CN113222656B - Programmed advertisement putting method, system, device, equipment and storage medium - Google Patents
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Abstract
The invention provides a programmed advertisement putting method, a system, a device, equipment and a storage medium: the method comprises the steps of obtaining log processing data and real-time characteristic data related to a programmed advertisement to be put in; carrying out feature processing on the log processing data and the real-time feature data, and estimating the click rate and the conversion rate of the programmed advertisement to be put on based on the feature processing result; calibrating the click rate and the conversion rate by using the historical posterior data, and setting a target ROI according to the calibrated click rate and conversion rate, so as to calculate the real bid of the programmed advertisement; and carrying out the price quotation of the programmed advertisement to be put and putting the program advertisement with the true bid. The invention solves the problems of cold start, flow quality fluctuation, unstable bidding and the like of advertisements in programmed delivery in the prior art, thereby causing poor delivery effect.
Description
Technical Field
The present invention relates to the field of advertisement delivery technologies, and in particular, to a method, a system, an apparatus, a device, and a storage medium for delivering a programmed advertisement.
Background
In the internet field, programmed advertisement delivery refers to a way that advertisers automatically complete buying and delivering advertisements through a media platform by programs and continuously optimize delivering effects by using feedback data in real time. The existing programming method is to manually put in for a period of time, automatically host and put in and optimize after enough data is accumulated, and the period of cold start for new advertisement programming becomes longer and the cost becomes higher; the flow quality of the media party is unstable, the effect fluctuation of the traditional model is large, automatic bidding is not facilitated, and the whole ROI is reduced.
Disclosure of Invention
The implementation of the invention provides a programmed advertisement putting method, a system, a device, equipment and a storage medium, which solve the problems of cold start, flow quality fluctuation, unstable bidding and the like of advertisements in programmed putting in the prior art, thereby causing poor putting effect.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method of programmed advertising comprising the steps of:
acquiring log processing data and real-time characteristic data related to a programmed advertisement to be put in;
carrying out feature processing on the log processing data and the real-time feature data, and estimating the click rate and the conversion rate of the programmed advertisement to be put on based on the feature processing result;
calibrating the click rate and the conversion rate by using the historical posterior data, and setting a target ROI according to the calibrated click rate and conversion rate, so as to calculate the real bid of the programmed advertisement;
and carrying out the price quotation of the programmed advertisement to be put and putting the program advertisement with the true bid.
As a further improvement of the present invention, the log processing data includes context scenes, advertisement behavior data, and media data;
the real-time features use scene features, advertisement features, and media features.
As a further improvement of the invention, the feature engineering processing package is used for discretizing all real-time features.
As a further improvement of the invention, the pre-estimating of the click rate and conversion rate of the programmed advertisement to be put on based on the feature processing result means that the deep FM model is adopted to perform automatic feature cross processing on the discretized features so as to obtain the pre-estimated click rate and conversion rate of the programmed advertisement.
As a further improvement of the present invention, calibrating click rate and conversion rate using historical posterior data includes:
and carrying out insurance regression on the predicted value of the click rate and the conversion rate of the pre-estimated programmed advertisement by using the real posterior data of the put advertisement.
As a further improvement of the invention, the calculation of the true bid for a programmed advertisement takes the steps of:
based on the calibrated estimated CTR/IVR, a target ROI is set, and an accurate bid is calculated, wherein the specific formula is as follows:
wherein,for bidding, pivr/pctr is a calibrated click rate value and conversion rate value respectively, and the ROI is a set target return on investment; price is price.
A programmed advertisement delivery system, comprising:
the acquisition module is used for acquiring log processing data and real-time characteristic data related to the programmed advertisement to be put in;
the estimating module is used for carrying out feature processing on the log processing data and the real-time feature data, and estimating the click rate and the conversion rate of the programmed advertisement to be put on based on the feature processing result;
the bidding module is used for calibrating the click rate and the conversion rate by utilizing the historical posterior data, and calculating the real bid of the programmed advertisement according to the calibrated click rate and conversion rate and the set target ROI;
and the delivery module is used for carrying out the price quotation of the programmed advertisement to be delivered and delivering the price quotation with the true bid.
A programmed advertising device, comprising:
an estimation model, comprising:
the log processing unit is used for acquiring log processing data related to the programmed advertisements to be put in;
the real-time feature unit is used for acquiring real-time feature data related to the programmed advertisement to be put in;
the feature engineering unit is used for discretizing all the real-time features;
the depth estimation unit is used for carrying out automatic feature intersection processing on the discretized features by adopting a deep FM model to obtain the click rate and the conversion rate of the estimated programmed advertisements;
and an intelligent bidding model, comprising:
an insurance regression unit for performing insurance regression on the predicted value of the click rate and conversion rate of the pre-programmed advertisement by using the real posterior data of the advertisement,
and a calculating bidding unit for calculating the real bid of the programmed advertisement according to the calibrated click rate and conversion rate and setting the target ROI.
An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the programmed advertising method when the computer program is executed.
A computer readable storage medium storing a computer program which when executed by a processor implements the steps of the programmed advertising method.
The beneficial effects of the invention are as follows:
in order to solve the problem of cold start of new advertisements, the advertisement putting is divided into two stages, namely a cold start stage and a putting optimization stage, wherein the cold start stage uses a click rate CTR estimation model to guide bidding due to insufficient data such as conversion, conversion data is accumulated by a certain magnitude, and the advertisement putting enters the putting optimization stage and uses a conversion rate IVR estimation model to guide bidding. The new ad setting will therefore translate the amount threshold to control the current programming delivery at that stage. The invention provides a whole set of solution for programmed advertisement delivery, which predicts intelligent bidding from a model, introduces two-section optimization, and solves the problem of cold start of new advertisements.
Furthermore, the invention discretizes all the characteristics on the model to solve the problem of unstable flow fluctuation.
Drawings
FIG. 1 is a flowchart of a method for delivering a programmed advertisement according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a first embodiment of a programmable advertisement delivery device;
FIG. 3 is a schematic diagram of a programmable advertisement delivery system;
fig. 4 is a schematic structural diagram of an electronic device.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to clearly describe the technical solution of the embodiments of the present invention, in the embodiments of the present invention, the terms "first", "second", etc. are used to distinguish the same item or similar items having substantially the same function or effect, and those skilled in the art will understand that the terms "first", "second", etc. do not limit the number and execution order.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
The term "comprising" when used herein refers to the presence of a feature, element or component, but does not preclude the presence or addition of one or more other features, elements or components.
The invention is described in further detail below with reference to the drawings and examples. The examples are given solely for the purpose of illustration and are not intended to limit the scope of the invention.
In order to solve the problem of cold start of new advertisements, the advertisement putting is divided into two stages, namely a cold start stage and a putting optimization stage, wherein the cold start stage uses a click rate CTR estimation model to guide bidding due to insufficient data such as conversion, conversion data is accumulated by a certain magnitude, and the advertisement putting enters the putting optimization stage and uses a conversion rate IVR estimation model to guide bidding. The new ad setting will therefore translate the amount threshold to control the current programming delivery at that stage.
Specifically, as shown in fig. 1, a first object of the present invention is to provide a method for delivering a programmed advertisement, which includes the following steps:
acquiring log processing data and real-time characteristic data related to a programmed advertisement to be put in;
carrying out feature processing on the log processing data and the real-time feature data, and estimating the click rate and the conversion rate of the programmed advertisement to be put on based on the feature processing result;
calibrating the click rate and the conversion rate by using the historical posterior data, and setting a target ROI according to the calibrated click rate and conversion rate, so as to calculate the real bid of the programmed advertisement;
and carrying out the price quotation of the programmed advertisement to be put and putting the program advertisement with the true bid.
The method aims to solve the problems of large flow mass difference, large model estimated fluctuation, unstable bidding and the like. In the pre-estimated model, all continuous features are discretized, and the continuous features are not directly used, so that fluctuation is prevented; and after model estimation, the posterior calibration is carried out on the pre-estimated value, and the method uses an order preserving regression algorithm for calibration.
Wherein the log processing data comprises context scenes, advertisement behavior data and media data; the real-time features use scene features, advertisement features, and media features.
The feature engineering processing package is to discretize all real-time features. Estimating the click rate and conversion rate of the programmed advertisement to be placed based on the feature processing result refers to performing automatic feature cross processing on the discretized features by adopting a deep FM model to obtain the click rate and conversion rate of the estimated programmed advertisement.
Calibrating click rate and conversion rate using historical posterior data includes: and carrying out insurance regression on the predicted value of the click rate and the conversion rate of the pre-estimated programmed advertisement by using the real posterior data of the put advertisement.
Based on the calibrated estimated CTR/IVR, a target ROI is set, an accurate bid is calculated, where price represents per conversion unit price,the specific formula for bidding is therefore as follows:
for example: let our target ROI be 10% at the present stage, per conversion unit price of $ 5, the current ad request model predicts PIVR to be 0.1, so the current bid = (0.1×5)/(1+0.1) $ 0.45$.
As shown in fig. 2, a second object of the present invention is to provide a programmable advertisement delivery device, where the system is mainly divided into a large number of modules: the model prediction and intelligent bidding module is used for predicting the click rate of the advertisement according to the context scene, the advertisement data and the media data by the CTR according to the classification of the model CTR and the IVR, and the IVR is used for representing the predicted advertisement conversion rate. The CTR and the IVR respectively correspond to model pre-evaluation values of two stages of advertisements; the intelligent bidding model performs model calibration by using historical posterior data according to the model pre-estimated value, and then sets the ROI to calculate the real bidding.
Wherein, the pre-estimated model comprises:
the log processing unit is used for acquiring log processing data related to the programmed advertisements to be put in;
the real-time feature unit is used for acquiring real-time feature data related to the programmed advertisement to be put in;
the feature engineering unit is used for discretizing all the real-time features;
the depth estimation unit is used for carrying out automatic feature intersection processing on the discretized features by adopting a deep FM model to obtain the click rate and the conversion rate of the estimated programmed advertisements;
an intelligent bidding model, comprising:
an insurance regression unit for performing insurance regression on the predicted value of the click rate and conversion rate of the pre-programmed advertisement by using the real posterior data of the advertisement,
and a calculating bidding unit for calculating the real bid of the programmed advertisement according to the calibrated click rate and conversion rate and setting the target ROI.
The model estimation module is divided into CTR and IVR estimation, and advertisement behavior data within 30 days are used for data; the feature uses scene features, advertisement features and media features to be 121 in total, the feature latitude is in the tens of millions, and in order to eliminate the influence of flow instability, all the features are subjected to discretization processing, such as advertisement display amount in a week, and barrel separation processing is performed; the model uses deep fm model, which has the advantage of automatic feature crossing.
An intelligent bidding module: firstly, carrying out insurance regression on the estimated value of the estimated model by utilizing the real posterior data of the advertisement, and finally calculating the real bid.
As shown in FIG. 2, the modules are largely divided into two large blocks of model predictive and intelligent bidding. Model estimation is carried out, the required characteristics of the model are extracted according to the log and the real-time data, and PCTR and PIVR are input into the model estimation. The intelligent bidding carries out insurance regression according to PCTR and PIVR estimated by the model, then calculates the real bidding according to the set target ROI, and then carries out quotation.
Another object of the present invention, as shown in fig. 3, is to provide a programmed advertisement delivery system, comprising:
a programmed advertisement delivery system, comprising: the acquisition module is used for acquiring log processing data and real-time characteristic data related to the programmed advertisement to be put in;
the estimating module is used for carrying out feature processing on the log processing data and the real-time feature data, and estimating the click rate and the conversion rate of the programmed advertisement to be put on based on the feature processing result;
the bidding module is used for calibrating the click rate and the conversion rate by utilizing the historical posterior data, and calculating the real bid of the programmed advertisement according to the calibrated click rate and conversion rate and the set target ROI;
and the delivery module is used for carrying out the price quotation of the programmed advertisement to be delivered and delivering the price quotation with the true bid.
As shown in fig. 4, a fourth object of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the programmed advertisement delivery method when executing the computer program.
It is a fifth object of the present invention to provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the programmed advertising method.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (7)
1. A method for programmed advertising comprising the steps of:
acquiring log processing data and real-time characteristic data related to a programmed advertisement to be put in;
carrying out feature processing on the log processing data and the real-time feature data, and estimating the click rate and the conversion rate of the programmed advertisement to be put on based on the feature processing result;
calibrating the click rate and the conversion rate by using the historical posterior data, and setting a target ROI according to the calibrated click rate and conversion rate, so as to calculate the real bid of the programmed advertisement;
carrying out the price quotation of the programmed advertisement to be put in and putting in by using the true bid;
estimating the click rate and conversion rate of the programmed advertisement to be put on based on the feature processing result, namely adopting a deep FM model to perform automatic feature cross processing on the discretized features to obtain the click rate and conversion rate of the estimated programmed advertisement;
calibrating click rate and conversion rate using historical posterior data includes:
performing insurance regression on the predicted value of the click rate and the conversion rate of the pre-estimated programmed advertisement by using the real posterior data of the put advertisement;
calculating the true bid for the programmed advertisement takes the steps of:
based on the calibrated estimated CTR/IVR, a target ROI is set, and an accurate bid is calculated, wherein the specific formula is as follows:
wherein,for bidding, pivr/pctr is a calibrated click rate value and conversion rate value respectively, and the ROI is a set target return on investment; price is expressed per conversion unit price.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the log processing data comprises context scenes, advertisement behavior data and media data;
the real-time features use scene features, advertisement features, and media features.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the feature engineering processing package is to discretize all real-time features.
4. A programmed advertising system, based on the method of any one of claims 1 to 3, comprising:
the acquisition module is used for acquiring log processing data and real-time characteristic data related to the programmed advertisement to be put in;
the estimating module is used for carrying out feature processing on the log processing data and the real-time feature data, and estimating the click rate and the conversion rate of the programmed advertisement to be put on based on the feature processing result;
the bidding module is used for calibrating the click rate and the conversion rate by utilizing the historical posterior data, and calculating the real bid of the programmed advertisement according to the calibrated click rate and conversion rate and the set target ROI;
and the delivery module is used for carrying out the price quotation of the programmed advertisement to be delivered and delivering the price quotation with the true bid.
5. A programmed advertising device, comprising:
an estimation model, comprising:
the log processing unit is used for acquiring log processing data related to the programmed advertisements to be put in;
the real-time feature unit is used for acquiring real-time feature data related to the programmed advertisement to be put in;
the feature engineering unit is used for discretizing all the real-time features;
the depth estimation unit is used for carrying out automatic feature intersection processing on the discretized features by adopting a deep FM model to obtain the click rate and the conversion rate of the estimated programmed advertisements;
and an intelligent bidding model, comprising:
an insurance regression unit for performing insurance regression on the predicted value of the click rate and conversion rate of the pre-programmed advertisement by using the real posterior data of the advertisement,
the calculating bidding unit is used for calculating the real bid of the programmed advertisement according to the calibrated click rate and conversion rate and the set target ROI;
calculating the true bid for the programmed advertisement takes the steps of:
based on the calibrated estimated CTR/IVR, a target ROI is set, and an accurate bid is calculated, wherein the specific formula is as follows:
wherein,for bidding, pivr/pctr are calibrated click rate values, respectivelyAnd conversion rate, ROI is set target return on investment rate; price is expressed per conversion unit price.
6. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the programmed advertising method of any one of claims 1-3 when the computer program is executed.
7. A computer readable storage medium storing a computer program which when executed by a processor performs the steps of the programmed advertising method of any one of claims 1-3.
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| CN116823353B (en) * | 2023-08-29 | 2024-01-19 | 阿里巴巴(成都)软件技术有限公司 | Method and equipment for predicting advertisement putting effect |
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