CN112435065B - Mobile internet non-standard position interactive advertisement putting method and system - Google Patents
Mobile internet non-standard position interactive advertisement putting method and system Download PDFInfo
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Abstract
The application relates to a method and a system for mobile internet non-bid-position interactive advertisement delivery, wherein the method for mobile internet non-bid-position interactive advertisement delivery can be used for pushing advertisement materials most relevant to current user information to a non-bid-position advertisement position by acquiring the current user information sent by an application program and calling a material recommendation algorithm so as to attract users to click. After the user clicks the advertisement material to enter the interactive page, the interactive assembly and the interactive program which are most relevant to the current user information are put into the interactive page by calling an interactive recommendation algorithm, so that the user participation degree can be improved through the mode of an interactive game, the user is stimulated to click the advertisement which is pushed subsequently, and the conversion rate of the advertisement which is pushed subsequently is improved. And finally, at least one advertisement which is most relevant to the current user information is put into the user mobile terminal, so that the association degree of the pushed advertisement and the user is further improved, and the conversion rate of the advertisement is further improved.
Description
Technical Field
The application relates to the technical field of advertisement delivery, in particular to a method and a system for non-bid-position interactive advertisement delivery of a mobile internet.
Background
In the mobile internet era, advertisements are put on mobile terminals, and the mobile terminals become a common advertisement putting means of various large advertisement platforms. When a user browses a mobile phone APP, such as a news APP, corresponding advertisements, such as information streams, are recommended when browsing news.
The traditional mobile internet non-standard position interactive advertisement putting method is characterized in that images or videos related to advertisement contents are put in a media APP so as to attract users to click. However, the advertisement delivery content is limited to the form of images and videos, so that the advertisement platform can only attract users to click according to advertisement creatives to perform advertisement conversion, the user motivation is not strong, the user participation rate is low, and the final advertisement conversion rate is low.
Disclosure of Invention
Based on the above, it is necessary to provide a method and a system for non-bid-location interactive advertisement delivery of a mobile internet for solving the problem of low advertisement conversion rate of the conventional method for non-bid-location interactive advertisement delivery of the mobile internet.
The application provides a mobile internet non-bid position interactive advertisement putting method, which comprises the following steps:
when a page with a non-standard position advertisement position in an application program of a user mobile terminal is opened, current user information sent by the application program is obtained, a material recommendation algorithm is called, advertisement materials most relevant to the current user information and interactive page links associated with the advertisement materials are pushed to the application program, so that the advertisement materials most relevant to the current user information are put into the non-standard position advertisement position;
when an interactive page associated with advertisement materials is opened, an interactive recommendation algorithm is called, and an interactive component most relevant to current user information and an interactive program corresponding to the interactive component are put into the interactive page;
when the interaction component most relevant to the current user information is clicked and the execution of the interaction program corresponding to the interaction component is completed, an advertisement recommendation algorithm is called, and at least one advertisement most relevant to the current user information is put into the user mobile terminal.
Further, obtaining current user information sent by an application program, calling a material recommendation algorithm, pushing advertisement materials most relevant to the current user information to the application program, and an interactive page link associated with the advertisement materials, wherein the method comprises the following steps:
when a page with a non-standard position advertisement position in an application program of a user mobile terminal is opened, receiving a material throwing request sent by the application program and current user information sent by the application program; the current user information includes a plurality of user characteristics;
traversing all advertisement materials in a local database, and sequentially calculating a first feature matching degree of each advertisement material and current user information;
the first feature matching degree is sequenced according to the sequence from big to small, and the advertisement materials with the largest first feature matching degree are selected as the advertisement materials most relevant to the current user information; n is a positive integer and less than or equal to 3;
and establishing association between N advertisement materials most relevant to the current user information and an interactive page, generating an interactive page link, and pushing N advertisement materials most relevant to the current user information to the application program to link with the interactive page.
Further, traversing all advertisement materials in the local database, and sequentially calculating a first feature matching degree of each advertisement material and the current user information, wherein the method comprises the following steps:
traversing all advertisement materials in a local database to obtain a characteristic label of each advertisement material;
selecting an advertisement material, and calculating a first characteristic matching degree of the advertisement material and current user information according to a formula 1;
equation 1;
wherein lambda is the first feature matching degree, A is the total number of feature labels identical to the user features, and B is the total number of the user features;
and repeatedly executing the step of calculating the first feature matching degree until the first feature matching degree of all the advertisement materials and the current user information is calculated.
Further, before traversing all the advertisement materials in the local database and sequentially calculating the first feature matching degree of each advertisement material and the current user information, the method further comprises the following steps:
calling CPC charging history records of the advertisement space;
judging whether the total cost benefit amount of the non-bid-position advertisement position in a preset time period is larger than a benefit threshold value or not;
if the total cost benefit amount of the non-bid-position advertisement position in the preset time period is greater than a benefit threshold value, executing the subsequent step of calculating the first feature matching degree;
if the total cost benefit amount of the non-bid-position advertisement position in the preset time period is smaller than or equal to the benefit threshold value, the follow-up steps are terminated.
Further, after determining whether the total amount of fee profit of the non-bid-position advertisement space within the preset time period is greater than the profit threshold, the method further comprises:
if the total cost benefit amount of the non-bid-position advertisement position in the preset time period is greater than a benefit threshold value, further calling a preset flow configuration;
matching the preset flow configuration with the current user information, and judging whether the current user falls in a user group range limited by the preset flow configuration according to a matching result;
if the current user falls in the user group range defined by the preset flow configuration, executing the subsequent step of calculating the first feature matching degree;
if the current user does not fall within the user group range defined by the preset flow configuration, the follow-up steps are terminated.
Further, invoking an interaction recommendation algorithm, and putting an interaction component most related to the current user information and an interaction program corresponding to the interaction component into the interaction page, wherein the interaction program comprises:
judging whether an interactive page associated with the advertisement material is accessed;
if the interactive page is accessed, traversing all the interactive components in the local database, and sequentially calculating the second feature matching degree of each interactive component and the current user information;
the second feature matching degree is sequenced according to the sequence from the big to the small, and the interaction component corresponding to the largest second feature matching degree is selected as the interaction component most relevant to the current user information;
and acquiring an interaction program corresponding to the interaction component most relevant to the current user information, pushing the interaction component most relevant to the user characteristics to the interaction page, and pushing the interaction program corresponding to the interaction component.
Further, invoking an interaction recommendation algorithm, and putting an interaction component most related to the current user information and an interaction program corresponding to the interaction component into the interaction page, wherein the interaction program comprises:
judging whether an interactive page associated with the advertisement material is accessed;
if the interactive page is accessed, traversing all the interactive components in the local database, and sequentially calculating the second feature matching degree of each interactive component and the current user information;
sequencing the second feature matching degrees according to the sequence from large to small, and selecting the first M interaction components with the largest second feature matching degrees as undetermined components; m is a positive integer and is less than or equal to 10;
invoking the user participation rate of each undetermined component in the non-standard position advertisement position, and selecting the undetermined component with the maximum user participation rate as the interaction component most relevant to the current user information;
and acquiring an interaction program corresponding to the interaction component most relevant to the current user information, pushing the interaction component most relevant to the user characteristics to the interaction page, and pushing the interaction program corresponding to the interaction component.
Further, invoking an advertisement recommendation algorithm to deliver at least one advertisement most relevant to the current user information to the user mobile terminal, including:
when the interactive component most relevant to the user characteristic is clicked and the interactive program corresponding to the interactive component is executed, the preset flow configuration is called;
filtering all advertisements to be put in a local database according to the preset flow configuration to obtain K advertisements to be put which accord with the preset flow configuration; k is a positive integer and is less than or equal to 50;
calculating the placement score of each advertisement to be placed according with the preset flow configuration in sequence;
sequencing the placement scores according to the sequence from big to small, and placing the top L advertisements to be placed with the largest placement scores to the user mobile terminal; l is a positive integer and L is less than or equal to 3.
Further, calculating the placement score of each advertisement to be placed according with the preset flow configuration in turn includes:
acquiring a preset cost benefit weight and a feature matching weight;
selecting an advertisement to be placed according with a preset flow configuration, and calculating the cost benefit score of the advertisement to be placed according to a formula 2;
equation 2;
wherein X is the cost benefit score of the advertisement to be placed, X i For the total amount of revenue for the ad to be placed in the ad slot,i is the cost gain weight, i is the serial number of the advertisement to be put;
calculating a third feature matching degree of the advertisement to be put and the current user information;
calculating the user characteristic matching score of the advertisement to be placed according to a formula 3;
equation 3;
wherein Y is the user feature matching score of the advertisement to be placed, Y i For a third feature matching of the advertisement to be placed with the current user information,the feature matching weight is that i is the sequence number of the advertisement to be put;
and calculating the sum of the cost benefit score and the user characteristic matching score of the advertisement to be placed, and taking the sum of the cost benefit score and the user characteristic matching score as the placement score of the advertisement to be placed.
And repeatedly executing the step of calculating the placement score until the placement score of each advertisement to be placed which accords with the preset flow configuration is calculated.
The application also provides an advertisement delivery system, comprising:
a user mobile terminal;
and the advertisement delivery platform is in communication connection with the user mobile terminal and is used for executing the mobile internet non-standard position interactive advertisement delivery method mentioned in the content.
The application relates to a method and a system for non-bid-position interactive advertisement delivery of a mobile Internet, which can realize pushing advertisement materials most relevant to current user information to a non-bid-position advertisement position to an application program by acquiring the current user information sent by the application program and calling a material recommendation algorithm so as to attract users to click. After the user clicks the advertisement material to enter the interactive page, the interactive assembly and the interactive program which are most relevant to the current user information are put into the interactive page by calling an interactive recommendation algorithm, so that the user participation degree can be improved through the mode of an interactive game, the user is stimulated to click the advertisement which is pushed subsequently, and the conversion rate of the advertisement which is pushed subsequently is improved. And finally, at least one advertisement which is most relevant to the current user information is put into the user mobile terminal, so that the association degree of the pushed advertisement and the user is further improved, and the conversion rate of the advertisement is further improved.
Drawings
FIG. 1 is a flowchart of a method for non-bid-location interactive advertisement delivery of a mobile Internet according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an advertisement delivery system according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The application provides a non-target position interactive advertisement putting method of a mobile internet. It should be noted that, the mobile internet non-bid location interactive advertisement delivery method provided by the application is applied to any type of advertisement delivery scheme.
In addition, the non-target location interactive advertisement putting method of the mobile internet is not limited to an execution subject. Optionally, the execution subject of the mobile internet non-bid location interactive advertisement delivery method provided by the application may be an advertisement delivery platform or an advertisement delivery end.
In an embodiment of the present application, the method for delivering non-bid-location interactive advertisement on the mobile internet includes the following steps S100 to S300:
s100, when a page with a non-bid-position advertisement position in an application program of a user mobile terminal is opened, the advertisement putting platform acquires current user information sent by the application program. Further, the advertisement delivery platform invokes a material recommendation algorithm to push advertisement materials most relevant to the current user information to the application program, and an interactive page link associated with the advertisement materials to enable the advertisement materials most relevant to the current user information to be delivered to the non-bid-location advertisement space.
Specifically, the advertisement material may be pictures or text. Alternatively, the advertising material may be dynamic pictures or text to attract user clicks. The application program of the user mobile terminal is a media application program, such as panning, curiosity, etc.
Non-bid-location ad slots mean non-standard locations where ads are planned to be placed. Non-target positions are concepts with respect to standard positions. The standard position is a media conspicuous position, and is a position that is easy for the user to observe and click. The advertisement put in the standard position has long exposure time and strong material showing capability, but the cost of putting the flow is higher.
The advertisement material is selected to be put in the non-standard position, and the advertisement material has high relativity with the user information, so that the advertisement material can be attracted to users to click, and meanwhile, the traffic cost of the advertisement position is low, and the advertisement position which is not utilized by the media application program originally can be utilized. For example, the user is female, and the advertising material placed may be small animals.
S200, when the interactive page associated with the advertisement material is opened, the advertisement delivery platform calls an interactive recommendation algorithm, and delivers the interactive component most relevant to the current user information and the interactive program corresponding to the interactive component to the interactive page.
Specifically, the advertising material is associated with an interactive page. After the user clicks the advertisement material, the application program of the user mobile terminal automatically jumps to the interactive page.
Further, an interactive component most relevant to the current user information and an interactive program corresponding to the interactive component are put into the interactive page. The interaction assembly and the interaction program are corresponding and matched, and under the support of the interaction assembly and the interaction program, the interaction page can be used for presenting an interaction game. Because the relativity of the interaction assembly and the current user information is high, after the user clicks the interaction assembly, the interaction program operates to guide the user to enter an interaction game. For example, the user is female, the interactive component may be a rabbit graphic, and the user may enter a "rabbit-in-rabbit" interactive game after clicking the rabbit graphic. After the user participates in the interactive game, the user participation degree is improved, so that the driving force of the user for clicking the advertisement later is improved.
And S300, when the interaction component most relevant to the current user information is clicked and the execution of the interaction program corresponding to the interaction component is finished, the advertisement delivery platform calls an advertisement recommendation algorithm and delivers at least one advertisement most relevant to the current user information to the user mobile terminal.
Specifically, the finally pushed advertisement has the highest relativity with the user information, so that the user can be stimulated to click the advertisement to the greatest extent by adding the previous interactive game, and the advertisement conversion is completed.
In this embodiment, by acquiring the current user information sent by the application program and invoking the material recommendation algorithm, the advertisement material most relevant to the current user information can be pushed to the non-standard advertisement position by the application program, so as to attract the user to click. After the user clicks the advertisement material to enter the interactive page, the interactive assembly and the interactive program which are most relevant to the current user information are put into the interactive page by calling an interactive recommendation algorithm, so that the user participation degree can be improved through the mode of an interactive game, the user is stimulated to click the advertisement which is pushed subsequently, and the conversion rate of the advertisement which is pushed subsequently is improved. And finally, at least one advertisement which is most relevant to the current user information is put into the user mobile terminal, so that the association degree of the pushed advertisement and the user is further improved, and the conversion rate of the advertisement is further improved.
In an embodiment of the present application, the step S100 includes the following steps S110 to S170:
s110, when a page with a non-bid-position advertisement position in an application program of the user mobile terminal is opened, the advertisement delivery platform receives a material delivery request sent by the application program and current user information sent by the application program. The current user information includes a plurality of user characteristics.
Specifically, when the user browses a certain page of the application program, if the page just has a non-bid-position advertisement position, for example, when the user browses a certain television episode of an aiqi art, one unobtrusive position in the lower right corner just is a preset non-bid-position advertisement position, then the application program of the user mobile terminal sends a material delivery request to the user mobile terminal. Meanwhile, the application program of the user mobile terminal acquires the current user information and sends the current user information to the advertisement putting platform in real time.
The current user information includes a plurality of user characteristics. The user characteristics may include identity information (name, age, gender, etc.), social information (home, home address, academic, income bracket, etc.), preference information (sports, social activities, books, etc.) of the user. Of course, the user features are not limited to the above. The user characteristics may be automatically obtained by the application when the user registers an application account. The user characteristics can also be obtained by the application program according to the browsing history information of the user in the process of using the application program by the user.
S130, traversing all advertisement materials in the local database by the advertisement delivery platform, and sequentially calculating the first feature matching degree of each advertisement material and the current user information.
Specifically, the local database of the advertisement delivery platform stores a plurality of advertisement materials in advance. The advertisement putting platform sequentially calculates the feature matching degree of each advertisement material and the current user information, and the feature matching degree is recorded as a first feature matching degree. The first feature matching degree represents a matching degree of the advertisement material and the current user information.
And S150, the advertisement putting platform sorts the first feature matching degrees according to the sequence from large to small, and selects the first N advertisement materials with the largest first feature matching degree as the advertisement materials most relevant to the current user information. N is a positive integer and less than or equal to 3.
Specifically, the greater the first feature matching degree, the higher the matching degree of the advertisement material and the current user information. Optionally, a plurality of advertisement materials with the largest matching degree of the first features can be selected so as to facilitate subsequent delivery.
S170, the advertisement delivery platform establishes association between N advertisement materials most relevant to the current user information and an interactive page to generate an interactive page link. Further, the advertisement delivery platform pushes N advertisement materials most relevant to the current user information to the application program to be linked with the interactive page.
Specifically, after step S170, step S100 further includes:
and S180, the advertisement delivery platform pushes N advertisement materials most relevant to the current user information and interactive page links associated with the advertisement materials to the application program, so that the advertisement materials most relevant to the current user information are delivered to the non-bid-position advertisement space.
Finally, the application program displays N advertisement materials most relevant to the current user information in the non-bid-position advertisement space. For example, N may be 3, and the advertisement materials with the largest matching degree of the first feature are the squirrel, the little rabbit and the little raccoon, so that the 3 advertisement materials may be placed in the advertisement position at the non-standard position, and the user is attracted to click. In this way, while the advertising material is displayed in a non-bid location advertising spot, it is less noticeable, but because of the higher relevance of the advertising material to the current user information, the user may prefer to click on the advertising material.
In this embodiment, by acquiring the current user information sent by the application program, the first feature matching degree of each advertisement material and the current user information is sequentially calculated, so that the advertisement material most relevant to the current user information can be pushed to the non-standard advertisement position by the application program, so as to attract the user to click.
In an embodiment of the present application, the step S130 includes the following steps S131 to S133:
s131, the advertisement delivery platform traverses all advertisement materials in the local database to obtain the feature tag of each advertisement material.
Specifically, each advertising material also has one or more feature tags, which are on the same principle as the user features that the current user information includes. For example, the advertising material may have a male, 21 years old, basketball 3 feature tags, and the current user information of the current user information may have a male, 20 years old, ball sports 3 feature tags.
S132, selecting an advertisement material, and calculating the first feature matching degree of the advertisement material and the current user information according to a formula 1 by the advertisement delivery platform.
Equation 1
Wherein λ is the first feature matching degree. A is the same feature tag total as the user feature. B is the total number of user features.
Specifically, for example, the advertisement material has a male, 25 to 30 years old, basketball 3 feature tags, and the current user information of the current user information has a male, 19 years old, basketball 3 feature tags. Then a is 2 and b is 3, then the first feature matching degree λ is 66.67%.
And S133, repeatedly executing the steps S131 to S132 until the first feature matching degree of all the advertisement materials and the current user information is calculated.
Specifically, after all the advertisement materials and the first feature matching degree of the current user information are calculated, a feature matching degree list can be generated, and the first feature matching degree of each advertisement material and the current user information is recorded.
In this embodiment, the first feature matching degree of each advertisement material and the current user information is calculated through the formula 1, so that the advertisement delivery platform can reasonably and accurately evaluate the correlation between the advertisement material and the current user information through the first feature matching degree, and a data basis is provided for delivering the advertisement material most relevant to the current user information in the non-standard position advertisement position.
In an embodiment of the present application, before the step S130, the step S100 further includes the following steps S121 to S124:
s121, the advertisement putting platform calls CPC charging history records of the advertisement positions at the non-standard positions.
Specifically, CPC is an english abbreviation of "Cost Per Click", i.e., a manner of charging once Per Click. Therefore, in step S121, the total amount of fee and profit of the non-bid advertisement space in the preset time period may also be calculated by retrieving the CPC billing history of the non-bid advertisement space.
After calculating the total amount of fee returns for the non-bid-position ad slots within the preset time period, a subsequent step S122 is performed.
S122, the advertisement putting platform judges whether the total cost and benefit amount of the non-bid-position advertisement space in the preset time period is larger than a benefit threshold value.
Specifically, the benefit threshold may be preset.
S123, if the total amount of fee profit of the non-bid-position advertisement space in the preset time period is greater than the profit threshold, executing the following step S130.
Specifically, if the total cost and benefit amount of the non-bid advertisement position in the preset time period is greater than the benefit threshold, it is indicated that advertisement is reasonable to be placed in the non-bid advertisement position, the benefit is acceptable, and the subsequent step of calculating the first feature matching degree of the advertisement material and the current user information can be executed.
S124, if the total amount of fee profit of the non-target advertisement position in the preset time period is less than or equal to the profit threshold, the subsequent steps are terminated.
Specifically, if the total cost profit amount of the non-bid advertisement position in the preset time period is smaller than or equal to the profit threshold value, it is indicated that profit loss is generated when the advertisement is placed in the non-bid advertisement position, and it is unreasonable to place the advertisement in the non-bid advertisement position, so that the first feature matching degree is calculated again later, and the user has no meaning when clicking the advertisement material, and the subsequent steps are terminated.
In this embodiment, before calculating the first feature matching degree of the advertisement material and the current user information, it is determined whether the total cost and benefit amount of the non-bid-position advertisement space in the preset time period is greater than the benefit threshold value, so that cost and benefit analysis on the non-bid-position advertisement space can be implemented, and advertisement material loss can be avoided.
In an embodiment of the present application, after the step S122, the step S100 further includes the following steps S125 to S128:
s125, if the total cost and benefit amount of the non-target advertisement position in the preset time period is greater than the benefit threshold value, the advertisement putting platform further calls the preset flow configuration.
Specifically, after calculating the fee benefit, the present embodiment further analyzes whether the user population range is in accordance with the preset flow configuration.
And S126, the advertisement delivery platform matches the preset flow configuration with the current user information, and judges whether the current user falls in the user group range limited by the preset flow configuration according to the matching result.
Specifically, the preset traffic profile defines a community of users range. The preset traffic profile represents advertiser demand or other hard specification or special restrictions.
S127, if the current user falls within the user group range defined by the preset flow configuration, a subsequent step S130 is executed.
Specifically, for example, the preset traffic configuration defines the user group range of the beijing users, if it is determined that the current user is the beijing user according to the current user information, it indicates that the current user falls within the user group range defined by the preset traffic configuration, and the subsequent step S130 may be performed.
And S128, if the current user does not fall within the user group range defined by the preset flow configuration, terminating the subsequent steps.
Specifically, for example, the advertisement put by us is game advertisement, the advertisement put by us is oriented to adult users, the preset flow configuration limits the range of the user group of the adult users, if the current user information or the current user is 14 years old child, the advertisement put platform automatically judges that the advertisement material and the advertisement can not be put continuously, and the follow-up steps are terminated. In this example, since the minors are prevented from being indulged in the network game, the user click advertising material which is not in the user group range can be effectively filtered out by the embodiment.
In this embodiment, whether the current user falls within the user group range defined by the preset traffic configuration is determined according to the current user information, so that users not within the user group range can be effectively filtered, and advertisement materials which appear in non-standard advertisement positions cannot be clicked.
In an embodiment of the present application, the step S200 includes the following steps S211 to S214:
s211, the advertisement delivery platform judges whether an interactive page associated with the advertisement material is accessed.
And S212, if the interactive page is accessed, traversing all the interactive components in the local database by the advertisement delivery platform, and sequentially calculating the second feature matching degree of each interactive component and the current user information.
And S213, the advertisement putting platform sorts the second feature matching degree according to the sequence from large to small, and selects the interaction component corresponding to the second feature matching degree with the largest numerical value as the interaction component most relevant to the current user information.
S214, the advertisement delivery platform acquires an interaction program corresponding to the interaction component most relevant to the current user information, and pushes the interaction component most relevant to the user characteristics and the interaction program corresponding to the interaction component to the interaction page.
Specifically, the calculation principle of the second feature matching degree is the same as that of the first feature matching degree, please refer to step S131 to step S133, which will not be repeated here.
In this embodiment, the second feature matching degree of each interaction component and the current user information is calculated, so that the advertisement delivery platform can reasonably and accurately evaluate the correlation between the interaction components and the current user information through the second feature matching degree, so that the advertisement delivery platform can automatically push the interaction component most relevant to the user features to the interaction page, thereby attracting the user to click the interaction components to start the interaction program, entering the interaction game, and improving the conversion rate of the subsequent delivered advertisements on the side surface.
In an embodiment of the present application, the step S200 includes the following steps S221 to S225:
s221, the advertisement delivery platform judges whether an interactive page associated with the advertisement material is accessed.
And S222, if the interactive page is accessed, traversing all the interactive components in the local database by the advertisement delivery platform, and sequentially calculating the second feature matching degree of each interactive component and the current user information.
S223, the advertisement putting platform sorts the second feature matching degrees according to the sequence from large to small, and selects the first M interaction components with the largest second feature matching degrees as undetermined components. M is a positive integer and less than or equal to 10.
S224, the advertisement putting platform invokes the user participation rate of each undetermined component in the non-standard position advertisement position, and selects the undetermined component with the largest user participation rate as the interaction component most relevant to the current user information.
S225, the advertisement delivery platform acquires an interaction program corresponding to the interaction component most relevant to the current user information, and pushes the interaction component most relevant to the user characteristics and the interaction program corresponding to the interaction component to the interaction page.
Specifically, steps S211 to S214 describe an embodiment of a method for confirming the interactive component most relevant to the current user information. The present embodiment describes another method for confirming the interactive component most relevant to the current user information.
In step S211 to step S214, the interaction component corresponding to the second feature matching degree with the largest value is selected as the interaction component most relevant to the current user information, and is unique.
In this embodiment, M interactive components with the largest matching degree of the second features are selected first, and then the interactive component with the largest user participation rate is further selected from the selected interactive components as the interactive component most relevant to the current user information.
The user engagement rate may be obtained by querying the interaction component click history. Which interaction component is clicked most often, the surface user participation rate is the largest.
In this embodiment, by coupling the evaluation result of the user participation rate and the evaluation result of the second feature matching degree, the finally obtained interaction component most relevant to the user feature has high matching degree with the user feature, and the user participation rate is high, so that the purpose of attracting the user to click can be achieved to the maximum.
In an embodiment of the present application, the step S300 includes the following steps S310 to S340:
s310, when the interaction component most relevant to the user characteristic is clicked and the interaction program corresponding to the interaction component is executed, the advertisement delivery platform invokes the preset flow configuration.
S320, filtering all advertisements to be delivered in the local database by the advertisement delivery platform according to the preset flow configuration to obtain K advertisements to be delivered which accord with the preset flow configuration. K is a positive integer and is less than or equal to 50.
S330, the advertisement delivery platform sequentially calculates the delivery score of each advertisement to be delivered, which accords with the preset flow configuration.
S340, the advertisement delivery platform sorts the delivery scores according to the order from large to small, and delivers the advertisement to be delivered with the largest delivery score of the first L to the user mobile terminal. L is a positive integer and L is less than or equal to 3.
Specifically, the foregoing steps S125 to S128 have described the analysis process of the current user and the preset traffic profile, and filter out the users that are not in the user group. In this embodiment, the filtering principle of step S320 is the same as that of step S125 to step S128, and will not be described again here.
Further, the advertisement putting platform sequentially calculates the putting score of each advertisement to be put according with the preset flow configuration, and puts the advertisement to be put with higher putting score. The final advertisement may be multiple.
In this embodiment, by filtering the advertisement to be placed, automatic filtering of the advertisement to be placed which does not conform to the preset flow configuration can be achieved.
In an embodiment of the present application, the step S330 includes the following steps S331 to S336:
s331, the advertisement putting platform obtains preset cost benefit weight and feature matching weight.
S332, selecting an advertisement to be placed according with the preset flow configuration, and calculating the cost benefit score of the advertisement to be placed according to the formula 2 by the advertisement placement platform.
Equation 2
Wherein X is the cost benefit score of the advertisement to be placed. X is X i The sum of the fee returns for the ad to be placed in the non-bid position.Is the cost benefit weight. i is the serial number of the advertisement to be put.
S333, the advertisement putting platform calculates the third feature matching degree of the advertisement to be put and the current user information.
S334, calculating the user characteristic matching score of the advertisement to be placed according to the formula 3.
Equation 3
Wherein Y is the user feature matching score of the advertisement to be placed. Y is Y i And the third feature matching degree of the advertisement to be put and the current user information is obtained.Weights are matched for the features. i is the serial number of the advertisement to be put.
S335, the advertisement putting platform calculates the sum of the cost benefit score of the advertisement to be put and the user characteristic matching score. And taking the sum of the fee benefit score and the user characteristic matching score as the putting score of the putting advertisement.
S336, repeating the steps S331 to S335 until the placement score of each advertisement to be placed which accords with the preset flow configuration is obtained.
Specifically, a specific calculation method of the placement score is described in this embodiment. In this embodiment, the house score is composed of a fee benefit score and a user feature matching score, so that the L advertisements to be delivered with the largest delivering scores are obtained, the fee benefit is the largest, the advertisement is most relevant to the current user information, and the weight can be allocated freely.
In this embodiment, by calculating the cost benefit score and the user feature matching score respectively and summing the two scores as the placement score, the matching degree and the cost benefit of the advertisement placed in the non-bid position advertisement position finally reach the maximization, and the weight of the matching degree and the cost benefit of the user can be freely allocated.
The application provides an advertisement delivery system.
In one embodiment of the present application, the advertisement delivery system includes a user mobile terminal and an advertisement delivery platform. And the advertisement putting platform is in communication connection with the user mobile terminal. The advertisement delivery platform is used for executing the mobile internet non-standard position interactive advertisement delivery method mentioned in the previous description.
Specifically, the foregoing has already described the beneficial effects of the mobile internet non-target location interactive advertisement delivery method, so the beneficial effects of the advertisement delivery system mentioned in this embodiment are not repeated.
The technical features of the above embodiments may be combined arbitrarily, and the steps of the method are not limited to the execution sequence, so that all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description of the present specification.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (6)
1. The non-bid position interactive advertisement putting method of the mobile internet is characterized by comprising the following steps of:
s100, when a page with a non-bid-position advertisement position in an application program of a user mobile terminal is opened, current user information sent by the application program is obtained, a material recommendation algorithm is called, advertisement materials most relevant to the current user information and interactive page links associated with the advertisement materials are pushed to the application program, and therefore the advertisement materials most relevant to the current user information are put into the non-bid-position advertisement position;
the step S100 includes:
s110, when a page with a non-bid-position advertisement position in an application program of a user mobile terminal is opened, receiving a material throwing request sent by the application program and current user information sent by the application program; the current user information includes a plurality of user characteristics;
s130, traversing all advertisement materials in a local database, and sequentially calculating a first feature matching degree of each advertisement material and current user information;
s150, sorting the first feature matching degrees according to the sequence from large to small, and selecting the first N advertisement materials with the largest first feature matching degree as the advertisement materials most relevant to the current user information; n is a positive integer and less than or equal to 3;
s170, establishing association between N advertisement materials most relevant to the current user information and an interactive page, generating an interactive page link, and pushing N advertisement materials most relevant to the current user information to the application program to link with the interactive page;
s200, when an interactive page associated with advertisement materials is opened, an interactive recommendation algorithm is called, and an interactive component most relevant to current user information and an interactive program corresponding to the interactive component are put into the interactive page;
the step S200 includes:
s211, judging whether an interactive page associated with the advertisement material is accessed;
s212, if the interactive page is accessed, traversing all the interactive components in the local database, and sequentially calculating the second feature matching degree of each interactive component and the current user information;
s213, sorting the second feature matching degree according to the order from large to small, and selecting the interaction component corresponding to the second feature matching degree with the largest numerical value as the interaction component most relevant to the current user information;
s214, acquiring an interaction program corresponding to the interaction component most relevant to the current user information, pushing the interaction component most relevant to the user characteristics to the interaction page, and pushing the interaction program corresponding to the interaction component;
s300, when the interaction component most relevant to the current user information is clicked and the execution of the interaction program corresponding to the interaction component is finished, invoking an advertisement recommendation algorithm, and putting at least one advertisement most relevant to the current user information to the user mobile terminal;
the step S300 includes:
s310, when the interaction component most relevant to the user characteristic is clicked and the interaction program corresponding to the interaction component is executed, the preset flow configuration is called;
s320, filtering all advertisements to be placed in a local database according to the preset flow configuration to obtain K advertisements to be placed which accord with the preset flow configuration; k is a positive integer and is less than or equal to 50;
s330, calculating the placement score of each advertisement to be placed according with the preset flow configuration in sequence;
the step S330 includes:
s331, acquiring a preset cost benefit weight and a feature matching weight;
s332, selecting an advertisement to be placed according with the preset flow configuration, and calculating the cost benefit score of the advertisement to be placed according to a formula 2;
wherein X is the cost benefit score of the advertisement to be placed, X i For the total amount of revenue for the ad to be placed in the non-bid position,i is the cost gain weight, i is the serial number of the advertisement to be put;
s333, calculating a third feature matching degree of the advertisement to be put and the current user information;
s334, calculating the user characteristic matching score of the advertisement to be placed according to the formula 3;
wherein Y is the user feature matching score of the advertisement to be placed, Y i For a third feature matching of the advertisement to be placed with the current user information,the feature matching weight is that i is the sequence number of the advertisement to be put;
s335, calculating the sum of the cost benefit score and the user characteristic matching score of the advertisement to be placed, and taking the sum of the cost benefit score and the user characteristic matching score as the placement score of the advertisement to be placed;
s336, repeatedly executing the steps S331 to S335 until the placement score of each advertisement to be placed which accords with the preset flow configuration is obtained;
s340, sorting the placement scores according to the order from large to small, and placing the first L advertisements to be placed with the largest placement scores to the user mobile terminal; l is a positive integer and L is less than or equal to 3.
2. The method for non-target location interactive advertisement delivery of mobile internet as set forth in claim 1, wherein said step S130 comprises:
s131, traversing all advertisement materials in the local database, and acquiring a characteristic label of each advertisement material;
s132, selecting an advertisement material, and calculating a first feature matching degree of the advertisement material and current user information according to a formula 1;
wherein lambda is the first feature matching degree, A is the total number of feature labels identical to the user features, and B is the total number of the user features;
and S133, repeatedly executing the steps S131 to S132 until the first feature matching degree of all the advertisement materials and the current user information is calculated.
3. The method for non-target location interactive advertisement delivery according to claim 2, wherein prior to said step S130, said step S100 further comprises:
s121, calling CPC charging history records of the non-bid-position advertisement space;
s122, judging whether the total cost benefit amount of the non-bid-position advertisement position in a preset time period is larger than a benefit threshold value;
s123, if the fee profit sum of the non-target advertisement position in the preset time period is greater than the profit threshold, executing the following step S130;
s124, if the total amount of fee profit of the non-target advertisement position in the preset time period is less than or equal to the profit threshold, the subsequent steps are terminated.
4. The method for non-target location interactive advertisement delivery according to claim 3, wherein after said step S122, said step S100 further comprises:
s125, if the total cost benefit amount of the non-target advertisement position in the preset time period is greater than a benefit threshold value, further calling a preset flow configuration;
s126, matching the preset flow configuration with the current user information, and judging whether the current user falls in the user group range limited by the preset flow configuration according to the matching result;
s127, if the current user falls within the user group range defined by the preset flow configuration, executing a subsequent step S130;
and S128, if the current user does not fall within the user group range defined by the preset flow configuration, terminating the subsequent steps.
5. The method for non-target location interactive advertisement delivery of mobile internet as set forth in claim 4, wherein said step S200 comprises:
s221, judging whether an interactive page associated with the advertisement material is accessed;
s222, if the interactive page is accessed, traversing all the interactive components in the local database, and sequentially calculating the second feature matching degree of each interactive component and the current user information;
s223, sorting the second feature matching degrees according to the sequence from large to small, and selecting the first M interaction components with the largest second feature matching degrees as undetermined components; m is a positive integer and is less than or equal to 10;
s224, the user participation rate of each undetermined component in the non-standard position advertisement position is called, and the undetermined component with the largest user participation rate is selected to be used as the interaction component most relevant to the current user information;
s225, acquiring an interaction program corresponding to the interaction component most relevant to the current user information, pushing the interaction component most relevant to the user characteristics to the interaction page, and pushing the interaction program corresponding to the interaction component.
6. An advertising system, comprising:
a user mobile terminal;
and the advertisement delivery platform is in communication connection with the user mobile terminal and is used for executing the mobile internet non-standard position interactive advertisement delivery method according to any one of claims 1-5.
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