Influence factor determination method and device, equipment and storage medium

Abstract

The invention discloses an influence factor determination method and device, equipment and a storage medium, and the method comprises the steps: obtaining a second number of candidate influence factors according to the correlation of each initial influence factor in a first number of initial influence factors; calculating respective weights and correction coefficients of the second number of candidate influence factors based on the hit times and the consumption data; for any user in the target user group, calculating the confidence coefficient of the user based on respective weights, hit times and / or correction coefficients of a second number of candidate influence factors; and determining a target influence factor of the user according to the confidence of the user. Colinear influence factors are removed through correlation analysis, the most critical influence factor of each user is determined based on the hit times of the influence factors and historical consumption data of the users, and the problem that in reality, the weight of each critical influence factor is often determined according to business experience, and the subjectivity is high, so that the accuracy is not high is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method, device, equipment and storage medium for determining influencing factors. Background Art

[0002] In the express delivery industry, customer operations analysis often requires analyzing the reasons for a decline in customer shipping costs over a period of time. Businesses hope to identify the most critical factors influencing this decline in customer shipping costs to support corresponding service assurance optimization plans.

[0003] In related technologies, there are two ways to calculate influencing factors: one is manual screening based on past experience. However, this method requires a high level of expertise from business personnel, and there's a high chance of missing important features. Furthermore, different business personnel's judgments vary based on experience, making it difficult to measure the accuracy of the results. Another method is to use big data algorithms to traverse all influencing factor dimensions. However, this method cannot guarantee computational efficiency when dealing with large amounts of data in the real world. Summary of the Invention

[0004] The main purpose of the present invention is to provide a method, device, equipment and storage medium for determining influencing factors, which uses correlation analysis to remove collinear influencing factors and determines the most critical influencing factors for each user based on the number of influencing factor hits and the user's historical consumption data, thereby solving the problem that the weights of various key influencing factors in reality are often determined based on business experience, which is highly subjective and leads to inaccuracy.

[0005] In order to achieve the above objectives, the embodiments of the present application provide the following technical solutions:

[0006] According to a first aspect of an embodiment of the present application, a method for determining an influencing factor is provided, the method comprising:

[0007] Obtaining a first number of initial influencing factors, a hit count of each initial influencing factor in the historical data, and consumption data of the target user group from historical data of the target user group;

[0008] Obtaining a second number of candidate influencing factors according to the correlation between each of the first number of initial influencing factors;

[0009] Calculating the weights and correction coefficients of the second number of candidate influencing factors based on the number of hits and the consumption data;

[0010] For any user in the target user group, calculating the user's confidence based on the weights, hit times, and / or correction coefficients of each of the second number of candidate influencing factors;

[0011] The target influencing factor of the user is determined according to the confidence level of the user.

[0012] Optionally, the calculating the weights of the second number of candidate influencing factors based on the number of hits and the consumption data includes:

[0013] Classifying the target user group into a first user group and a second user group based on the consumption data, wherein the first user group is a user group whose consumption amount in the current period has increased compared with the consumption amount in the previous period; and the second user group is a user group whose consumption amount in the current period has decreased compared with the consumption amount in the previous period;

[0014] For each candidate influencing factor of the second number of candidate influencing factors, respectively counting the number of hits of the first user group and the second user group in the candidate influencing factor;

[0015] The weight of the candidate influencing factor is calculated according to the number of hits of the first user group and the second user group in the candidate influencing factor respectively.

[0016] Optionally, the calculating correction coefficients of the second number of candidate influencing factors based on the number of hits and the consumption data includes:

[0017] Classifying the second number of candidate influencing factors into a user-unaware influencing factor group and a user-aware influencing factor group based on influencing factor attributes;

[0018] Calculate the ratio of the number of hits of the second user group to the number of hits of the first user group in the group of users with no perceived influencing factors, as the ratio of the group with no perceived influencing factors;

[0019] Calculating a ratio of the number of hits of the second user group to the first user group in the user group with perceived influencing factors as the ratio of the group with perceived influencing factors;

[0020] The correction coefficient is calculated according to the proportion of the group without the perceived influencing factors and the proportion of the group with the perceived influencing factors.

[0021] Optionally, calculating the user's confidence level for each user based on the weights, hit times, and / or correction coefficients of the second number of candidate influencing factors includes:

[0022] For each user-unaware influencing factor of the user, calculate the product of the number of hits of the user in the user-unaware influencing factor and the weight;

[0023] For each user-perceived influencing factor of the user, calculate the product of the number of hits of the user in the user-unperceived influencing factor, the weight and the correction coefficient;

[0024] The maximum value of the product of all the user's non-perceived influencing factors and the product of all the user's perceivable influencing factors is determined as the user's confidence.

[0025] Optionally, determining the target influencing factor of each user according to the confidence level of each user includes:

[0026] Obtain the influencing factors of the corresponding candidate influencing factors according to the confidence level of each user;

[0027] The influencing factors of the candidate influencing factors are determined as the target influencing factors of the user.

[0028] Optionally, obtaining a second number of candidate influencing factors according to the correlation between the initial influencing factors in the first number of initial influencing factors includes:

[0029] Obtaining a plurality of pairs of correlation influencing factors according to the correlation between each of the first number of initial influencing factors;

[0030] For each pair of correlation influencing factors, one of the correlation influencing factors is screened according to the set influencing factor attribute condition to obtain the second number of candidate influencing factors.

[0031] Optionally, for any pair of correlation influencing factors, retaining one of the correlation influencing factors according to a set rule includes:

[0032] If the attributes of the influencing factors in the correlation influencing factors include both user-unaware influencing factors and user-aware influencing factors, then the correlation influencing factors whose attributes are user-aware influencing factors are retained;

[0033] If the pair of correlation influencing factors have the same influencing factor attributes, one of the correlation influencing factors is randomly retained.

[0034] According to a second aspect of an embodiment of the present application, a device for determining an influencing factor is provided, the device comprising:

[0035] A data acquisition module, configured to acquire, from the historical data of the target user group, a first number of initial influencing factors, a hit count of each initial influencing factor in the historical data, and consumption data of the target user group;

[0036] a correlation analysis module, configured to obtain a second number of candidate influencing factors based on the correlation of each of the first number of initial influencing factors;

[0037] a parameter calculation module, configured to calculate the weights and correction coefficients of the second number of candidate influencing factors based on the number of hits and the consumption data;

[0038] a confidence calculation module, configured to calculate, for any user in the target user group, the confidence of the user based on the weights, hit times and / or correction coefficients of the second number of candidate influencing factors;

[0039] The target influencing factor determination module is used to determine the target influencing factor of the user according to the confidence level of the user.

[0040] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0041] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer-readable instructions are stored. The computer-readable instructions can be executed by a processor to implement the method described in the first aspect above.

[0042] In summary, the embodiments of the present application provide an influencing factor determination method, apparatus, device and storage medium, which obtains a first number of initial influencing factors, the number of hits of each initial influencing factor in the historical data, and the consumption data of the target user group from the historical data of the target user group; obtains a second number of candidate influencing factors based on the correlation of each initial influencing factor in the first number of initial influencing factors; calculates the weights and correction coefficients of each of the second number of candidate influencing factors based on the number of hits and the consumption data; for any user in the target user group, calculates the user's confidence based on the weights, number of hits and / or correction coefficients of each of the second number of candidate influencing factors; and determines the target influencing factors of the user based on the user's confidence. Correlation analysis is used to remove collinear influencing factors, and the most critical influencing factors of each user are determined based on the number of hits of the influencing factors and the user's historical consumption data, which solves the problem that the weights of each key influencing factor in reality are often established based on business experience, which is highly subjective and leads to inaccuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0044] The structures, proportions, sizes, etc. illustrated in this specification are intended solely to complement the contents disclosed herein and to facilitate understanding and reading by persons skilled in the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall remain within the scope of the technical contents disclosed herein.

[0045] Figure 1 A flow chart of a method for determining influencing factors provided in an embodiment of the present application;

[0046] Figure 2 A block diagram of a device for determining influencing factors provided in an embodiment of the present application;

[0047] Figure 3 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown;

[0048] Figure 4 A schematic diagram of a computer-readable storage medium provided in an embodiment of the present application is shown.

[0049] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0051] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0052] In addition, the terms "first," "second," and so on, used in this disclosure are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referenced. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this disclosure, "plurality" means at least two, such as two or three, unless otherwise specifically defined.

[0053] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0054] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0055] Figure 1 A flow chart of a method for determining influencing factors provided in an embodiment of the present application is shown, wherein the method includes:

[0056] Step 101: Obtaining a first number of initial influencing factors, the number of hits of each initial influencing factor in the historical data, and consumption data of the target user group from historical data of the target user group;

[0057] Step 102: Obtain a second number of candidate influencing factors based on the correlation between the initial influencing factors in the first number of initial influencing factors;

[0058] Step 103: Calculating the weights and correction coefficients of the second number of candidate influencing factors based on the hit counts and the consumption data;

[0059] Step 104: For any user in the target user group, calculate the user's confidence based on the weights, hit times and / or correction coefficients of the second number of candidate influencing factors;

[0060] Step 105: Determine the target influencing factors of the user according to the user's confidence level.

[0061] In a possible implementation, in step 102, obtaining a second number of candidate influencing factors according to the correlation of each of the first number of initial influencing factors includes:

[0062] According to the correlation of each initial influencing factor in the first number of initial influencing factors, several pairs of correlation influencing factors are obtained; for each pair of correlation influencing factors, one of the correlation influencing factors is screened according to the set influencing factor attribute conditions to obtain the second number of candidate influencing factors.

[0063] In a possible implementation, for any pair of correlation influencing factors, retaining one of the correlation influencing factors according to a set rule includes:

[0064] If the influencing factor attributes in the pair of correlation influencing factors include both user-unaware influencing factors and user-perceived influencing factors, then the correlation influencing factors whose influencing factor attributes are user-perceived influencing factors are retained; if the pair of correlation influencing factors have the same influencing factor attributes, then one of the correlation influencing factors is randomly retained.

[0065] In a possible implementation, in step 103, calculating the weights of the second number of candidate influencing factors based on the hit count and the consumption data includes:

[0066] Based on the consumption data, the target user group is classified into a first user group and a second user group, the first user group being a user group whose consumption amount in this period has increased compared with the consumption amount in the previous period; the second user group being a user group whose consumption amount in this period has decreased compared with the consumption amount in the previous period; for each candidate influencing factor among the second number of candidate influencing factors, the number of hits of the first user group and the second user group in the candidate influencing factor is counted respectively; the weight of the candidate influencing factor is calculated according to the number of hits of the first user group and the second user group in the candidate influencing factor respectively.

[0067] In a possible implementation, calculating correction coefficients for the second number of candidate influencing factors based on the number of hits and the consumption data includes:

[0068] Based on the influencing factor attributes, the second number of candidate influencing factors are classified into a user-unaware influencing factor group and a user-aware influencing factor group; the ratio of the number of hits of the second user group and the first user group in the user-unaware influencing factor group is calculated as the non-aware influencing factor group ratio; the ratio of the number of hits of the second user group and the first user group in the user-aware influencing factor group is calculated as the aware influencing factor group ratio; and a correction coefficient is calculated according to the non-aware influencing factor group ratio and the aware influencing factor group ratio.

[0069] In a possible implementation, in step 104, calculating the user's confidence level for each user based on the weights, hit counts, and / or correction coefficients of the second number of candidate influencing factors includes:

[0070] For each user-imperceptible influencing factor of the user, the product of the number of hits of the user in the user-imperceptible influencing factor and the weight is calculated; for each user-perceived influencing factor of the user, the product of the number of hits of the user in the user-imperceptible influencing factor, the weight and the correction coefficient is calculated; the maximum value between the product of all user-imperceptible influencing factors and the product of all user-perceived influencing factors is determined as the confidence of the user.

[0071] In a possible implementation, in step 105, determining the target influencing factor of each user according to the confidence level of each user includes:

[0072] Obtain the influencing factors of the corresponding candidate influencing factors according to the confidence level of each user; and determine the influencing factors of the candidate influencing factors as the target influencing factors of the user.

[0073] The following is a further detailed description of the method for determining influencing factors provided in the embodiments of the present application.

[0074] Phase 1: Obtain the first number of initial influencing factors from the historical data of the target user group, the number of hits for each initial influencing factor in the historical data, and the consumption data of the target user group. Data preparation and raw data analysis are performed to determine the initial n key influencing factors.

[0075] Based on business attributes, data analysis was used to determine business rules. These rules included the logic for determining whether customer spending was declining, the data analysis period, and the observation period for whether spending was declining. Based on these business rules, a preliminary list of n key influencing factors was identified. After identifying these n key influencing factors based on the business logic, the number of hits for each key influencing factor was obtained.

[0076] The hit count data is the number of times each influencing factor is involved in the statistical database. For example, the hit count is: the influencing factor is "whether the courier is unaware / not delivering to the door to complain", and during this period, there were two complaints because the courier was unaware and did not deliver to the door, so the hit count is 2.

[0077] Phase II: Correlation Analysis (Collinearity Calculation): Based on the correlation of each of the first number of initial influencing factors, several pairs of correlation influencing factors are obtained; for each pair of correlation influencing factors, one of the correlation influencing factors is screened based on the set influencing factor attribute conditions to obtain the second number of candidate influencing factors. If the influencing factor attributes in the pair of correlation influencing factors include both user-unaware influencing factors and user-aware influencing factors, the correlation influencing factor with the influencing factor attribute of user-aware influencing factors is retained; if the pair of correlation influencing factors have the same influencing factor attributes, one of the correlation influencing factors is randomly retained.

[0078] This stage specifically includes the following steps:

[0079] Step 1: Perform correlation analysis on the n influencing factors of the original data, obtain the correlation influencing factors, and output a collinearity heat map; the correlation analysis method can be to calculate the Pearson correlation coefficient pairwise.

[0080] Step 2: Filter out the influencing factor pairs whose correlation influencing factors exceed the set threshold.

[0081] Step 3: Eliminate the selected influencing factors again. The principle of this elimination is to prioritize process-related factors and retain customer complaint-related factors. For example, if there are two relevant influencing factors, one related to customer complaints and the other to process, eliminate the process-related factor. If both factors are related to process, eliminate one, and the remaining factor is also related to process.

[0082] First, perform a correlation analysis to eliminate common influencing factors. Then, calculate the importance coefficient and correction coefficient for the remaining influencing factors. Based on this data, determine the number of hits for the remaining n influencing factors and the change in spending during the statistical period. Based on the change in spending, classify all users into customer group A (whose spending has declined) and customer group B (whose spending has not declined).

[0083] The third stage: Calculate the weights (importance coefficient TGI) of the selected candidate influencing factors based on the number of hits and consumption data.

[0084] Based on the consumption data, the target user group is classified into a first user group and a second user group, the first user group being a user group whose consumption amount in this period has increased compared with the consumption amount in the previous period; the second user group being a user group whose consumption amount in this period has decreased compared with the consumption amount in the previous period; for each candidate influencing factor among the second number of candidate influencing factors, the number of hits of the first user group and the second user group in the candidate influencing factor is counted respectively; the weight of the candidate influencing factor is calculated according to the number of hits of the first user group and the second user group in the candidate influencing factor respectively.

[0085] For example, the proportion of the hit frequency of n influencing factors to the overall hit frequency of the influencing factor is calculated, and the proportion of the influencing factor in group A / the proportion of the influencing factor in group B is used as the importance coefficient of the influencing factor (characterizing the relationship between the characteristics of the influencing factor and the decline in consumption amount). The importance coefficient I is calculated for n influencing factors in turn. n Calculated according to the following formula:

[0086] I n =(C A / C A+B ) / (CB / C A+B )

[0087] Among them, n is the nth key influencing factor initially selected, C is the number of hits of the nth key influencing factor within the time period, A represents the user group whose consumption amount has declined, and B represents the user group whose consumption amount has not declined.

[0088] Phase 4: Calculate the correction coefficients for each of the selected candidate influencing factors based on the number of hits and consumption data. Distinguish and categorize process influencing factors (which customers are unaware of) and factors that influence customer complaints (which they are aware of), and perform coefficient correction.

[0089] Based on the influencing factor attributes, the second number of candidate influencing factors are classified into a user-unaware influencing factor group and a user-aware influencing factor group; the ratio of the number of hits of the second user group and the first user group in the user-unaware influencing factor group is calculated as the non-aware influencing factor group ratio; the ratio of the number of hits of the second user group and the first user group in the user-aware influencing factor group is calculated as the aware influencing factor group ratio; and a correction coefficient is calculated according to the non-aware influencing factor group ratio and the aware influencing factor group ratio.

[0090] For example, to address the differences in the impact of process-related factors and customer complaint-related factors on results, the original data was divided into two groups: Group 1 (customer group with data only on process-related factors) and Group 2 (customer group with data only on customer complaint-related factors). The frequency ratios of consumption amount decline and non-decline within each group were calculated, yielding R1 and R2, respectively, for the frequency ratios of consumption amount decline. The R1 / R2 ratio was used as the correction coefficient between process-related factors and customer complaint-related factors.

[0091]

[0092]

[0093]

[0094] C: The number of hits on these key influencing factors within the group of influencing factors during the time period;

[0095] E&F: The declining and non-declining customer segments in Group 1;

[0096] G&H: Declining and non-declining customer segments in Group 2.

[0097] The fifth stage: for any user in the target user group, the user's confidence is calculated based on the weights, hit counts and / or correction coefficients of the second number of candidate influencing factors, and the target influencing factor of the user is determined based on the user's confidence.

[0098] For each user's non-perceived influencing factor, the product of the number of hits for the user in that non-perceived influencing factor and its weight is calculated. For each user's perceived influencing factor, the product of the number of hits for the user in that non-perceived influencing factor, its weight, and the correction coefficient is calculated. The maximum value of the product of all non-perceived influencing factors and the product of all perceived influencing factors is determined as the user's confidence score. The influencing factor corresponding to the confidence score is the most critical influencing factor.

[0099] The confidence score S is calculated according to the following formula:

[0100] S=MAX(I n ×F×C n )

[0101] Among them, I n is the importance coefficient, n represents the nth key influencing factor among the initially selected key influencing factors, and C represents the number of hits against the nth key influencing factor within the time period. F is the correction coefficient, which only needs to be added before process-related influencing factors, not customer complaint-related influencing factors. If the factor is a process-related influencing factor, F is 1, indicating that no correction is required. If the factor is a customer complaint-related influencing factor, it needs to be multiplied by the correction coefficient F.

[0102] The TGI approach is used to determine weights (importance coefficients and correction coefficients), and correlation analysis is used to remove collinearity factors. Ultimately, the most critical attributable factor is determined by ranking n key influencing factors. This addresses the problem that the weights of key influencing factors are often determined based on business experience, which is highly subjective, poorly interpretable, inaccurate, and unable to eliminate highly correlated factors.

[0103] In summary, the embodiment of the present application provides a method for determining influencing factors, which obtains a first number of initial influencing factors, the number of hits of each initial influencing factor in the historical data, and the consumption data of the target user group from the historical data of the target user group; obtains a second number of candidate influencing factors based on the correlation of each initial influencing factor in the first number of initial influencing factors; calculates the weights and correction coefficients of each of the second number of candidate influencing factors based on the number of hits and the consumption data; calculates the confidence of the user for any user in the target user group based on the weights, number of hits and / or correction coefficients of each of the second number of candidate influencing factors; and determines the target influencing factors of the user based on the confidence of the user. Correlation analysis is used to remove collinear influencing factors, and the most critical influencing factors of each user are determined based on the number of hits of the influencing factors and the historical consumption data of the user, which solves the problem that the weights of each key influencing factor in reality are often established based on business experience, which is highly subjective and leads to inaccuracy.

[0104] Based on the same technical concept, the embodiment of the present application also provides an influencing factor determination device, such as Figure 2 As shown, the device includes:

[0105] The data acquisition module 201 is configured to acquire a first number of initial influencing factors, a hit count of each initial influencing factor in the historical data, and consumption data of the target user group from the historical data of the target user group;

[0106] A correlation analysis module 202 is configured to obtain a second number of candidate influencing factors based on the correlation of each of the first number of initial influencing factors;

[0107] A parameter calculation module 203 is configured to calculate the weights and correction coefficients of the second number of candidate influencing factors based on the hit count and the consumption data;

[0108] A confidence calculation module 204 is configured to calculate, for any user in the target user group, the confidence of the user based on the weights, hit counts, and / or correction coefficients of the second number of candidate influencing factors;

[0109] The target influencing factor determination module 205 is configured to determine the target influencing factor of the user according to the user's confidence level.

[0110] The present application also provides an electronic device corresponding to the method provided in the above embodiment. Figure 3, which shows a schematic diagram of an electronic device provided in some embodiments of the present application. The electronic device 20 may include: a processor 200, a memory 201, a bus 202, and a communication interface 203. The processor 200, the communication interface 203, and the memory 201 are connected via the bus 202. The memory 201 stores a computer program executable on the processor 200. When the processor 200 executes the computer program, it executes the method provided in any of the aforementioned embodiments of the present application.

[0111] The memory 201 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element and at least one other network element are connected via at least one physical port 203 (which may be wired or wireless), and may utilize the Internet, a wide area network, a local area network, a metropolitan area network, or the like.

[0112] The bus 202 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used to store programs. The processor 200 executes the programs upon receiving execution instructions. The methods disclosed in any of the aforementioned embodiments of the present application may be applied to or implemented by the processor 200.

[0113] The processor 200 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 200 or by software instructions. The above processor 200 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory 201 , and the processor 200 reads the information in the memory 201 and completes the steps of the above method in combination with its hardware.

[0114] The electronic device provided in the embodiments of the present application and the method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented by them.

[0115] The present application also provides a computer-readable storage medium corresponding to the method provided in the above embodiment. Figure 4 The computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the method provided by any of the aforementioned embodiments is executed.

[0116] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0117] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0118] It should be noted that:

[0119] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other device. Various general-purpose devices may also be used in conjunction with the teachings herein. Based on the above description, it is apparent that the structure required for constructing such devices is suitable. In addition, the present application is not directed to any specific programming language. It should be understood that various programming languages ​​may be utilized to implement the present application described herein, and the above description of specific languages ​​is provided for the purpose of disclosing the best mode of implementation of the present application.

[0120] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0121] Similarly, it should be understood that in order to streamline the present application and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the claims below, inventive aspects lie in fewer than all the features of the individual embodiments disclosed above. Accordingly, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim itself serving as a separate embodiment of the present application.

[0122] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0123] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of this application and to form different embodiments. For example, in the claims below, any of the claimed embodiments may be used in any combination.

[0124] The various component embodiments of the present application can be implemented in hardware, or implemented in a software module running on one or more processors, or implemented in a combination thereof. Those skilled in the art will appreciate that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the creation device of the virtual machine according to an embodiment of the present application. The application can also be implemented as a part or all of the equipment or device program (for example, computer program and computer program product) for performing the method described herein. Such a program realizing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0125] It should be noted that the above embodiments illustrate rather than limit the present application, and that a person skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0126] The above description is merely a preferred embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0127] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for determining influencing factors, characterized in that: The method comprises: Obtaining a first number of initial influencing factors, a hit count of each initial influencing factor in the historical data, and consumption data of the target user group from historical data of the target user group; Obtaining a second number of candidate influencing factors according to the correlation between each of the first number of initial influencing factors; Calculating the weights and correction coefficients of the second number of candidate influencing factors based on the number of hits and the consumption data; For any user in the target user group, calculating the user's confidence based on the weights, hit times, and / or correction coefficients of each of the second number of candidate influencing factors; The target influencing factor of the user is determined according to the confidence level of the user.

2. The method according to claim 1, wherein Calculating the weights of the second number of candidate influencing factors based on the number of hits and the consumption data includes: Classifying the target user group into a first user group and a second user group based on the consumption data, wherein the first user group is a user group whose consumption amount in the current period has increased compared with the consumption amount in the previous period; and the second user group is a user group whose consumption amount in the current period has decreased compared with the consumption amount in the previous period; For each candidate influencing factor of the second number of candidate influencing factors, respectively counting the number of hits of the first user group and the second user group in the candidate influencing factor; The weight of the candidate influencing factor is calculated according to the number of hits of the first user group and the second user group in the candidate influencing factor respectively.

3. The method according to claim 2, wherein The calculating of correction coefficients of a second number of candidate influencing factors based on the number of hits and the consumption data includes: Classifying the second number of candidate influencing factors into a user-unaware influencing factor group and a user-aware influencing factor group based on influencing factor attributes; Calculate the ratio of the number of hits of the second user group to the number of hits of the first user group in the group of users with no perceived influencing factors, as the ratio of the group with no perceived influencing factors; Calculating a ratio of the number of hits of the second user group to the first user group in the user group with perceived influencing factors as the ratio of the group with perceived influencing factors; The correction coefficient is calculated according to the proportion of the group without the perceived influencing factors and the proportion of the group with the perceived influencing factors.

4. The method according to claim 3, wherein The step of calculating the user's confidence level for each user based on the weights, hit times, and / or correction coefficients of the second number of candidate influencing factors includes: For each user-unaware influencing factor of the user, calculate the product of the number of hits of the user in the user-unaware influencing factor and the weight; For each user-perceived influencing factor of the user, calculate the product of the number of hits of the user in the user-unperceived influencing factor, the weight and the correction coefficient; The maximum value of the product of all the user's non-perceived influencing factors and the product of all the user's perceivable influencing factors is determined as the user's confidence.

5. The method according to claim 4, wherein Determining the target influencing factor of each user according to the confidence level of each user includes: Obtain the influencing factors of the corresponding candidate influencing factors according to the confidence level of each user; The influencing factors of the candidate influencing factors are determined as the target influencing factors of the user.

6. The method according to claim 3, wherein The obtaining of a second number of candidate influencing factors according to the correlation of each of the first number of initial influencing factors includes: Obtaining a plurality of pairs of correlation influencing factors according to the correlation between each of the first number of initial influencing factors; For each pair of correlation influencing factors, one of the correlation influencing factors is screened according to the set influencing factor attribute condition to obtain the second number of candidate influencing factors.

7. The method according to claim 6, wherein For any pair of correlation influencing factors, retaining one of the correlation influencing factors according to the set rule includes: If the attributes of the influencing factors in the correlation influencing factors include both user-unaware influencing factors and user-aware influencing factors, then the correlation influencing factors whose attributes are user-aware influencing factors are retained; If the pair of correlation influencing factors have the same influencing factor attributes, one of the correlation influencing factors is randomly retained.

8. A device for determining influencing factors, characterized in that: The device comprises: A data acquisition module, configured to acquire, from the historical data of the target user group, a first number of initial influencing factors, a hit count of each initial influencing factor in the historical data, and consumption data of the target user group; a correlation analysis module, configured to obtain a second number of candidate influencing factors based on the correlation of each of the first number of initial influencing factors; a parameter calculation module, configured to calculate the weights and correction coefficients of the second number of candidate influencing factors based on the number of hits and the consumption data; a confidence calculation module, configured to calculate, for any user in the target user group, the confidence of the user based on the weights, hit times and / or correction coefficients of the second number of candidate influencing factors; The target influencing factor determination module is used to determine the target influencing factor of the user according to the confidence level of the user.

9. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Computer-readable instructions are stored thereon, and the computer-readable instructions can be executed by a processor to implement the method according to any one of claims 1 to 7.

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