CN110827080A - Directional pushing method and device - Google Patents
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Abstract
Description
Technical Field
The application relates to the field of internet advertisements, in particular to a directional pushing method and a directional pushing device.
Background
At present, marketing means of enterprises mainly adopt modes of propaganda orders, large-screen delivery and the like, the marketing means depends on geographical positions and artificial propagation, and internet advertisements are more effective than traditional marketing means under the background of high-speed development of the internet.
Aiming at the entrepreneurs, in the prior art, advertisement, meeting invitation and the like are widely pushed to a large number of users, so that information can be spread widely enough to ensure that real entrepreneurs can receive pushed messages, but the mode causes that a large number of pushing are invalid and accurate information delivery cannot be achieved, and non-entrepreneurs can be possibly given poor impression, and meanwhile, the mode is high in cost, poor in marketing efficiency and easy to leak personal sensitive information (mobile phone numbers, mailboxes and the like).
Disclosure of Invention
In view of the above, an object of the present application is to provide a directional pushing method and apparatus, which are used to solve the problem of how to accurately push a message to a specific group of people in the prior art.
In a first aspect, an embodiment of the present application provides a directional pushing method, where the method includes:
acquiring historical network behavior information of alternative users;
for each alternative user, classifying the historical network behavior information of the alternative user according to a preset classification strategy, and calculating a classification reference value based on the classified historical network behavior information; the classification strategy comprises a website browsing class, an application software using class, a high-frequency keyword searching class and a geographic position class;
and calculating the total reference value of each alternative user according to the classification reference value of each alternative user, setting the alternative users with the total reference value exceeding a preset threshold value as target users, and pushing target information to the target users.
According to a first aspect, the present embodiments provide a first possible implementation manner of the first aspect, wherein the classification reference value is calculated by using the following formula:
wherein S is a classification reference value, n is the number of network behavior items contained in the classification, and muiAs weights for network behavior items, αiIs a reference value of the network behavior item.
According to the first aspect, this embodiment provides a second possible implementation manner of the first aspect, where calculating, for the classification reference value of each candidate user, a total reference value of the candidate user includes:
obtaining various classification historical reference values according to the pushed historical target information, and determining classification weights according to the obtained various classification historical reference values;
and calculating the weighted sum of each classification reference value of each alternative user and the corresponding classification weight to obtain the total reference value of the alternative user.
According to the first aspect, an embodiment of the present application provides a third possible implementation manner of the first aspect, wherein setting, as a target user, an alternative user for which the total reference value exceeds a preset threshold includes:
obtaining a standard value of each classification reference value according to each classification reference value of the historical target user;
and setting the alternative users of which the total reference value exceeds a preset threshold value and all reference values reach the standard value as target users.
In a second aspect, an embodiment of the present application provides a directional pushing device, including:
the acquisition module is used for acquiring historical network behavior information of the alternative users;
the computing module is used for classifying the historical network behavior information of each alternative user according to a preset classification strategy and computing a classification reference value based on the classified historical network behavior information; the classification strategy comprises a website browsing class, an application software using class, a high-frequency keyword searching class and a geographic position class;
and the pushing module is used for calculating the total reference value of each alternative user according to the classification reference value of each alternative user, setting the alternative users with the total reference value exceeding a preset threshold value as target users, and pushing target information to the target users.
According to a second aspect, the present embodiments provide a first possible implementation of the second aspect, wherein the calculating module calculates the classification reference value by using the following formula:
wherein S is a classification reference value, n is the number of network behavior items contained in the classification, and muiAs weights for network behavior items, αiIs a reference value of the network behavior item.
According to a second aspect, the present embodiments provide a second possible implementation manner of the second aspect, where the pushing module includes:
the weighting unit is used for obtaining various classification historical reference values according to the pushed historical target information and determining classification weights according to the obtained various classification historical reference values;
and the first operation unit is used for calculating the weighted sum of each classification reference value and the corresponding classification weight of each candidate user to obtain the total reference value of the candidate user.
According to a second aspect, the present embodiments provide a second possible implementation manner of the second aspect, where the pushing module includes:
the second arithmetic unit is used for obtaining the standard value of each classification reference value according to each classification reference value of the historical target user;
and the screening unit is used for setting the alternative users of which the total reference value exceeds a preset threshold value and all reference values reach the standard value as target users.
In a third aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the method according to any one of the first aspect and possible implementation manners 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, which, when executed by a processor, performs the steps of the method of any one of the above first aspect and possible implementations thereof.
According to the directional pushing method and device provided by the embodiment of the application, historical network behavior information of each alternative user is analyzed, the historical network behavior information of the alternative users is classified, classification reference values of the classifications are calculated, then a total reference value of the alternative users is calculated by using the classification reference values of the classifications, the alternative users with the total reference value exceeding a preset threshold value are screened out, and the alternative users serving as target users for pushing target information are pushed to carry out information pushing. The directional pushing method and the directional pushing device provided by the embodiment of the application can be used for screening specific target crowds from users and carrying out accurate pushing, and the efficiency of pushing the advertisement information 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 directional pushing method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a directional pushing method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a directional pushing device according to an embodiment of the present disclosure;
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.
An embodiment of the present application provides a directional pushing method, as shown in fig. 1, including the following steps:
s101, acquiring historical network behavior information of an alternative user;
step S102, aiming at each alternative user, classifying the historical network behavior information of the alternative user according to a preset classification strategy, and calculating a classification reference value based on the classified historical network behavior information; the classification strategies comprise a website browsing class, an application software using class, a high-frequency keyword searching class and a geographic position class;
step S103, calculating a total reference value of each alternative user according to the classification reference value of each alternative user, setting the alternative users with the total reference value exceeding a preset threshold value as target users, and pushing target information to the target users.
The historical network behavior information comprises browsing frequency and duration of the alternative user to the website, using frequency and duration of the alternative user to the application software, high-frequency search keywords and search frequency used by the alternative user to search in the website or the application software, and occurrence frequency of the alternative user positioned at a specific place. After the historical network information of the alternative users is classified, the classification reference value is calculated for each classification, the total reference value of the alternative users is further calculated, the alternative users with the total reference value exceeding a preset threshold value are used as target users, target user crowd labels are added to the target users, so that the target users can be subjected to subsequent continuous message pushing, redundant time for recalculating the same type of target crowd pushing every time is saved, and efficiency is improved.
In an alternative embodiment, the classification reference value is calculated using the following equation:
wherein S is a classification reference value, n is the number of network behavior items contained in the classification, and muiAs weights for network behavior items, αiIs a reference value of the network behavior item.
Under one classification, the historical network behavior information of the candidate user may include a plurality of items under the classification, for example, if the target group is the creator group, the network browsing classification of the candidate user includes items such as a smart network, a media finding network, a fast plastic network, a smart network, and the like, each item has a weight corresponding to the item, a reference value of a website is obtained according to the browsing frequency and duration of each website (item), and the sum of products of the reference value of each website and the weight corresponding to the website is used as the classification reference value of the classification.
In an optional embodiment, in step S103, for the classification reference value of each candidate user, calculating a total reference value of the candidate user, as shown in fig. 2, includes:
step S1031, obtaining various classification history reference values according to the pushed history target information, and determining classification weights according to the obtained various classification history reference values;
step S1032, for each candidate user, calculating a weighted sum of each classification reference value and the corresponding classification weight of the candidate user to obtain a total reference value of the candidate user.
The information of the historical target information is analyzed, the historical reference value of each classification is calculated, and the importance degree of each classification reference value of the target user crowd in the judgment of the total reference value, namely the classification weight, can be obtained through the historical reference value of each classification. The weight is added to each classification reference value in the calculation of the total reference value, so that the reliability of the total reference value is improved, and the target user can be effectively screened.
In an optional embodiment, step S103, setting, as the target user, the candidate users whose total reference value exceeds the preset threshold, includes:
step 1033, obtaining a standard value of each classification reference value according to each classification reference value of the historical target user;
step 1034, setting the candidate users whose total reference value exceeds the preset threshold value and all reference values reach the standard value as target users.
For example, if the candidate user is a worker of a certain website, the browsing frequency and the browsing duration of the candidate user on the website and the similar websites are high, which causes the website browsing classification reference value of the user to be extremely high, and the obtained total reference value of the user also reaches the preset threshold value. In order to avoid the above situation, the average value of the classification reference values of the historical target users is calculated as the standard value of the classification reference value for each classification reference value through the classification reference values of the historical target users.
And performing secondary screening on the alternative users of which the total reference value reaches the preset threshold value by using the standard values of all the classification reference values so as to further improve the accuracy of screening the target users.
An embodiment of the present application further provides a directional pushing device, as shown in fig. 3, the device includes:
an obtainingmodule 30, configured to obtain historical network behavior information of an alternative user;
thecalculation module 31 is configured to, for each candidate user, classify the historical network behavior information of the candidate user according to a preset classification policy, and calculate a classification reference value based on the classified historical network behavior information; the classification strategies comprise a website browsing class, an application software using class, a high-frequency keyword searching class and a geographic position class;
and the pushingmodule 32 is configured to calculate a total reference value of each candidate user according to the classification reference value of each candidate user, set the candidate user whose total reference value exceeds a preset threshold as a target user, and push target information to the target user.
The historical network behavior information comprises browsing frequency and duration of the alternative user to the website, using frequency and duration of the alternative user to the application software, high-frequency search keywords and search frequency used by the alternative user to search in the website or the application software, and occurrence frequency of the alternative user positioned at a specific place. After the historical network information of the alternative users is classified, the classification reference value is calculated for each classification, the total reference value of the alternative users is further calculated, the alternative users with the total reference value exceeding a preset threshold value are used as target users, target user crowd labels are added to the target users, so that the target users can be subjected to subsequent continuous message pushing, redundant time for recalculating the same type of target crowd pushing every time is saved, and efficiency is improved.
In an alternative embodiment, the calculatingmodule 31 calculates the classification reference value by using the following formula:
wherein S is a classification reference value, n is the number of network behavior items contained in the classification, and muiAs weights for network behavior items, αiIs a reference value of the network behavior item.
Under one classification, the historical network behavior information of the candidate user may include a plurality of items under the classification, for example, if the target group is the creator group, the network browsing classification of the candidate user includes items such as a smart network, a media finding network, a fast plastic network, a smart network, and the like, each item has a weight corresponding to the item, a reference value of a website is obtained according to the browsing frequency and duration of each website (item), and the sum of products of the reference value of each website and the weight corresponding to the website is used as the classification reference value of the classification.
In an alternative embodiment, the pushingmodule 32 includes:
a weighting unit 321, configured to obtain each classification history reference value according to the pushed history target information, and determine a classification weight according to each obtained classification history reference value;
a first operation unit 322, configured to calculate, for each candidate user, a weighted sum of each classification reference value and the corresponding classification weight of the candidate user, so as to obtain a total reference value of the candidate user.
The information of the historical target information is analyzed, the historical reference value of each classification is calculated, and the importance degree of each classification reference value of the target user crowd in the judgment of the total reference value, namely the classification weight, can be obtained through the historical reference value of each classification. The weight is added to each classification reference value in the calculation of the total reference value, so that the reliability of the total reference value is improved, and the target user can be effectively screened.
In an alternative embodiment, the pushingmodule 32 includes:
the second arithmetic unit 323 is used for obtaining the standard value of each classification reference value according to each classification reference value of the historical target user;
and a screening unit 324, configured to set, as a target user, a candidate user whose total reference value exceeds a preset threshold and all reference values reach the standard value.
For example, if the candidate user is a worker of a certain website, the browsing frequency and the browsing duration of the candidate user on the website and the similar websites are high, which causes the website browsing classification reference value of the user to be extremely high, and the obtained total reference value of the user also reaches the preset threshold value. In order to avoid the above situation, the average value of the classification reference values of the historical target users is calculated as the standard value of the classification reference value for each classification reference value through the classification reference values of the historical target users.
And performing secondary screening on the alternative users of which the total reference value reaches the preset threshold value by using the standard values of all the classification reference values so as to further improve the accuracy of screening the target users.
Corresponding to a directional pushing method in fig. 1, an embodiment of the present application further provides acomputer device 400, as shown in fig. 4, the device includes a memory 401, aprocessor 402, and a computer program stored on the memory 401 and executable on theprocessor 402, where theprocessor 402 implements the directional pushing method when executing the computer program.
Specifically, the memory 401 and theprocessor 402 can be general memories and processors, which are not limited in this embodiment, and when theprocessor 402 runs a computer program stored in the memory 401, the directional push method can be executed, so as to solve the problem in the prior art of how to accurately push a message to a specific group of people.
Corresponding to one directional pushing method in fig. 1, 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 one directional pushing method described above.
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 executed, the above directional pushing method can be executed, and a problem of how to accurately push a message to a specific population in the prior art is solved. The directional pushing method and the directional pushing device provided by the embodiment of the application can be used for screening specific target crowds from users and carrying out accurate pushing, and the efficiency of pushing the advertisement information 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 directional pushing method, comprising:
acquiring historical network behavior information of alternative users;
for each alternative user, classifying the historical network behavior information of the alternative user according to a preset classification strategy, and calculating a classification reference value based on the classified historical network behavior information; the classification strategy comprises a website browsing class, an application software using class, a high-frequency keyword searching class and a geographic position class;
and calculating the total reference value of each alternative user according to the classification reference value of each alternative user, setting the alternative users with the total reference value exceeding a preset threshold value as target users, and pushing target information to the target users.
2. The method of claim 1, wherein the classification reference value is calculated using the formula:
wherein S is a classification reference value, n is the number of network behavior items contained in the classification, and muiAs weights for network behavior items, αiIs a reference value of the network behavior item.
3. The method of claim 1, wherein calculating the total reference value of each candidate user for the classification reference value of the candidate user comprises:
obtaining various classification historical reference values according to the pushed historical target information, and determining classification weights according to the obtained various classification historical reference values;
and calculating the weighted sum of each classification reference value of each alternative user and the corresponding classification weight to obtain the total reference value of the alternative user.
4. The method according to claim 1, wherein setting the candidate users with the total reference value exceeding a preset threshold as target users comprises:
obtaining a standard value of each classification reference value according to each classification reference value of the historical target user;
and setting the alternative users of which the total reference value exceeds a preset threshold value and all reference values reach the standard value as target users.
5. A directional pushing device, comprising:
the acquisition module is used for acquiring historical network behavior information of the alternative users;
the computing module is used for classifying the historical network behavior information of each alternative user according to a preset classification strategy and computing a classification reference value based on the classified historical network behavior information; the classification strategy comprises a website browsing class, an application software using class, a high-frequency keyword searching class and a geographic position class;
and the pushing module is used for calculating the total reference value of each alternative user according to the classification reference value of each alternative user, setting the alternative users with the total reference value exceeding a preset threshold value as target users, and pushing target information to the target users.
6. The apparatus of claim 5, wherein the calculation module calculates the classification reference value using the following equation:
wherein S is a classification reference value, n is the number of network behavior items contained in the classification, and muiAs weights for network behavior items, αiIs a reference value of the network behavior item.
7. The apparatus of claim 5, wherein the push module comprises:
the weighting unit is used for obtaining various classification historical reference values according to the pushed historical target information and determining classification weights according to the obtained various classification historical reference values;
and the first operation unit is used for calculating the weighted sum of each classification reference value and the corresponding classification weight of each candidate user to obtain the total reference value of the candidate user.
8. The apparatus of claim 5, wherein the push module comprises:
the second arithmetic unit is used for obtaining the standard value of each classification reference value according to each classification reference value of the historical target user;
and the screening unit is used for setting the alternative users of which the total reference value exceeds a preset threshold value and all reference values reach the standard value as target users.
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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