CN112633948A - Target audience proportion calculation method and device - Google Patents
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Abstract
The application provides a target audience proportion calculation method and a target audience proportion calculation device, wherein the method comprises the following steps: acquiring the maximum audience occupancy of a target area, the audience number of a plurality of advertisements in a preset time period, and an audience sample set obtained through research; calculating the minimum sample size of the total audience according to the maximum audience proportion and the audience number of the advertisements; performing structure adjustment on the audience sample set according to a preset sample structure weighting adjustment algorithm to obtain a template sample set and a weight of each type of sample in the template sample set; the number of samples in the template sample set exceeds the minimum sample size of the general audience, and the sample structure is consistent with the netizen structure of the target area; and calculating the target audience occupation ratio of the target advertisement according to the monitoring log of the target advertisement, the template sample set, the weight of each type of sample in the template sample set and preset target audience attributes.
Description
Technical Field
The application relates to the field of data analysis, in particular to a target audience proportion calculation method and device.
Background
In advertisement monitoring, a target audience refers to a group targeted by an advertisement or marketing campaign, and the common attributes of the group are gender, age, education level, income, and the like. The target audience ratio is the ratio of the target audience to the total audience, and is an important judgment index for judging the advertisement putting effect.
In the prior art, netizen information with population attributes is generally obtained in the form of questionnaire research and is made into a netizen sample library, and basic data of netizens comprise netizen ID, gender, age, region, education degree, income and the like. Then, dividing the number of target audiences in human units in the netizen sample library by the number of all groups watching the advertisements, and calculating the target audience ratio. The larger the data of the netizen sample library is, the more reasonable the population distribution in the sample database is, the more truly the information of the whole netizen can be reflected, and the target audience ratio calculated based on the research sample is closer to the target audience ratio calculated based on the whole netizen. The labor cost to investigate enough sample data is enormous.
Disclosure of Invention
In view of the above, an object of the present application is to provide a method and an apparatus for calculating a target audience share, so as to solve the problem of how to reduce the calculation difficulty of the target audience share in the prior art.
In a first aspect, an embodiment of the present application provides a target audience proportion calculation method, where the method includes:
acquiring the maximum audience occupancy of a target area, the audience number of a plurality of advertisements in a preset time period, and an audience sample set obtained through research;
calculating the minimum sample size of the total audience according to the maximum audience proportion and the audience number of the advertisements;
performing structure adjustment on the audience sample set according to a preset sample structure weighting adjustment algorithm to obtain a template sample set and a weight of each type of sample in the template sample set; the number of samples in the template sample set exceeds the minimum sample size of the general audience, and the sample structure is consistent with the netizen structure of the target area;
and calculating the target audience occupation ratio of the target advertisement according to the monitoring log of the target advertisement, the template sample set, the weight of each type of sample in the template sample set and preset target audience attributes.
In some embodiments, the performing structure adjustment on the audience sample set according to a preset sample structure weighting adjustment algorithm to obtain a weight of each type of sample in the template sample set includes:
obtaining weights of various samples according to the population number of the target area, the netizen number of the target area, the preset attribute dimension, the preset grouping under the preset attribute dimension and the preset multiple of the maximum weight and the minimum weight;
and carrying out structure adjustment on the audience sample set according to the weight values of the various samples to obtain a template sample set and the weight value of each sample in the template sample set.
In some embodiments, the preset target audience attributes include at least one of the following attribute dimensions: gender, age, school calendar, personal income, average income of family.
In some embodiments, the calculating a target audience ratio of the target advertisement according to the monitoring log of the target advertisement, the template sample set, the weight of each type of sample in the template sample set, and a preset target audience attribute includes:
acquiring a monitoring log of the target advertisement, and performing identity identification matching on audiences in the monitoring log and samples in the template sample set to obtain a template audience set of the target advertisement;
screening out a target audience set of the target advertisement from a template audience set of the target advertisement according to preset target audience attributes;
and calculating the ratio of the weight sum of all samples in the target audience set to the weight sum of all samples in the template audience set to obtain the target audience ratio of the target advertisement.
In a second aspect, an embodiment of the present application provides a target audience proportion calculation apparatus, including:
the acquisition module is used for acquiring the maximum audience proportion of the target area, the audience number of a plurality of advertisements in a preset time period, and an audience sample set obtained through research;
the estimation module is used for calculating the minimum sample size of the total audience according to the maximum audience occupancy and the audience number of the advertisements;
the adjusting module is used for carrying out structure adjustment on the audience sample set according to a preset sample structure weighting adjusting algorithm to obtain a template sample set and a weight of each type of sample in the template sample set; the number of samples in the template sample set exceeds the minimum sample size of the general audience, and the sample structure is consistent with the netizen structure of the target area;
and the calculation module is used for calculating the target audience occupation ratio of the target advertisement according to the monitoring log of the target advertisement, the template sample set, the weight of each type of sample in the template sample set and preset target audience attributes.
In some embodiments, the adjustment module comprises:
the weight unit is used for obtaining weights of various samples according to the population number of the target area, the netizen number of the target area, the preset attribute dimension, the preset grouping under the preset attribute dimension and the preset multiple of the maximum weight and the minimum weight;
and the adjusting unit is used for carrying out structure adjustment on the audience sample set according to the weight values of the various samples to obtain a template sample set and the weight value of each type of sample in the template sample set.
In some embodiments, the preset target audience attributes in the calculation module include at least one of the following attribute dimensions: gender, age, school calendar, personal income, average income of family.
In some embodiments, the calculation module comprises:
the matching unit is used for acquiring a monitoring log of the target advertisement, and performing identity identification matching on audiences in the monitoring log and samples in the template sample set to obtain a template audience set of the target advertisement;
the screening unit is used for screening out a target audience set of the target advertisement from the template audience set of the target advertisement according to preset target audience attributes;
and the calculating unit is used for calculating the ratio of the weight sum of all the samples in the target audience set to the weight sum of all the samples in the template audience set to obtain the target audience ratio of the target advertisement.
In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method in any one of the above first aspects when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs the steps of the method in any one of the above first aspects.
According to the target audience occupation ratio calculation method provided by the embodiment of the application, the minimum sample size of the total audience is calculated according to the maximum audience occupation ratio of a target area and the audience number of a plurality of advertisements in a preset time period, then the structure of an audience sample set obtained through research is adjusted according to a preset sample structure weighting adjustment algorithm, a template sample set and the weight of each type of samples in the template sample set are obtained, wherein the sample size exceeds the minimum sample size of the total audience, the sample structure is consistent with the netizen structure of the target area, and finally the target audience occupation ratio of the target advertisement is calculated according to the monitoring log of the target advertisement, the template sample set, the weight of each type of samples in the template sample set and the preset target audience attribute. According to the target audience occupation ratio calculation method provided by the embodiment of the application, limited audience samples are adjusted into template samples consistent with the network civil structure of the target area in a weighting mode, and the target audience occupation ratio is calculated by taking the weight as basic data, so that the calculation difficulty of the target audience occupation ratio is reduced, and the calculation accuracy is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flowchart of a target audience share calculating method according to an embodiment of the present disclosure;
fig. 2 is a flowchart illustrating a specific method for calculating a target audience share according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a target audience proportion calculation apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a target audience proportion calculation method, as shown in fig. 1, which comprises the following steps:
s101, acquiring the maximum audience occupancy of a target area, the audience number of a plurality of advertisements in a preset time period, and an audience sample set obtained through research;
step S102, calculating the minimum sample size of the total audience according to the maximum audience occupancy and the audience number of the advertisements;
step S103, carrying out structure adjustment on the audience sample set according to a preset sample structure weighting adjustment algorithm to obtain a template sample set and a weight of each type of sample in the template sample set; the number of samples in the template sample set exceeds the minimum sample size of the general audience, and the sample structure is consistent with the netizen structure of the target area;
and step S104, calculating the target audience ratio of the target advertisement according to the monitoring log of the target advertisement, the template sample set, the weight of each type of sample in the template sample set and preset target audience attributes.
Specifically, the target area may be a county, a city, a province, a region, a country, or the like, the maximum audience share ratio of the target area is calculated according to the preset grouping in each attribute dimension, the ratio of the grouped netizens in the target area to the total netizens is calculated, and the maximum value of the ratio is used as the maximum audience share ratio in the target area. The grouping of the attribute dimensions divides one attribute dimension into a plurality of groups according to attribute values, for example, the age dimension can be divided into 18-22 years old, 22-30 years old, 30-40 years old, 40-50 years old, more than 50 years old, and 5 groups. The audience size of an advertisement refers to the number of netizens watching the advertisement.
Before calculating the minimum sample size of the total audience, the minimum sample size of a single advertisement needs to be calculated, assuming that the target audience occupation ratio calculated based on the whole netizens in an advertisement campaign (including at least one advertisement) is P, the traffic of the campaign traffic and the intersecting sample is s, an unbiased estimation of which s is P can be obtained by a statistical theory, and the estimated variance is:
σ2＝P*(1-P)/n
wherein σ2The variance of the unbiased estimate of the target audience share across the population, and n is the number of target audiences in the campaign.
Taking the 95% confidence level as an example, the estimated maximum error M satisfies:
then, after the maximum audience share of the target area is obtained, the maximum allowable error of the target area can be calculated according to the above formula.
Then, the minimum sample size n of the single advertisement in the target area is calculated by the following formula0：
Wherein, P0Is the maximum audience share of the target area, M0Is the maximum allowable error for the target area.
Then, the minimum sample size n is calculated one by one0And (3) calculating the minimum sample size of the total audience by using the value corresponding to the 5 th percentile in the beta as the minimum sample proportion phi of the total audience in order to ensure that 95 percent of advertisements are in accordance with the minimum sample size:
wherein, sampleminIs the minimum sample size, U, of the total audiencetotalIs the number of net citizens.
Audience samples far exceeding the minimum sample size of the total audience are obtained through research and are used as an audience sample set, then the structure of the audience sample set is adjusted in a weighting mode to obtain a template sample set, the sample size of the template sample set is greatly reduced, but the netizen structure is the same as that of a target area, and the sample size is larger than the minimum sample size of the total audience.
In the process of weighting adjustment, each type of sample is endowed with a corresponding weight, and the weight of a single sample of the same type is equal to the weight of the sample of the same type.
And finally, screening samples in a template sample set by obtaining a monitoring log of the target advertisement of which the audience occupation ratio is to be calculated, and calculating the target audience occupation ratio corresponding to the preset target audience attribute under the target advertisement according to the weight of each sample.
In some embodiments, the step S103 of performing structure adjustment on the audience sample set according to a preset sample structure weighting adjustment algorithm to obtain a weight of each type of sample in the template sample set includes:
step 1031, obtaining weights of various samples according to the population number of the target area, the number of netizens of the target area, the preset attribute dimension, the preset grouping under the preset attribute dimension and the preset multiple of the maximum weight and the minimum weight;
and 1032, performing structure adjustment on the audience sample set according to the weights of the various samples to obtain a template sample set and the weight of each sample in the template sample set.
Specifically, by establishing an objective function and constraint conditions, weights of various samples are reasonably distributed to adjust the sample structure of the audience sample set, so that a template sample set is obtained.
Specific objective functions and constraints are illustrated below by taking five attribute dimensions into account gender, age, academic history, personal income, and average family income:
weighting dimension: a, B, C, E, G E { gender, age, academic history, personal income, average income of family }.
A is the gender dimension; a isiIs a gender grouping, a is the maximum grouping number, wherein i is less than or equal to a;
b is the age dimension; bjIs an age group, b is the maximum group number, wherein j is less than or equal to b;
c is the academic dimension; c. CkIs a study calendar group, c is the maximum group number, wherein k is less than or equal to c;
e is the personal income dimension; e.g. of the typelIs a personal income group, e is the maximum group number, wherein l is less than or equal to e;
g is the average income dimension of the household; gnIs the average income grouping of the family, and g is the maximum grouping number, wherein n is less than or equal to g.
The objective function is:
the constraint conditions include:
wmax-a*wmin≤0
wherein the content of the first and second substances,
is the number of samples;
is the weight; w is aminIs the weight minimum; w is amaxIs the maximum value of the weight; α is a preset multiple, typically set to 15; k is a radical of0Is an adjustment parameter;
and
it is the slack variable that is used to adjust the weighting error,
is the population boundary of the target area,
is the netizen boundary of the target area;
is a preset value;
is the number of net citizens;
is a preset coefficient.
Through the target function and the constraint conditions, the weights of various samples and the proportion of the various samples in the template sample set can be calculated, and further the quantity of the various samples in the audience sample set is adjusted.
In some embodiments, the preset target audience attributes include at least one of the following attribute dimensions: gender, age, school calendar, personal income, average income of family.
Specifically, the target audience is screened from the samples of which the monitoring logs are overlapped with the template sample set through preset target audience attributes.
The required groups can be respectively set through at least one dimension of attribute dimensions such as gender, age, school calendar, personal income, family average income and the like, and are combined into preset target audience attributes.
In some embodiments, the step S104 of calculating the target audience ratio of the target advertisement according to the monitoring log of the target advertisement, the template sample set, the weight of each type of sample in the template sample set, and the preset target audience attribute includes, as shown in fig. 2:
step S201, obtaining a monitoring log of a target advertisement, and performing identity identification matching on audiences in the monitoring log and samples in the template sample set to obtain a template audience set of the target advertisement;
step S202, screening out a target audience set of the target advertisement from the template audience set of the target advertisement according to preset target audience attributes;
step S203, calculating the ratio of the weight sum of all samples in the target audience set to the weight sum of all samples in the template audience set to obtain the target audience ratio of the target advertisement.
Specifically, the monitoring log of the target advertisement includes IDs (Identity documents) of all audiences of the target advertisement. And matching the monitoring log with the template sample set by taking the ID as a basis, wherein the audience overlapped with the monitoring log and the template sample set is used as the template audience set of the target advertisement. Then, a preset target audience attribute screens out a target audience from the template audience set to serve as the target audience set.
The sum of the weights corresponding to each ID in the template audience set is used as an audience total weight of the target advertisement, the sum of the weights corresponding to each ID in the target audience set is used as a target audience total weight of the target advertisement, the ratio of the target audience total weight to the audience total weight of the target advertisement is the target audience occupation ratio of the target advertisement, for example, only two dimensions of gender and age are considered, and the formula of the target audience occupation ratio T is as follows:
an embodiment of the present application further provides a target audience share calculating apparatus, as shown in fig. 3, the apparatus includes:
the acquisition module 30 is configured to acquire the maximum audience occupancy of the target area, the audience number of the multiple advertisements in a preset time period, and an audience sample set obtained through research;
an estimation module 31, configured to calculate a minimum sample size of a total audience according to the maximum audience occupancy and the audience number of the multiple advertisements;
an adjusting module 32, configured to perform structure adjustment on the audience sample set according to a preset sample structure weighting adjustment algorithm, to obtain a template sample set and a weight of each type of sample in the template sample set; the number of samples in the template sample set exceeds the minimum sample size of the general audience, and the sample structure is consistent with the netizen structure of the target area;
and the calculating module 33 is configured to calculate a target audience ratio of the target advertisement according to the monitoring log of the target advertisement, the template sample set, the weight of each type of sample in the template sample set, and a preset target audience attribute.
In some embodiments, the adjusting module 32 includes:
a weight unit 321, configured to obtain weights of various samples according to the population number of the target area, the netizen number of the target area, the preset attribute dimension, the preset group under the preset attribute dimension, and a preset multiple of the maximum weight and the minimum weight;
the adjusting unit 322 is configured to perform structure adjustment on the audience sample set according to the weights of the various types of samples, so as to obtain a template sample set and a weight of each type of sample in the template sample set.
In some embodiments, the preset target audience attributes in the calculation module 33 include at least one of the following attribute dimensions: gender, age, school calendar, personal income, average income of family.
In some embodiments, the calculating module 33 includes:
the matching unit 331 is configured to obtain a monitoring log of a target advertisement, and perform identity matching between an audience in the monitoring log and a sample in the template sample set to obtain a template audience set of the target advertisement;
a screening unit 332, configured to screen out a target audience set of the target advertisement from the template audience set of the target advertisement according to a preset target audience attribute;
the calculating unit 333 is configured to calculate a ratio between the weight sum of all the samples in the target audience set and the weight sum of all the samples in the template audience set, so as to obtain a target audience ratio of the target advertisement.
Corresponding to the target audience share calculation method in fig. 1, an embodiment of the present application further provides a computer device 400, as shown in fig. 4, the device includes a memory 401, a processor 402, and a computer program stored in the memory 401 and executable on the processor 402, wherein the processor 402 implements the target audience share calculation method when executing the computer program.
Specifically, the memory 401 and the processor 402 can be general memories and processors, which are not limited in particular, and when the processor 402 runs a computer program stored in the memory 401, the target audience share calculation method can be executed, so that the problem of how to reduce the calculation difficulty of the target audience share in the prior art is solved.
Corresponding to a target audience share calculation method in fig. 1, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the target audience share calculation method.
Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, and the like, when a computer program on the storage medium is run, the target audience proportion calculation method can be executed, and the problem of how to reduce the calculation difficulty of the target audience proportion in the prior art is solved And calculating the target audience occupation ratio of the target advertisement according to the template sample set, the weight of each type of sample in the template sample set and preset target audience attributes. According to the target audience occupation ratio calculation method provided by the embodiment of the application, limited audience samples are adjusted into template samples consistent with the network civil structure of the target area in a weighting mode, and the target audience occupation ratio is calculated by taking the weight as basic data, so that the calculation difficulty of the target audience occupation ratio is reduced, and the calculation accuracy is improved.
In the embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the present disclosure, which should be construed in light of the above teachings. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for calculating a target audience fraction, comprising:
acquiring the maximum audience occupancy of a target area, the audience number of a plurality of advertisements in a preset time period, and an audience sample set obtained through research;
calculating the minimum sample size of the total audience according to the maximum audience proportion and the audience number of the advertisements;
performing structure adjustment on the audience sample set according to a preset sample structure weighting adjustment algorithm to obtain a template sample set and a weight of each type of sample in the template sample set; the number of samples in the template sample set exceeds the minimum sample size of the general audience, and the sample structure is consistent with the netizen structure of the target area;
and calculating the target audience occupation ratio of the target advertisement according to the monitoring log of the target advertisement, the template sample set, the weight of each type of sample in the template sample set and preset target audience attributes.
2. The method of claim 1, wherein the performing structure adjustment on the audience sample set according to a preset sample structure weight adjustment algorithm to obtain a weight of each type of sample in the template sample set comprises:
obtaining weights of various samples according to the population number of the target area, the netizen number of the target area, the preset attribute dimension, the preset grouping under the preset attribute dimension and the preset multiple of the maximum weight and the minimum weight;
and carrying out structure adjustment on the audience sample set according to the weight values of the various samples to obtain a template sample set and the weight value of each sample in the template sample set.
3. The method of claim 1, wherein the preset target audience attributes include at least one of the following attribute dimensions: gender, age, school calendar, personal income, average income of family.
4. The method of claim 1, wherein calculating the target audience share of the target advertisement according to the monitoring log of the target advertisement, the template sample set, the weight of each type of sample in the template sample set, and preset target audience attributes comprises:
acquiring a monitoring log of the target advertisement, and performing identity identification matching on audiences in the monitoring log and samples in the template sample set to obtain a template audience set of the target advertisement;
screening out a target audience set of the target advertisement from a template audience set of the target advertisement according to preset target audience attributes;
and calculating the ratio of the weight sum of all samples in the target audience set to the weight sum of all samples in the template audience set to obtain the target audience ratio of the target advertisement.
5. A target audience share computing apparatus, comprising:
the acquisition module is used for acquiring the maximum audience proportion of the target area, the audience number of a plurality of advertisements in a preset time period, and an audience sample set obtained through research;
the estimation module is used for calculating the minimum sample size of the total audience according to the maximum audience occupancy and the audience number of the advertisements;
the adjusting module is used for carrying out structure adjustment on the audience sample set according to a preset sample structure weighting adjusting algorithm to obtain a template sample set and a weight of each type of sample in the template sample set; the number of samples in the template sample set exceeds the minimum sample size of the general audience, and the sample structure is consistent with the netizen structure of the target area;
and the calculation module is used for calculating the target audience occupation ratio of the target advertisement according to the monitoring log of the target advertisement, the template sample set, the weight of each type of sample in the template sample set and preset target audience attributes.
6. The apparatus of claim 5, wherein the adjustment module comprises:
the weight unit is used for obtaining weights of various samples according to the population number of the target area, the netizen number of the target area, the preset attribute dimension, the preset grouping under the preset attribute dimension and the preset multiple of the maximum weight and the minimum weight;
and the adjusting unit is used for carrying out structure adjustment on the audience sample set according to the weight values of the various samples to obtain a template sample set and the weight value of each type of sample in the template sample set.
7. The apparatus of claim 5, wherein the preset target audience attributes in the computing module include at least one of the following attribute dimensions: gender, age, school calendar, personal income, average income of family.
8. The apparatus of claim 5, wherein the computing module comprises:
the matching unit is used for acquiring a monitoring log of the target advertisement, and performing identity identification matching on audiences in the monitoring log and samples in the template sample set to obtain a template audience set of the target advertisement;
the screening unit is used for screening out a target audience set of the target advertisement from the template audience set of the target advertisement according to preset target audience attributes;
and the calculating unit is used for calculating the ratio of the weight sum of all the samples in the target audience set to the weight sum of all the samples in the template audience set to obtain the target audience ratio of the target advertisement.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of the preceding claims 1-4 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method of any one of the preceding claims 1 to 4.
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|---|---|---|---|---|
| CN113538047A (en) * | 2021-07-09 | 2021-10-22 | 湖南快乐阳光互动娱乐传媒有限公司 | Advertisement filtering statistical method and device, electronic equipment and storage medium |
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