CN112819552A - Advertisement pushing method and device and computer readable storage medium - Google Patents
Advertisement pushing method and device and computer readable storage medium Download PDFInfo
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Abstract
The application discloses an advertisement pushing method and device and a computer readable storage medium, relates to the technical field of artificial intelligence, and can improve the accuracy of advertisement pushing by combining static attribute information and dynamic attribute information of a user. The method comprises the following steps: the advertisement pushing device acquires at least one type of static attribute information of a user and historical behavior data of the user; then, the advertisement pushing device determines at least one kind of dynamic attribute information of the user according to the historical behavior data of the user; then, the advertisement pushing device selects at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static attribute information of the user, the at least one dynamic attribute information of the user and the at least one static label and the at least one dynamic label of the candidate advertisements in the advertisement library. Wherein the at least one dynamic attribute information of the user comprises a preference type of the user and/or a consumption level of the user.
Description
Technical Field
The embodiment of the application relates to the technical field of artificial intelligence, in particular to an advertisement pushing method and device and a computer readable storage medium.
Background
With the development of internet technology, the number of customers and product types of each e-commerce website is increasing, the advertisement position of the home page of the e-commerce website becomes more and more scarce, and the advertisement of one side of thousands of people cannot meet the operation requirement. Therefore, how to push the advertisement to the client according to the requirement of the client from a plurality of advertisements becomes especially important.
In the existing advertisement push method, a user is pushed an advertisement through a static tag (information such as age, occupation, income, and the like) of the user.
However, the advertisement pushed to the user according to the above advertisement pushing method often does not meet the actual requirements of the user, and the accuracy of advertisement pushing is low.
Disclosure of Invention
The application provides an advertisement pushing method, an advertisement pushing device and a computer readable storage medium, and the accuracy of advertisement pushing can be improved by combining static attribute information and dynamic attribute information of a user.
In order to achieve the purpose, the technical scheme is as follows:
in a first aspect, the present application provides an advertisement push method, including: the advertisement pushing device acquires at least one type of static attribute information of a user and historical behavior data of the user; then, the advertisement pushing device determines at least one kind of dynamic attribute information of the user according to the historical behavior data of the user; then, the advertisement pushing device selects at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static attribute information of the user, the at least one dynamic attribute information of the user and the at least one static label and the at least one dynamic label of the candidate advertisements in the advertisement library. Wherein the at least one dynamic attribute information of the user comprises a preference type of the user and/or a consumption level of the user.
According to the technical scheme, the candidate advertisements are attached with the static labels corresponding to the static attribute information, and the dynamic labels corresponding to the dynamic attribute information are attached. Due to different static attribute information of users, the demands for advertisements are different, for example, the demands for advertisements are different for users of different ages or different professions; in addition, the dynamic attribute information of the user may reflect the advertisement type and/or consumption level of the user's preferences. Therefore, the method and the device can analyze the actual requirements of the user by analyzing the static attribute information and the dynamic information of the user, and finally determine the target advertisement which meets the actual requirements of the user from the candidate advertisements and push the target advertisement to the user based on the static label and the dynamic label of the candidate advertisements. Therefore, the advertisement pushing accuracy can be improved.
Optionally, in a possible design manner, the "selecting at least one target advertisement to be pushed for a user from candidate advertisements according to at least one static attribute information of the user, at least one dynamic attribute information of the user, and at least one static tag and at least one dynamic tag of the candidate advertisements in the advertisement library" may include:
and matching the at least one kind of static attribute information of the user with each static label of each advertisement in the candidate advertisements in sequence, matching the at least one kind of dynamic attribute information of the user with each dynamic label of each advertisement in sequence, and selecting at least one target advertisement to be pushed for the user from the candidate advertisements.
Optionally, in another possible design manner, the "sequentially matching at least one static attribute information of a user with each static tag of each advertisement in the candidate advertisements, and sequentially matching at least one dynamic attribute information of the user with each dynamic tag of each advertisement, and selecting at least one target advertisement to be pushed for the user from the candidate advertisements" may include:
determining at least one static target label of each advertisement and at least one dynamic target label of each advertisement; and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static target label of each advertisement and the at least one dynamic target label of each advertisement.
The static target label is matched with any static attribute information of the user and matched with any static label of the advertisement; the dynamic target label is a label matched with any dynamic attribute information of the user and matched with any dynamic label of the advertisement.
Optionally, in another possible design manner, the "selecting at least one targeted advertisement to be pushed for the user from the candidate advertisements according to at least one static targeted label of each advertisement and at least one dynamic targeted label of each advertisement" may include:
and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the static weight, the first weight of each static target label, the dynamic weight and the second weight of each dynamic target label.
The static weight is used for representing the weight of the static attribute information, and the dynamic weight is used for representing the weight of the dynamic attribute information.
Optionally, in another possible design manner, the "selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the static weight, the first weight of each static target tag, the dynamic weight, and the second weight of each dynamic target tag" may include:
determining the static matching degree of each advertisement and the user according to the static weight and the first weight of each static target label; determining the dynamic matching degree of each advertisement and the user according to the dynamic weight and the second weight of each dynamic target label; and then, selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the static matching degree of each advertisement and the user and the dynamic matching degree of each advertisement and the user.
Optionally, in another possible design manner, the "determining a static matching degree of each advertisement with the user according to the static weight and the first weight of each static target tag; and determining a dynamic matching degree of each advertisement to the user according to the dynamic weight and the second weight of each dynamic target label may include:
respectively calculating the product of the static weight and the first weight of each static target label of the first advertisement, and determining the sum of all the products as the static matching degree of the first advertisement and the user; respectively calculating the product of the dynamic weight and the second weight of each dynamic target label of the first advertisement, and determining the sum of all the products as the dynamic matching degree of the first advertisement and the user; the first advertisement is any one of the candidate advertisements.
Optionally, in another possible design manner, the "selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the static matching degree of each advertisement with the user and the dynamic matching degree of each advertisement with the user" may include:
determining the target matching degree of each advertisement; the target matching degree of the second advertisement is the sum of the static matching degree of the second advertisement and the dynamic matching degree of the second advertisement; the second advertisement is any one of the candidate advertisements; and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the target matching degree of each advertisement.
Optionally, in another possible design manner, the "selecting at least one targeted advertisement to be pushed for the user from the candidate advertisements according to the target matching degree of each advertisement" may include:
acquiring the number N of the advertisement positions; and determining N advertisements with the highest target matching degree from the candidate advertisements as N target advertisements to be pushed to the user. N is a positive integer.
Optionally, in another possible design, the "historical behavior data of the user" may include: at least one of transaction data, shopping cart data, collection data, and browsing data.
Optionally, in another possible design, the "determining at least one dynamic attribute information of the user according to the historical behavior data of the user" may include:
counting the type of each piece of data in the historical behavior data; and determining the type with the largest occurrence number in the historical behavior data as the preference type of the user.
Optionally, in another possible design, the "determining at least one dynamic attribute information of the user according to the historical behavior data of the user" may include:
counting the consumption level of each piece of data in the historical behavior data according to a preset rule; the preset rule comprises a corresponding relation between the consumption amount and the consumption grade; and determining the consumption grade with the highest occurrence frequency in the historical behavior data as the consumption grade of the user.
Optionally, in another possible design manner, the "static attribute information" may include: at least one of income information, occupation information, academic information, age information, and gender information.
In a second aspect, the present application provides an advertisement push apparatus, including: the device comprises an acquisition module and a determination module;
the acquisition module is used for acquiring at least one type of static attribute information of a user and historical behavior data of the user;
the determining module is used for determining at least one type of dynamic attribute information of the user according to the historical behavior data of the user acquired by the acquiring module; wherein the at least one dynamic attribute information of the user comprises a preference type of the user and/or a consumption level of the user;
and the determining module is further used for selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one piece of static attribute information of the user, the at least one piece of dynamic attribute information of the user and the at least one static label and the at least one dynamic label of the candidate advertisements in the advertisement library.
Optionally, in a possible design manner, the determining module is specifically configured to: and matching the at least one kind of static attribute information of the user with each static label of each advertisement in the candidate advertisements in sequence, matching the at least one kind of dynamic attribute information of the user with each dynamic label of each advertisement in sequence, and selecting at least one target advertisement to be pushed for the user from the candidate advertisements.
Optionally, in another possible design manner, the determining module is further specifically configured to:
determining at least one static target label of each advertisement and at least one dynamic target label of each advertisement; the static target label is a label matched with any static attribute information of the user and matched with any static label of the advertisement; the dynamic target label is a label matched with any dynamic attribute information of the user and matched with any dynamic label of the advertisement; and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static target label of each advertisement and the at least one dynamic target label of each advertisement.
Optionally, in another possible design manner, the determining module is further specifically configured to:
selecting at least one target advertisement to be pushed for a user from the candidate advertisements according to the static weight, the first weight of each static target label, the dynamic weight and the second weight of each dynamic target label; the static weight is used for representing the weight of the static attribute information, and the dynamic weight is used for representing the weight of the dynamic attribute information.
Optionally, in another possible design manner, the determining module is further specifically configured to:
determining the static matching degree of each advertisement and the user according to the static weight and the first weight of each static target label; determining the dynamic matching degree of each advertisement and the user according to the dynamic weight and the second weight of each dynamic target label;
and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the static matching degree of each advertisement and the user and the dynamic matching degree of each advertisement and the user.
Optionally, in another possible design manner, the determining module is further specifically configured to:
respectively calculating the product of the static weight and the first weight of each static target label of the first advertisement, and determining the sum of all the products as the static matching degree of the first advertisement and the user; respectively calculating the product of the dynamic weight and the second weight of each dynamic target label of the first advertisement, and determining the sum of all the products as the dynamic matching degree of the first advertisement and the user; the first advertisement is any one of the candidate advertisements.
Optionally, in another possible design manner, the determining module is further specifically configured to:
determining the target matching degree of each advertisement; the target matching degree of the second advertisement is the sum of the static matching degree of the second advertisement and the dynamic matching degree of the second advertisement; the second advertisement is any one of the candidate advertisements;
and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the target matching degree of each advertisement.
Optionally, in another possible design manner, the determining module is further specifically configured to:
acquiring the number N of the advertisement positions; n is a positive integer;
and determining N advertisements with the highest target matching degree from the candidate advertisements as N target advertisements to be pushed to the user.
Optionally, in another possible design, the historical behavior data of the user includes: at least one of transaction data, shopping cart data, collection data, and browsing data.
Optionally, in another possible design manner, the determining module is further specifically configured to:
counting the type of each piece of data in the historical behavior data;
and determining the type with the largest occurrence number in the historical behavior data as the preference type of the user.
Optionally, in another possible design manner, the determining module is further specifically configured to:
counting the consumption level of each piece of data in the historical behavior data according to a preset rule; the preset rule comprises a corresponding relation between the consumption amount and the consumption grade;
and determining the consumption grade with the highest occurrence frequency in the historical behavior data as the consumption grade of the user.
Optionally, in another possible design manner, the determining module is further specifically configured to: the static attribute information includes: at least one of income information, occupation information, academic information, age information, and gender information.
In a third aspect, the present application provides an advertisement delivery device, including a memory, a processor, a bus, and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus; when the advertisement push device is running, the processor executes the computer-executable instructions stored by the memory to cause the advertisement push device to perform the advertisement push method as provided by the first aspect above.
Optionally, the advertisement push device may further include a transceiver, and the transceiver is configured to perform the steps of transceiving data, signaling or information under the control of the processor of the advertisement push device, for example, acquiring at least one of static attribute information of the user and historical behavior data of the user.
Further optionally, the advertisement push device may be a physical machine for implementing advertisement push, or may be a part of the physical machine, for example, a system on chip in the physical machine. The system-on-chip is configured to enable the advertisement delivery apparatus to implement the functions referred to in the first aspect, such as receiving, sending or processing data and/or information referred to in the advertisement delivery method. The chip system includes a chip and may also include other discrete devices or circuit structures.
In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions are stored, and when the instructions are executed by a computer, the computer executes the advertisement push method provided in the first aspect.
In a fifth aspect, the present application provides a computer program product comprising computer instructions which, when run on a computer, cause the computer to perform the advertisement push method as provided in the first aspect.
It should be noted that all or part of the computer instructions may be stored on the computer readable storage medium. The computer-readable storage medium may be packaged with a processor of the advertisement delivery device, or may be packaged separately from the processor of the advertisement delivery device, which is not limited in this application.
For the descriptions of the second, third, fourth and fifth aspects in this application, reference may be made to the detailed description of the first aspect; in addition, for the beneficial effects described in the second aspect, the third aspect, the fourth aspect and the fifth aspect, reference may be made to the beneficial effect analysis of the first aspect, and details are not repeated here.
In the present application, the names of the above advertisement delivery devices do not limit the devices or functional modules themselves, and in practical implementations, the devices or functional modules may appear by other names. Insofar as the functions of the respective devices or functional modules are similar to those of the present application, they fall within the scope of the claims of the present application and their equivalents.
These and other aspects of the present application will be more readily apparent from the following description.
Drawings
Fig. 1 is a schematic structural diagram of an advertisement push system according to an embodiment of the present application;
fig. 2 is a schematic flowchart of an advertisement delivery method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of another advertisement pushing method provided in an embodiment of the present application;
fig. 4 is a schematic flowchart of another advertisement delivery method according to an embodiment of the present application;
fig. 5 is a schematic flowchart of another advertisement delivery method according to an embodiment of the present application;
fig. 6 is a schematic flowchart of another advertisement delivery method according to an embodiment of the present application;
fig. 7 is a schematic flowchart of another advertisement delivery method according to an embodiment of the present application;
fig. 8 is a schematic flowchart of another advertisement delivery method according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an advertisement push device according to an embodiment of the present application;
fig. 10 is a schematic structural diagram of another advertisement push device according to an embodiment of the present application.
Detailed Description
The advertisement push method, apparatus and computer-readable storage medium provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second" and the like in the description and drawings of the present application are used for distinguishing different objects or for distinguishing different processes for the same object, and are not used for describing a specific order of the objects.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the description of the present application, the meaning of "a plurality" means two or more unless otherwise specified.
With the development of internet technology, the number of customers and product types of each e-commerce website is increasing, the advertisement position of the home page of the e-commerce website becomes more and more scarce, and the advertisement of one side of thousands of people cannot meet the operation requirement. Therefore, how to push the advertisement to the client according to the requirement of the client from a plurality of advertisements becomes especially important.
In the existing advertisement push method, a user is pushed an advertisement through a static tag (information such as age, occupation, income, and the like) of the user. However, the advertisement pushed to the user according to the above advertisement pushing method often does not meet the actual requirements of the user, and the accuracy of advertisement pushing is low.
In view of the problems in the prior art, an embodiment of the present application provides an advertisement push method, in which a static tag corresponding to static attribute information is attached to a candidate advertisement, and a dynamic tag corresponding to dynamic attribute information is attached to the candidate advertisement. The demand for advertisements may vary due to the variation of the static attribute information of the user, while the dynamic attribute information of the user may reflect the advertisement type and/or consumption level of the user's preferences. Therefore, the embodiment of the application can analyze the actual requirements of the user according to the static attribute information and the dynamic attribute information of the user, so that the accuracy of advertisement pushing is improved.
The advertisement pushing method provided by the embodiment of the application can be applied to the advertisement pushing system shown in fig. 1. Referring to fig. 1, the advertisement push system may include: the advertisement push device 02 is connected with each user terminal 01.
The advertisement delivery device 02 may be a physical machine (e.g., a server), or may be a Virtual Machine (VM) deployed on the physical machine.
The advertisement push device 02 is configured to obtain historical behavior data and at least one type of static attribute information of each user terminal 01, so as to implement advertisement push for a user.
The user terminal 01 may be a mobile phone, a tablet computer, a desktop computer, a laptop computer, a notebook computer, an ultra-mobile personal computer (UMPC), a handheld computer, a netbook, a Personal Digital Assistant (PDA), a wearable electronic device, a virtual reality device, or other different types of terminals.
Among them, the user terminal 01 may be installed with an electronic commerce Application (APP). When the user browses on the interface of the e-commerce APP of the user terminal 01, the advertisement push device 02 can display the determined target advertisement on the advertisement position of the e-commerce APP.
It can be understood that fig. 1 only shows 3 user terminals 01, and in practical application of the embodiment of the present application, the number of the user terminals 01 is not limited.
The following describes an advertisement push method provided by the present application with reference to the advertisement push system shown in fig. 1.
In this embodiment, the advertisement delivery device 02 processes each user terminal 01 in the same way. The following description will take an example of the processing of the advertisement delivery device 02 for one user terminal 01.
Referring to fig. 2, the advertisement push method provided in the embodiment of the present application includes S201 to S203:
s201, the advertisement push device acquires at least one type of static attribute information of the user and historical behavior data of the user.
Wherein the static attribute information may include at least one of income information, occupation information, academic information, age information, and gender information. The historical behavior data includes: at least one of transaction data, shopping cart data, collection data, and browsing data.
It is understood that, in practical applications, the static attribute information may be other information used for characterizing the identity of the user, and the historical behavior data may be other behavior data of the user, which is not limited in this embodiment of the present application.
Optionally, in a possible implementation manner, when the user registers an account in the e-commerce APP, the user may fill the static attribute information in the e-commerce APP interface of the user terminal, and the user terminal may store the static attribute information of the user. When the advertisement push device pushes the advertisement for the user, the static attribute information of the user can be obtained from the user terminal.
In a possible implementation manner, when a user transacts or browses an e-commerce APP, a user terminal may record historical behavior data of the user, and when an advertisement push device pushes an advertisement for the user, the advertisement push device may obtain the historical behavior data of the user from the user terminal.
Certainly, in practical application, the advertisement push device may also obtain the static attribute information of the user and the historical behavior data of the user in other manners, which is not limited in this embodiment of the application.
S202, the advertisement pushing device determines at least one dynamic attribute information of the user according to the historical behavior data of the user.
Wherein the at least one dynamic attribute information of the user comprises a preference type of the user and/or a consumption level of the user. Exemplary user preference types may include: automobiles, household appliances, and homes. Different consumption levels correspond to different ranges of consumption amounts.
Optionally, in a possible implementation manner, the advertisement push device may count the type of each piece of data in the historical behavior data, and then determine the type with the largest occurrence number in the historical behavior data as the preference type of the user.
Optionally, in a possible implementation manner, the advertisement push device may count a consumption level of each piece of data in the historical behavior data according to a preset rule, and then determine a consumption level with the highest occurrence frequency in the historical behavior data as the consumption level of the user.
The preset rule may be a rule determined in advance manually, and the preset rule includes a corresponding relationship between the consumption amount and the consumption level. For example, the consumption amount between 0 and 5000 yuan can be a low consumption level, the consumption amount between 5000 and 50000 yuan can be a medium consumption level, and the consumption amount above 50000 yuan can be a high consumption level.
S203, the advertisement pushing device selects at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static attribute information of the user, the at least one dynamic attribute information of the user and the at least one static label and the at least one dynamic label of the candidate advertisements in the advertisement library.
Since the user information includes static attribute information and dynamic attribute information, the candidate advertisements may be tagged with static tags and dynamic tags. Illustratively, the candidate advertisements include advertisement A, advertisement B, and advertisement C. Advertisement a, advertisement B, and advertisement C correspond to different revenue ratings and corresponding consumer groups have different professions. For example, advertisement a may be labeled with two static labels, namely, revenue class a and finance practitioner, advertisement B may be labeled with two static labels, namely, revenue class a and IT practitioner, and advertisement C may be labeled with two static labels, namely, revenue class B and service practitioner, according to revenue class and occupation. In addition, advertisement A, advertisement B, and advertisement C correspond to different advertisement types and consumption levels. For example, two dynamic labels of a consumption level a and an automobile type can be attached to the advertisement a, two dynamic labels of a consumption level a and a household type can be attached to the advertisement B, and two dynamic labels of a consumption level B and a household type can be attached to the advertisement C according to the advertisement type and the consumption level.
Optionally, in a possible implementation manner, the advertisement pushing apparatus may match at least one static attribute information of the user with each static tag of each advertisement in the candidate advertisements in sequence, and match at least one dynamic attribute information of the user with each dynamic tag of each advertisement in sequence.
Optionally, in a possible implementation manner, after the matching is completed, the advertisement pushing device may determine at least one static target tag of each advertisement and at least one dynamic target tag of each advertisement. And then selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static target label of each advertisement and the at least one dynamic target label of each advertisement.
The static target label is a label matched with any kind of static attribute information of the user and matched with any kind of static label of the advertisement, and the dynamic target label is a label matched with any kind of dynamic attribute information of the user and matched with any kind of dynamic label of the advertisement.
Illustratively, if the static attribute information of a certain user corresponds to income bracket a and financial practitioners, it may be determined that advertisement a includes two static target tags, namely income bracket a and financial practitioners. Advertisement B includes a static target label, i.e., revenue class a.
In addition, the dynamic attribute information of the user corresponds to a consumption level A and a car class, and the advertisement A comprises two dynamic target tags, namely the consumption level A and the car class. Advertisement B includes a dynamic target label, namely consumption level a.
Advertisement C has no static target tags and no dynamic target tags. In one possible implementation, advertisement C may be determined directly as not matching the current user.
Optionally, in a possible implementation manner, the advertisement pushing device may select at least one target advertisement to be pushed for the user from the candidate advertisements according to the static weight, the first weight of each static target tag, the dynamic weight, and the second weight of each dynamic target tag.
The static weight is used for representing the weight of the static attribute information, and the dynamic weight is used for representing the weight of the dynamic attribute information. The static weights and the dynamic weights may be values determined in advance by an operator. Illustratively, the static weight may be 0.4 and the dynamic weight may be 0.6.
Optionally, in a possible implementation manner, the advertisement pushing device may determine a static matching degree of each advertisement with the user according to the static weight and the first weight of each static target tag; determining the dynamic matching degree of each advertisement and the user according to the dynamic weight and the second weight of each dynamic target label; and then, selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the static matching degree of each advertisement and the user and the dynamic matching degree of each advertisement and the user.
Optionally, taking the first advertisement as an example, the advertisement pushing device may calculate the product of the static weight and the first weight of each static target tag of the first advertisement, and determine the sum of all the products as the static matching degree of the first advertisement and the user; and respectively calculating products of the dynamic weight and the second weight of each dynamic target label of the first advertisement, and determining the sum of all the products as the dynamic matching degree of the first advertisement and the user.
Wherein the first advertisement is any one of the candidate advertisements.
Optionally, taking the second advertisement as an example, the advertisement pushing device may determine a target matching degree of each advertisement; and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the target matching degree of each advertisement.
The target matching degree of the second advertisement is the sum of the static matching degree of the second advertisement and the dynamic matching degree of the second advertisement; the second advertisement is any one of the candidate advertisements.
Illustratively, the target matching degree is represented by T, the static matching degree is represented by T1, the dynamic matching degree is represented by T2, the static weight is represented by R1, R2 represents the dynamic weight, ar0 represents the first weight of the income grade, ar1 represents the first weight of the occupation of the user, ar3 represents the second weight of the preference type of the user, and ar4 represents the second weight of the consumption grade.
For example, for the foregoing advertisement a, the static matching degree T1 of advertisement a with the user is R1(ar0+ ar1), the dynamic matching degree T2 of advertisement a with the user is R2(ar2+ ar3), and the target matching degree T of advertisement a with the user is R1(ar0+ ar1) + R2(ar2+ ar 3).
As another example, for the foregoing advertisement B, the static matching degree T1 of advertisement B with the user is R1 ar0, the dynamic matching degree T2 of advertisement B with the user is R2 ar4, and the target matching degree T1 ar0+ R2 ar4 of advertisement a with the user.
Optionally, in a possible implementation manner, after determining the target matching degree of each advertisement with the user, the advertisement pushing device may further obtain the number N of advertisement slots, and then determine N advertisements with the highest target matching degree from the candidate advertisements, as the N target advertisements to be pushed to the user. Therefore, the advertisement pushing device can select the advertisement which best meets the user requirement for the user according to the proper amount of data of the advertisement position.
Wherein N is a positive integer.
In the advertisement push method provided by the embodiment of the application, the static label corresponding to the static attribute information is attached to the candidate advertisement, and the dynamic label corresponding to the dynamic attribute information is attached to the candidate advertisement. Due to different static attribute information of users, the demands for advertisements are different, for example, the demands for advertisements are different for users of different ages or different professions; in addition, the dynamic attribute information of the user may reflect the advertisement type and/or consumption level of the user's preferences. Therefore, the embodiment of the application can analyze the actual demand of the user by analyzing the static attribute information and the dynamic information of the user, and finally determines the target advertisement which meets the actual demand of the user from the candidate advertisements and pushes the target advertisement to the user based on the static label and the dynamic label of the candidate advertisements. Therefore, the advertisement pushing accuracy can be improved.
In summary, as shown in fig. 3, step S203 in fig. 2 may be replaced with S2031:
s2031, the advertisement pushing device matches the at least one kind of static attribute information of the user with each static label of each advertisement in the candidate advertisements in sequence, matches the at least one kind of dynamic attribute information of the user with each dynamic label of each advertisement in sequence, and selects at least one target advertisement to be pushed for the user from the candidate advertisements.
Alternatively, as shown in fig. 4, step S2031 in fig. 3 may be replaced with S2032 to S2033:
s2032, the advertisement pushing device determines at least one static target label of each advertisement and at least one dynamic target label of each advertisement.
S2033, the advertisement pushing device selects at least one target advertisement to be pushed for the user from the candidate advertisements according to at least one static target label of each advertisement and at least one dynamic target label of each advertisement.
Alternatively, as shown in fig. 5, step S2033 in fig. 4 may be replaced with step S2034:
s2034, the advertisement pushing device selects at least one target advertisement to be pushed for the user from the candidate advertisements according to the static weight, the first weight of each static target label, the dynamic weight and the second weight of each dynamic target label.
Alternatively, as shown in fig. 6, step S2034 in fig. 5 may be replaced with S20341 to S20342:
s20341, the advertisement pushing device determines the static matching degree of each advertisement and the user according to the static weight and the first weight of each static target label.
S20342, the advertisement pushing device selects at least one target advertisement to be pushed for the user from the candidate advertisements according to the static matching degree of each advertisement and the user and the dynamic matching degree of each advertisement and the user.
Alternatively, as shown in fig. 7, step S202 in fig. 2 may be replaced with S2021-S2022:
s2021, the advertisement pushing device counts the type of each piece of data in the historical behavior data.
S2022, the advertisement pushing device determines the type with the largest occurrence number in the historical behavior data as the preference type of the user.
Alternatively, as shown in fig. 8, step S202 in fig. 2 may be replaced with S2023-S2024:
s2023, the advertisement pushing device counts the consumption level of each piece of data in the historical behavior data according to a preset rule.
S2024, the advertisement pushing device determines the consumption level with the largest occurrence number in the historical behavior data as the consumption level of the user.
As shown in fig. 9, an embodiment of the present application further provides an advertisement push device, which may be the advertisement push device 02 in the advertisement push system according to fig. 1 in the foregoing embodiment. This advertisement pusher includes: an acquisition module 21 and a determination module 22.
The obtaining module 21 executes S201 in the above method embodiment, and the determining module 22 executes S202 and S203 in the above method embodiment.
Specifically, the obtaining module 21 is configured to obtain at least one type of static attribute information of the user and historical behavior data of the user;
a determining module 22, configured to determine at least one dynamic attribute information of the user according to the historical behavior data of the user acquired by the acquiring module 21; wherein the at least one dynamic attribute information of the user comprises a preference type of the user and/or a consumption level of the user;
the determining module 22 is further configured to select at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static attribute information of the user, the at least one dynamic attribute information of the user, and the at least one static tag and the at least one dynamic tag of the candidate advertisements in the advertisement library.
Optionally, the determining module 22 is specifically configured to: and matching the at least one kind of static attribute information of the user with each static label of each advertisement in the candidate advertisements in sequence, matching the at least one kind of dynamic attribute information of the user with each dynamic label of each advertisement in sequence, and selecting at least one target advertisement to be pushed for the user from the candidate advertisements.
Optionally, the determining module 22 is further specifically configured to:
determining at least one static target label of each advertisement and at least one dynamic target label of each advertisement; the static target label is a label matched with any static attribute information of the user and matched with any static label of the advertisement; the dynamic target label is a label matched with any dynamic attribute information of the user and matched with any dynamic label of the advertisement; and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static target label of each advertisement and the at least one dynamic target label of each advertisement.
Optionally, the determining module 22 is further specifically configured to:
selecting at least one target advertisement to be pushed for a user from the candidate advertisements according to the static weight, the first weight of each static target label, the dynamic weight and the second weight of each dynamic target label; the static weight is used for representing the weight of the static attribute information, and the dynamic weight is used for representing the weight of the dynamic attribute information.
Optionally, the determining module 22 is further specifically configured to:
determining the static matching degree of each advertisement and the user according to the static weight and the first weight of each static target label; determining the dynamic matching degree of each advertisement and the user according to the dynamic weight and the second weight of each dynamic target label;
and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the static matching degree of each advertisement and the user and the dynamic matching degree of each advertisement and the user.
Optionally, the determining module 22 is further specifically configured to:
respectively calculating the product of the static weight and the first weight of each static target label of the first advertisement, and determining the sum of all the products as the static matching degree of the first advertisement and the user; respectively calculating the product of the dynamic weight and the second weight of each dynamic target label of the first advertisement, and determining the sum of all the products as the dynamic matching degree of the first advertisement and the user; the first advertisement is any one of the candidate advertisements.
Optionally, the determining module 22 is further specifically configured to:
determining the target matching degree of each advertisement; the target matching degree of the second advertisement is the sum of the static matching degree of the second advertisement and the dynamic matching degree of the second advertisement; the second advertisement is any one of the candidate advertisements;
and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the target matching degree of each advertisement.
Optionally, the determining module 22 is further specifically configured to:
acquiring the number N of the advertisement positions; n is a positive integer;
and determining N advertisements with the highest target matching degree from the candidate advertisements as N target advertisements to be pushed to the user.
Optionally, the historical behavior data of the user includes: at least one of transaction data, shopping cart data, collection data, and browsing data.
Optionally, the determining module 22 is further specifically configured to:
counting the type of each piece of data in the historical behavior data;
and determining the type with the largest occurrence number in the historical behavior data as the preference type of the user.
Optionally, the determining module 22 is further specifically configured to:
counting the consumption level of each piece of data in the historical behavior data according to a preset rule; the preset rule comprises a corresponding relation between the consumption amount and the consumption grade;
and determining the consumption grade with the highest occurrence frequency in the historical behavior data as the consumption grade of the user.
Optionally, the determining module 22 is further specifically configured to: the static attribute information includes: at least one of income information, occupation information, academic information, age information, and gender information.
Optionally, the advertisement delivery apparatus may further include a storage module, where the storage module is configured to store the program code of the advertisement delivery apparatus.
As shown in fig. 10, an advertisement push device according to an embodiment of the present application further includes a memory 41, a processor 42, a bus 43, and a communication interface 44; the memory 41 is used for storing computer execution instructions, and the processor 42 is connected with the memory 41 through a bus 43; when the advertisement push device is operated, the processor 42 executes the computer-executable instructions stored in the memory 41 to cause the advertisement push device to perform the advertisement push method provided as the above-described embodiments.
In particular implementations, processor 42(42-1 and 42-2) may include one or more Central Processing Units (CPUs), such as CPU0 and CPU1 shown in FIG. 10, as one example. And as an example, the advertisement push device may include a plurality of processors 42, such as processor 42-1 and processor 42-2 shown in fig. 10. Each of the processors 42 may be a single-Core Processor (CPU) or a multi-Core Processor (CPU). Processor 42 may refer herein to one or more devices, circuits, and/or processing cores that process data (e.g., computer program instructions).
The memory 41 may be, but is not limited to, a read-only memory 41 (ROM) or other type of static storage device that can store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 41 may be self-contained and coupled to the processor 42 via a bus 43. The memory 41 may also be integrated with the processor 42.
In a specific implementation, the memory 41 is used for storing data in the present application and computer-executable instructions corresponding to software programs for executing the present application. The processor 42 may perform various functions of the advertisement delivery device by running or executing software programs stored in the memory 41 and invoking data stored in the memory 41.
The communication interface 44 is any device, such as a transceiver, for communicating with other devices or communication networks, such as a control system, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), and the like. The communication interface 44 may include a receiving unit implementing a receiving function and a transmitting unit implementing a transmitting function.
The bus 43 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an extended ISA (enhanced industry standard architecture) bus, or the like. The bus 43 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 10, but this is not intended to represent only one bus or type of bus.
As an example, in conjunction with fig. 9, the function implemented by the acquisition module in the advertisement push device is the same as the function implemented by the receiving unit in fig. 10, the function implemented by the determination module in the advertisement push device is the same as the function implemented by the processor in fig. 10, and the function implemented by the storage module in the advertisement push device is the same as the function implemented by the memory in fig. 10.
For the explanation of the related contents in this embodiment, reference may be made to the above method embodiments, which are not described herein again.
Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
The embodiment of the present application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed by a computer, the computer is enabled to execute the advertisement push method provided by the foregoing embodiment.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, any suitable combination of the foregoing, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). In embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should 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 (15)
1. An advertisement pushing method, comprising:
acquiring at least one type of static attribute information of a user and historical behavior data of the user;
determining at least one kind of dynamic attribute information of the user according to the historical behavior data of the user; wherein the at least one dynamic attribute information of the user comprises a preference type of the user and/or a consumption level of the user;
and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static attribute information of the user, the at least one dynamic attribute information of the user and the at least one static label and the at least one dynamic label of the candidate advertisements in the advertisement library.
2. The advertisement pushing method according to claim 1, wherein the selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static attribute information of the user, the at least one dynamic attribute information of the user, and the at least one static tag and the at least one dynamic tag of the candidate advertisements in the advertisement library comprises:
and matching the at least one kind of static attribute information of the user with each static label of each advertisement in the candidate advertisements in sequence, matching the at least one kind of dynamic attribute information of the user with each dynamic label of each advertisement in sequence, and selecting at least one target advertisement to be pushed for the user from the candidate advertisements.
3. The advertisement push method according to claim 2, wherein the matching at least one static attribute information of the user with each static tag of each advertisement in the candidate advertisements in turn, and matching at least one dynamic attribute information of the user with each dynamic tag of each advertisement in turn, and selecting at least one target advertisement to be pushed for the user from the candidate advertisements comprises:
determining at least one static target label of each advertisement and at least one dynamic target label of each advertisement; the static target label is a label matched with any static label and matched with any static attribute information of the user; the dynamic target label is matched with any one dynamic label and matched with any one dynamic attribute information of the user;
and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static target label of each advertisement and the at least one dynamic target label of each advertisement.
4. The advertisement pushing method according to claim 3, wherein said selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static target label of each advertisement and the at least one dynamic target label of each advertisement comprises:
selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the static weight, the first weight of each static target label, the dynamic weight and the second weight of each dynamic target label; the static weight is used for representing the weight of the static attribute information, and the dynamic weight is used for representing the weight of the dynamic attribute information.
5. The advertisement pushing method according to claim 4, wherein the selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the static weight, the first weight of each static target label, the dynamic weight and the second weight of each dynamic target label comprises:
determining a static matching degree of each advertisement and the user according to the static weight and the first weight of each static target label; determining the dynamic matching degree of each advertisement and the user according to the dynamic weight and the second weight of each dynamic target label;
and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the static matching degree of each advertisement and the user and the dynamic matching degree of each advertisement and the user.
6. The advertisement pushing method according to claim 5, wherein the static matching degree of each advertisement with the user is determined according to the static weight and the first weight of each static target label; and determining the dynamic matching degree of each advertisement and the user according to the dynamic weight and the second weight of each dynamic target label, wherein the determining comprises the following steps:
respectively calculating the product of the static weight and the first weight of each static target label of the first advertisement, and determining the sum of all the products as the static matching degree of the first advertisement and the user; respectively calculating products of the dynamic weight and a second weight of each dynamic target label of the first advertisement, and determining the sum of all the products as the dynamic matching degree of the first advertisement and the user; the first advertisement is any one of the candidate advertisements.
7. The advertisement pushing method according to claim 5, wherein the selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the static matching degree of each advertisement with the user and the dynamic matching degree of each advertisement with the user comprises:
determining the target matching degree of each advertisement; the target matching degree of the second advertisement is the sum of the static matching degree of the second advertisement and the dynamic matching degree of the second advertisement; the second advertisement is any one of the candidate advertisements;
and selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the target matching degree of each advertisement.
8. The method of claim 7, wherein the selecting at least one target advertisement to be pushed for the user from the candidate advertisements according to the target matching degree of each advertisement comprises:
acquiring the number N of the advertisement positions; n is a positive integer;
and determining N advertisements with the highest target matching degree from the candidate advertisements as N target advertisements to be pushed to the user.
9. The advertisement pushing method according to any one of claims 1 to 8, wherein the historical behavior data of the user includes: at least one of transaction data, shopping cart data, collection data, and browsing data.
10. The advertisement pushing method according to claim 1, wherein the determining at least one dynamic attribute information of the user according to the historical behavior data of the user comprises:
counting the type of each piece of data in the historical behavior data;
and determining the type with the largest occurrence number in the historical behavior data as the preference type of the user.
11. The advertisement pushing method according to claim 1, wherein the determining at least one dynamic attribute information of the user according to the historical behavior data of the user comprises:
counting the consumption level of each piece of data in the historical behavior data according to a preset rule; the preset rule comprises a corresponding relation between the consumption amount and the consumption grade;
and determining the consumption grade with the largest occurrence number in the historical behavior data as the consumption grade of the user.
12. The advertisement pushing method according to claim 1, wherein the static attribute information includes: at least one of income information, occupation information, academic information, age information, and gender information.
13. An advertisement push apparatus, comprising:
the acquisition module is used for acquiring at least one type of static attribute information of a user and historical behavior data of the user;
the determining module is used for determining at least one type of dynamic attribute information of the user according to the historical behavior data of the user acquired by the acquiring module; wherein the at least one dynamic attribute information of the user comprises a preference type of the user and/or a consumption level of the user;
the determining module is further configured to select at least one target advertisement to be pushed for the user from the candidate advertisements according to the at least one static attribute information of the user, the at least one dynamic attribute information of the user, and the at least one static tag and the at least one dynamic tag of the candidate advertisements in the advertisement library.
14. An advertisement push device is characterized by comprising a memory, a processor, a bus and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through the bus;
when the advertisement push device is running, the processor executes the computer-executable instructions stored by the memory to cause the advertisement push device to perform the advertisement push method of any one of claims 1-12.
15. A computer-readable storage medium having stored therein instructions, which when executed by a computer, cause the computer to execute the advertisement push method according to any one of claims 1 to 12.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113707335A (en) * | 2021-09-06 | 2021-11-26 | 挂号网（杭州）科技有限公司 | Method, device, electronic equipment and storage medium for determining target reception user |
| CN113868529A (en) * | 2021-09-29 | 2021-12-31 | 平安银行股份有限公司 | Knowledge recommendation method and device, electronic equipment and readable storage medium |
| CN113868528A (en) * | 2021-09-29 | 2021-12-31 | 平安银行股份有限公司 | Information recommendation method and device, electronic equipment and readable storage medium |
| CN114596126A (en) * | 2022-04-26 | 2022-06-07 | 土巴兔集团股份有限公司 | Advertisement recommendation method and device |
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2021
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113707335A (en) * | 2021-09-06 | 2021-11-26 | 挂号网（杭州）科技有限公司 | Method, device, electronic equipment and storage medium for determining target reception user |
| CN113868529A (en) * | 2021-09-29 | 2021-12-31 | 平安银行股份有限公司 | Knowledge recommendation method and device, electronic equipment and readable storage medium |
| CN113868528A (en) * | 2021-09-29 | 2021-12-31 | 平安银行股份有限公司 | Information recommendation method and device, electronic equipment and readable storage medium |
| CN114596126A (en) * | 2022-04-26 | 2022-06-07 | 土巴兔集团股份有限公司 | Advertisement recommendation method and device |
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