CN111681042A - Advertisement recommendation method, server and terminal equipment - Google Patents
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Abstract
The application is applicable to the technical field of communication, and provides an advertisement recommendation method, a server and a terminal device, wherein the advertisement recommendation method comprises the following steps: the server acquires user behavior characteristics sent by the terminal equipment; determining a distribution channel and a distribution template according to the user behavior characteristics and the commodity information; the advertisement is generated according to the distribution template and the commodity information, the distribution channel and the generated advertisement are sent to the terminal equipment, and the terminal equipment is instructed to recommend the advertisement through the distribution channel, so that the delivered advertisement is targeted, and can adapt to different crowds, the probability of viewing the advertisement by a user is improved, and the advertisement effect is further improved.
Description
Technical Field
The application belongs to the technical field of communication, and particularly relates to an advertisement recommendation method, a server and terminal equipment.
Background
The existing advertisement forms are generally traditional television advertisements, webpage advertisements, video advertisements, social channel advertisements and the like, potential consumers are obtained mainly by adopting a broad-casting strategy, or simple interest advertisement forms such as friend circle forwarding, microblog forwarding lottery and the like are adopted, advertisement putting is not targeted, the probability of viewing advertisements by users is low, and the advertisement effect is not ideal.
Disclosure of Invention
In view of this, the embodiment of the application provides an advertisement recommendation method, a server and a terminal device, so as to solve the problems in the prior art that advertisement delivery is not targeted, the probability of a user viewing an advertisement is low, and the advertisement effect is not ideal.
A first aspect of an embodiment of the present application provides an advertisement recommendation method, which is applied to a server, and includes:
acquiring user behavior characteristics and commodity information sent by terminal equipment;
determining a distribution channel and a distribution template according to the user behavior characteristics and the commodity information;
and generating advertisements according to the distribution templates and the commodity information, sending the distribution channels and the generated advertisements to the terminal equipment, and indicating the terminal equipment to recommend the advertisements through the distribution channels.
In a possible implementation manner of the first aspect, the determining a distribution channel and a distribution template according to the user behavior feature and the commodity information includes:
generating a user social portrait according to the user behavior characteristics;
determining a distribution channel according to the user social portrait;
and determining a distribution template according to the user social portrait, the distribution channel and the commodity information.
In a possible implementation manner of the first aspect, the generating the user social representation according to the user behavior feature includes:
generating a feature label according to the user behavior feature;
and determining the weight corresponding to the feature tag according to the acquisition time of the user behavior feature.
In a possible implementation manner of the first aspect, the determining a distribution template according to the user social representation, the distribution channel, and the merchandise information includes:
and determining the distribution template according to the user social portrait, the distribution channel, the commodity information and a preset corresponding relation, wherein the preset corresponding relation is the corresponding relation of the user social portrait, the distribution channel, the commodity information and the distribution template.
In a possible implementation manner of the first aspect, after the sending the distribution channel and the generated advertisement to the terminal device and instructing the terminal device to recommend the advertisement through the distribution channel, the advertisement recommendation method further includes:
and counting transaction records corresponding to the advertisements.
In a possible implementation manner of the first aspect, after the counting the transaction records corresponding to the advertisements, the advertisement recommendation method further includes:
and updating the preset corresponding relation according to the transaction record.
A second aspect of the embodiments of the present application provides an advertisement recommendation method, which is applied to a terminal device, and the advertisement recommendation method includes:
acquiring user behavior characteristics;
sending the user behavior characteristics to a server, instructing the server to determine a distribution channel and a distribution template according to the user behavior characteristics and the commodity information, and generating an advertisement according to the distribution template and the commodity information;
and acquiring the distribution channel and the advertisement sent by the server, and recommending the advertisement through the distribution channel.
In a possible implementation manner of the second aspect, the obtaining the user behavior feature includes:
when the distribution request information of the user is obtained, user behavior characteristics are obtained, wherein the user behavior characteristics comprise user personal behavior characteristics and user social behavior characteristics.
A third aspect of embodiments of the present application provides a server, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the advertisement recommendation method according to the first aspect.
A fourth aspect of the embodiments of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the advertisement recommendation method according to the second aspect.
A fifth aspect of embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the advertisement recommendation method according to the first aspect.
A sixth aspect of embodiments of the present application provides a computer program product, which, when run on a terminal device, causes the terminal device to execute the advertisement recommendation method according to the first aspect.
Compared with the prior art, the embodiment of the application has the advantages that: the server acquires the user behavior characteristics and the commodity information sent by the terminal equipment, determines a distribution channel and a distribution template according to the user behavior characteristics and the commodity information, generates an advertisement according to the distribution template and the commodity information, sends the distribution channel and the generated advertisement to the terminal equipment, and instructs the terminal equipment to recommend the advertisement through the distribution channel. Because the activity degree of user groups with different user behavior characteristics in each distribution channel is different, and the interest degree of users with different user behavior characteristics in commodities is different, the effect of recommending advertisements in different distribution channels is different, the distribution channel in which users interested in commodities are more active can be determined according to the user behavior characteristics, and the pertinence of advertisement putting can be improved by recommending advertisements in the distribution channel. Because the advertisement that generates according to the distribution template of difference is different to different users' attraction degree, and can generate the advertisement of different forms according to the distribution template of difference to make the advertisement form diversified, with the crowd that adapts to different, improve the probability that the user looked over the advertisement, and then promote advertisement effect.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the embodiments or the description of the prior art will be briefly described below.
FIG. 1 is a diagram of an application scenario of an advertisement recommendation method provided in an embodiment of the present application;
FIG. 2 is a flowchart illustrating an implementation of an advertisement recommendation method according to an embodiment of the present application;
FIG. 3 is a flow diagram of substeps of a method for advertisement recommendation provided by an embodiment of the present application;
FIG. 4 is a flowchart illustrating an implementation of an advertisement recommendation method according to another embodiment of the present application;
FIG. 5 is a flowchart illustrating a specific implementation of an advertisement recommendation method according to an embodiment of the present application;
FIG. 6 is a schematic diagram of an advertisement recommendation device according to an embodiment of the present application;
FIG. 7 is a schematic diagram of an advertisement recommendation device according to another embodiment of the present application;
FIG. 8 is a schematic diagram of a server provided by an embodiment of the present application;
fig. 9 is a schematic diagram of a terminal device provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Referring to fig. 1, fig. 1 is an application scenario diagram of an advertisement recommendation method according to an embodiment of the present application, in which a server 100 obtains user behavior characteristics sent by a terminal device 200, determines a distribution channel and a distribution template according to the user behavior characteristics and commodity information, generates an advertisement according to the distribution template and the commodity information, sends the distribution channel and the generated advertisement to the terminal device 200, and instructs the terminal device 200 to recommend the advertisement through the distribution channel. Because the activity degree of user groups with different user behavior characteristics in each distribution channel is different, and the interest degree of users with different user behavior characteristics in commodities is different, the effect of recommending advertisements in different distribution channels is different, the distribution channel in which users interested in commodities are more active can be determined according to the user behavior characteristics, and the pertinence of advertisement putting can be improved by recommending advertisements in the distribution channel. Because the advertisement that generates according to the distribution template of difference is different to different users' attraction degree, and can generate the advertisement of different forms according to the distribution template of difference to make the advertisement form diversified, with the crowd that adapts to different, improve the probability that the user looked over the advertisement, and then promote advertisement effect.
Referring to fig. 2, fig. 2 shows an implementation flow of the advertisement recommendation method provided in an embodiment of the present application, an execution subject of the advertisement recommendation method of the embodiment is a server, and a process thereof is detailed as follows:
s101: and acquiring the user behavior characteristics sent by the terminal equipment.
Wherein the user behavior characteristics comprise user personal behavior characteristics and user social behavior characteristics, and the user personal behavior characteristics may comprise at least one of the following: gender, age, interests, hobbies, school calendar, address, income, record of purchased products, etc. The social behavior characteristics of the user comprise social platforms used by the user, the number of friends of the user on each social platform, the type of information shared by the user on each social platform, and the like. Under the condition of user authorization, the user behavior characteristics can be sent to the server after the terminal device collects the user information, the server counts the user behavior characteristics, or the terminal device counts the collected user information to obtain the user behavior characteristics and sends the user behavior characteristics to the server.
S102: and determining a distribution channel and a distribution template according to the user behavior characteristics and the commodity information.
Specifically, the distribution channels and the distribution templates are determined according to the preset corresponding relation among the user behavior characteristics, the distribution channels and the distribution templates, and meanwhile, the distribution templates suitable for the commodities are generated by combining specific commodity information.
As shown in fig. 3, in one possible implementation, this step includes S201-S203.
S201: and generating a user social portrait according to the user behavior characteristics.
Specifically, the server classifies the user behavior characteristics according to the characteristic labels corresponding to the user behavior characteristics, and counts the obtained characteristic labels to obtain the user social portrait. For example, the user social representation of user A includes at least one of: shenzhen, official, electronic products, clothing, WeChat, QQ, Taobao, microblog and other feature labels.
In a possible implementation manner, the user social portrait includes feature tags and weights corresponding to the feature tags, the server generates corresponding feature tags according to the user behavior features, and determines the weights corresponding to the feature tags according to the acquisition time of the user behavior features and/or the number of the user behavior features corresponding to the feature tags. For example, if the user behavior feature acquired by the server at the current time interval is the login WeChat, the feature tag is generated as the WeChat, the weight is set to 1, and if the user does not log in the WeChat at the next time interval, the weight of the feature tag WeChat is multiplied by 0.9. Meanwhile, when the preset interval time is reached, the times of logging in WeChat of the user are counted, and the weight of the characteristic label WeChat is updated according to the logging times, so that the generated user portrait can reflect the behavior characteristics of the user in time, and the accuracy of the generated user social portrait is improved.
S202: and determining a distribution channel according to the user social portrait.
Specifically, the distribution channel is determined according to the social representation of the user and the determination rule of the distribution channel. For example, if the social representation of the user includes a type corresponding to a current commodity, it indicates that the user is interested in the commodity, and the user of the social platform frequently used by the user is similar to the interest and hobbies of the current user, and may also be interested in the commodity, and the social platform frequently used by the user is used as a distribution channel. For another example, a social platform with a large number of friends of the user is determined according to the social portrait of the user, and the social platform is used as a distribution channel, so that the popularization range of the advertisement is improved. For another example, the information types shared by the users on the social platforms are determined according to the social portrait of the users, and the social platforms with the shared information types similar to the types corresponding to the current commodities are used as distribution channels, so that the probability of viewing the advertisements is improved.
S203: and determining a distribution template according to the user social portrait, the distribution channel and the commodity information.
In particular, different distribution templates are required because the same item needs to merge the access rules of the platform in different distribution channels, i.e. in different social platforms. Different user groups corresponding to different distribution channels are different, for example, the number of teenager users of the QQ platform is large, the number of young female users of the microblog platform is large, the number of sports enthusiasts of the sports forum platform is large, and the number of game players of the game forum platform is large, so that different distribution templates need to be set for different distribution channels to meet the requirements of users. In the embodiment of the application, the preset corresponding relation among the user social portrait, the distribution channel, the commodity information and the distribution template is established in advance, and the server determines the distribution template according to the user social portrait, the distribution channel, the commodity information and the preset corresponding relation.
For example, as shown in table 1, for users corresponding to the same type of user social representation, a distribution template corresponding to the user with the interest is set in combination with user behaviors corresponding to the user social representation, for example, in combination with the interest of the user, and a correspondence relationship between a distribution channel, commodity information, and the distribution template is established. And determining a distribution template corresponding to the user of the social portrait according to the corresponding relation.
TABLE 1
S103: and generating advertisements according to the distribution templates and the commodity information, sending the distribution channels and the generated advertisements to the terminal equipment, and indicating the terminal equipment to recommend the advertisements through the distribution channels.
Specifically, if the server receives a request of sharing the advertisement from the user, the server generates an advertisement according to the distribution template and the commodity information, and sends the distribution channel and the generated advertisement to the terminal device, and the terminal device recommends the generated advertisement through each distribution channel.
In a possible implementation mode, after the server sends the generated advertisement to the terminal device, the generated advertisement is associated with the identity of the user, the transaction record of the advertisement is counted, so that the transaction condition of the advertisement recommended by the user associated with the advertisement is calculated, the reward is provided for the user according to the transaction condition, and the enthusiasm of the user for participating in and sharing the advertisement is improved.
In another possible implementation manner, the server counts the transaction conditions of the advertisements recommended by the users in each distribution channel according to the transaction records of the advertisements, so that the distribution channels and the distribution templates corresponding to the social portrait of the users are adjusted, the preset corresponding relations among the social portrait of the users, the distribution channels, the commodity information and the distribution templates are updated, and therefore the more accurate distribution channels and distribution templates are selected when the advertisements are recommended next time.
In the embodiment, the server acquires the user behavior characteristics and the commodity information sent by the terminal equipment, determines the distribution channel and the distribution template according to the user behavior characteristics and the commodity information, generates the advertisement according to the distribution template and the commodity information, sends the distribution channel and the generated advertisement to the terminal equipment, and instructs the terminal equipment to recommend the advertisement through the distribution channel. Because the activity degree of user groups with different user behavior characteristics in each distribution channel is different, and the interest degree of users with different user behavior characteristics in commodities is different, the effect of recommending advertisements in different distribution channels is different, the distribution channel in which users with interest in commodities are more active can be determined according to the user behavior characteristics, and the advertisement recommending in the distribution channel can improve the advertisement putting accuracy. Because the advertisement that generates according to the distribution template of difference is different to different users' attraction degree, and can generate the advertisement of different forms according to the distribution template of difference to make the advertisement form diversified, with the crowd that adapts to different, improve the probability that the user looked over the advertisement, and then promote advertisement effect.
Referring to fig. 4, fig. 4 shows an implementation flow of an advertisement recommendation method according to another embodiment of the present application, an execution subject of the advertisement recommendation method according to the embodiment is a terminal device, and a process of the advertisement recommendation method according to the embodiment is detailed as follows:
s301: and acquiring user behavior characteristics.
In a possible implementation manner, when detecting that a user browses advertisements, the server sends a distribution desire request to the terminal device in a push or pop window mode to be displayed to the user, and if the user agrees to distribute, that is, the terminal device obtains distribution request information of the user, user behavior characteristics are obtained according to the distribution request information, so that user experience is improved. Wherein the user behavior characteristics comprise user personal behavior characteristics and user social behavior characteristics, and the user personal behavior characteristics may comprise at least one of the following: gender, age, interests, hobbies, school calendar, address, income, record of purchased products, etc. The social behavior characteristics of the user comprise social platforms used by the user, the number of friends of the user on each social platform, the type of information shared by the user on each social platform, and the like.
S302: and sending the user behavior characteristics to a server, instructing the server to determine a distribution channel and a distribution template according to the user behavior characteristics and the commodity information, and generating an advertisement according to the distribution template and the commodity information.
The method for determining the distribution channel and the distribution template by the server according to the user behavior characteristics and the commodity information and generating the advertisement according to the distribution template and the commodity information is the same as that of S102 and S103 in the above embodiment, and details are not repeated here.
S303: and acquiring the distribution channel and the advertisement sent by the server, and recommending the advertisement through the distribution channel.
Specifically, after the terminal device acquires the distribution channels and the advertisements, the corresponding advertisements are recommended through the distribution channels according to the corresponding relations between the distribution channels and the advertisements, and the corresponding advertisements can be viewed by the user at the contacts of each distribution channel.
In the above embodiment, the terminal device acquires the user behavior characteristics, sends the user behavior characteristics to the server, instructs the server to determine the distribution channel and the distribution template according to the user behavior characteristics and the commodity information, and generates the advertisement according to the distribution template and the commodity information. And the terminal equipment acquires the distribution channel and the generated advertisement sent by the server and recommends the advertisement through the distribution channel. Because the activity degree of user groups with different user behavior characteristics in each distribution channel is different, and the interest degree of users with different user behavior characteristics in commodities is different, the effect of recommending advertisements in different distribution channels is different, the distribution channel in which users with interest in commodities are more active can be determined according to the user behavior characteristics, and the advertisement recommending in the distribution channel can improve the advertisement putting accuracy. Because the advertisement that generates according to the distribution template of difference is different to different users' attraction degree, and can generate the advertisement of different forms according to the distribution template of difference to make the advertisement form diversified, with the crowd that adapts to different, improve the probability that the user looked over the advertisement, and then promote advertisement effect.
Referring to fig. 5, fig. 5 shows a specific implementation flow of the advertisement recommendation method provided in the embodiment of the present application, and as shown in fig. 5, the advertisement recommendation method provided in the embodiment of the present application includes:
s401: the terminal equipment acquires the user behavior characteristics and sends the user behavior characteristics to the server.
S402: and the server determines a distribution channel and a distribution template according to the user behavior characteristics and the commodity information and generates an advertisement according to the distribution template and the commodity information.
S403: and the server sends the distribution channel and the generated advertisement to the terminal equipment.
S404: and the terminal equipment recommends the advertisement through the distribution channel.
S401 to S404 are the same as the above embodiments, and are not described herein again.
For example, when the user a browses the WeChat on the terminal device, the user sees the advertisement of the clothing B, and after the user watches the advertisement, the server pushes information on whether the user shares the advertisement to the user through the terminal device. If the user selects to share the advertisement, the server acquires the behavior characteristics of the user, generates a user social portrait according to the behavior characteristics of the user, and determines a distribution channel according to the user social portrait and a determination rule of the distribution channel, for example, the determined distribution channel is WeChat, microblog and QQ. And the server determines a distribution template corresponding to each distribution channel for the commodity according to the preset corresponding relation among the user social portrait, the distribution channels, the commodity information and the distribution templates, generates an advertisement according to the distribution template, associates the advertisement with the identity of the user A, and recommends the associated advertisement to each distribution channel. And the server counts the transaction records of the clothing B, and calculates the reward of the user A according to the quantity associated with the identity of the user A in the transaction records. In the above embodiment, the terminal device acquires the user behavior characteristics, sends the user behavior characteristics to the server, the server determines the distribution channel and the distribution template according to the user behavior characteristics and the commodity information, so that the distribution channel in which the user is active and the distribution template adaptive to the distribution channel can be determined, the server generates the advertisement according to the distribution template and the commodity information, the distribution channel and the advertisement are sent to the terminal device, the terminal device recommends the advertisement through the distribution channel, so that the advertisement form is diversified, the advertisement is adaptive to different crowds, the probability of viewing the advertisement by the user is improved, and the advertisement effect is further improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 6 to 7 respectively show structural block diagrams of the advertisement recommendation device provided in the embodiment of the present application, corresponding to the advertisement recommendation method described in the above embodiment, and for convenience of explanation, only the parts related to the embodiment of the present application are shown.
As shown in fig. 6, an advertisement recommendation apparatus according to an embodiment of the present application includes,
the acquisition module 10 is used for acquiring user behavior characteristics and commodity information sent by the terminal equipment;
the determining module 20 is used for determining a distribution channel and a distribution template according to the user behavior characteristics and the commodity information;
and the recommending module 30 is used for generating advertisements according to the distribution templates and the commodity information, sending the distribution channels and the generated advertisements to the terminal equipment and indicating the terminal equipment to recommend the advertisements through the distribution channels.
In one possible implementation, the determining module 20 includes:
the generating unit is used for generating a user social portrait according to the user behavior characteristics;
the first determining unit is used for determining a distribution channel according to the user social portrait;
and the second determining unit is used for determining a distribution template according to the user social representation, the distribution channel and the commodity information.
In a possible implementation manner, the user social representation includes feature tags and weights corresponding to the feature tags, and the generating unit is specifically configured to:
generating a feature label according to the user behavior feature;
and determining the weight corresponding to the feature tag according to the acquisition time of the user behavior feature.
In a possible implementation manner, the second determining unit is specifically configured to:
and determining the distribution template according to the user social portrait, the distribution channel, the commodity information and a preset corresponding relation, wherein the preset corresponding relation is the corresponding relation of the user social portrait, the distribution channel, the commodity information and the distribution template.
In one possible implementation manner, the advertisement recommendation apparatus further includes:
and the counting module is used for counting the transaction records corresponding to the advertisements.
In one possible implementation, the statistics module is further configured to:
and updating the preset corresponding relation according to the transaction record.
As shown in fig. 7, another embodiment of the advertisement recommendation device of the present application includes,
an input module 40, configured to obtain user behavior characteristics;
the sending module 50 is used for sending the user behavior characteristics to a server, instructing the server to determine a distribution channel and a distribution template according to the user behavior characteristics and the commodity information, and generating advertisements according to the distribution template and the commodity information;
and an output module 60, configured to acquire the distribution channel and the advertisement sent by the server, and recommend the advertisement through the distribution channel.
In a possible implementation manner, the input module 40 is specifically configured to:
when the distribution request information of the user is obtained, user behavior characteristics are obtained, wherein the user behavior characteristics comprise user personal behavior characteristics and user social behavior characteristics.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
Fig. 8 is a schematic diagram of a server provided in an embodiment of the present application. As shown in fig. 8, the server of this embodiment includes: a processor 11, a memory 12 and a computer program 13 stored in said memory 12 and executable on said processor 11. The processor 11, when executing the computer program 13, implements the steps in the above-described advertisement recommendation method embodiments, such as the steps S101 to S103 shown in fig. 1. Alternatively, the processor 11, when executing the computer program 13, implements the functions of each module/unit in each device embodiment described above, for example, the functions of the modules 10 to 30 shown in fig. 6.
Illustratively, the computer program 13 may be partitioned into one or more modules/units, which are stored in the memory 12 and executed by the processor 11 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 13 in the terminal device.
Those skilled in the art will appreciate that fig. 8 is merely an example of a server and is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or different components, e.g., the server may also include input-output devices, network access devices, buses, etc.
The Processor 11 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 12 may be an internal storage unit of the server, such as a hard disk or a memory of the server. The memory 12 may also be an external storage device of the server, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a flash memory Card (FlashCard), and the like, provided on the server. Further, the memory 12 may also include both an internal storage unit of the server and an external storage device. The memory 12 is used for storing the computer program and other programs and data required by the server. The memory 12 may also be used to temporarily store data that has been output or is to be output.
Fig. 9 is a schematic diagram of a terminal device provided in an embodiment of the present application. As shown in fig. 9, the terminal device of this embodiment includes: a processor 21, a memory 22 and a computer program 23 stored in said memory 22 and executable on said processor 21. The processor 21 implements the steps in the above-mentioned advertisement recommendation method embodiment, such as steps S301 to S303 shown in fig. 4, when executing the computer program 23. Alternatively, the processor 21, when executing the computer program 23, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 40 to 60 shown in fig. 7.
Illustratively, the computer program 23 may be partitioned into one or more modules/units, which are stored in the memory 22 and executed by the processor 21 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 23 in the terminal device.
Those skilled in the art will appreciate that fig. 9 is merely an example of a terminal device and is not limiting and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input output devices, network access devices, buses, etc.
The Processor 21 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 22 may be an internal storage unit of the terminal device, such as a hard disk or a memory of the terminal device. The memory 22 may also be an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device. Further, the memory 22 may also include both an internal storage unit and an external storage device of the terminal device. The memory is used for storing the computer program and other programs and data required by the terminal device. The memory 22 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, 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 through some interfaces, devices or units, 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 of 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 integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An advertisement recommendation method applied to a server is characterized by comprising the following steps:
acquiring user behavior characteristics sent by terminal equipment;
determining a distribution channel and a distribution template according to the user behavior characteristics and the commodity information;
and generating an advertisement according to the distribution template and the commodity information, sending the distribution channel and the generated advertisement to the terminal equipment, and indicating the terminal equipment to recommend the advertisement through the distribution channel.
2. The advertisement recommendation method of claim 1, wherein the determining distribution channels and distribution templates from the user behavior characteristics and the goods information comprises:
generating a user social portrait according to the user behavior characteristics;
determining a distribution channel according to the user social portrait;
and determining a distribution template according to the user social portrait, the distribution channel and the commodity information.
3. The advertisement recommendation method of claim 2, wherein the user social representation includes feature tags and weights corresponding to the feature tags, the generating a user social representation from the user behavior features comprising:
generating a feature label according to the user behavior feature;
and determining the weight corresponding to the feature tag according to the acquisition time of the user behavior feature.
4. The advertisement recommendation method of claim 2, wherein said determining a distribution template from said user social representation, said distribution channel and merchandise information comprises:
and determining the distribution template according to the user social portrait, the distribution channel, the commodity information and a preset corresponding relation, wherein the preset corresponding relation is the corresponding relation of the user social portrait, the distribution channel, the commodity information and the distribution template.
5. The advertisement recommendation method according to claim 4, wherein after said transmitting the distribution channel and the generated advertisement to the terminal apparatus instructing the terminal apparatus to recommend the advertisement through the distribution channel, the advertisement recommendation method further comprises:
and counting transaction records corresponding to the advertisements.
6. The advertisement recommendation method of claim 5, after said counting transaction records corresponding to said advertisements, said advertisement recommendation method further comprising:
and updating the preset corresponding relation according to the transaction record.
7. An advertisement recommendation method is applied to terminal equipment, and is characterized by comprising the following steps:
acquiring user behavior characteristics;
sending the user behavior characteristics to a server, instructing the server to determine a distribution channel and a distribution template according to the user behavior characteristics and the commodity information, and generating an advertisement according to the distribution template and the commodity information;
and acquiring the distribution channel and the advertisement sent by the server, and recommending the advertisement through the distribution channel.
8. The advertisement recommendation method of claim 7, wherein the obtaining user behavior characteristics comprises:
when the distribution request information of the user is obtained, user behavior characteristics are obtained, wherein the user behavior characteristics comprise user personal behavior characteristics and user social behavior characteristics.
9. A server comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 6 when executing the computer program.
10. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 7 to 8 when executing the computer program.
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| CN202010461254.9A CN111681042A (en) | 2020-05-27 | 2020-05-27 | Advertisement recommendation method, server and terminal equipment |
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