0.5258547844255081.

[0085] Step 1223 , generating a first reference group according to users corresponding to a first target behavior difference, wherein the first target behavior difference is a behavior difference smaller than a parameter behavior difference among a plurality of behavior differences in the first sorting sequence.

[0086] For example, Figure 6 As shown, users whose behavior difference is smaller than the reference behavior difference are grouped together to form a first reference group. The users in the first reference group have high behavior similarity and similar needs.

[0087] Referring to the above Table 2, the behavior difference L between every two users in the above Table 2 is grouped to generate a first reference group. The specific grouping process is shown in Tables 3 to 5 below.

[0088]

[0089] Table 3

[0090] Referring to Table 3 above, Table 3 shows the preliminary grouping process taking U1 as an example. It can be seen from Table 3 that the behavioral difference between U1 and U2 and U7 is less than the reference behavioral difference, so U1, U2 and U7 are preliminarily determined to be members of the first reference group. In order to more comprehensively identify all members of the first reference group, as shown in Table 4, the behavioral difference between U2 and U7 and other users is further examined. From Table 4, it can be seen that the behavioral difference between U2 and U5, and between U7 and U8 is also less than the reference behavioral difference, so U5 and U8 are also included in the first reference group.

[0091]

[0092] Table 4

[0093] By repeating the above steps and iteratively checking the behavioral differences between the newly introduced members and other users, it can be seen that U5 and U9 also meet the grouping conditions, while U8 has no other members introduced.

[0094] Therefore, U1, U2, U5, U7, U8, and U9 are classified into the same user group, and the first round of iteration ends. As a result, the first reference group including U1, U2, U5, U7, U8, and U9 is finally determined. The users in the first reference group not only have small behavioral differences between each other, but also share similar needs.

[0095] Step 1224 , generating a to-be-processed group according to users corresponding to a second target behavior difference, wherein the second target behavior difference is a behavior difference greater than or equal to the parameter behavior difference among a plurality of behavior differences in the first sorting sequence.

[0096] Exemplarily, users whose behavior difference is greater than or equal to the reference behavior difference are grouped together to form a to-be-processed group. The behavior difference of users in this to-be-processed group is relatively large. Referring to the above example, the to-be-processed group includes U3, U4, U6, and U10.

[0097] Step 1225: Determine a second reference group according to the difference between the to-be-processed group and the reference behavior.

[0098] Exemplarily, by generating the second reference group, more refined grouping of users can be achieved, the personalized needs of different user groups can be further met, and the accuracy of resource allocation can be improved.

[0099] In the embodiment of the present application, through the refined user grouping method, not only the user group with the most similar behavior pattern is identified, such as the first reference group, but also the second reference group and more levels of groups are generated by further subdividing the group to be processed, thereby achieving a comprehensive and detailed classification of multiple users. This method helps to understand the needs of different user groups more accurately, and through reasonable grouping strategies, it can also optimize resource allocation, improve resource utilization efficiency, and thus improve overall business results and user satisfaction.

[0100] In some embodiments of the present application, the second reference group includes M second reference groups, where M is an integer greater than 1. Based on this, Figure 7 As shown, the above step 1225 may specifically include steps 12251 to 12257.

[0101] Step 12251, taking the to-be-processed group as the current group, and taking the reference behavior difference as the current reference behavior difference.

[0102] Step 12252, using a preset update rule to update the current reference behavior difference to obtain an updated reference behavior difference, the updated reference behavior difference being greater than the current reference behavior difference.

[0103] Exemplarily, the preset update rule refers to a rule or algorithm for determining how to adjust the reference behavior difference. The updated reference behavior difference refers to the new reference behavior difference value after adjustment by the preset update rule. Specifically, the preset update rule can be a fixed incremental value, such as increasing a fixed percentage or a fixed value each time, or a dynamically calculated value based on the current group characteristics, such as determined according to the distribution of user behavior differences within the current group.

[0104] Exemplarily, the updated reference behavior difference can be calculated using the following formula (3):

[0105] D i+1 =D i ×(1+lr) (3)

[0106] In formula (3), D i Indicates the difference between the current reference behavior, D i+1 Represents the updated reference behavior difference, lr represents the incremental value, lr∈[0.01, 0.1].

[0107] Step 12253, traverse the behavior difference between every two users in the current group, output the mth second reference group according to the user corresponding to the third target behavior difference, the third target behavior difference being the behavior difference that is smaller than the updated reference behavior difference among the multiple behavior differences in the first sorting sequence, and generate an updated to-be-processed group according to the user corresponding to the fourth target behavior difference, the fourth target behavior difference being the behavior difference that is greater than or equal to the updated reference behavior difference among the multiple behavior differences in the first sorting sequence.

[0108] Exemplarily, the current group refers to the set of users currently being processed in the loop. After updating the reference behavior difference, the behavior difference between all users in the current group is re-evaluated to prepare for a new round of group division. The mth second reference group refers to the mth second reference group among the M second reference groups generated during the loop and containing users with similar behaviors. The updated to-be-processed group refers to the set of users that is updated during the loop and needs to be further subdivided.

[0109] Referring to the above example, the reference behavior difference is updated to 0.578440263. Then, the to-be-processed groups, namely U3, U4, U6 and U10, are further grouped according to 0.578440263. The results are shown in Table 5 below.

[0110] user Group U1 1 U2 1 U3 2 U4 2 U5 1 U6 2 U7 1 U8 1 U9 1 U10 2

[0111] Table 5

[0112] In Table 5, group 1 represents the first reference group, and group 2 represents the second reference group. It can be seen from Table 5 that at this time, multiple users have been divided into two groups, and no further iteration is required for grouping.

[0113] Step 12254, determine whether there is a user in the updated to-be-processed group. If the judgment result is yes, proceed to step 12255; if the judgment result is no, proceed to step 12257.

[0114] Step 12255, update the updated pending group to the current group, and execute step 12256.

[0115] Exemplarily, when there are users in the updated to-be-processed group, the updated to-be-processed group is set as the current group in the next cycle, so as to continue to perform the group division operation.

[0116] Step 12256, update m=m+1, m=1,2,3...M-1.

[0117] Exemplarily, if there are still users in the current group who have not been assigned to any second reference group, the value of the counter m is increased to prepare for generating the next second reference group. Specifically, check whether the current group is empty, if not, execute m=m+1.

[0118] Step 12257, obtain M second reference groups.

[0119] In an embodiment of the present application, the gradual refinement and precise division of user groups can be achieved. Each cycle will further subdivide the users in the current group into a second reference group and a new group to be processed based on the updated reference behavior difference. This process will continue until all users in the group to be processed are successfully assigned to the corresponding second reference group. This method not only improves the accuracy of user grouping, but also ensures that users in each group have a high degree of behavioral similarity, thereby providing strong support for subsequent personalized recommendations, resource allocation, etc. At the same time, the method can flexibly respond to the complexity and diversity of different user groups by dynamically adjusting the reference behavior difference, thereby achieving more refined user management.

[0120] Furthermore, in step 130, by determining the user's contribution, key users in the group can be more accurately identified, providing a basis for subsequent personalized resource allocation. This helps ensure that business resources can be more accurately allocated to the users who need them most or can play the most important role, thereby improving the accuracy of resource allocation.

[0121] In the embodiments of the present application, Figure 8 As shown, the above step 130 may specifically include steps 131 to 133. The specific description is as follows.

[0122] Step 131 : generating a first behavior difference parameter set corresponding to a reference group according to the behavior difference between every two first users among at least two first users.

[0123] Exemplarily, first, for each first user in the reference group, the behavior difference between the first user and all other first users in the reference group is calculated. Then, all these calculated behavior differences are taken as elements and assembled into a data structure to form a first behavior difference parameter set as shown in Table 6 below.

[0124] U1 U2 U5 U7 U8 U9 U1 0 0.322587 0.654841 0.525854 0.590395 0.729929 U2 0.322587 0 0.410934 0.430286 0.682037 0.536627 U5 0.654841 0.410934 0 0.554088 0.802212 0.460681 U7 0.525854 0.430286 0.554088 0 0.392732 0.886638 U8 0.590395 0.682037 0.802212 0.392732 0 1.186133 U9 0.729929 0.536627 0.460681 0.886638 1.186133 0

[0125] Table 6

[0126] Step 132 , screening the behavior difference degree related to the i-th first user in the reference group in the first behavior difference parameter set to generate a first target behavior difference parameter set.

[0127] Exemplarily, the first behavior difference parameter set is traversed to screen out all the behavior differences related to the i-th first user, that is, the behavior difference between the i-th user and each other first user in the reference group. Then, these screened behavior differences are collected into a new data structure to form a first target behavior difference parameter set.

[0128] Step 133: Determine the contribution of the i-th first user in the reference group according to the first behavior difference parameter set and the first target behavior difference parameter set.

[0129] In the embodiment of the present application, not only the behavioral difference between every two first users is taken into consideration, but also the relative position of each first user is accurately located by generating a first behavior difference parameter set and screening a first target behavior difference parameter set, thereby providing a reference basis for subsequent personalized recommendations and resource allocation, thereby effectively improving the accuracy and refinement of user grouping.

[0130] In the embodiment of the present application, in order to improve the calculation accuracy of the contribution of the i-th first user in the reference group, as Fig. 9As shown, the above step 133 may specifically include steps 1331 to 1333.

[0131] Step 1331 , performing square sum processing on the behavior difference degrees in the first behavior difference parameter set to obtain a first parameter.

[0132] Exemplarily, each behavior difference in the first behavior difference parameter set is squared and then summed. The squaring operation can amplify the influence of the behavior difference, so that a larger behavior difference occupies a larger proportion in the sum. Through the square sum processing, a comprehensive indicator that can reflect the overall behavior difference between the i-th first user and other first users in the reference group is obtained. Specifically, the behavior difference between the i-th first user and each other first user in the reference group is first calculated, and then these behavior differences are squared and summed.

[0133] Step 1332, performing square sum processing on the behavior difference degrees in the first target behavior difference parameter set to obtain a second parameter.

[0134] Exemplarily, the first target behavior difference parameter set is used to analyze the behavior difference of the i-th user in the reference group, and by squaring each behavior difference in the first target behavior difference parameter set and summing them, a comprehensive indicator that can reflect the overall difference between the i-th first user and other first users in the reference group is obtained. Table 7 below shows the sum of squares S of the behavior difference between each first user in the first reference group and each other first user in the reference group.

[0135] S U1 1.6907665 U2 1.21122006 U5 1.76047122 U7 1.70905107 U8 3.01843668 U9 3.22603324

[0136] Table 7

[0137] Step 1333: Determine the contribution of the i-th first user in the reference group according to the first parameter and the second parameter.

[0138] Exemplarily, the contribution is used to characterize the importance of the i-th first user in the reference group. Specifically, the contribution can be obtained by combining the first parameter and the second parameter by proportion, difference, ratio or mathematical function. For example, the contribution can be calculated by ratio, by taking the ratio of the second parameter to the first parameter as the contribution. This ratio can reflect the ratio of the difference in behavior between the i-th user and the reference group as a whole to the difference in the overall degree within the reference group, and can be used to evaluate the contribution of the i-th first user.

[0139] In the embodiment of the present application, through square sum processing, steps 1331 and 1332 respectively obtain two comprehensive indicators, namely the first parameter and the second parameter, and then determine the contribution of the i-th first user based on the first parameter and the second parameter. In this way, not only the uniqueness of the behavior of the i-th first user is considered, but also the behavioral characteristics of the reference group as a whole are considered, thereby effectively improving the objectivity and accuracy of the calculation of the contribution of the i-th user.

[0140] In some embodiments of the present application, in order to improve the accuracy of calculating the contribution of the i-th first user, the above step 1333 may specifically include calculating the first parameter and the second parameter using a preset logarithmic function to obtain the contribution of the i-th first user in the reference group.

[0141] Exemplarily, the contribution V of the i-th first user in the reference group is calculated using the following formula (4): i :

[0142]

[0143] In formula (4), a represents the second parameter, and b represents the first parameter.

[0144] Combined with Table 6, the behavior difference in the first behavior difference parameter set is squared and processed to obtain the first parameter, which is 12.61597877. Finally, combined with Table 7 and formula (4), the contribution of each first user in the first reference group is calculated, and the result is shown in Table 8 below.

[0145] <![CDATA[Contribution V i > U1 0.872837315 U2 1.017697895 U5 0.85529202 U7 0.868165909 U8 0.62113888 U9 0.592252112

[0146] Table 8

[0147] Referring to Table 8, it can be seen that user U2 has the highest contribution to the first reference group in the first reference group.

[0148] Therefore, the logarithmic function is a monotonically increasing function that can amplify the differences between smaller values ​​and compress the differences between larger values, which helps to highlight the relative differences between individuals in the contribution calculation. By calculating the ratio of the sum of squares of the distances between each first user and all other first users, and then taking the logarithm, the degree of deviation of the user from the central trend of the group can be measured. If the ratio is high, the logarithmic value will be small, indicating that the user is closer to other users and has a greater contribution to the reference group.

[0149] Then, step 140 is involved, which allocates business resources corresponding to the business scenario to the target users in the group according to the contribution determined in step 130. The business scenario refers to a specific business environment or background, which determines the type, quantity and allocation method of the business resources to be allocated. Business resources refer to various resources invested in supporting business operations and development, including but not limited to funds, time, manpower, material resources, etc. Specifically, such as coupons, recommended content, exclusive services, etc. Exemplarily, the resource allocation strategy can be flexibly designed according to business needs and user contribution. For example, users with high contribution can be provided with more discounts or more personalized services; while users with lower contribution may obtain fewer resources or more general services.

[0150] In some embodiments of the present application, in order to effectively ensure that business resources can be effectively utilized, the target user includes the first user in the reference group, that is, the user group that needs to be evaluated for contribution and resource allocation. Fig.10 As shown, the above step 140 may specifically include steps 141a to 142a.

[0151] Step 141a, calculating the contribution ratio of each first user in the reference group to the reference group according to the contribution of each first user in the reference group.

[0152] Exemplarily, the contribution ratio refers to the ratio of each first user's contribution to the total contribution of all first users in the reference group. Specifically, the contribution of each first user in the reference group is first calculated, and the total of these contributions is calculated, and then the contribution of each first user is divided by the total contribution to obtain the contribution ratio of the user in the reference group.

[0153] Step 142a: allocate business resources to each first user in the reference group according to the business resources in the business scenario and the contribution ratio of each first user.

[0154] Exemplarily, the amount of resources that should be allocated to each first user is calculated based on the total amount of available business resources in the current business scenario and the contribution ratio of each first user.

[0155] Suppose there is a reference group of 100 users on an e-commerce platform. They contribute value to the platform by purchasing goods, sharing links, and writing reviews. The platform decides to allocate coupons as business resources based on the user's contribution. The platform first calculates the contribution of each user, and then calculates the contribution ratio of each user. Finally, based on the contribution ratio, coupons of different denominations are allocated to the top 20 users with the highest contribution.

[0156] In an embodiment of the present application, business resources are reasonably allocated according to the contribution of the first user in the reference group, thereby improving the fairness and efficiency of resource allocation. Specifically, users with high contributions will obtain more resources that match their contributions, so that business resources can achieve maximum benefits.

[0157] In other embodiments of the present application, the target user includes a second user. In order to identify the second user who has a high contribution to the business scenario and ensure that resources can be allocated to truly valuable users, based on this, Fig.11 As shown, the above step 140 may specifically include steps 141b to 143b.

[0158] Step 141b: sort the first users in the reference group in descending order of contribution to obtain a second sorting sequence.

[0159] Exemplarily, the first users in the reference group are sorted according to their contributions to the service, and the obtained second sorting sequence reflects the relative contribution of the first users in the reference group.

[0160] Step 142b: Filter the second user from the second sorting sequence according to a preset rule.

[0161] Exemplarily, the preset rules refer to specific criteria or conditions for screening the second user, such as contribution threshold, ratio limit, etc. The preset rules can be flexibly set according to business needs, resource conditions, and other factors. The target user refers to the second user, that is, the user who is determined to need to allocate business resources after screening. Specifically, the preset rules may include but are not limited to the following forms.

[0162] Contribution threshold: By setting a specific contribution value as the threshold, only users whose contribution exceeds this threshold can be included in the category of second users. This method is simple and direct, and can quickly screen out high-contribution users.

[0163] Ratio restriction: According to the total number of first users in the second ranking sequence, a specific ratio is set, such as the top 10%, top 20, etc., and users with the highest contribution ranking are selected as second users. This method helps to maintain a certain resource concentration while taking into account the user diversity of the reference group.

[0164] Composite conditions combine multiple screening conditions such as contribution, activity, historical performance, etc., and comprehensively evaluate the user's comprehensive value through weighted scoring or logical judgment to determine the second user.

[0165] Step 143b: allocate business resources corresponding to the business scenario to the second user.

[0166] Exemplarily, when allocating business resources corresponding to the business scenario to the second user, a reasonable allocation strategy can be set according to business needs. For example, differentiated resource allocation is implemented according to the contribution of the second user. The higher the contribution of the second user, the higher the amount or quality of resources obtained. This method helps to ensure that business resources can be concentrated on supporting the second user who contributes the most to the business scenario.

[0167] In the embodiment of the present application, through contribution-based resource allocation, it is possible to ensure that business resources are used more reasonably and effectively, while enhancing user satisfaction with business activities.

[0168] The service resource allocation method provided in the embodiment of the present application can be executed by a service resource allocation device. In the embodiment of the present application, the service resource allocation device performing service resource allocation is taken as an example to illustrate the service resource allocation device provided in the embodiment of the present application.

[0169] Based on the same inventive concept, the present application also provides a service resource allocation device. Fig.12 Provide detailed explanation.

[0170] Fig.12 A schematic diagram of the structure of a service resource allocation device provided in some embodiments of the present application is shown.

[0171] like Fig.12 As shown, the service resource allocation device 1200 may specifically include:

[0172] An acquisition module 1210 is used to acquire user behavior data of each user among multiple users in a business scenario;

[0173] The grouping module 1220 is used to group the multiple users according to the user behavior data of each user in the multiple users in the business scenario to obtain at least two reference groups, where the reference groups include at least two first users, and the user behavior data of users in any two reference groups in the at least two reference groups in the business scenario show different behavior differences;

[0174] A first determination module 1230, configured to determine a contribution of each first user in a reference group according to a behavior difference between each two first users among at least two first users, wherein the contribution is used to characterize an importance of the first user in the reference group for a business scenario;

[0175] The allocation module 1240 is configured to allocate business resources corresponding to the business scenario to the target user in the reference group according to the contribution of each first user in the reference group.

[0176] In an embodiment of the present application, the user behavior data of multiple users in a business scenario is obtained by the acquisition module 1210, and then the multiple users are grouped by the grouping module 1220, so that user groups with similar behavioral characteristics and needs, that is, at least two reference groups, can be identified. Subsequently, the first determination module 1230 is used to calculate the importance of each first user in the reference group relative to the entire reference group, that is, the contribution. Then, the allocation module 1240 is used to allocate business resources corresponding to the business scenario to the target users in the reference group according to the determined contribution. In this way, through a resource allocation strategy based on contribution, accurate services for target users can be achieved, which helps to optimize business resource configuration and improve the accuracy of business resource allocation.

[0177] The service resource allocation device 1200 in the embodiment of the present application is described in detail below, as shown below.

[0178] In some embodiments of the present application, the first determining module 1230 in the embodiments of the present application may specifically include:

[0179] A first generating submodule, configured to generate a first behavior difference parameter set corresponding to a reference group according to a behavior difference degree between each two first users among at least two first users;

[0180] A second generating submodule is used to select the behavior difference degree related to the i-th first user in the reference group in the first behavior difference parameter set to generate a first target behavior difference parameter set;

[0181] The first determination submodule is used to determine the contribution of the i-th first user in the reference group according to the first behavior difference parameter set and the first target behavior difference parameter set.

[0182] In some embodiments of the present application, the first determining submodule in the embodiments of the present application may specifically include:

[0183] A first determining unit, configured to perform square sum processing on the behavior difference degree in the first behavior difference parameter set to obtain a first parameter;

[0184] A second determination unit is used to perform square sum processing on the behavior difference degree in the first target behavior difference parameter set to obtain a second parameter;

[0185] The third determining unit is configured to determine the contribution of the i-th first user in the reference group according to the first parameter and the second parameter.

[0186] In some embodiments of the present application, the third determination unit in the embodiments of the present application may be specifically configured to calculate the first parameter and the second parameter using a preset logarithmic function to obtain the contribution of the i-th first user in the reference group.

[0187] In some embodiments of the present application, the grouping module 1220 in the embodiments of the present application may specifically include:

[0188] A first calculation submodule, configured to calculate the behavior difference between every two users in the business scenario according to the user behavior data of each user in the business scenario;

[0189] The second determination submodule is used to group the multiple users according to the behavioral difference between every two users in the multiple users in the business scenario to obtain at least two reference groups.

[0190] In some embodiments of the present application, the user behavior data of each user in a business scenario includes a behavior metric value corresponding to each business type in J business types of each user in the business scenario, where J is an integer greater than 1. The first calculation submodule in the embodiment of the present application may specifically include:

[0191] A first calculation unit is used to calculate the behavior difference between every two users under the jth service type according to the behavior metric value of the first target user under the jth service type and the behavior metric value of the second target user under the jth service type, where every two users include the first target user and the second target user, j∈[1,2,3,...,J];

[0192] The fourth determining unit is used to determine the behavior difference between every two users in the business scenario according to the behavior difference between every two users in each business type of the J business types.

[0193] In some embodiments of the present application, the at least two reference groups include a first reference group and a second reference group; the second determination submodule in the embodiments of the present application may specifically include:

[0194] A first sorting unit is used to sort the behavior difference between every two users in the business scenario among the multiple users in an ascending order of the behavior difference to obtain a first sorting sequence;

[0195] A first screening unit, configured to screen reference behavior differences from a plurality of behavior differences in a first sorting sequence, wherein an order of the reference behavior differences in the first sorting sequence satisfies a preset condition;

[0196] A first generating unit is used to generate a first reference group according to users corresponding to a first target behavior difference, where the first target behavior difference is a behavior difference that is smaller than a parameter behavior difference among a plurality of behavior differences in a first sorting sequence;

[0197] A second generating unit is used to generate a to-be-processed group according to users corresponding to a second target behavior difference, where the second target behavior difference is a behavior difference that is greater than or equal to the parameter behavior difference among a plurality of behavior differences in the first sorting sequence;

[0198] The fifth determining unit is used to determine a second reference group according to the difference between the to-be-processed group and the reference behavior.

[0199] In some embodiments of the present application, the second reference group includes M second reference groups, where M is an integer greater than 1. The fifth determining unit in the embodiments of the present application may be specifically used to:

[0200] The group to be processed is used as the current group, and the reference behavior difference is used as the current reference behavior difference. The following operations are performed cyclically until all users in the group to be processed belong to the corresponding second reference group:

[0201] The current reference behavior difference is updated using a preset update rule to obtain an updated reference behavior difference, wherein the updated reference behavior difference is greater than the current reference behavior difference;

[0202] Traversing the behavior difference between every two users in the current group, outputting the mth second reference group according to the user corresponding to the third target behavior difference, the third target behavior difference being a behavior difference that is smaller than the updated reference behavior difference among the multiple behavior differences in the first sorting sequence, and generating an updated to-be-processed group according to the user corresponding to the fourth target behavior difference, the fourth target behavior difference being a behavior difference that is greater than or equal to the updated reference behavior difference among the multiple behavior differences in the first sorting sequence;

[0203] If there are users in the updated pending group, updating the updated pending group as the current group;

[0204] Update m=m+1, m=1,2,3...M-1.

[0205] In some embodiments of the present application, the target user includes the first user in the reference group; the allocation module in the embodiments of the present application may specifically include:

[0206] A second calculation submodule, configured to calculate a contribution ratio of each first user in the reference group to the reference group according to the contribution of each first user in the reference group;

[0207] The first allocation submodule is used to allocate business resources to each first user in the reference group according to the business resources in the business scenario and the contribution ratio of each first user.

[0208] In some other embodiments of the present application, the target user includes the second user; the allocation module in the embodiment of the present application may specifically include:

[0209] A sorting submodule, used to sort the first users in the reference group in descending order of contribution to obtain a second sorting sequence;

[0210] A screening submodule, used for screening the second user from the second sorting sequence according to a preset rule;

[0211] The second allocation submodule is used to allocate business resources corresponding to the business scenario to the second user.

[0212] Fig.13 A schematic diagram of the structure of an electronic device provided in some embodiments of the present application is shown.

[0213] The electronic device may include a processor 1310 and a memory 1320 storing computer program instructions.

[0214] Specifically, the processor 1310 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0215] The memory 1320 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 1320 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 1320 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 1320 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 1320 is a non-volatile solid-state memory.

[0216] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0217] The processor 1310 implements any one of the service resource allocation methods in the above embodiments by reading and executing computer program instructions stored in the memory 1320 .

[0218] In one example, the electronic device may further include a communication interface 1330 and a bus 1340. Fig.13 As shown, the processor 1310, the memory 1320, and the communication interface 1330 are connected via a bus 1340 and communicate with each other.

[0219] The communication interface 1330 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0220] Bus 1340 includes hardware, software or both, and the parts of electronic equipment are coupled to each other.For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 1340 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0221] The electronic device can execute the service resource allocation method in the embodiment of the present application, thereby realizing the combination Figures 1 to 12 The described service resource allocation method and device.

[0222] In addition, in combination with the service resource allocation method in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any service resource allocation method in the above embodiment is implemented.

[0223] In addition, in combination with the service resource allocation method in the above embodiment, the embodiment of the present application can provide a computer program product for implementation. The computer program product includes a computer program, and when the computer program is processed and executed, any of the above service resource allocation methods is implemented.

[0224] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0225] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0226] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0227] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0228] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for allocating business resources, It is characterized in that include: Obtain user behavior data of each user in a business scenario among multiple users; According to the user behavior data of each user in the multiple users in the business scenario, the multiple users are grouped to obtain at least two reference groups, wherein the reference groups include at least two first users, and the user behavior data of users in any two reference groups in the at least two reference groups in the business scenario show different behavior differences; Determining a contribution of each first user in the reference group according to a behavior difference between each two first users among the at least two first users, wherein the contribution is used to characterize an importance of the first user in the reference group for the business scenario; According to the contribution of each first user in the reference group, business resources corresponding to the business scenario are allocated to the target user in the reference group.

2. The method according to claim 1, It is characterized in that The determining, according to the behavior difference between each two first users among the at least two first users, the contribution of each first user in the reference group comprises: generating a first behavior difference parameter set corresponding to the reference group according to a behavior difference degree between each two first users among the at least two first users; Screening the behavior difference degree related to the i-th first user in the reference group from the first behavior difference parameter set to generate a first target behavior difference parameter set; The contribution of the i-th first user in the reference group is determined according to the first behavior difference parameter set and the first target behavior difference parameter set.

3. The method according to claim 2, It is characterized in that The determining, according to the first behavior difference parameter set and the first target behavior difference parameter set, the contribution of the i-th first user in the reference group includes: Performing square sum processing on the behavior difference degrees in the first behavior difference parameter set to obtain a first parameter; Performing square sum processing on the behavior difference degrees in the first target behavior difference parameter set to obtain a second parameter; A contribution degree of an i-th first user in the reference group is determined according to the first parameter and the second parameter.

4. The method according to claim 3, It is characterized in that The determining, according to the first parameter and the second parameter, the contribution of the i-th first user in the reference group includes: The first parameter and the second parameter are calculated using a preset logarithmic function to obtain a contribution of the i-th first user in the reference group.

5. The method according to claim 1, It is characterized in that The step of grouping the multiple users according to the user behavior data of each user in the multiple users in the business scenario to obtain at least two reference groups includes: Calculating, according to the user behavior data of each user among the multiple users in the business scenario, the behavior difference between every two users among the multiple users in the business scenario; The multiple users are grouped according to the difference in behaviors between every two users in the multiple users in the business scenario to obtain at least two reference groups.

6. The method according to claim 5, It is characterized in that The user behavior data of each user in the business scenario includes a behavior metric value corresponding to each business type in J business types of each user in the business scenario, where J is an integer greater than 1; The calculating, according to the user behavior data of each user among the multiple users in the business scenario, the behavior difference between every two users among the multiple users in the business scenario includes: According to every two users among the multiple users, according to the behavior metric value of the first target user under the jth business type and the behavior metric value of the second target user under the jth business type, calculating the behavior difference between the every two users under the jth business type, wherein the every two users include the first target user and the second target user, j∈[1,2,3,...,J]; According to the behavior difference between each two users in each service type of the J service types, the behavior difference between each two users in the service scenario is determined.

7. The method according to any one of claims 5 or 6, It is characterized in that The at least two reference groups include a first reference group and a second reference group; The step of grouping the multiple users according to the difference in behaviors between every two users in the multiple users in the business scenario to obtain at least two reference groups includes: sorting the behavior difference between every two users in the plurality of users in the business scenario in an ascending order of the behavior difference to obtain a first sorting sequence; Selecting a reference behavior difference from a plurality of behavior differences in the first sorting sequence, wherein the order of the reference behavior difference in the first sorting sequence meets a preset condition; generating a first reference group according to users corresponding to a first target behavior difference, wherein the first target behavior difference is a behavior difference among the plurality of behavior differences in the first sorting sequence that is smaller than a parameter behavior difference; generating a to-be-processed group according to users corresponding to a second target behavior difference, wherein the second target behavior difference is a behavior difference greater than or equal to a parameter behavior difference among the plurality of behavior differences in the first sorting sequence; The second reference group is determined according to the difference between the to-be-processed group and the reference behavior.

8. The method according to claim 7, It is characterized in that The second reference group includes M second reference groups, where M is an integer greater than 1; The step of determining the second reference group according to the difference between the to-be-processed group and the reference behavior includes: Taking the group to be processed as the current group and the reference behavior difference as the current reference behavior difference, the following operations are performed cyclically until all users in the group to be processed belong to the corresponding second reference group: The current reference behavior difference is updated using a preset update rule to obtain an updated reference behavior difference, wherein the updated reference behavior difference is greater than the current reference behavior difference; Traversing the behavior difference between every two users in the current group, outputting the mth second reference group according to the user corresponding to the third target behavior difference, wherein the third target behavior difference is a behavior difference that is smaller than the updated reference behavior difference among the multiple behavior differences in the first sorting sequence, and generating an updated to-be-processed group according to the user corresponding to the fourth target behavior difference, wherein the fourth target behavior difference is a behavior difference that is greater than or equal to the updated reference behavior difference among the multiple behavior differences in the first sorting sequence; If there is a user in the updated to-be-processed group, updating the updated to-be-processed group as the current group; Update m=m+1, m=1,2,3...M-1.

9. The method according to claim 1, It is characterized in that The target user includes a first user in the reference group; The allocating the business resources corresponding to the business scenario to the target user in the reference group according to the contribution of each first user in the reference group includes: Calculating, according to the contribution of each first user in the reference group, the contribution ratio of each first user in the reference group to the reference group; The business resources are allocated to each first user in the reference group according to the business resources in the business scenario and the contribution ratio of each first user.

10. The method according to claim 1, It is characterized in that The target user includes a second user; The allocating the business resources corresponding to the business scenario to the target user in the reference group according to the contribution of each first user in the reference group includes: Sorting the first users in the reference group in descending order of contribution to obtain a second sorting sequence; According to a preset rule, selecting a second user from the second sorting sequence; Allocate business resources corresponding to the business scenario to the second user.

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