CN116777524A - Interactive advertisement putting method and related device based on artificial intelligence - Google Patents
Interactive advertisement putting method and related device based on artificial intelligence Download PDFInfo
- Publication number
- CN116777524A CN116777524A CN202310880481.9A CN202310880481A CN116777524A CN 116777524 A CN116777524 A CN 116777524A CN 202310880481 A CN202310880481 A CN 202310880481A CN 116777524 A CN116777524 A CN 116777524A
- Authority
- CN
- China
- Prior art keywords
- advertisement
- target
- delivery
- target client
- historical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000002452 interceptive effect Effects 0.000 title claims abstract description 54
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000006243 chemical reaction Methods 0.000 claims abstract description 17
- 230000003993 interaction Effects 0.000 claims description 51
- 230000015572 biosynthetic process Effects 0.000 claims description 27
- 238000003786 synthesis reaction Methods 0.000 claims description 27
- 230000004044 response Effects 0.000 claims description 21
- 239000000463 material Substances 0.000 claims description 20
- 238000004590 computer program Methods 0.000 claims description 15
- 238000013507 mapping Methods 0.000 claims description 15
- 238000002716 delivery method Methods 0.000 claims description 13
- 238000000605 extraction Methods 0.000 claims description 5
- 230000002194 synthesizing effect Effects 0.000 claims description 5
- 239000002699 waste material Substances 0.000 abstract description 7
- 238000012545 processing Methods 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 6
- 239000002131 composite material Substances 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Data Mining & Analysis (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Marketing (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Business, Economics & Management (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The application relates to the technical field of business data processing, and particularly discloses an interactive advertisement putting method and a related device based on artificial intelligence, wherein the method firstly generates a plurality of intermediate target advertisement putting according to advertisement putting requirement information of an advertisement putting party, then acquires a plurality of target clients of the target advertisement putting based on the advertisement putting requirement information, finally determines the target advertisement putting in the plurality of intermediate target advertisement putting based on historical online data of the target client in a preset time period for each target client, determines an interactive mode of the target client and the target advertisement putting based on the historical online data of the target client in the preset time period, and sends the target advertisement putting to terminal equipment of the target client based on the target advertisement putting and the interactive mode. The method can improve the advertisement conversion rate of the interactive advertisement, thereby reducing the waste of the interactive advertisement resources.
Description
Technical Field
The application relates to the technical field of business data processing, in particular to an interactive advertisement putting method based on artificial intelligence and a related device.
Background
With the development of the digital age, the form of advertisement delivery is continuously changed, and the advertising industry continuously seeks more efficient and targeted advertisement delivery means from traditional printing materials to network advertisements. The interactive advertisement is a common advertisement delivery mode at present, and the existing interactive advertisement delivery method also has the problem of advertisement resource waste caused by low advertisement conversion rate.
Disclosure of Invention
The application provides an interactive advertisement putting method and a related device based on artificial intelligence, which are used for improving the advertisement conversion rate of interactive advertisements so as to avoid the problem of advertisement resource waste.
In a first aspect, the present application provides an artificial intelligence based interactive advertisement delivery method, including:
responding to an advertisement putting request of an advertisement putting party, acquiring advertisement putting demand information of the advertisement putting party, and determining the advertisement field of targeted advertisement according to the advertisement putting demand information based on a preset advertisement field decision model;
acquiring a plurality of advertisement generating templates matched with the advertisement field based on big data;
generating a plurality of intermediate targeted advertisements according to the advertisement delivery demand information based on a plurality of advertisement generation templates;
Acquiring a plurality of target clients of the target advertisement based on the advertisement delivery demand information;
for each target client, determining the target advertisement in a plurality of intermediate target advertisements based on historical online data of the target client in a preset time period, determining an interaction mode of the target client and the target advertisement based on the historical online data of the target client in the preset time period, and transmitting the target advertisement to terminal equipment of the target client based on the target advertisement and the interaction mode.
In one implementation, the obtaining, based on big data, a plurality of advertisement generation templates matching the advertisement domain includes:
constructing a historical advertisement database based on big data, wherein the historical advertisement database comprises a plurality of mapping relations, and the mapping relations are the mapping relations between the historical advertisements and the information sets of the historical advertisements; wherein the information set comprises advertisement characteristics of the historical advertisements and response information of users to the historical advertisements;
determining a plurality of pending delivery forms of the targeted advertisement according to the advertisement domain and the advertisement characteristics of the historical advertisement based on the advertisement database;
Determining a plurality of target delivery forms of the target delivery advertisement based on the plurality of the target delivery forms and response information of the user to the historical advertisement corresponding to the target delivery forms;
and determining a plurality of advertisement generating templates matched with the advertisement field in a preset advertisement generating template database based on the target delivery forms.
In one implementation, the advertisement features of the historical advertisement include a product name of the historical advertisement and a delivery form of the historical advertisement, and the determining, based on the advertisement database, a plurality of pending delivery forms of the targeted advertisement according to the advertisement domain and the advertisement features of the historical advertisement includes:
extracting semantic features of the advertising field and semantic features of each product name based on a preset semantic feature extraction model;
respectively calculating first similarity between semantic features of the advertising field and semantic features of each product name;
and respectively comparing each first similarity with a first preset similarity, and determining the delivery form of the historical advertisement corresponding to the similarity as the undetermined delivery form of the target delivered advertisement if the first similarity is larger than the first preset similarity.
In one implementation, each of the advertisement generating templates includes a material library and an advertisement generating template, and the generating a plurality of intermediate targeted advertisements according to the advertisement delivery demand information based on a plurality of the advertisement generating templates includes:
generating a plurality of element tag information based on the advertisement delivery demand information;
searching in the material library based on the element tag information to determine a plurality of initial advertisement synthetic elements;
respectively inputting a plurality of initial advertisement synthesized elements into a preset standard element tag identification model to obtain standard element tag information corresponding to the initial advertisement synthesized elements;
calculating the second similarity of the element tag information corresponding to each initial advertisement synthesis element and the standard element tag information;
comparing the magnitude relation between each second similarity and a second preset similarity;
if the second similarity is greater than the second preset similarity, determining the initial advertisement synthesis element corresponding to the second similarity as an advertisement synthesis element;
if the second similarity is smaller than the second preset similarity, acquiring advertisement synthesized elements from a cloud according to the element tag information corresponding to the second similarity, and storing the advertisement synthesized elements acquired from the cloud into the material library;
And synthesizing the advertisement synthesis element and the advertisement synthesis element acquired from the cloud into the intermediate targeted advertisement based on the advertisement generation template.
In one implementation, the determining the interaction mode of the target client and the targeted advertisement based on the historical online data of the target client in the preset time period includes:
based on the historical online data of the target client in the preset time period, acquiring response information of the target client to advertisements put to terminal equipment of the target client in the preset time period;
determining a mode of interaction between the target client and the advertisement in the preset time period based on response information of the target client to the advertisement put to the terminal equipment of the target client in the preset time period; wherein the way in which the target client interacts with the advertisement within the preset time period comprises at least one of;
aiming at each mode of interaction between the target client and the advertisement in a preset time period, calculating the percentage of the conversion times of the advertisement to the throwing times of the advertisement;
and determining the mode corresponding to the maximum percentage as the interaction mode of the target client and the target advertisement.
In one implementation, the sending the targeted advertisement to the terminal device of the targeted client based on the targeted advertisement and the interaction mode includes:
acquiring identity information of the target client, and optimizing the target advertisement based on the identity information;
optimizing the delivery form of the targeted advertisement based on the interaction mode;
acquiring media currently browsed by the target client on the terminal equipment;
and delivering the optimized targeted delivery advertisement to the media in the optimized delivery form.
In a second aspect, the present application provides an artificial intelligence based interactive advertisement delivery system, comprising:
the determining module is used for responding to the advertisement putting request of the advertisement putting party, acquiring the advertisement putting demand information of the advertisement putting party, and determining the advertisement field of the targeted advertisement according to the advertisement putting demand information based on a preset advertisement field decision model;
the first acquisition module is used for acquiring a plurality of advertisement generating templates matched with the advertisement field based on big data;
the generation module is used for generating a plurality of intermediate targeted advertisement according to the advertisement delivery demand information based on a plurality of advertisement generation templates;
The second acquisition module is used for acquiring a plurality of target clients of the target advertisement based on the advertisement delivery demand information;
the sending module is used for determining the target advertisement in a plurality of intermediate target advertisements based on historical online data of the target client in a preset time period, determining an interaction mode of the target client and the target advertisement based on the historical online data of the target client in the preset time period, and sending the target advertisement to terminal equipment of the target client based on the target advertisement and the interaction mode.
In a third aspect, the present application provides a terminal device, where the terminal device includes a processor, a memory, and a computer program stored on the memory and executable by the processor, where the computer program, when executed by the processor, implements any of the artificial intelligence based interactive advertisement delivery methods described above.
In a fourth aspect, the present application provides a computer readable storage medium, where a computer program is stored on the computer readable storage medium, where any one of the above-mentioned interactive advertisement delivery methods based on artificial intelligence is implemented when the computer program is executed by the processor.
The application provides an artificial intelligence-based interactive advertisement delivery method, an artificial intelligence-based interactive advertisement delivery system, equipment and a storage medium, wherein the artificial intelligence-based interactive advertisement delivery method firstly generates a plurality of intermediate target delivery advertisements according to advertisement delivery demand information of an advertisement delivery party, then acquires a plurality of target clients of the target delivery advertisements based on the advertisement delivery demand information, finally determines the target delivery advertisements in the plurality of intermediate target delivery advertisements based on historical online data of the target clients in a preset time period for each target client, determines an interaction mode of the target clients and the target delivery advertisements based on the historical online data of the target clients in the preset time period, and sends the target delivery advertisements to terminal equipment of the target clients based on the target delivery advertisements and the interaction mode. The method can improve the advertisement conversion rate of the interactive advertisement, thereby reducing the waste of the interactive advertisement resources.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an artificial intelligence-based interactive advertisement delivery method according to an embodiment of the present application;
FIG. 2 is a schematic block diagram of an artificial intelligence based interactive advertising system according to an embodiment of the present application;
fig. 3 is a schematic block diagram of a structure of a terminal device according to an embodiment of the present application.
Description of the embodiments
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification 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.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
With the development of the digital age, the form of advertisement delivery is continuously changed, and the advertising industry continuously seeks more efficient and targeted advertisement delivery means from traditional printing materials to network advertisements. The interactive advertisement is a common advertisement delivery mode at present, and the existing interactive advertisement delivery method also has the problem of advertisement resource waste caused by low advertisement conversion rate. Therefore, the embodiment of the application provides an interactive advertisement putting method, an interactive advertisement putting system, interactive advertisement putting equipment and a storage medium based on artificial intelligence so as to solve the problems.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a flowchart of an artificial intelligence-based interactive advertisement delivery method according to an embodiment of the present application, as shown in fig. 1, the artificial intelligence-based interactive advertisement delivery method according to an embodiment of the present application includes steps S100 to S500.
Step S100, responding to an advertisement putting request of an advertisement putting party, acquiring advertisement putting demand information of the advertisement putting party, and determining the advertisement field of targeted advertisement according to the advertisement putting demand information based on a preset advertisement field decision model.
The advertisement domain decision model is obtained based on neural network training, and the advertisement domain refers to the domain of the product corresponding to the advertisement delivery demand information.
And step 200, acquiring a plurality of advertisement generating templates matched with the advertisement field based on big data.
And step S300, generating a plurality of intermediate targeted advertisement according to the advertisement delivery demand information based on a plurality of advertisement generation templates.
Wherein, the advertisement generated by each advertisement generating template is different in release form, and the release form of the advertisement comprises, but is not limited to, forms of video, cartoon, picture and the like.
It will be appreciated that steps S100 through S300 may generate a variety of different forms of advertisements to meet the preferences of different users, thereby improving the click rate and conversion rate of the advertisements.
Step S400, a plurality of target clients of the target advertisement are obtained based on the advertisement delivery demand information.
Illustratively, step S400 may be implemented by:
constructing a user database based on big data, wherein the user database comprises historical online data of a plurality of users;
constructing a historical portrait model of each user based on the historical online data of each user in the database;
and respectively inputting the advertisement demand information into each historical portrait model so that each historical portrait model outputs a score for the advertisement delivery demand information, and determining the user corresponding to the score as a target client for delivering the advertisement according to the score when the score is larger than a preset score.
Step S500, for each target client, determining the target advertisement in a plurality of intermediate target advertisements based on historical online data of the target client in a preset time period, determining an interaction mode of the target client and the target advertisement based on the historical online data of the target client in the preset time period, and sending the target advertisement to terminal equipment of the target client based on the target advertisement and the interaction mode.
The preset time period refers to a period of time before and connected with the moment of responding to the advertisement putting request of the advertisement putting party.
It can be understood that the preference of the user varies with time, so that the target advertisement is determined in the plurality of intermediate target advertisements based on the historical online data of the target client in the preset time period, and the interaction mode of the target client and the target advertisement is determined based on the historical online data of the target client in the preset time period, so that the determined target advertisement and the determined interaction mode of the target advertisement better accord with the current preference of the user, and the conversion rate of the interactive advertisement is improved.
According to the interactive advertisement putting method based on artificial intelligence, a plurality of intermediate target advertisement putting advertisements are firstly generated according to advertisement putting demand information of advertisement putting parties, then a plurality of target clients of the target advertisement putting advertisements are obtained based on the advertisement putting demand information, finally, for each target client, the target advertisement putting advertisements are determined in the plurality of intermediate target advertisement putting advertisements based on historical online data of the target client in a preset time period, the interaction mode of the target client and the target advertisement putting advertisements is determined based on the historical online data of the target client in the preset time period, and the target advertisement putting advertisements are sent to terminal equipment of the target client based on the target advertisement putting advertisements and the interaction mode. The method can improve the advertisement conversion rate of the interactive advertisement, thereby reducing the waste of the interactive advertisement resources.
In some embodiments, step S200 obtains a plurality of advertisement generation templates matching the advertisement domain based on big data, including steps S210 to S240.
Step S210, constructing a historical advertisement database based on big data, wherein the historical advertisement database comprises a plurality of mapping relations, and the mapping relations are mapping relations between historical advertisements and information sets of the historical advertisements; wherein the information set comprises advertisement characteristics of the historical advertisements and response information of users to the historical advertisements.
Step S220, based on the advertisement database, a plurality of undetermined delivery forms of the targeted advertisement are determined according to the advertisement field and the advertisement characteristics of the historical advertisement.
Illustratively, step S220 may be implemented by:
carrying out relevance analysis on each advertisement feature in the historical advertisement database and the advertisement field respectively to obtain a plurality of relevance coefficients;
and respectively comparing each association coefficient with a preset association coefficient, and determining the release form of the historical advertisement corresponding to the association coefficient as the undetermined release form of the target release advertisement when the association coefficient is not smaller than the preset association coefficient.
Step S230, determining a plurality of target delivery forms of the target delivery advertisement based on the plurality of the target delivery forms and response information of the user to the historical advertisement corresponding to the target delivery form.
And step S240, determining a plurality of advertisement generating templates matched with the advertisement field in a preset advertisement generating template database based on a plurality of target delivery forms.
It will be appreciated that one of the targeted delivery forms corresponds to one of the advertisement generation templates.
According to the method, on one hand, a plurality of advertisement generating templates matched with the advertisement field can be obtained, advertisements in various different throwing forms are generated in the subsequent advertisement generating process, so that favorites of different target clients are met, the click rate and conversion rate of interactive advertisements are improved, on the other hand, the method, the device and the system determine the target throwing forms of the target throwing advertisements based on the response information of the plurality of target throwing forms and the historical advertisements corresponding to the target throwing forms of users, determine the advertisement generating templates matched with the advertisement field in a preset advertisement generating template database based on the plurality of target throwing forms, and further improve the click rate and conversion rate of advertisements generated by using the advertisement generating templates, and reduce waste of interactive advertisement resources.
In some embodiments, the advertisement characteristics of the historical advertisement include a product name of the historical advertisement and a delivery form of the historical advertisement, and step S220 determines a plurality of pending delivery forms of the targeted advertisement according to the advertisement domain and the advertisement characteristics of the historical advertisement based on the advertisement database, including steps S221 to S223.
Step S221, extracting semantic features of the advertising field and semantic features of each product name based on a preset semantic feature extraction model.
Step S222, calculating a first similarity between the semantic features of the advertisement domain and the semantic features of each product name.
Step S223, comparing each first similarity with a first preset similarity, and if the first similarity is greater than the first preset similarity, determining the delivery form of the historical advertisement corresponding to the similarity as the pending delivery form of the targeted delivery advertisement.
In some embodiments, each of the advertisement generating templates includes a material library and an advertisement generating template, and step S300 generates a plurality of intermediate targeted advertisements according to the advertisement delivery requirement information based on a plurality of the advertisement generating templates, including steps S310 to S380.
Step S310, generating a plurality of element tag information based on the advertisement putting requirement information.
And step 320, searching in the material library based on the element tag information to determine a plurality of initial advertisement synthetic elements.
Step S330, inputting the plurality of initial advertisement synthesized elements into a preset standard element tag identification model respectively to obtain standard element tag information corresponding to the plurality of initial advertisement synthesized elements respectively.
Step S340, calculating a second similarity between the element tag information corresponding to each initial advertisement composite element and the standard element tag information.
And S350, comparing the magnitude relation between each second similarity and a second preset similarity.
Step S360, if the second similarity is greater than the second preset similarity, determining the initial advertisement synthesis element corresponding to the second similarity as an advertisement synthesis element.
Step S370, if the second similarity is smaller than the second preset similarity, acquiring an advertisement synthesized element from the cloud according to the element tag information corresponding to the second similarity, and storing the advertisement synthesized element acquired from the cloud into the material library.
It may be understood that if the second similarity is smaller than the second preset similarity, it is indicated that the advertisement composite element of the element tag information corresponding to the similarity does not exist in the material library, and the advertisement composite element needs to be acquired from the cloud according to the element tag information corresponding to the second similarity.
And step 380, synthesizing the advertisement synthesis element and the advertisement synthesis element acquired from the cloud into the intermediate targeted advertisement based on the advertisement generation template.
It should be noted that, steps S310 to S380 are required to be executed for each advertisement generating template.
The method provided by the embodiment can improve the matching degree of the intermediate targeted advertisement and the advertisement delivery demand information, and can expand the material library to improve the variety of advertisement synthesis elements in the material library.
In some embodiments, the determining the interaction mode of the target client and the targeted advertisement in step S500 based on the historical online data of the target client in the preset time period includes steps S511 to S514.
Step S511, based on the historical online data of the target client in the preset time period, response information of the target client to advertisements put to terminal equipment of the target client in the preset time period is obtained.
Step S512, determining a mode of interaction between the target client and the advertisement in the preset time period based on response information of the target client to the advertisement put on the terminal device of the target client in the preset time period; the method for the target client to interact with the advertisement in the preset time period comprises at least one mode.
The interaction modes include, but are not limited to, game interaction, red-burst interaction, prize-winning bid interaction and the like.
Step S513, for each mode of interaction between the target client and the advertisement in the preset time period, calculates the percentage of the conversion times of the advertisement to the delivery times of the advertisement.
Step S514, determining the mode corresponding to the maximum percentage as the interaction mode of the target client and the target advertisement.
In some embodiments, the step S500 of sending the targeted advertisement to the terminal device of the targeted client based on the targeted advertisement and the interaction mode includes steps S531 to S534.
And step S531, acquiring the identity information of the target client, and optimizing the target advertisement based on the identity information.
Illustratively, if the target client is an elderly person, the font of the targeted advertisement is changed to a large font.
And step S532, optimizing the delivery form of the targeted advertisement based on the interaction mode.
If the interaction mode is a mode of winning a bid, a question is set according to the content of the target advertisement, and the question is set on a first page of the target advertisement.
Step S533, acquiring the media currently browsed by the target client on the terminal equipment.
And step 534, delivering the optimized targeted advertisement to the media in the optimized delivery form.
After the target advertisement is sent to the terminal equipment of the target client by adopting the method provided by the embodiment, the attention of the target client to the target advertisement can be improved, so that the click rate and the conversion rate of the target advertisement are improved.
Referring to fig. 2, fig. 2 is a schematic block diagram illustrating a structure of an artificial intelligence-based interactive advertisement delivery system 100 according to an embodiment of the present application, where the artificial intelligence-based interactive advertisement delivery system 100 shown in fig. 2 includes:
the determining module 110 is configured to respond to an advertisement delivery request of an advertisement delivery party, obtain advertisement delivery demand information of the advertisement delivery party, and determine an advertisement domain of a targeted advertisement according to the advertisement delivery demand information based on a preset advertisement domain decision model.
A first obtaining module 120, configured to obtain a plurality of advertisement generating templates matched with the advertisement domain based on big data.
And the generating module 130 is used for generating a plurality of intermediate targeted advertisements according to the advertisement delivery demand information based on a plurality of advertisement generating templates.
And a second obtaining module 140, configured to obtain a plurality of target clients of the targeted advertisement based on the advertisement delivery requirement information.
The sending module 150 is configured to determine, for each target client, the target advertisement among the plurality of intermediate target advertisements based on historical online data of the target client in a preset time period, determine an interaction mode of the target client and the target advertisement based on the historical online data of the target client in the preset time period, and send the target advertisement to a terminal device of the target client based on the target advertisement and the interaction mode.
In some embodiments, the first acquisition module 120 includes:
the construction unit is used for constructing a historical advertisement database based on big data, wherein the historical advertisement database comprises a plurality of mapping relations, and the mapping relations are the mapping relations between the historical advertisements and the information sets of the historical advertisements; wherein the information set comprises advertisement characteristics of the historical advertisements and response information of users to the historical advertisements.
And the first determining unit is used for determining a plurality of undetermined delivery forms of the targeted delivery advertisement according to the advertisement field and the advertisement characteristics of the historical advertisement based on the advertisement database.
And the second determining unit is used for determining a plurality of target delivery forms of the target delivery advertisement based on the plurality of the target delivery forms and response information of the user to the historical advertisement corresponding to the target delivery form.
And the third determining unit is used for determining a plurality of advertisement generating templates matched with the advertisement field in a preset advertisement generating template database based on the target delivery forms.
In some embodiments, the advertisement characteristics of the history advertisement include a product name of the history advertisement and a put form of the history advertisement, and the first determining unit is configured to perform the steps of:
extracting semantic features of the advertising field and semantic features of each product name based on a preset semantic feature extraction model;
respectively calculating first similarity between semantic features of the advertising field and semantic features of each product name;
and respectively comparing each first similarity with a first preset similarity, and determining the delivery form of the historical advertisement corresponding to the similarity as the undetermined delivery form of the target delivered advertisement if the first similarity is larger than the first preset similarity.
In some embodiments, each of the advertisement generation templates includes a material library and advertisement generation templates, the generation module 130 includes:
and the generating unit is used for generating a plurality of element tag information based on the advertisement putting requirement information.
And a fourth determining unit for determining a plurality of initial advertisement composition elements based on the element tag information retrieved from the material library.
The first acquisition unit is used for respectively inputting the plurality of initial advertisement synthesized elements into a preset standard element tag identification model so as to acquire standard element tag information corresponding to the plurality of initial advertisement synthesized elements.
And the calculating unit is used for calculating the second similarity of the element label information corresponding to each initial advertisement synthesized element and the standard element label information.
And the comparison unit is used for comparing the magnitude relation between each second similarity and a second preset similarity.
And a fifth determining unit, configured to determine the initial advertisement synthesis element corresponding to the second similarity as an advertisement synthesis element if the second similarity is greater than the second preset similarity.
And the second acquisition unit is used for acquiring advertisement synthesis elements from the cloud according to the element tag information corresponding to the second similarity if the second similarity is smaller than the second preset similarity, and storing the advertisement synthesis elements acquired from the cloud into the material library.
And the synthesis unit is used for synthesizing the advertisement synthesis element and the advertisement synthesis element acquired from the cloud into the intermediate targeted advertisement based on the advertisement generation template.
In some embodiments, the transmitting module 150 includes:
and a third obtaining unit, configured to obtain, based on the historical online data of the target client in the preset time period, response information of the target client to an advertisement put into a terminal device of the target client in the preset time period.
A sixth determining unit, configured to determine, based on response information of the target client to the advertisement delivered to the terminal device of the target client in the preset time period, a manner in which the target client interacts with the advertisement in the preset time period; the method for the target client to interact with the advertisement in the preset time period comprises at least one mode.
The conversion calculation unit is used for calculating the percentage of the conversion times of the advertisement to the throwing times of the advertisement according to each mode of interaction between the target client and the advertisement in a preset time period.
And a seventh determining unit, configured to determine a mode corresponding to the maximum percentage as an interaction mode between the target client and the target advertisement.
In some embodiments, the transmitting module 150 includes:
the first optimizing unit is used for acquiring the identity information of the target client and optimizing the target advertisement based on the identity information.
And the second optimizing unit is used for optimizing the delivery form of the targeted advertisement based on the interaction mode.
And the fourth acquisition unit is used for acquiring the media currently browsed by the target client on the terminal equipment.
And the delivery unit is used for delivering the optimized targeted advertisement to the media in the optimized delivery form.
It should be noted that, for convenience and brevity of description, specific working processes of the above-described apparatus and each module and unit may refer to corresponding processes in the foregoing embodiment of the interactive advertisement delivery method based on artificial intelligence, which are not described herein again.
The artificial intelligence based interactive advertisement delivery system 100 provided in the above embodiment may be implemented in the form of a computer program that can be run on the terminal device 200 as shown in fig. 3.
Referring to fig. 3, fig. 3 is a schematic block diagram of a structure of a terminal device 200 according to an embodiment of the present application, where the terminal device 200 includes a processor 201 and a memory 202, and the processor 201 and the memory 202 are connected through a system bus 203, and the memory 202 may include a nonvolatile storage medium and an internal memory.
The non-volatile storage medium may store a computer program. The computer program comprises program instructions that, when executed by the processor 201, cause the processor 201 to perform any of the artificial intelligence based interactive advertising methods described above.
The processor 201 is used to provide computing and control capabilities supporting the operation of the overall terminal device 200.
The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium that, when executed by the processor 201, causes the processor 201 to perform any of the artificial intelligence based interactive advertising methods described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation of the terminal device 200 related to the present application, and that a specific terminal device 200 may include more or less components than those shown in the drawings, or may combine some components, or have a different arrangement of components.
It should be appreciated that the processor 201 may be a central processing unit (Central Processing Unit, CPU), and the processor 201 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In some embodiments, the processor 201 is configured to execute a computer program stored in the memory to implement the following steps:
responding to an advertisement putting request of an advertisement putting party, acquiring advertisement putting demand information of the advertisement putting party, and determining the advertisement field of targeted advertisement according to the advertisement putting demand information based on a preset advertisement field decision model;
acquiring a plurality of advertisement generating templates matched with the advertisement field based on big data;
generating a plurality of intermediate targeted advertisements according to the advertisement delivery demand information based on a plurality of advertisement generation templates;
acquiring a plurality of target clients of the target advertisement based on the advertisement delivery demand information;
for each target client, determining the target advertisement in a plurality of intermediate target advertisements based on historical online data of the target client in a preset time period, determining an interaction mode of the target client and the target advertisement based on the historical online data of the target client in the preset time period, and transmitting the target advertisement to terminal equipment of the target client based on the target advertisement and the interaction mode.
In some embodiments, the processor 201, when implementing the multiple advertisement generation templates matching the advertisement domain based on big data acquisition, is configured to implement:
constructing a historical advertisement database based on big data, wherein the historical advertisement database comprises a plurality of mapping relations, and the mapping relations are the mapping relations between the historical advertisements and the information sets of the historical advertisements; wherein the information set comprises advertisement characteristics of the historical advertisements and response information of users to the historical advertisements;
determining a plurality of pending delivery forms of the targeted advertisement according to the advertisement domain and the advertisement characteristics of the historical advertisement based on the advertisement database;
determining a plurality of target delivery forms of the target delivery advertisement based on the plurality of the target delivery forms and response information of the user to the historical advertisement corresponding to the target delivery forms;
and determining a plurality of advertisement generating templates matched with the advertisement field in a preset advertisement generating template database based on the target delivery forms.
In some embodiments, the advertisement characteristics of the historical advertisement include a product name of the historical advertisement and a delivery form of the historical advertisement, and the processor 201 is configured, when implementing the determining, based on the advertisement database, a plurality of pending delivery forms of the targeted advertisement according to the advertisement domain and the advertisement characteristics of the historical advertisement, to implement:
Extracting semantic features of the advertising field and semantic features of each product name based on a preset semantic feature extraction model;
respectively calculating first similarity between semantic features of the advertising field and semantic features of each product name;
and respectively comparing each first similarity with a first preset similarity, and determining the delivery form of the historical advertisement corresponding to the similarity as the undetermined delivery form of the target delivered advertisement if the first similarity is larger than the first preset similarity.
In some embodiments, each of the advertisement generating templates includes a material library and an advertisement generating template, and when implementing the generating a plurality of intermediate targeted advertisements based on the advertisement generating templates and according to the advertisement delivery requirement information, the processor 201 is configured to implement:
generating a plurality of element tag information based on the advertisement delivery demand information;
searching in the material library based on the element tag information to determine a plurality of initial advertisement synthetic elements;
respectively inputting a plurality of initial advertisement synthesized elements into a preset standard element tag identification model to obtain standard element tag information corresponding to the initial advertisement synthesized elements;
Calculating the second similarity of the element tag information corresponding to each initial advertisement synthesis element and the standard element tag information;
comparing the magnitude relation between each second similarity and a second preset similarity;
if the second similarity is greater than the second preset similarity, determining the initial advertisement synthesis element corresponding to the second similarity as an advertisement synthesis element;
if the second similarity is smaller than the second preset similarity, acquiring advertisement synthesized elements from a cloud according to the element tag information corresponding to the second similarity, and storing the advertisement synthesized elements acquired from the cloud into the material library;
and synthesizing the advertisement synthesis element and the advertisement synthesis element acquired from the cloud into the intermediate targeted advertisement based on the advertisement generation template.
In some embodiments, when implementing the determining, based on the historical online data of the target client in the preset time period, an interaction mode of the target client with the targeted advertisement, the processor 201 is configured to implement:
based on the historical online data of the target client in the preset time period, acquiring response information of the target client to advertisements put to terminal equipment of the target client in the preset time period;
Determining a mode of interaction between the target client and the advertisement in the preset time period based on response information of the target client to the advertisement put to the terminal equipment of the target client in the preset time period; wherein the way in which the target client interacts with the advertisement within the preset time period comprises at least one of;
aiming at each mode of interaction between the target client and the advertisement in a preset time period, calculating the percentage of the conversion times of the advertisement to the throwing times of the advertisement;
and determining the mode corresponding to the maximum percentage as the interaction mode of the target client and the target advertisement.
In some embodiments, when implementing the sending of the targeted advertisement to the terminal device of the targeted client based on the targeted advertisement and the interaction mode, the processor 201 is configured to implement:
acquiring identity information of the target client, and optimizing the target advertisement based on the identity information;
optimizing the delivery form of the targeted advertisement based on the interaction mode;
acquiring media currently browsed by the target client on the terminal equipment;
And delivering the optimized targeted delivery advertisement to the media in the optimized delivery form.
It should be noted that, for convenience and brevity of description, the specific working process of the terminal device 200 described above may refer to the corresponding process of the interactive advertisement delivery method based on artificial intelligence, and will not be described herein.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by one or more processors to enable the one or more processors to realize the interactive advertisement putting method based on artificial intelligence.
The computer readable storage medium may be an internal storage unit of the terminal device 200 of the foregoing embodiment, for example, a hard disk or a memory of the terminal device 200. The computer readable storage medium may also be an external storage device of the terminal device 200, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which the terminal device 200 is equipped with.
While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (9)
1. An artificial intelligence based interactive advertisement delivery method is characterized by comprising the following steps:
responding to an advertisement putting request of an advertisement putting party, acquiring advertisement putting demand information of the advertisement putting party, and determining the advertisement field of targeted advertisement according to the advertisement putting demand information based on a preset advertisement field decision model;
acquiring a plurality of advertisement generating templates matched with the advertisement field based on big data;
generating a plurality of intermediate targeted advertisements according to the advertisement delivery demand information based on a plurality of advertisement generation templates;
acquiring a plurality of target clients of the target advertisement based on the advertisement delivery demand information;
for each target client, determining the target advertisement in a plurality of intermediate target advertisements based on historical online data of the target client in a preset time period, determining an interaction mode of the target client and the target advertisement based on the historical online data of the target client in the preset time period, and transmitting the target advertisement to terminal equipment of the target client based on the target advertisement and the interaction mode.
2. The artificial intelligence based interactive advertising method as claimed in claim 1, wherein the obtaining a plurality of advertisement generation templates matching the advertisement domain based on big data comprises:
constructing a historical advertisement database based on big data, wherein the historical advertisement database comprises a plurality of mapping relations, and the mapping relations are the mapping relations between the historical advertisements and the information sets of the historical advertisements; wherein the information set comprises advertisement characteristics of the historical advertisements and response information of users to the historical advertisements;
determining a plurality of pending delivery forms of the targeted advertisement according to the advertisement domain and the advertisement characteristics of the historical advertisement based on the advertisement database;
determining a plurality of target delivery forms of the target delivery advertisement based on the plurality of the target delivery forms and response information of the user to the historical advertisement corresponding to the target delivery forms;
and determining a plurality of advertisement generating templates matched with the advertisement field in a preset advertisement generating template database based on the target delivery forms.
3. The artificial intelligence based interactive advertising method as claimed in claim 2, wherein the advertisement characteristics of the history advertisement include a product name of the history advertisement and a delivery form of the history advertisement, the determining a plurality of pending delivery forms of the targeted advertisement based on the advertisement database according to the advertisement field and the advertisement characteristics of the history advertisement includes:
Extracting semantic features of the advertising field and semantic features of each product name based on a preset semantic feature extraction model;
respectively calculating first similarity between semantic features of the advertising field and semantic features of each product name;
and respectively comparing each first similarity with a first preset similarity, and determining the delivery form of the historical advertisement corresponding to the similarity as the undetermined delivery form of the target delivered advertisement if the first similarity is larger than the first preset similarity.
4. The artificial intelligence based interactive advertising method as claimed in claim 1, wherein each of the advertisement generating templates includes a material library and an advertisement generating template, the generating a plurality of intermediate targeted advertisements based on the advertisement generating templates according to the advertisement delivery demand information includes:
generating a plurality of element tag information based on the advertisement delivery demand information;
searching in the material library based on the element tag information to determine a plurality of initial advertisement synthetic elements;
respectively inputting a plurality of initial advertisement synthesized elements into a preset standard element tag identification model to obtain standard element tag information corresponding to the initial advertisement synthesized elements;
Calculating the second similarity of the element tag information corresponding to each initial advertisement synthesis element and the standard element tag information;
comparing the magnitude relation between each second similarity and a second preset similarity;
if the second similarity is greater than the second preset similarity, determining the initial advertisement synthesis element corresponding to the second similarity as an advertisement synthesis element;
if the second similarity is smaller than the second preset similarity, acquiring advertisement synthesized elements from a cloud according to the element tag information corresponding to the second similarity, and storing the advertisement synthesized elements acquired from the cloud into the material library;
and synthesizing the advertisement synthesis element and the advertisement synthesis element acquired from the cloud into the intermediate targeted advertisement based on the advertisement generation template.
5. The artificial intelligence based interactive advertising method according to claim 1, wherein the determining the interaction mode of the target client with the target advertising based on the historical online data of the target client in the preset time period comprises:
based on the historical online data of the target client in the preset time period, acquiring response information of the target client to advertisements put to terminal equipment of the target client in the preset time period;
Determining a mode of interaction between the target client and the advertisement in the preset time period based on response information of the target client to the advertisement put to the terminal equipment of the target client in the preset time period; wherein the way in which the target client interacts with the advertisement within the preset time period comprises at least one of;
aiming at each mode of interaction between the target client and the advertisement in a preset time period, calculating the percentage of the conversion times of the advertisement to the throwing times of the advertisement;
and determining the mode corresponding to the maximum percentage as the interaction mode of the target client and the target advertisement.
6. The interactive advertising method based on artificial intelligence according to claim 1, wherein the sending the targeted advertising to the terminal device of the targeted client based on the targeted advertising and the interactive mode comprises:
acquiring identity information of the target client, and optimizing the target advertisement based on the identity information;
optimizing the delivery form of the targeted advertisement based on the interaction mode;
acquiring media currently browsed by the target client on the terminal equipment;
And delivering the optimized targeted delivery advertisement to the media in the optimized delivery form.
7. An artificial intelligence based interactive advertising system, comprising:
the determining module is used for responding to the advertisement putting request of the advertisement putting party, acquiring the advertisement putting demand information of the advertisement putting party, and determining the advertisement field of the targeted advertisement according to the advertisement putting demand information based on a preset advertisement field decision model;
the first acquisition module is used for acquiring a plurality of advertisement generating templates matched with the advertisement field based on big data;
the generation module is used for generating a plurality of intermediate targeted advertisement according to the advertisement delivery demand information based on a plurality of advertisement generation templates;
the second acquisition module is used for acquiring a plurality of target clients of the target advertisement based on the advertisement delivery demand information;
the sending module is used for determining the target advertisement in a plurality of intermediate target advertisements based on historical online data of the target client in a preset time period, determining an interaction mode of the target client and the target advertisement based on the historical online data of the target client in the preset time period, and sending the target advertisement to terminal equipment of the target client based on the target advertisement and the interaction mode.
8. A terminal device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program when executed by the processor implements the artificial intelligence based interactive advertising method of any one of claims 1 to 6.
9. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, wherein the computer program, when executed by a processor, implements the artificial intelligence based interactive advertising method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310880481.9A CN116777524A (en) | 2023-07-18 | 2023-07-18 | Interactive advertisement putting method and related device based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310880481.9A CN116777524A (en) | 2023-07-18 | 2023-07-18 | Interactive advertisement putting method and related device based on artificial intelligence |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116777524A true CN116777524A (en) | 2023-09-19 |
Family
ID=87985937
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310880481.9A Pending CN116777524A (en) | 2023-07-18 | 2023-07-18 | Interactive advertisement putting method and related device based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116777524A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117314530A (en) * | 2023-11-15 | 2023-12-29 | 北京京拍档科技股份有限公司 | Advertisement putting method and device based on user characteristic analysis advertisement |
| CN117391776A (en) * | 2023-10-17 | 2024-01-12 | 北京维科维众科技发展有限公司 | Advertisement putting method and device, electronic equipment and storage medium |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130025051A (en) * | 2011-09-01 | 2013-03-11 | 에스케이플래닛 주식회사 | Method and system for serving advertizement service based on history, client device and advertizement service apparatus for advertizement service based on history |
| WO2018098780A1 (en) * | 2016-12-01 | 2018-06-07 | 深圳前海达闼云端智能科技有限公司 | Interactive advertisement presentation method, terminal, and smart city interaction system |
| CN110570232A (en) * | 2019-08-05 | 2019-12-13 | 科大讯飞股份有限公司 | Internet advertisement putting method, device, server and storage medium |
| CN111144944A (en) * | 2019-12-27 | 2020-05-12 | 浙江致梦大数据有限公司 | Advertisement putting method and device, electronic equipment and storage medium |
| CN111882362A (en) * | 2020-07-31 | 2020-11-03 | 苏州云开网络科技有限公司 | Artificial intelligence advertisement delivery system based on 5G communication network |
| CN114140150A (en) * | 2021-10-26 | 2022-03-04 | 杭州微财网络科技有限公司 | Interactive advertisement delivery method and system |
| CN115170202A (en) * | 2022-07-25 | 2022-10-11 | 厦门诸云纵横科技有限公司 | Advertisement accurate delivery interaction method and system based on big data analysis |
| CN115545789A (en) * | 2022-10-19 | 2022-12-30 | 杭州面朝信息科技有限公司 | Advertisement putting method and device, electronic equipment and storage medium |
| CN115775163A (en) * | 2022-12-07 | 2023-03-10 | 山东浪潮质量链科技有限公司 | Programmed advertisement putting method, device and medium based on big data |
| CN116308556A (en) * | 2023-05-25 | 2023-06-23 | 北京吉欣科技有限公司 | Advertisement pushing method and system based on Internet of things |
| CN116402539A (en) * | 2023-06-08 | 2023-07-07 | 北京吉欣科技有限公司 | Method, system, equipment and storage medium for guest development based on big data |
-
2023
- 2023-07-18 CN CN202310880481.9A patent/CN116777524A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130025051A (en) * | 2011-09-01 | 2013-03-11 | 에스케이플래닛 주식회사 | Method and system for serving advertizement service based on history, client device and advertizement service apparatus for advertizement service based on history |
| WO2018098780A1 (en) * | 2016-12-01 | 2018-06-07 | 深圳前海达闼云端智能科技有限公司 | Interactive advertisement presentation method, terminal, and smart city interaction system |
| CN110570232A (en) * | 2019-08-05 | 2019-12-13 | 科大讯飞股份有限公司 | Internet advertisement putting method, device, server and storage medium |
| CN111144944A (en) * | 2019-12-27 | 2020-05-12 | 浙江致梦大数据有限公司 | Advertisement putting method and device, electronic equipment and storage medium |
| CN111882362A (en) * | 2020-07-31 | 2020-11-03 | 苏州云开网络科技有限公司 | Artificial intelligence advertisement delivery system based on 5G communication network |
| CN114140150A (en) * | 2021-10-26 | 2022-03-04 | 杭州微财网络科技有限公司 | Interactive advertisement delivery method and system |
| CN115170202A (en) * | 2022-07-25 | 2022-10-11 | 厦门诸云纵横科技有限公司 | Advertisement accurate delivery interaction method and system based on big data analysis |
| CN115545789A (en) * | 2022-10-19 | 2022-12-30 | 杭州面朝信息科技有限公司 | Advertisement putting method and device, electronic equipment and storage medium |
| CN115775163A (en) * | 2022-12-07 | 2023-03-10 | 山东浪潮质量链科技有限公司 | Programmed advertisement putting method, device and medium based on big data |
| CN116308556A (en) * | 2023-05-25 | 2023-06-23 | 北京吉欣科技有限公司 | Advertisement pushing method and system based on Internet of things |
| CN116402539A (en) * | 2023-06-08 | 2023-07-07 | 北京吉欣科技有限公司 | Method, system, equipment and storage medium for guest development based on big data |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117391776A (en) * | 2023-10-17 | 2024-01-12 | 北京维科维众科技发展有限公司 | Advertisement putting method and device, electronic equipment and storage medium |
| CN117314530A (en) * | 2023-11-15 | 2023-12-29 | 北京京拍档科技股份有限公司 | Advertisement putting method and device based on user characteristic analysis advertisement |
| CN117314530B (en) * | 2023-11-15 | 2024-03-19 | 北京京拍档科技股份有限公司 | Advertisement putting method and device based on user characteristic analysis advertisement |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116777524A (en) | Interactive advertisement putting method and related device based on artificial intelligence | |
| CN106951422B (en) | Webpage training method and device, and search intention identification method and device | |
| KR102122373B1 (en) | Method and apparatus for obtaining user portrait | |
| US10529011B2 (en) | Method and system of determining an optimal value of an auction parameter for a digital object | |
| US20240143700A1 (en) | Multimodal Image Classifier using Textual and Visual Embeddings | |
| CN112231584B (en) | Data pushing method and device based on small sample transfer learning and computer equipment | |
| CN111651704B (en) | Content recommendation method, computing device and storage medium | |
| CN111782772A (en) | Text automatic generation method, device, equipment and medium based on OCR technology | |
| WO2019113977A1 (en) | Method, device, and server for processing written articles, and storage medium | |
| US10949000B2 (en) | Sticker recommendation method and apparatus | |
| WO2017157149A1 (en) | Social network-based recommendation method and apparatus, server and storage medium | |
| JP7206072B2 (en) | Improved request handling | |
| CN111582913B (en) | Advertisement recommendation method and device | |
| US9665941B2 (en) | Object segmentation | |
| CN116402539B (en) | Method, system, equipment and storage medium for guest development based on big data | |
| JP6501939B2 (en) | Model generation device, information distribution device, model generation method, information distribution method, model generation program, information distribution program, estimation device and estimation method | |
| US20230252991A1 (en) | Artificial Assistant System Notifications | |
| CN112989213B (en) | Content recommendation method, device and system, electronic equipment and storage medium | |
| CN111209725B (en) | Text information generation method and device and computing equipment | |
| CN116701637B (en) | Zero sample text classification method, system and medium based on CLIP | |
| CN111382254A (en) | Electronic business card recommendation method, device, equipment and computer readable storage medium | |
| US11087045B1 (en) | Apparatus and method for option data object performance prediction and modeling | |
| US20200279299A1 (en) | Systems and methods for generating personalized advertisements | |
| JP7126808B2 (en) | Information processing device and program for information processing device | |
| US11928568B1 (en) | Training and using machine learning models to place effective mixed-modality messages |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |