CN111179030B - Advertisement bidding method and device and electronic equipment - Google Patents
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Abstract
The present disclosure relates to an advertisement bidding method, apparatus, electronic device, and computer-readable medium. The method comprises the following steps: acquiring target user flow data, wherein the target user flow data comprises user information; inputting the user information into a conversion rate estimation model to generate conversion rate data; generating a bid function from the conversion rate data and the bid coefficients; determining bid data based on the bid function and budget data; and bidding the target user traffic according to the bidding data. The advertisement bidding method, the advertisement bidding device, the electronic equipment and the computer readable medium can enable an advertiser to accurately control the price of the user flow, effectively improve the advertising efficiency of the advertiser, and enable the advertiser to overall control the advertisement expense and coordinate the expense use.
Description
Technical Field
The present disclosure relates to the field of computer information processing, and in particular, to an advertisement bidding method, an apparatus, an electronic device, and a computer readable medium.
Background
In recent years, online advertising has been increasingly high in specific gravity throughout the advertising industry. The proportion of real-time bidding advertisements in online advertisements is increased year by year due to the good conversion effect. The DSP (Demand-Side Platform) is used as a Demand Platform, and bidding attempts are carried out on each exposure through an advertisement trading Platform (AdExchange). For each bid request of AdExchange, the DSP tries to map the user browsing the media website and App to the user which can be identified by the DSP according to the user information or equipment information, and then performs flow screening, click rate/conversion rate estimation and the like according to the user portraits mined by the DSP from the user history behaviors, so as to maximize the ROI (return on investment).
The current common advertisement delivery flow is: the advertiser logs in the background of the service end, audience users, bids and budgets are set according to the targeting conditions, the media side determines target users according to the setting of the advertiser, and then advertisements are put on the target users. The media side generates a unified advertisement bid for the target user according to the setting of the advertiser to bid the advertisement, and the advertisement of the advertiser is played after the bid is successful. In practice, different users may have different value to one advertiser, and the same bid is used for all users, either to make the main stream of advertisements lose importance to the client, or to make the advertiser waste money on low value users.
Accordingly, there is a need for a new advertising bidding method, apparatus, electronic device, and computer-readable medium.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the disclosure provides an advertisement bidding method, an advertisement bidding device, an electronic device and a computer readable medium, which can enable an advertiser to accurately control the price of user traffic, effectively improve the advertising efficiency of the advertiser, and enable the advertiser to generally control advertisement expenses and coordinate the use of the expenses.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to an aspect of the present disclosure, an advertisement bidding method is presented, the method comprising: acquiring target user flow data, wherein the target user flow data comprises user information; inputting the user information into a conversion rate estimation model to generate conversion rate data; generating a bid function from the conversion rate data and the bid coefficients; determining bid data based on the bid function and budget data; and bidding the target user traffic according to the bid data so as to promote advertisement.
Optionally, the method further comprises: and training the polar gradient lifting model through historical user flow data to generate the conversion rate estimation model.
Optionally, obtaining the user traffic data includes: and screening the user traffic through a screening strategy preset at the media to acquire the target user traffic data.
Optionally, generating a bid function from the conversion rate data and the bid coefficients includes: determining an overall conversion rate; generating a base bid coefficient based on the overall conversion rate and the conversion rate data; and mapping based on the base bid coefficients to determine a bid function.
Optionally, determining the overall conversion rate includes: determining a time coefficient according to the historical data; determining budget data according to the service data; and determining the overall conversion rate from the time coefficient and the budget data.
Optionally, mapping based on the base bid coefficients to determine a bid function includes: mapping based on the base bid coefficients to determine a target interval from a plurality of intervals; and determining the bid function based on the target interval.
Optionally, mapping based on the base bid coefficients to determine a bid function includes:
Wherein Coef is a bid function, coef_base is based on bid data, Is a mapping function of different intervals.
Optionally, determining bid data based on the bid function and budget data includes: determining a target bid coefficient based on the bid function; and determining bid data based on the bid coefficients and the budget data.
Optionally, bidding the target user traffic according to the bid data to perform advertisement promotion, including: transmitting the bid data to a media party; the media party bidding on a target user traffic based on the bid data; and pushing the preset advertisement for the target user after the bidding is successful.
Optionally, the method further comprises: and recording a bid result so as to update the conversion rate estimation model.
According to an aspect of the present disclosure, there is provided an advertisement bidding apparatus, the apparatus comprising: the user flow module is used for acquiring target user flow data, wherein the target user flow data comprises user information; the conversion rate module is used for inputting the user information into a conversion rate estimation model to generate conversion rate data; a bid function module for generating a bid function from the conversion rate data and the bid coefficients; a bid data module for determining bid data based on the bid function and budget data; and the advertisement promotion module is used for bidding the target user flow according to the bid data so as to promote advertisement.
Optionally, the method further comprises: and the model training module is used for training the polar gradient lifting model through historical user flow data to generate the conversion rate estimation model.
Optionally, the user traffic module is further configured to screen the user traffic through a screening policy preset at the media to obtain the target user traffic data.
Optionally, the bid function module includes: a parameter unit for determining an overall conversion rate; a coefficient unit for generating a base bid coefficient based on the overall conversion rate and the conversion rate data; and a function unit for mapping based on the base bid coefficients to determine a bid function.
Optionally, the parameter unit is further configured to determine a time coefficient according to the historical data; determining budget data according to the service data; and determining the overall conversion rate from the time coefficient and the budget data.
Optionally, the function unit is further configured to map based on the base bid coefficient to determine a target interval from a plurality of intervals; and determining the bid function based on the target interval.
Optionally, the method comprises:
Wherein Coef is a bid function, coef_base is based on bid data, Is a mapping function of different intervals.
Optionally, the bid data module comprises: a target unit for determining a target bid coefficient based on the bid function; and a data unit for determining bid data based on the bid coefficients and the budget data.
Optionally, the advertisement promotion module includes: a transmitting unit for transmitting the bid data to a media party; a bidding unit, configured to bid a target user traffic by the media party based on the bidding data; and the promotion unit is used for pushing the preset advertisement for the target user after the bidding is successful.
Optionally, the method further comprises: and the updating unit is used for recording the bidding result so as to update the conversion rate estimation model.
According to an aspect of the present disclosure, there is provided an electronic device including: one or more processors; a storage means 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 are caused to implement the methods as described above.
According to an aspect of the present disclosure, a computer-readable medium is presented, on which a computer program is stored, which program, when being executed by a processor, implements a method as described above.
According to the advertisement bidding method, the advertisement bidding device, the electronic equipment and the computer readable medium, target user flow data are obtained, wherein the target user flow data comprise user information; inputting the user information into a conversion rate estimation model to generate conversion rate data; generating a bid function from the conversion rate data and the bid coefficients; determining bid data based on the bid function and budget data; and bidding the target user flow according to the bidding data to perform advertisement popularization, so that the advertiser can accurately control the price of the user flow, the advertising efficiency of the advertiser is effectively improved, the advertiser can also perform overall control on advertising expenses, and the use of the expenses is coordinated overall.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely examples of the present disclosure and other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a system block diagram illustrating an advertising bidding method and apparatus, according to an exemplary embodiment.
FIG. 2 is a flow chart illustrating an advertising bidding method, according to an exemplary embodiment.
FIG. 3 is a flow chart illustrating an advertising bidding method, according to another exemplary embodiment.
FIG. 4 is a flow chart illustrating an advertising bidding method, according to another exemplary embodiment.
FIG. 5 is a block diagram illustrating an advertising bidding appliance, according to an exemplary embodiment.
Fig. 6 is a block diagram of an electronic device, according to an example embodiment.
Fig. 7 is a block diagram of a computer-readable medium shown according to an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another element. Accordingly, a first component discussed below could be termed a second component without departing from the teachings of the concepts of the present disclosure. As used herein, the term "and/or" includes any one of the associated listed items and all combinations of one or more.
Those skilled in the art will appreciate that the drawings are schematic representations of example embodiments and that the modules or flows in the drawings are not necessarily required to practice the present disclosure, and therefore, should not be taken to limit the scope of the present disclosure.
The terms involved in this disclosure are explained as follows:
XGBoost: XGBoost is an open source software library which provides a gradient lifting framework for C++, java, python, R and Julia, and is widely used for various machine learning tasks, wherein a main user performs user conversion rate estimation;
Personalized advertisement: personalized advertising is a very powerful tool that helps to improve the relevance of the advertisements delivered to the user, and thus the return on investment of the advertiser. Advertisers may infer the interests of the user based on the websites or applications being used by the user to visit. In this way, advertisers can target advertisement series based on these interests, providing a better experience for both the user and the advertiser;
An advertiser: is a legal person, other economic organization or individual who designs, makes, or issues advertisements for marketing goods or providing services, by themselves or entrusts others. It is an important participant in market economies and advertising campaigns, and its subject matter is closely related to its organization morphology. It can be a legal person or a natural person;
personalized bidding: based on personalized advertising technology, advertisers can auction proper advertisement positions to show and expose advertisements of obtained target users, and offer different bids for different target users;
RTA: (REAL TIME API) a real-time conversion interface for returning conversion data;
CVR: (Conversion Rate) estimation;
Coef: bidding coefficients;
DSP: a (Demand-Side Platform);
DMP: a data management platform for storing data such as audience information, user information, etc. of each advertiser;
ROI: return on investment.
According to the advertising bidding method and device, CVR (continuously variable ratio) estimation is carried out on user data provided by a DMP (digital media player) by using a xgboost model, so that the conversion rate condition of the user under the current flow is obtained, the conversion price of the user is carried out by combining the current overall conversion rate condition of an advertiser, and the conversion price is transmitted to an RTA (real time analysis) for bidding on the flow, so that the aim of improving the overall conversion rate of the advertiser is fulfilled. The present disclosure is described in detail below in connection with specific embodiments.
FIG. 1 is a system block diagram illustrating an advertising bidding method and apparatus, according to an exemplary embodiment.
FIG. 1 is a block diagram of an advertisement bidding system, according to an exemplary embodiment.
As shown in fig. 1, the system architecture 10 may include user terminals 101, 102, 103, a network 104, a media server 105, and an advertiser server 106. The network 104 is a medium used to provide a communication link between the user terminals 101, 102, 103 and the media server 105; the network 104 also serves as a medium to provide a communication link between the media server 105 and the advertiser server 106. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the media server 105 via the network 104 using the user terminals 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as financial services applications, shopping applications, web browser applications, instant messaging tools, mailbox clients, social platform software, etc., may be installed on the user terminals 101, 102, 103.
The user terminals 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The media server 105 may be a server providing various services, such as a background management server providing support for news browsing-type websites browsed by the user using the user terminals 101, 102, 103. The background management server can analyze the received user data and the like and push advertisements to the user.
The advertiser server 106 may be a server providing various financial services, and when a user browses news websites using the user terminals 101, 102, 103, the advertiser server 106 may provide advertisement information of the websites to a target user.
The media server 105 may, for example, push user traffic data to the advertiser platform; advertiser server 106 may, for example, obtain target user traffic data including user information therein; advertiser server 106 may, for example, input the user information into a conversion rate prediction model to generate conversion rate data; the advertiser server 106 may generate a bid function, for example, from the conversion rate data and the bid coefficients; the advertiser server 106 may determine bid data, for example, based on the bid function and budget data; the advertiser server 106 may bid on the target user traffic, for example, according to the bid data, for advertising promotion.
After successful bid of the advertiser server 106, the media server 105 may, for example, present the advertiser's preset advertisement in the user terminal 103 (which may also be the user terminal 101 or 102).
The server 105 may be an entity server, or may be formed of a plurality of servers, for example, it should be noted that the advertisement bidding method provided in the embodiment of the present disclosure may be executed by the server 105, and accordingly, the advertisement bidding device may be disposed in the server 105. And the receiving end provided to the user for receiving the advertisement is typically located in the terminal device 101, 102, 103.
FIG. 2 is a flow chart illustrating an advertising bidding method, according to an exemplary embodiment. The advertisement bidding method 20 includes at least steps S202 to S210.
As shown in fig. 2, in S202, target user traffic data including user information is acquired. Comprising the following steps: and screening the user traffic through a screening strategy preset at the media to acquire the target user traffic data.
In S204, the user information is input into a conversion rate estimation model to generate conversion rate data.
In one embodiment, further comprising: and training the polar gradient lifting model through historical user flow data to generate the conversion rate estimation model.
More specifically, in an application scenario, user data provided by a DMP performs user conversion rate estimation through a xgboost model, the conversion rate of a user is scored through the model, a xgboost model is trained according to accumulated user conversion data, after flow enters a dsp platform, a trained xgboost model is used for scoring first, a conversion confidence value pCVR of the user is obtained, and the conversion confidence value is returned to a price coefficient module after being combined with the requirement of a service on whether the user conversion rate is put in or not.
In S206, a bid function is generated from the conversion rate data and the bid coefficients. Comprising the following steps: determining an overall conversion rate; generating a base bid coefficient based on the overall conversion rate and the conversion rate data; and mapping based on the base bid coefficients to determine a bid function.
In one embodiment, determining the overall conversion rate includes: determining a time coefficient according to the historical data; determining budget data according to the service data; and determining the overall conversion rate from the time coefficient and the budget data.
Details of "generating a bid function from the conversion rate data and the bid coefficients" will be described in the embodiment of fig. 3.
In S208, bid data is determined based on the bid function and budget data. Comprising the following steps: determining a target bid coefficient based on the bid function; and determining bid data based on the bid coefficients and the budget data.
More specifically, in connection with business requirements, different user bid coefficients are converted into a plurality of bid functions to obtain new bid coefficients pCoef; the bid bid= pCoef of the user is calculated by combining the provided bid slit and the bid coefficient pCoef.
In S210, bidding is performed on the target user traffic according to the bid data to perform advertisement promotion. Comprising the following steps: transmitting the bid data to a media party; the media party bidding on a target user traffic based on the bid data; and pushing the preset advertisement for the target user after the bidding is successful.
The media side sorts the relevant advertisements according to eCPM, and the advertiser only needs to estimate the conversion rate because the conversion value is already determined by the advertiser. Wherein ecpm=bid pCTR pCVR. Bid is positively correlated to conversion rate for the advertiser. Therefore, the advertiser can analyze and process the data provided by the DMP, CVR estimation is performed on the user by using xgboost model, the conversion rate condition of the user under the current flow is obtained, and the conversion price of the user is performed by combining the current overall conversion rate condition of the advertiser, so that the competition value is obtained.
According to the advertising bidding method disclosed by the invention, target user flow data is obtained, wherein the target user flow data comprises user information; inputting the user information into a conversion rate estimation model to generate conversion rate data; generating a bid function from the conversion rate data and the bid coefficients; determining bid data based on the bid function and budget data; and bidding the target user flow according to the bidding data to perform advertisement popularization, so that the advertiser can accurately control the price of the user flow, the advertising efficiency of the advertiser is effectively improved, the advertiser can also perform overall control on advertising expenses, and the use of the expenses is coordinated overall.
According to the advertising bidding method, xgboost models can be used for estimating the conversion rate of users, so that the conversion condition of user flow is prejudged in advance, and the cost is effectively reduced. The advertisement bidding method can bid the user by using the conversion rate, achieves the aim of converting the high conversion user into the high price and converting the user into the low price, and effectively improves the throwing efficiency of advertisers. The advertisement bidding method disclosed by the invention not only carries out basic bidding, but also carries out time period control on the overall budget, thereby avoiding the situation that the budget is consumed for a short time.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
FIG. 3 is a flow chart illustrating an advertising bidding method, according to another exemplary embodiment. The flow shown in fig. 3 is a detailed description of S206 "generate a bid function from the conversion rate data and the bid coefficient" in the flow shown in fig. 2.
As shown in fig. 3, in S302, the overall conversion rate is determined. Comprising the following steps: determining a time coefficient according to the historical data; determining budget data according to the service data; and determining the overall conversion rate from the time coefficient and the budget data.
More specifically, the time coefficient time coef is calculated, and the user time coef in the current time window is calculated by analyzing the conversion rate of the large disk accumulated in the prior period in different time periods. Calculating the budget_split of the time period, and calculating the total value of the current available budget by combining the time coef. The advertiser-side tray conversion rate adrCVR for the current slot may also be calculated, for example.
In S304, a base bid coefficient is generated based on the overall conversion rate and the conversion rate data. The bid coefficient coef_base= (pCVR/adrCVR).
In S306, a mapping is performed based on the base bid coefficients to determine a target interval from a plurality of intervals. And the bid coefficients are secondarily mapped to different intervals to realize segmentation of different user crowd bid functions.
In S308, the bid function is determined based on the target interval. May include:
Wherein Coef is a bid function, coef_base is based on bid data, Is a mapping function of different intervals. i denotes different intervals, R is the number of user function interval divisions, e.g. a curve with coef >1 for the user with a slope of 0.2, a curve with coef < = 1 with a slope of 0.6.
FIG. 4 is a flow chart illustrating an advertising bidding method, according to another exemplary embodiment. The flow shown in fig. 4 is a detailed description of the "training the polar gradient lifting model to generate the slew rate estimation model by historical user flow data".
As shown in fig. 4, in S402, basic data of a first user who has historically performed floor conversion is acquired. The base data for the first user may include user base data, user conversion time, user debit data, user status data, and the like.
In S404, the base data of the second user who has not historically performed the floor conversion is acquired. The base data for the second user may include user base data, user browsing time, user-adapted platform, and so forth.
In S406, training the polar gradient lifting model according to the basic data of the first user and the second user, so as to generate the conversion rate estimation model.
Wherein XGBoost is an iterative decision tree algorithm consisting of a plurality of decision trees, and the conclusions of all the trees are accumulated to make a final answer. XGBoost is a widely applied algorithm that can be used for classification, regression and feature selection.
In S408, the floor conversion data of the target user is tracked to update the conversion rate estimation model with the floor conversion data of the target user. The bid of the advertiser is transmitted to the media party to bid the user, and the information of conversion of the user flow, success or failure of bidding and the like is recorded for subsequent iteration.
Those skilled in the art will appreciate that all or part of the steps implementing the above described embodiments are implemented as a computer program executed by a CPU. The above-described functions defined by the above-described methods provided by the present disclosure are performed when the computer program is executed by a CPU. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic disk or an optical disk, etc.
Furthermore, it should be noted that the above-described figures are merely illustrative of the processes involved in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
The following are device embodiments of the present disclosure that may be used to perform method embodiments of the present disclosure. For details not disclosed in the embodiments of the apparatus of the present disclosure, please refer to the embodiments of the method of the present disclosure.
FIG. 5 is a block diagram illustrating an advertising bidding appliance, according to an exemplary embodiment. As shown in fig. 5, the advertisement bidding device 50 includes: the user traffic module 502, conversion rate module 504, bid function module 506, bid data module 508, advertisement promotion module 510, advertisement bidding device 50 may further include: model training module 512.
The user traffic module 502 is configured to obtain target user traffic data, where the target user traffic data includes user information; the user traffic module 502 is further configured to screen the user traffic through a screening policy preset at the media to obtain the target user traffic data.
The conversion rate module 504 is configured to input the user information into a conversion rate estimation model, so as to generate conversion rate data;
The bid function module 506 is for generating a bid function from the conversion rate data and the bid coefficients; the bid function module 506 includes: a parameter unit for determining an overall conversion rate; the parameter unit is also used for determining a time coefficient according to the historical data; determining budget data according to the service data; and determining the overall conversion rate from the time coefficient and the budget data. A coefficient unit for generating a base bid coefficient based on the overall conversion rate and the conversion rate data; and a function unit for mapping based on the base bid coefficients to determine a bid function. The function unit is further configured to map based on the base bid coefficient to determine a target interval from a plurality of intervals; and determining the bid function based on the target interval.
The bid data module 508 is for determining bid data based on the bid function and budget data; the bid data module 508 includes: a target unit for determining a target bid coefficient based on the bid function; and a data unit for determining bid data based on the bid coefficients and the budget data.
The advertisement promotion module 510 is configured to bid the target user traffic according to the bid data for advertisement promotion. The advertisement promotion module 510 includes: a transmitting unit for transmitting the bid data to a media party; a bidding unit, configured to bid a target user traffic by the media party based on the bidding data; and the promotion unit is used for pushing the preset advertisement for the target user after the bidding is successful.
The model training module 512 is configured to train the polar gradient lifting model to generate the conversion rate estimation model according to the historical user flow data. The model training module 512 further includes: and the updating unit is used for recording the bidding result so as to update the conversion rate estimation model.
According to the advertisement bidding device, target user flow data is obtained, wherein the target user flow data comprises user information; inputting the user information into a conversion rate estimation model to generate conversion rate data; generating a bid function from the conversion rate data and the bid coefficients; determining bid data based on the bid function and budget data; and bidding the target user flow according to the bidding data to perform advertisement popularization, so that the advertiser can accurately control the price of the user flow, the advertising efficiency of the advertiser is effectively improved, the advertiser can also perform overall control on advertising expenses, and the use of the expenses is coordinated overall.
Fig. 6 is a block diagram of an electronic device, according to an example embodiment.
An electronic device 600 according to such an embodiment of the present disclosure is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 6, the electronic device 600 is in the form of a general purpose computing device. Components of electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different system components (including the memory unit 620 and the processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program code executable by the processing unit 610 such that the processing unit 610 performs steps according to various exemplary embodiments of the present disclosure described in the above-described electronic prescription flow processing methods section of the present specification. For example, the processing unit 610 may perform the steps as shown in fig. 2,3, and 4.
The memory unit 620 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 6201 and/or cache memory unit 6202, and may further include Read Only Memory (ROM) 6203.
The storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 630 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 600 may also communicate with one or more external devices 600' (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and/or any devices (e.g., routers, modems, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 650. Also, electronic device 600 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 600, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, as shown in fig. 7, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the above-described method according to the embodiments of the present disclosure.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium 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 readable storage 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.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, 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 computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The computer-readable medium carries one or more programs, which when executed by one of the devices, cause the computer-readable medium to perform the functions of: acquiring target user flow data, wherein the target user flow data comprises user information; inputting the user information into a conversion rate estimation model to generate conversion rate data; generating a bid function from the conversion rate data and the bid coefficients; determining bid data based on the bid function and budget data; and bidding the target user traffic according to the bid data so as to promote advertisement.
Those skilled in the art will appreciate that the modules may be distributed throughout several devices as described in the embodiments, and that corresponding variations may be implemented in one or more devices that are unique to the embodiments. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or in combination with the necessary hardware. Thus, the technical solutions according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and include several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that this disclosure is not limited to the particular arrangements, instrumentalities and methods of implementation described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (14)
1. An advertising bidding method, comprising:
screening the user traffic through a screening strategy preset at the media to obtain target user traffic data, wherein the target user traffic data comprises user information;
Training the polar gradient lifting model by utilizing historical user flow data to generate a conversion rate estimation model, wherein the method comprises the following steps of:
estimating the conversion rate of the historical user through a xgboost model by utilizing the flow data of the historical user, scoring the conversion rate of the historical user through the xgboost model, and training a xgboost model according to the accumulated conversion rate data of the historical user; or alternatively
Acquiring basic data of a first user which is subjected to ground conversion in history and basic data of a second user which is not subjected to ground conversion in history, and training xgboost models through the basic data of the first user and the second user;
inputting the user information into a trained conversion rate estimation model to generate conversion rate data;
Generating a bid function from the conversion rate data and the bid coefficients includes:
Calculating a time coefficient of a user in a current time window by analyzing conversion rates of accumulated large disc data in different time periods;
calculating time period budget data according to the service data and calculating the total value of the current budget by combining the time coefficients;
Determining an overall conversion rate by the time coefficient and the budget data;
generating a base bid coefficient based on the overall conversion rate and the conversion rate data; and
Mapping based on the base bid coefficients to determine a bid function;
Determining a target bid coefficient based on the bid function, determining bid data based on the target bid coefficient and budget data; and
And bidding the target user flow according to the bidding data.
2. The method of claim 1, wherein mapping based on the base bid coefficients to determine a bid function comprises:
mapping based on the bid coefficients to determine a target interval from a plurality of intervals;
Secondarily mapping the bidding coefficients to different areas to realize segmentation of bidding functions of different user groups; the bid function is determined based on the target interval.
3. The method of claim 1, wherein mapping based on the base bid coefficients to determine a bid function comprises:
Wherein, As a bidding function,/>Based on bid data,/>And R is the number of user function interval divisions for mapping functions of different intervals.
4. The method of claim 1, wherein determining target bid coefficients based on the bid function, determining bid data from the bid coefficients and budget data, further comprises:
Converting the basic bidding coefficients of different users into a plurality of bidding functions to obtain new bidding coefficients;
The new bid coefficients are combined with the budget data to calculate bid data.
5. The method of claim 1, wherein bidding the target user traffic in accordance with the bid data comprises:
transmitting the bid data to a media party;
The media party bidding on a target user traffic based on the bid data; and
And pushing the preset advertisement for the target user after successful bidding.
6. The method as recited in claim 1, further comprising:
and recording a bid result so as to update the conversion rate estimation model.
7. An advertising bidding device, comprising:
the system comprises a user flow module, a user flow module and a user flow module, wherein the user flow module is used for screening user flow through a screening strategy preset at a media position to obtain target user flow data, and the target user flow data comprises user information;
The model training module is used for training the polar gradient lifting model through historical user flow data to generate a conversion rate estimation model, and comprises the following steps: estimating the conversion rate of the historical user through a xgboost model by utilizing the flow data of the historical user, scoring the conversion rate of the historical user through the xgboost model, and training a xgboost model according to the accumulated conversion rate data of the historical user; or acquiring basic data of a first user which is converted into the ground historically and basic data of a second user which is not converted into the ground historically, and training xgboost models through the basic data of the first user and the second user;
The conversion rate module is used for inputting the user information into the trained conversion rate estimation model to generate conversion rate data;
A bid function module for generating a bid function from the conversion rate data and the bid coefficients, comprising:
The parameter unit is used for calculating the time coefficient of the user in the current time window by analyzing the conversion rate of the accumulated large disc data in different time periods; calculating time period budget data according to the service data and calculating the total value of the current budget by combining the time coefficients; determining an overall conversion rate by the time coefficient and the budget data;
a coefficient unit for generating a base bid coefficient based on the overall conversion rate and the conversion rate data; and
A function unit for mapping based on the base bid coefficients to determine a bid function; a bid data module including a target unit for determining a target bid coefficient based on the bid function; a data unit for determining bid data based on the target bid coefficients and budget data;
And the advertisement promotion module is used for bidding the target user flow according to the bid data.
8. The apparatus of claim 7, wherein the function unit is further to
Mapping based on the base bid coefficients to determine a target interval from a plurality of intervals; secondarily mapping the bidding coefficients to different areas to realize segmentation of bidding functions of different user groups; and determining the bid function based on the target interval.
9. The apparatus as claimed in claim 7, comprising:
Wherein, As a bidding function,/>Based on bid data,/>And R is the number of user function interval divisions for mapping functions of different intervals.
10. The apparatus of claim 7, wherein,
The target unit further includes: converting the basic bidding coefficients of different users into a plurality of bidding functions to obtain new bidding coefficients;
A data unit, further comprising: the new bid coefficients are combined with the budget data to calculate bid data.
11. The apparatus of claim 7, wherein the advertising promotion module comprises:
a transmitting unit for transmitting the bid data to a media party;
A bidding unit, configured to bid a target user traffic by the media party based on the bidding data; and
And the promotion unit is used for pushing preset advertisements to the target users after successful bidding.
12. The apparatus of claim 7, wherein the model training module further comprises:
and the updating unit is used for recording the bidding result so as to update the conversion rate estimation model.
13. An electronic device, comprising:
One or more processors;
A storage means for storing one or more programs;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-6.
14. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-6.
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