CN110889725A - Online advertisement CTR estimation method, device, equipment and storage medium - Google Patents

Online advertisement CTR estimation method, device, equipment and storage medium Download PDF

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CN110889725A
CN110889725A CN201911174546.8A CN201911174546A CN110889725A CN 110889725 A CN110889725 A CN 110889725A CN 201911174546 A CN201911174546 A CN 201911174546A CN 110889725 A CN110889725 A CN 110889725A
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CN110889725B (en
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聂佳彬
何世福
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Shenzhen Caixiangyun Technology Co ltd
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Shenzhen Suishou Jinfu Information Technology Co Ltd
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Abstract

The embodiment of the invention discloses a method, a device, equipment and a storage medium for estimating CTR of an online advertisement, wherein the method comprises the following steps: acquiring the accumulated exposure times of the target advertisement; confirming an adjusting parameter according to the accumulated exposure times; acquiring a real-time CTR; obtaining a model prediction CTR; and adjusting the model pre-estimated CTR according to the adjustment parameters and the real-time CTR to obtain a target pre-estimated CTR. The method and the device realize real-time correction of the model pre-estimated CTR through real-time data, reduce the difference between the target pre-estimated CTR and the actual CTR, ensure the individuation of the model pre-estimated CTR, ensure the goodness of fit between the target pre-estimated CTR and the actual CTR, and solve the problems of ineffective exposure or insufficient exposure caused by inaccurate model pre-estimated CTR.

Description

Online advertisement CTR estimation method, device, equipment and storage medium
Technical Field
The embodiment of the invention relates to the technical field of Internet, in particular to a method, a device, equipment and a storage medium for estimating the CTR of an online advertisement.
Background
The network advertisement is a high-tech advertisement operation mode which is transmitted to internet users through a network. It uses the method of advertisement banner, text link and multimedia on the web site to publish or publish advertisement on the internet by means of network advertisement putting platform.
CTR (Click-Through-Rate) is a common term for internet advertisements, and refers to the Click arrival Rate of a web advertisement (photo advertisement/text advertisement/keyword advertisement/ranking advertisement/video advertisement, etc.), i.e., the actual number of clicks of the advertisement divided by the advertisement presentation amount (Show content). CTR estimation means that the click rate of the recommended content (advertisement) of the user is estimated on the Internet platform according to the user interest, habits and the like, and the Internet platform sorts the advertisements according to the estimated value of CTR.
The existing online advertisement CTR prediction model is modeled by using historical data due to a T +1 training mode, then CTR prediction is carried out by using the model on the same day, and due to the fact that data updating is not timely, model prediction is inaccurate, namely, prediction of CTR is higher or lower relative to actual CTR, invalid exposure is caused when prediction of CTR is higher, actual exposure is insufficient when prediction of CTR is lower, and bad influence is brought to online advertisement sequencing.
Disclosure of Invention
The embodiment of the invention provides an online advertisement CTR estimation method, device, equipment and storage medium, which are used for reducing the difference between estimated CTR and actual CTR and reducing the influence caused by inaccurate model estimation CTR.
In a first aspect, an embodiment of the present invention provides an online advertisement CTR estimation method, including:
acquiring the accumulated exposure times of the target advertisement;
confirming an adjusting parameter according to the accumulated exposure times;
acquiring a real-time CTR;
obtaining a model prediction CTR;
and adjusting the model pre-estimated CTR according to the adjustment parameters and the real-time CTR to obtain a target pre-estimated CTR.
Further, the acquiring the real-time CTR includes:
acquiring accumulated click times;
and calculating the real-time CTR according to the accumulated exposure times and the accumulated click times.
Further, the adjusting the model pre-estimated CTR according to the adjustment parameter and the real-time CTR to obtain a target pre-estimated CTR includes:
and combining the real-time CTR and the model predicted CTR according to the adjusting parameters to obtain a target predicted CTR.
Further, obtaining the model predictive CTR includes:
obtaining CTR estimated characteristics;
determining a CTR pre-estimation model;
and obtaining the model prediction CTR according to the CTR prediction characteristics and the CTR prediction model.
Further, the calculating an adjustment parameter according to the cumulative exposure number includes:
determining a first parameter and a second parameter;
and calculating an adjusting parameter according to the accumulated exposure times, the first parameter and the second parameter.
Further, the targeted advertisement is determined by a targeted advertising campaign ID.
In a second aspect, an embodiment of the present invention provides an online advertisement CTR estimation apparatus, including:
the accumulated exposure times acquisition module is used for acquiring the accumulated exposure times of the target advertisement;
the adjustment parameter calculation module is used for calculating an adjustment parameter according to the accumulated exposure times;
the real-time CTR acquisition module is used for acquiring a real-time CTR;
the model pre-estimation CTR obtaining module is used for obtaining a model pre-estimation CTR;
and the target prediction CTR obtaining module is used for adjusting the model prediction CTR according to the adjusting parameters and the real-time CTR to obtain the target prediction CTR.
Further, the real-time CTR obtaining module includes:
the click frequency acquisition unit is used for acquiring the accumulated click frequency;
and the real-time CTR calculating unit is used for calculating the real-time CTR according to the accumulated exposure times and the accumulated click times.
In a third aspect, an embodiment of the present invention provides a computer device, including:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the online advertisement CTR prediction method provided by any embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the online advertisement CTR prediction method provided in any embodiment of the present invention.
The online advertisement CTR estimation method provided by the embodiment of the invention obtains the accumulated exposure times of the target advertisement; confirming an adjusting parameter according to the accumulated exposure times; acquiring a real-time CTR; obtaining a model prediction CTR; and adjusting the model pre-estimated CTR according to the adjustment parameters and the real-time CTR to obtain a target pre-estimated CTR. The method and the device realize real-time correction of the model pre-estimated CTR through real-time data, reduce the difference between the target pre-estimated CTR and the actual CTR, ensure the individuation of the model pre-estimated CTR, ensure the goodness of fit between the target pre-estimated CTR and the actual CTR, and solve the problems of ineffective exposure or insufficient exposure caused by inaccurate model pre-estimated CTR.
Drawings
Fig. 1 is a schematic flow chart of a method for estimating CTR of an online advertisement according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an online advertisement CTR predicting device according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, a first parameter may be referred to as a second parameter, and similarly, a second parameter may be referred to as a first parameter, without departing from the scope of the present application. The first parameter and the second parameter are both parameters, but they are not the same parameter. The terms "first", "second", etc. are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "plurality", "batch" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Example one
Fig. 1 is a schematic flow chart of a method for estimating a CTR of an online advertisement according to an embodiment of the present invention, which is applicable to estimating a CTR of an online advertisement. As shown in fig. 1, a method for estimating a CTR of an online advertisement according to an embodiment of the present invention includes:
and S110, acquiring the accumulated exposure times of the target advertisement.
Specifically, the target advertisement is an advertisement which needs to be subjected to CTR estimation, and is determined by a target advertisement plan ID, wherein the advertisement plan ID is equivalent to an identity authentication of the advertisement, one advertisement corresponds to one advertisement plan ID, and the advertisement plan ID is a string of numbers or codes.
The exposure times refer to the number of times that a webpage with an advertisement is browsed, and the accumulated exposure times refer to the total exposure times of the advertisement from a certain preset time point to a current time point. In this embodiment, the statistical time of the cumulative exposure times is preferably a total exposure time that is not more than 24 hours from the current time point, for example, the cumulative exposure time is a total exposure time of the target advertisement within 2 hours from the current time point. Because information on the internet changes rapidly, when the counting time is too long, the accumulated exposure times become historical exposure times, and the reference significance for real-time calculation is not great.
And S120, confirming an adjusting parameter according to the accumulated exposure times.
Specifically, the adjustment parameter is a parameter calculated from the cumulative number of exposures.
Further, a method for confirming the adjustment parameter according to the accumulated exposure times includes steps S121 to S122 (not shown in the figure).
S121, determining a first parameter and a second parameter;
and S122, calculating an adjusting parameter according to the accumulated exposure times, the first parameter and the second parameter.
Specifically, if the adjustment parameter is recorded as α, α is calculated according to the formula (1-1).
Figure BDA0002289627810000061
Wherein n represents the accumulated exposure times, k is a first parameter, f and k are determined by a technician, k represents the effective exposure times of the target advertisement approved by the technician, and f is equivalent to an adjustment parameter.
And S130, acquiring the real-time CTR.
Specifically, the real-time CTR reflects the actual click through rate of the targeted advertisement.
Further, a method of acquiring a real-time CTR includes steps S131 to S132 (not shown in the figure).
S131, acquiring the accumulated click times.
Specifically, the number of clicks refers to the number of times that a target advertisement on a web page is clicked, and the cumulative number of clicks refers to the total number of clicks from a preset time point to a current time point. The statistical time of the cumulative number of clicks is the same as the statistical time of the cumulative number of exposures.
And S132, calculating the real-time CTR according to the accumulated exposure times and the accumulated click times.
Specifically, real-time CTR is denoted as CTRposThen CTRposThe calculation was carried out according to the formula (1-2).
Figure BDA0002289627810000071
Where n represents the cumulative number of exposures and m represents the cumulative number of clicks.
S140, obtaining model prediction CTR.
Specifically, the model prediction CTR is given by a CTR prediction model, and in order to predict the CTR of the advertisement, a prediction model may be used for calculation, for example, LR (logistic Regression), GBDT (Gradient boosting decision Tree), FM (Factorization Machine), FFM (Field-aware decomposition Machine), and the like. The models usually use historical data as model training data, and users usually update the model data after a certain time, that is, the model data is not updated in real time, while the change and update of internet data are rapid, and the estimated CTR of the model obtained by using the historical data as the model training data is often inaccurate.
Further, a method for obtaining the model prediction CTR includes steps S141 to S143 (not shown in the figure).
And S141, obtaining the CTR estimated characteristics.
Specifically, the factors that can affect the click through rate of an advertisement are called CTR estimation characteristics, such as advertisement creativity, advertiser industry, advertisement audience age, advertisement placement time, and the like. The CTR predicted features generally need to be subjected to feature engineering data transformation to be used by the model.
And S142, determining the CTR prediction model.
Specifically, the CTR prediction model is formed by a plurality of models, for example, LR, GBDT, FM, FFM, etc., and the technician may use the existing CTR prediction model or may model the CTR prediction model by using historical data.
S143, obtaining the model prediction CTR according to the CTR prediction characteristics and the CTR prediction model.
Specifically, the CTR estimated characteristics are input into the CTR estimated model, and the estimated CTR of the model can be obtained.
S150, adjusting the model pre-estimated CTR according to the adjusting parameters and the real-time CTR to obtain a target pre-estimated CTR.
Specifically, the model pre-estimated CTR is adjusted according to the adjustment parameters and the real-time CTR, so that the deviation between the finally obtained target pre-estimated CTR and the real-time CTR is reduced.
Further, a method for adjusting the model pre-estimated CTR according to the adjustment parameter and the real-time CTR to obtain a target pre-estimated CTR includes: and combining the real-time CTR and the model predicted CTR according to the adjusting parameters to obtain a target predicted CTR.
Marking the target estimated CTR as CTRcomThen CTRcomThe calculation was carried out according to the formula (1-3).
CTRcom＝α*CTRpos+(1-α)*CTRpre(1-3)
Wherein α is the tuning parameter, CTRposIs a real-time CTR, CTRpreCTR is estimated for the model.
For example, in a spring jacket advertisement, the CTR in spring is obviously higher than that in summer, and if the estimated CTR of the model is not updated in time, the estimated CTR of the model obtained in summer is higher, f is 2, k is 2, n is 10, m is 3, α is 0.69 according to formula (1-1), and CTR is obtained according to formula (1-2)pos0.3 if CTRpreCTR obtained from formula (1-3) ═ 0.6com0.393. It can be seen that although the deviation between the estimated CTR and the actual CTR of the model is large, the estimated CTR and the actual CTR are obtained after the correction of the expressions (1-1) to (1-3)The target prediction CTR greatly reduces the deviation between the model prediction CTR and the actual CTR.
The online advertisement CTR estimation method provided by the embodiment of the invention obtains the accumulated exposure times of the target advertisement; confirming an adjusting parameter according to the accumulated exposure times; acquiring a real-time CTR; obtaining a model prediction CTR; and adjusting the model pre-estimated CTR according to the adjustment parameters and the real-time CTR to obtain a target pre-estimated CTR. The method and the device realize real-time correction of the model pre-estimated CTR through real-time data, reduce the difference between the target pre-estimated CTR and the actual CTR, ensure the individuation of the model pre-estimated CTR, ensure the goodness of fit between the target pre-estimated CTR and the actual CTR, and solve the problems of ineffective exposure or insufficient exposure caused by inaccurate model pre-estimated CTR.
Example two
Fig. 2 is a schematic structural diagram of an online advertisement CTR prediction apparatus according to a second embodiment of the present invention, which is applicable to performing CTR prediction on an online advertisement. The online advertisement CTR estimation device provided by the second embodiment of the invention can realize the online advertisement CTR estimation method provided by any embodiment of the invention, basically realize the corresponding functions and structures of the method, and refer to the description of any method embodiment of the invention for the content which is not closely described in the embodiment.
As shown in fig. 2, an online advertisement CTR estimation apparatus provided by the second embodiment of the present invention includes: an accumulated exposure time obtaining module 210, an adjustment parameter calculating module 220, a real-time CTR obtaining module 230, a model pre-estimation CTR obtaining module 240, and a target pre-estimation CTR obtaining module 250.
The cumulative exposure times obtaining module 210 is configured to obtain cumulative exposure times of the target advertisement;
the adjustment parameter calculation module 220 is configured to calculate an adjustment parameter according to the accumulated exposure times;
the real-time CTR obtaining module 230 is configured to obtain a real-time CTR;
the model predicted CTR obtaining module 240 is used for obtaining a model predicted CTR;
the target predicted CTR obtaining module 250 is configured to adjust the model predicted CTR according to the adjustment parameter and the real-time CTR to obtain a target predicted CTR.
Further, the real-time CTR obtaining module 230 includes:
the accumulated click number obtaining unit is used for obtaining the accumulated click number;
and the real-time CTR calculating unit is used for calculating the real-time CTR according to the accumulated exposure times and the accumulated click times.
Further, the target prediction CTR obtaining module 250 is specifically configured to:
and combining the real-time CTR and the model predicted CTR according to the adjusting parameters to obtain a target predicted CTR.
Further, the model pre-estimation CTR obtaining module 240 is specifically configured to:
obtaining CTR estimated characteristics;
determining a CTR pre-estimation model;
and obtaining the model prediction CTR according to the CTR prediction characteristics and the CTR prediction model.
Further, the adjustment parameter calculating module 220 is specifically configured to:
determining a first parameter and a second parameter;
and calculating an adjusting parameter according to the accumulated exposure times, the first parameter and the second parameter.
Further, the targeted advertisement is determined by a targeted advertising campaign ID.
The online advertisement CTR estimation device provided by the embodiment of the invention realizes real-time correction of the model estimated CTR through real-time data, reduces the difference between the target estimated CTR and the actual CTR, ensures the individuation of the model estimated CTR, ensures the goodness of the target estimated CTR and the actual CTR, and solves the problems of ineffective exposure or insufficient exposure caused by inaccurate model estimated CTR.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present invention. FIG. 3 illustrates a block diagram of an exemplary computer device 312 suitable for use in implementing embodiments of the present invention. The computer device 312 shown in FIG. 3 is only an example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 3, computer device 312 is in the form of a general purpose computer device. The components of computer device 312 may include, but are not limited to: one or more processors 316, a storage device 328, and a bus 318 that couples the various system components including the storage device 328 and the processors 316.
Bus 318 represents one or more of any of several types of bus structures, including a memory device bus or memory device controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer device 312 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 312 and includes both volatile and nonvolatile media, removable and non-removable media.
Storage 328 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 330 and/or cache Memory 332. The computer device 312 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 334 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk such as a Compact disk Read-Only Memory (CD-ROM), Digital Video disk Read-Only Memory (DVD-ROM) or other optical media may be provided. In these cases, each drive may be connected to bus 318 by one or more data media interfaces. Storage 328 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 340 having a set (at least one) of program modules 342 may be stored, for example, in storage 328, such program modules 342 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 342 generally perform the functions and/or methodologies of the described embodiments of the invention.
The computer device 312 may also communicate with one or more external devices 314 (e.g., keyboard, pointing computer device, display 324, etc.), with one or more computer devices that enable a user to interact with the computer device 312, and/or with any computer device (e.g., network card, modem, etc.) that enables the computer device 312 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 322. Moreover, computer device 312 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network, such as the internet) via Network adapter 320. As shown in FIG. 3, network adapter 320 communicates with the other modules of computer device 312 via bus 318. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the computer device 312, including but not limited to: microcode, computer device drivers, Redundant processors, external disk drive Arrays, Redundant Array of Independent Disks (RAID) systems, tape drives, and data backup storage systems, to name a few.
The processor 316 executes various functional applications and data processing by executing programs stored in the storage 328, for example, implementing an online advertisement CTR prediction method provided by any embodiment of the present invention, and the method may include:
acquiring the accumulated exposure times of the target advertisement;
confirming an adjusting parameter according to the accumulated exposure times;
acquiring a real-time CTR;
obtaining a model prediction CTR;
and adjusting the model pre-estimated CTR according to the adjustment parameters and the real-time CTR to obtain a target pre-estimated CTR.
Example four
The fourth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the online advertisement CTR prediction method provided in any embodiment of the present invention, and the method may include:
acquiring the accumulated exposure times of the target advertisement;
confirming an adjusting parameter according to the accumulated exposure times;
acquiring a real-time CTR;
obtaining a model prediction CTR;
and adjusting the model pre-estimated CTR according to the adjustment parameters and the real-time CTR to obtain a target pre-estimated CTR.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. An online advertisement CTR estimation method is characterized by comprising the following steps:
acquiring the accumulated exposure times of the target advertisement;
confirming an adjusting parameter according to the accumulated exposure times;
acquiring a real-time CTR;
obtaining a model prediction CTR;
and adjusting the model pre-estimated CTR according to the adjustment parameters and the real-time CTR to obtain a target pre-estimated CTR.
2. The method of claim 1, wherein the obtaining the real-time CTR comprises:
acquiring accumulated click times;
and calculating the real-time CTR according to the accumulated exposure times and the accumulated click times.
3. The method of claim 2, wherein the adjusting the model predicted CTR according to the adjustment parameters and the real-time CTR to obtain a target predicted CTR comprises:
and combining the real-time CTR and the model predicted CTR according to the adjusting parameters to obtain a target predicted CTR.
4. The method of claim 1, wherein obtaining the model prediction CTR comprises:
obtaining CTR estimated characteristics;
determining a CTR pre-estimation model;
and obtaining the model prediction CTR according to the CTR prediction characteristics and the CTR prediction model.
5. The method of claim 1, wherein said calculating an adjustment parameter based on said cumulative number of exposures comprises:
determining a first parameter and a second parameter;
and calculating an adjusting parameter according to the accumulated exposure times, the first parameter and the second parameter.
6. The method of claims 1-5, wherein the targeted advertisement is determined by a targeted advertising campaign ID.
7. An online advertisement CTR estimation device, comprising:
the accumulated exposure times acquisition module is used for acquiring the accumulated exposure times of the target advertisement;
the adjustment parameter calculation module is used for calculating an adjustment parameter according to the accumulated exposure times;
the real-time CTR acquisition module is used for acquiring a real-time CTR;
the model pre-estimation CTR obtaining module is used for obtaining a model pre-estimation CTR;
and the target prediction CTR obtaining module is used for adjusting the model prediction CTR according to the adjusting parameters and the real-time CTR to obtain the target prediction CTR.
8. The apparatus of claim 7, wherein the real-time CTR acquisition module comprises:
the click frequency acquisition unit is used for acquiring the accumulated click frequency;
and the real-time CTR calculating unit is used for calculating the real-time CTR according to the accumulated exposure times and the accumulated click times.
9. A computer device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the online advertising CTR prediction method of any one of claims 1-6.
10. A computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the online advertisement CTR prediction method according to any one of claims 1 to 6.
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