CN110766427A

CN110766427A - Advertisement bidding method and system

Info

Publication number: CN110766427A
Application number: CN201810828790.0A
Authority: CN
Inventors: 李莉; 王志勇; 张小洵; 盖坤
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba East China Co ltd
Priority date: 2018-07-25
Filing date: 2018-07-25
Publication date: 2020-02-07
Anticipated expiration: 2038-07-25
Also published as: CN110766427B

Abstract

The application discloses an advertisement bidding method and system. Wherein, the method comprises the following steps: acquiring a flow pushing condition; determining the target flow to be compared and the price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the traffic pushing condition; and comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow. The method and the device solve the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of the user.

Description

Advertisement bidding method and system

Technical Field

The application relates to the field of traffic pushing, in particular to an advertisement bidding method and system.

Background

In the field of internet advertisement promotion, the current mainstream internet traffic pushing mode can be that RTB shows advertisements, advertisers firstly set prices for audiences who want to reach, and a platform decides which advertiser's originality is shown for the traffic through a mode based on price auction. Therefore, the price setting is very critical for the advertiser, and the price not only determines the amount of the acquired traffic, but also influences the final advertisement putting effect.

The attributes of traffic on the internet vary widely, and are inherently different from the value of advertisers. However, in the conventional RTB display advertisement system, advertisers can only bid uniformly on a coarse granularity (targeted audience resource site), and cannot bid in real time according to the quality/value difference of each traffic, which results in an unsatisfactory delivery effect of the advertisers. Some internet platforms then introduced a method of adjusting the price of the reference price, and different adjustment coefficients may be set for traffic attributes such as different devices/periods/areas/page contents to change the bid (e.g., float-90% to 900% on the basis of the reference price).

At present, DSP of each large advertisement develops towards programming and intellectualization, the intelligent bidding function basically becomes the bidding of an advertisement platform, the market application range of intelligent bidding is very wide, and the commercial value is obvious. Similar intelligent bidding product functionality is also provided by the industry's large DSPs.

However, the existing intelligent bidding products still have the following defects: the current intelligent bidding tool has a single standard for evaluating the flow price value, is usually an index of partial short-term business benefits such as click rate, conversion rate and the like, does not support an advertiser to define the flow price value, and cannot meet the personalized requirements of the advertiser in different marketing scenes; at present, a bidding strategy of an intelligent bidding tool is opaque, the link of how the flow value is mapped to the final bid is often determined by a platform black box, and an advertiser has no way to participate in customization.

Aiming at the problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of users, an effective solution is not provided at present.

Disclosure of Invention

The embodiment of the application provides an advertisement bidding method and system, which at least solve the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of users.

According to an aspect of an embodiment of the present application, there is provided an advertisement bidding method, including: acquiring a flow pushing condition; determining the target flow to be compared and the price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the traffic pushing condition; and comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.

According to another aspect of the embodiments of the present application, there is also provided an advertisement bidding method, including: displaying the flow pushing condition; displaying a target flow to be compared and a target flow price, wherein the target flow and the target flow price are determined according to the flow pushing condition; displaying a recommendation result corresponding to the flow pushing condition; and displaying a comparison result of the target flow, wherein the comparison result is obtained by comparing the target flow based on the flow pushing condition.

According to another aspect of the embodiments of the present application, there is also provided an advertisement bidding method, including: acquiring a flow pushing condition; determining the target flow to be compared and the price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the traffic pushing condition; obtaining the price of the target flow according to the flow pushing condition; and comparing the target flow based on the price of the target flow to obtain a comparison result of the target flow.

According to another aspect of the embodiments of the present application, there is also provided a method for comparing prices of traffic, including: displaying the flow pushing condition; displaying the price of the target flow to be compared, wherein the target flow and the target flow price are determined according to the flow pushing condition; displaying a recommendation result corresponding to the flow pushing condition; displaying the price of the target flow, wherein the price of the target flow is obtained according to the flow pushing condition; and displaying a comparison result of the target flow, wherein the comparison result is obtained by comparing the target flow based on the price of the target flow.

According to another aspect of the embodiments of the present application, there is also provided a storage medium including a stored program, wherein when the program runs, a device on which the storage medium is located is controlled to perform the following steps: acquiring a flow pushing condition; determining the target flow to be compared and the price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the traffic pushing condition; and comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.

According to another aspect of the embodiments of the present application, there is also provided a processor, configured to execute a program, where the program executes the following steps: acquiring a flow pushing condition; determining the target flow to be compared and the price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the traffic pushing condition; and comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.

According to another aspect of embodiments of the present application, there is also provided an advertisement bidding system, including: a processor; and a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: acquiring a flow pushing condition; determining the target flow to be compared and the price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the traffic pushing condition; and comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.

According to another aspect of embodiments of the present application, there is also provided an advertisement bidding system, including: the first setting module is used for acquiring flow pushing conditions; the second setting module is used for determining the target flow to be compared and the price of the target flow according to the flow pushing condition; the estimation module is used for generating a recommendation result corresponding to the flow pushing condition; and the bidding module is used for comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.

In the embodiment of the application, after the traffic pushing condition is obtained, the target traffic and the price of the target traffic which need to be compared can be determined according to the traffic pushing condition, a recommendation result corresponding to the traffic pushing condition is further generated, and the target traffic is compared based on the traffic pushing condition to obtain a comparison result of the target traffic, so that the purpose of comparing the traffic price is achieved.

It is easy to notice that, compared with the prior art, the advertiser can set the traffic pushing condition according to the promotion purpose, and can determine the target traffic and the corresponding price according to the traffic pushing condition set by the advertiser, further generate the recommendation result corresponding to the traffic pushing condition to guide the advertiser to adjust the traffic pushing condition, so as to achieve the technical effects of increasing the control force of floating bid of the advertiser on different value traffic, reducing the trial and error cost of the advertiser, and improving the application feasibility and the usability of the comparison method.

Therefore, the technical problem that the advertisement bidding method in the prior art cannot meet personalized requirements of users is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

fig. 1 is a block diagram of a hardware structure of a computer terminal (or mobile device) for implementing an advertisement bidding method according to an embodiment of the present application;

FIG. 2 is a flow chart of a method of advertising bidding according to embodiment 1 of the present application;

FIG. 3 is an interactive schematic diagram of an alternative bid function setting according to an embodiment of the present application;

FIG. 4 is an alternative advertisement bidding system architecture diagram according to an embodiment of the present application;

FIG. 5 is a flow chart of an optional promotion setup phase according to an embodiment of the present application;

FIG. 6 is an interactive schematic diagram of an alternative traffic value score setting according to an embodiment of the present application;

FIG. 7 is an interaction diagram illustrating an alternative delivery projection presentation according to an embodiment of the present application;

FIG. 8 is a flow diagram of an alternative traffic real-time bidding phase according to an embodiment of the present application;

fig. 9 is a flowchart of an advertisement bidding method according to embodiment 2 of the present application;

FIG. 10 is a flow chart of a method of advertising bidding according to embodiment 3 of the present application;

FIG. 11 is a flow chart of a method of advertising bidding according to embodiment 4 of the present application;

fig. 12 is a schematic view of an advertisement bidding apparatus according to embodiment 5 of the present application;

fig. 13 is a schematic view of an advertisement bidding apparatus according to embodiment 6 of the present application;

fig. 14 is a schematic view of an advertisement bidding apparatus according to embodiment 7 of the present application;

fig. 15 is a schematic view of an advertisement bidding apparatus according to embodiment 8 of the present application;

FIG. 16 is a schematic diagram of an advertisement bidding system according to an embodiment of the present application; and

fig. 17 is a block diagram of a computer terminal according to an embodiment of the present application.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

First, some terms or terms appearing in the description of the embodiments of the present application are applicable to the following explanations:

RTB: real Time Bidding, Real Time Bidding.

And (4) DSP: the Demand-side platform serves an advertiser and helps the advertiser to carry out advertisement putting on the Internet or the mobile Internet.

DMP: data Management Platform, Data Management Platform.

CTR: click Through Rate.

CVR: conversion Rate, click Conversion Rate.

CPC: cost Per Click, single Click charge.

CPM: cost Per mill, thousands of showings charges.

OCPC: optimized Cost Per Click, optimized cpc bid.

OCPM: optimized CPM, optimized CPM bid.

GBDT: gradient Boosting Decision Tree, Gradient lifting Tree Algorithm.

LR: logistic Regression, Logistic Regression algorithm.

Example 1

There is also provided, in accordance with an embodiment of the present application, an embodiment of an advertisement bidding method, it should be noted that the steps illustrated in the flowchart of the accompanying drawings may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.

The method provided by the first embodiment of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Fig. 1 illustrates a hardware configuration block diagram of a computer terminal (or mobile device) for implementing an advertisement bidding method. As shown in fig. 1, the computer terminal 10 (or mobile device 10) may include one or more (shown as 102a, 102b, … …, 102 n) processors 102 (the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

It should be noted that the one or more processors 102 and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuit may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computer terminal 10 (or mobile device). As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).

The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the advertisement bidding method in the embodiment of the present application, and the processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, so as to implement the advertisement bidding method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or mobile device).

It should be noted here that, in some embodiments, the computer device (or mobile device) shown in fig. 1 has a touch display (also referred to as a "touch screen" or "touch display screen"). In some embodiments, the computer device (or mobile device) shown in fig. 1 above has a Graphical User Interface (GUI) with which a user can interact by touching finger contacts and/or gestures on a touch-sensitive surface, where the human interaction functionality optionally includes the following interactions: executable instructions for creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, emailing, call interfacing, playing digital video, playing digital music, and/or web browsing, etc., for performing the above-described human-computer interaction functions, are configured/stored in one or more processor-executable computer program products or readable storage media.

Under the operating environment, the application provides an advertisement bidding method as shown in fig. 2. Fig. 2 is a flowchart of an advertisement bidding method according to embodiment 1 of the present application. As shown in fig. 2, the method may include the steps of:

in step S22, a traffic pushing condition is acquired.

Optionally, the traffic pushing condition may include: the flow attribute parameters are used for determining target flow, the value scoring conditions are used for determining value scores of the target flow, and the price conditions are used for representing the mapping relation between the value scores of the target flow and the prices of the target flow.

Optionally, the mapping relationship may include at least one of the following: linear, exponential, power-exponential, logarithmic.

Specifically, in the field of internet advertisement promotion, the traffic attribute parameter may be a basic promotion parameter setting performed by an advertiser; the bidding strategy that the advertiser can formulate according to the advertisement promotion requirement includes a traffic value evaluation criterion (i.e., the value scoring condition described above) and a bidding function (i.e., the price condition described above), for example, the bidding function can be a linear function, an exponential function, a power exponential function, a logarithmic function, and the like.

Further, the traffic attribute parameter includes at least one of: pushing time, region, oriented crowd, resource location, creativity and total budget; the value scoring conditions include: scoring dimensions and scoring methods; the price conditions include: price conditions and custom price conditions in the condition template.

Specifically, in the field of internet advertisement promotion, the traffic attribute parameters may include: the advertisement delivery time period, region, targeted crowd, resource position, creative idea, total budget and the like can not be limited to the above, and other popularization parameters can also be included; the traffic value evaluation standard can comprise a scoring dimension and a scoring method, wherein the scoring dimension can refer to the characteristic of judging the value of each traffic by an advertiser, and various characteristics about the traffic, which can be obtained or processed by an advertisement platform, can be provided for the advertiser to serve as the scoring dimension, such as the population attributes of the traffic, such as the region, age, education level and the like, consumption habits of consumption frequency, consumption amount and the like, and shopping preferences of tendency commodity categories, brands and the like; the scoring method may refer to a specific score calculation means at a given scoring dimension. The scoring method has various implementation manners, for example, a calculation method for performing weighted summation based on a user-defined scoring dimension weight may be adopted, and a method for training a scoring model based on annotation sample data uploaded by an advertiser may also be adopted. The price condition can be a bid function for obtaining a corresponding price according to the value score of the flow.

Preferably, the scoring method comprises: a scoring model, the scoring model including at least one of: a linear weighted sum model, a model trained based on user data.

Specifically, in the field of internet advertisement promotion, generally, the bid function is a monotonically increasing function of the traffic value score, that is, the higher the traffic value score is, the higher the corresponding bid is. Assuming that the value score of the traffic ranges from 0 to 1, the bid range (highest/lowest value) is determined after the form of the bid function f (x) is given. Therefore, the bid function contains information of both the functional form and the bid range.

There are a number of implementations on the setting of the bid function. For example, to relieve the advertiser of input burden, the platform may provide several commonly used function types as alternative templates (linear, logarithmic, power, etc.), and after the advertiser selects a function type and gives a bid range, the platform automatically computes the function. For example, as shown in fig. 3, the advertiser may choose a linear function, with a bid range of 3-10, and a bid function f (X) ═ 7X + 3.

Another way is for the advertiser to enter the function formula at his or her own discretion. For example, as shown in fig. 3, in order to make the interaction experience of the advertiser more intuitive and friendly, the platform may display and preview information of the function, such as a bid range, a function curve, and the like, to help the advertiser make modification adjustments.

And step S24, determining the target flow and the price of the target flow which need to be compared according to the flow pushing condition.

Specifically, in the field of internet advertisement promotion, the target traffic may be a traffic in which an advertiser participates in bidding for advertisement delivery of the advertiser, and the price of the target traffic may be a bidding price provided by the advertiser for bidding on the target traffic.

In step S26, a recommendation result corresponding to the traffic volume push condition is generated.

Specifically, in the field of internet advertisement promotion, the advertisement delivery result can be pre-estimated (namely, the recommendation result) for the advertiser according to the information input by the advertiser, so that the advertiser can be helped to pre-judge the rationality of the promotion parameter setting, the trial and error cost is reduced, the operation experience of the advertiser is improved, the beneficial effect is brought, and meanwhile, the advertisement delivery method and the advertisement delivery method are different from other schemes in flow and front-end display.

And step S28, comparing the target flow based on the flow pushing condition to obtain the comparison result of the target flow.

Specifically, in the field of internet advertisement promotion, after a recommendation result is generated, an advertiser can estimate delivery results provided by a viewing system, and if a place which is not expected to exist is found, the flow pushing condition can be updated; if no unexpected places are found to exist, advertising bids can be placed based on this information.

In an optional scheme, a bid of a target flow rate can be calculated according to information input by an advertiser, real-time bidding is performed on the target flow rate, subsequently, a winning advertiser is judged according to a bidding mechanism and a settlement mode of a platform (that is, the comparison result is obtained), and the winning advertiser is charged.

An information description of a preferred embodiment of the present application is given below with reference to fig. 4 to 7, taking the field of internet advertisement promotion as an example. Fig. 4 is an alternative advertisement bidding system architecture diagram according to an embodiment of the present application, fig. 5 is a flowchart of an alternative promotion setting stage according to an embodiment of the present application, fig. 6 is an interaction diagram of an alternative traffic value score setting according to an embodiment of the present application, and fig. 7 is an interaction diagram of an alternative delivery forecast display according to an embodiment of the present application.

As shown in fig. 4, the system may include: a basic popularization parameter setting module, a bidding strategy setting module, a delivery result pre-estimating module and an online bidding module. The advertiser can set basic promotion parameters through the basic promotion parameter setting module, and specifically, the basic promotion parameter setting module can set basic promotion parameters including orientation, resource positions, creativity, total budget and the like. The advertiser can make a bidding strategy through the bidding strategy setting module, specifically can set a flow value evaluation standard through the flow value evaluation standard setting module, and sets a bidding function through the bidding function setting module. The advertiser can check corresponding delivery result prediction through the delivery result prediction module, specifically, the delivery result prediction can be obtained through calculation of the bidding simulation and result statistics module, the delivery result prediction is displayed to the advertiser through the prediction result display module, the advertiser judges whether a place which is not in line with the prediction exists or not, and when the place which is not in line with the prediction exists, the advertiser can increase a limiting rule through the rule limiting module and finish the promotion setting stage. The method comprises the steps of conducting real-time bidding on flow of advertisers participating in bidding through an online bidding module, specifically, scoring the flow according to a flow value evaluation standard set in a promotion setting stage through a flow value score calculation module to obtain a value score of the flow, further conducting calculation through a flow score mapping bidding calculation module according to a bidding function set in the promotion setting stage to obtain a corresponding bid, and finally conducting real-time bidding on the flow according to the calculated bid through a real-time bidding module to obtain a final bidding result.

As shown in fig. 5, the promotion setting phase process may include the following steps:

step S51, the advertiser sets the basic promotion parameters, including the time slot, region, targeted crowd, resource location, creative idea, total budget, etc.

At step S52, the advertiser formulates a bidding strategy including traffic value assessment criteria and a bidding function.

Alternatively, the flow value evaluation criterion may include two factors, namely a scoring dimension and a scoring method.

For example, as shown in FIG. 6, an advertiser may enter traffic value scoring criteria, including two scoring methods, respectively: the method comprises the steps of customizing scoring dimension weight, performing linear weighting and solving, uploading marking sample data, training a scoring model, and enabling an advertiser to select any scoring method according to needs. For example, when an advertiser selects the first scoring method, the advertiser may select a scoring dimension and set the corresponding weight; when the advertiser selects the second scoring method, the advertiser can upload the annotation sample data by clicking the upload button and select the scoring dimension. After the advertiser has completed the setup, the advertiser may click on the next button to begin setting up the bid function.

As shown in FIG. 3, the advertiser can set the bid function, including two setting modes, respectively: using the templates and custom input functions, the advertiser can select any one of the settings as desired. For example, when an advertiser chooses to use a template, the advertiser can set a bid range and select a functional form, so that a bid function can be automatically generated; when the advertiser selects a custom input function, the advertiser can input a bid function so that a bid function preview can be made, including a bid range and corresponding function image. After the advertiser finishes the meeting, the advertiser can click the next key to estimate before putting. The advertiser may also resume the setup by clicking on the back button.

And step S53, according to the input information of the advertiser in the steps and in combination with the historical bidding log, the system provides estimation of the delivery result, including crowd price value distribution, bidding distribution, estimated display/click/consumption and the like.

For example, as shown in fig. 7, pre-placement estimation can be performed according to the setting of the advertiser, and value score distribution estimation, bid distribution estimation and placement effect estimation can be obtained. The value score distribution estimation and the bid distribution estimation can display different value score intervals or traffic distribution corresponding to the bid intervals in a histogram mode. The impression effect estimate may display the expected number of available impressions, clicks, and spending amounts.

And step S54, the advertiser modifies or further limits the promotion setting or bidding strategy according to the delivery result estimation of the steps, and the delivery is started to take effect after confirmation.

Optionally, the advertiser may return for modification if it finds that there are places that are not as expected, looking at the placement estimate provided by the system. In addition to returning to modify the promotional settings or bidding strategy, qualifying conditions may be further added.

For example, as shown in FIG. 7, advertisers have found that low value traffic under 0.3 points is comparatively high and wish to spend limited budgets on high value traffic, and in this case may add a qualifying rule that qualifies traffic for a delivery value >0.3 points. After adding the qualifying rules, the advertiser may manually click an update button to trigger an update of the pre-estimated results. And further clicking OK to confirm that the release button starts to take effect, calculating the bid of the flow of the advertiser participating in the bidding in real time, and bidding the flow in real time to obtain the final bidding result.

According to the scheme provided by the embodiment 1 of the present application, after the traffic pushing condition is obtained, the target traffic and the price of the target traffic which need to be compared can be determined according to the traffic pushing condition, a recommendation result corresponding to the traffic pushing condition is further generated, and the target traffic is compared based on the traffic pushing condition to obtain a comparison result of the target traffic, so that the purpose of comparing the traffic price is achieved.

Therefore, the scheme of the embodiment 1 provided by the application solves the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of the user.

In the above embodiment of the present application, in step S26, generating a recommendation result corresponding to the traffic pushing condition includes:

step S262, obtaining a history comparison log, wherein the history comparison log includes: a number of attribute features regarding traffic, maximum price, and number of clicks.

Optionally, the attribute characteristic about the traffic may include at least one of: pushing time, region, directional crowd and resource position of the flow.

Specifically, in the field of internet advertising, the history comparison log may be a bidding log of one or more days of history, assuming that the access traffic per day is relatively stable. In the embodiment of the present application, the bidding log of one day of history is taken as an example for explanation, and the history bidding log may include the directional label carried by the traffic, the highest bid, the estimated pctr for the advertiser, and the characteristic attribute of the traffic.

Step S264, comparing the log according to the traffic pushing condition and the history to obtain a first traffic set and a second traffic set, wherein the first traffic set comprises the traffic set successfully matched with the traffic attribute parameters, and the second traffic set comprises the traffic set successfully matched with the price condition in the first traffic set.

In an optional scheme, in the field of internet advertisement promotion, all the competitive traffic sets (i.e., the first traffic set) meeting the advertisement promotion setting conditions can be screened out from the bidding candidate sets according to information such as time intervals, regions, orientations, resource locations and the like of traffic, and further a competitive traffic set (the second traffic set) can be obtained according to bids of advertisers.

Step S266, generating a recommendation result, where the recommendation result includes: the value distribution and price distribution of the first traffic set, and the estimated number of impressions, clicks and consumption of the second traffic set.

In an optional scheme, in the field of internet advertisement promotion, each bidding flow in a bidding flow set can be traversed, medium distribution and bidding distribution of all bidding flows are calculated according to a bidding strategy set by an advertiser, and the medium distribution and the bidding distribution are displayed in a chart mode such as a histogram and a histogram. For all the flows in the flow set capable of being bid, the information of the putting effect is obtained through statistics, including the number of showings (total number of bid flows), the number of clicks (sum of predicted pctr of bid flows), and the amount of consumption (sum of highest bid of bid flows), as shown in fig. 7.

In the above embodiment of the present application, step S264 compares the log according to the traffic pushing condition and the history to obtain a first traffic set and a second traffic set, where the steps include:

in step S2640, a plurality of attribute features related to the flow rate are matched with the flow rate attribute parameters.

Step 2642, obtaining the traffic with the attribute characteristics successfully matched with the traffic attribute parameters, and obtaining a first traffic set.

In an optional scheme, in the field of internet advertisement promotion, all the competitive traffic sets meeting the advertisement promotion setting conditions can be screened out from the competitive candidate sets according to the information of time intervals, regions, orientations, resource positions and the like of traffic.

Step 2644, obtaining a price of each flow in the first flow set according to the price condition.

In an alternative approach, the value scores for each flow in the set of biddable flows and the corresponding bids may be calculated according to a bidding strategy set by the advertiser.

Step S2646 compares the highest price of each flow in the first set of flows with the price of each flow.

Step 2648, obtain the flow with the highest price less than the price, and obtain the second flow set.

In an optional scheme, in the field of internet advertisement promotion, for each competitive traffic, it may be determined whether an advertiser bid for the traffic is higher than the highest bid of other advertisers, that is, whether the calculated bid is greater than the highest bid of the traffic in the historical bidding log, and if so, the traffic is considered as competitive traffic.

In the above embodiment of the present application, in step S266, generating a recommendation result includes:

step 2661, a value score and a price of each flow in the first flow set are obtained according to the value scoring condition and the price condition.

Step 2662, performing statistics on the value score of each flow in the first flow set to obtain a value distribution.

Step 2663, the price of each flow in the first flow set is counted to obtain a price distribution.

In an optional scheme, in the field of internet advertisement promotion, each bidding flow in a set of bidding flows can be traversed, value scores and corresponding bids of the flows are calculated according to bidding strategies set by advertisers, and the price values and the distribution of the bids of all the bidding flows are counted.

Step 2664, counting each flow in the second flow set to obtain the number of display times.

Step 2665, counting the number of clicks of each flow in the second flow set to obtain the number of clicks.

Step 2666, counting the maximum price of each flow in the second flow set to obtain the consumption amount.

In an optional scheme, in the field of internet advertisement promotion, the information of the delivery effect, including the estimated number of shows, clicks and the consumption amount, can be counted for all the traffics that can be bid for.

In the above embodiment of the present application, after the step S26 generates the recommendation result corresponding to the traffic pushing condition, the method further includes the following steps:

step S210, adjusting the traffic pushing condition, and/or obtaining an input limiting rule to obtain a new traffic pushing condition.

In an alternative approach, in the field of internet advertising, advertisers view impression predictions provided by the system and may return to modify if there are places found to be undesirable. For example, finding that the predicted consumption is higher than the set budget, one may return to increasing the total budget for delivery; and (5) the bid distribution is over concentrated, the bid differentiation degree of the high-quality flow and the low-quality flow is not high, and the bid function is returned to be modified.

In addition to returning to modify the promotional settings or bidding strategy, qualifying conditions may be further added. For example, as shown in fig. 7, a flow rate with a dispensing value >0.3 points is defined. In addition to the value, the bid range may be further defined. After adding the qualifying rules, the advertiser may manually click an update button to trigger an update of the pre-estimated results.

It should be noted that adding the constraint condition may also be regarded as updating the bid function. For example, the limiting value >0.3, and the range of values corresponding to F (X) is limited to x ∈ (0.3,1 ].

Step S212, comparing the target flow based on the new flow pushing condition to obtain a comparison result.

In an alternative scheme, in the field of internet advertisement promotion, after the setting of an advertiser is completed, bids of target traffic can be calculated in real time according to traffic pushing conditions set by the advertiser, and the traffic can be bid in real time.

In the above embodiment of the present application, in step S28, comparing the target traffic based on the traffic pushing condition to obtain a comparison result of the target traffic, where the comparison result includes:

step S282, score the target traffic according to the value scoring condition, to obtain a value score of the target traffic.

In an optional scheme, in the field of internet advertisement promotion, for an access traffic, according to information such as time interval, region, orientation, resource location and the like of the traffic, matching is performed with a promotion condition set by an advertiser, and the matched traffic is the traffic of the advertiser participating in bidding. At this time, based on a traffic value evaluation standard preset by an advertiser, the attribute characteristics of the traffic are obtained at the same time, and the traffic is scored (generally normalized to an interval, such as 0-1).

And step S284, obtaining the price of the target flow according to the price condition and the value score of the target flow.

For example, the advertiser may formulate a bid function of f (X) ═ 7 × X +3 (with the proviso that X ∈ (0.3, 1)), assuming that traffic 1 is worth 0.2 and traffic 2 is worth 0.6, the corresponding bids are as shown in table 1 below.

TABLE 1

Flow ID	Flow value score	Bid price (Yuan)
			1	0.2	0
2	0.6	7.2

Step S286, comparing the target flow based on the price of the target flow to obtain a comparison result.

In an alternative approach, in the field of internet advertising, the traffic can be bid on in real time using computed bids. Subsequently, the winning advertiser is judged according to a bidding mechanism and a settlement mode of the platform, and is charged, belonging to a general flow for displaying advertisement bidding and not described in detail in the application.

An information description of a preferred embodiment of the present application is given below with reference to fig. 8, taking the field of internet advertisement promotion as an example. Figure 8 is a flow diagram of an alternative traffic real-time bidding phase according to an embodiment of the present application.

As shown in fig. 8, the flow real-time bidding phase flow may include the following steps:

step S81, for the flow rate of the advertiser participating in bidding, the value score of the flow rate is calculated in real time according to the flow rate value evaluation standard established by the advertiser.

Step S82, according to the bid function formulated by the advertiser, maps the value score of the traffic to a bid.

In step S83, real-time bidding is performed on the traffic volume using the bid price calculated in the above step.

An example of two scoring methods is illustrated below in conjunction with fig. 6, including a specific implementation of how advertisers enter on the interface during the ad placement phase, and how the real-time bidding phase scores each traffic.

For embodiments based on custom scoring dimensional weights, the summation is linearly weighted.

In the advertisement setting stage, an advertiser browses a candidate set of scoring dimensions provided by a platform, selects a plurality of scoring dimensions from the candidate set, and assumes that the promotion purpose of the advertiser A at this time is to promote a high-end automobile newly promoted by a certain brand, so brand preference, consumption level and education level are selected to distinguish promotion values of different flows; the advertiser sets a normalized weight for each scoring dimension, assuming 0.6, 0.3, 0.1, respectively.

In the flow real-time bidding stage, firstly calculating the characteristic value of the flow on the scoring dimension, and then performing linear weighted average according to the weight of the scoring dimension:

wherein, Score_iRepresents the value score, f, of the ith flow_ijRepresenting the characteristic value, w, of the flow i in the j-dimension scoring dimension_jRepresenting the (normalized) weights of the j-th scoring dimension, assuming that the scores for traffic 1 and traffic 2 over the 3 scoring dimensions selected by the advertiser are shown in table 2, the final value scores are 0.56 and 0.42, respectively.

TABLE 2

Flow Id	Brand preference	Consumption hierarchy	Degree of education	Value score
					1	0.7	0.3	0.5	0.70.6+0.30.3+0.5*0.1＝0.56
2	0.5	0.5	0.3	0.50.6+0.30.3+0.3*0.1＝0.42

Embodiments of a scoring model are trained for advertiser upload of annotation sample data.

The advertisement setting stage comprises a label sample collection and uploading stage and a model training stage, wherein in the label sample collection and uploading stage, in the brand/store management process of an advertiser, ID information (ID of identification identities such as website user names, telephones, mailboxes and the like) of users can be collected, the group of users are scored according to the value scoring standard of the advertiser, and data files (excel, txt and the like) are uploaded according to a format specified by a platform.

For example, the advertiser a accumulates more than 3 million members information in the offline store, and the advertiser processes the consumer lifetime Value LTV (Life cycle total Value, which is called Life Time Value) according to the online consumption information of the group of members, and normalizes the LTV to the range of 0 to 1 to obtain the Value score of the member. Advertiser A wishes to use the data of offline store members as template to identify the value of online traffic through the data and model capability of the platform (traffic similar to high-value members in characteristic dimensions, corresponding value score is generally higher). Advertisers can select the scoring dimension of their interest as a feature for model training.

In the model training stage, preprocessing dirty data filtering, format unification and the like is carried out on data files submitted by an advertiser; assuming that a pre-processed user feature table is stored in a database (or a third-party DMP) of the advertisement platform, as shown in Table 3, a feature value of an advertiser marking sample ID on a selected feature dimension is searched and obtained in a user ID matching mode, and a training sample is generated; the marked samples are used for training a scoring model, common machine learning methods such as GBDT, LR and deep learning can be adopted, and a plurality of algorithms are available in the industry and academia.

TABLE 3

And in the flow real-time bidding stage, acquiring the characteristic value of the flow on the selected scoring dimension, taking the characteristic value as the input of a scoring model, and obtaining the value score of the flow after a series of operations of the scoring model.

It should be noted that, in general, model training requires a period of computation time, and therefore, unlike the first method, the scoring of traffic cannot be immediately effective and cannot be started until the model training is completed.

According to the scheme, in the advertisement promotion setting stage, an advertiser formulates bidding strategies (including flow value evaluation standards and bidding functions) according to promotion requirements, and meanwhile, the system outputs delivery estimation results according to input and historical bidding information of the advertiser so as to assist the advertiser in judging the rationality of strategy setting and further adjusting; in the real-time bidding stage, the value of each flow is calculated according to the flow value evaluation standard set by the advertiser and the flow attribute, the value is mapped into the bid based on the bid function, and the flow is bid in real time by using the price. The method can better meet the personalized requirements of advertisers on intelligent bidding, helps the advertisers to reduce trial and error cost through result estimation, and has better usability. Meanwhile, for the platform, the intelligent bidding method has good expandability and universality, and can be deployed on various advertisement platforms (searching advertisements, brand advertisements and the like).

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.

Example 2

An embodiment of a method for advertising bidding is also provided according to an embodiment of the present application.

Fig. 9 is a flowchart of an advertisement bidding method according to embodiment 2 of the present application. As shown in fig. 9, the method may include the steps of:

in step S92, the flow rate push condition is displayed.

And step S94, displaying the target flow and the price of the target flow which need to be compared, wherein the target flow and the price of the target flow are determined according to the flow pushing condition.

In step S96, a recommendation result corresponding to the traffic volume push condition is displayed.

And step S98, displaying a comparison result of the target flow, wherein the comparison result is obtained by comparing the target flow based on the flow pushing condition.

According to the scheme provided by the embodiment 2 of the present application, after the flow pushing condition is displayed, the target flow and the price of the target flow which need to be compared are determined according to the flow pushing condition, and the target flow and the price of the target flow are displayed, the recommended result corresponding to the flow pushing condition is displayed, the target flow is compared based on the flow pushing condition, the comparison result of the target flow is obtained and displayed, and therefore the purpose of comparing the flow price is achieved.

Therefore, the scheme of the embodiment 2 provided by the application solves the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of the user.

In the above embodiments of the present application, the recommendation result includes: the method comprises the following steps of obtaining a log according to a flow pushing condition and an obtained history, wherein the log comprises the value distribution and the price distribution of a first flow set, and the estimated display times, the click number and the consumption amount of a second flow set, the first flow set comprises the flow set successfully matched with a flow attribute parameter, the second flow set comprises the flow set successfully matched with a price condition in the first flow set, the first flow set and the second flow set are obtained by comparing the log according to the flow pushing condition and the obtained history, and the history comparison log comprises the following steps: attribute characteristics of the plurality of flows, maximum price, and number of hits.

In an optional scheme, in the field of internet advertisement promotion, all the competitive traffic sets (i.e., the first traffic set) meeting the advertisement promotion setting conditions can be screened out from the bidding candidate sets according to information such as time intervals, regions, orientations, resource locations and the like of traffic, and further a competitive traffic set (the second traffic set) can be obtained according to bids of advertisers. The media distribution and the bid distribution of all the bidding flows can be calculated according to the bidding strategy set by the advertiser by traversing all the bidding flows in the bidding flow set, and the media distribution and the bid distribution are displayed in a histogram, a histogram and other chart modes. For all the flows in the flow set capable of being bid, the information of the putting effect is obtained through statistics, including the number of showings (total number of bid flows), the number of clicks (sum of predicted pctr of bid flows), and the amount of consumption (sum of highest bid of bid flows), as shown in fig. 7.

In the above embodiments of the present application, the method further includes the following steps:

step S910, displaying a new traffic pushing condition, where the new traffic pushing condition is obtained by adjusting the traffic pushing condition and/or obtaining a limiting rule.

Step S912, displaying a comparison result, wherein the comparison result is obtained by comparing the target traffic based on the new traffic pushing condition.

In the above embodiment of the application, the comparison result is obtained by comparing the target traffic based on the price of the target traffic, the price of the target traffic is obtained according to the price condition and the value score of the target traffic, and the value score of the target traffic is obtained by scoring the target traffic according to the value scoring condition.

In an optional scheme, in the field of internet advertisement promotion, for an access traffic, according to information such as time interval, region, orientation, resource location and the like of the traffic, matching is performed with a promotion condition set by an advertiser, and the matched traffic is the traffic of the advertiser participating in bidding. At this time, based on a traffic value evaluation standard preset by an advertiser, the attribute characteristics of the traffic are obtained at the same time, and the traffic is scored (generally normalized to an interval, such as 0-1). For example, the bid function defined by the advertiser is f (X) ═ 7 × X +3 (with the constraint of X ∈ (0.3, 1)), and assuming that the value of traffic 1 is 0.2 and the value of traffic 2 is 0.6, the corresponding bids are shown in table 1.

Example 3

Fig. 10 is a flowchart of an advertisement bidding method according to embodiment 3 of the present application. As shown in fig. 10, the method may include the steps of:

step S102, acquiring flow pushing conditions.

And step S104, determining the target flow and the price of the target flow which need to be compared according to the flow pushing condition.

Step S106 generates a recommendation result corresponding to the traffic push condition.

And step S108, obtaining the price of the target flow according to the flow pushing condition.

Step S1010, comparing the target flow based on the price of the target flow to obtain a comparison result of the target flow.

According to the scheme provided by the above embodiment 3 of the present application, after the flow pushing condition is obtained, the target flow and the price of the target flow which need to be compared can be determined according to the flow pushing condition, the recommendation result corresponding to the flow pushing condition is further generated, the price of the target flow is obtained according to the flow pushing condition, the target flow is compared based on the price of the target flow, the comparison result of the target flow is obtained, and therefore the purpose of comparing the flow price is achieved.

Therefore, the scheme of the embodiment 3 provided by the application solves the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of the user.

In the above embodiment of the present application, step S106 is to generate a recommendation result corresponding to the traffic pushing condition, and includes:

step S1062, obtaining a history comparison log, wherein the history comparison log comprises: a number of attribute features regarding traffic, maximum price, and number of clicks.

Step S1064, comparing the log according to the traffic pushing condition and the history to obtain a first traffic set and a second traffic set, wherein the first traffic set comprises the traffic set successfully matched with the traffic attribute parameters, and the second traffic set comprises the traffic set successfully matched with the price condition in the first traffic set.

Step S1066, generating a recommendation result, wherein the recommendation result comprises: the value distribution and price distribution of the first traffic set, and the estimated number of impressions, clicks and consumption of the second traffic set.

The implementation manners of the steps S1062 to S1066 are the same as the implementation manners of the steps S262 to S266 in the embodiment 1, the specific implementation manner of the step S1064 is the same as the implementation manners of the steps S2640 to S2648 in the embodiment 1, and the specific implementation manner of the step S1066 is the same as the implementation manners of the steps S2661 to S2666 in the embodiment 1, which is not repeated herein.

In the above embodiment of the present application, after the step S106 generates the recommendation result corresponding to the traffic pushing condition, the method further includes the following steps:

step S1012, adjust the traffic pushing condition, and/or obtain the input limiting rule, so as to obtain a new traffic pushing condition.

And step S1014, obtaining the price of the target flow according to the new flow pushing condition.

The implementation manners of steps S1012 to S1014 are the same as the implementation manners of steps S210 to S212 in embodiment 1, and are not described herein again.

In the above embodiment of the present application, in step S108, obtaining the price of the target flow according to the flow pushing condition includes:

and step S1082, scoring the target flow according to the value scoring conditions to obtain the value score of the target flow.

And step S1084, obtaining the price of the target flow according to the price condition and the value score of the target flow.

The implementation manners of the steps S1082 to S1084 are the same as the implementation manners of the steps S282 to S284 in the embodiment 1, and are not described herein again.

Example 4

Fig. 11 is a flowchart of an advertisement bidding method according to embodiment 4 of the present application. As shown in fig. 11, the method may include the steps of:

step S112, displaying the traffic pushing condition.

And step S114, displaying the target flow and the price of the target flow which need to be compared, wherein the target flow and the price of the target flow are determined according to the flow pushing condition.

Step S116, a recommendation result corresponding to the traffic push condition is displayed.

And step S118, displaying the price of the target flow, wherein the price of the target flow is obtained according to the flow pushing condition.

Step S1110, displaying a comparison result of the target flow, where the comparison result is obtained by comparing the target flow based on the price of the target flow.

According to the scheme provided by the foregoing embodiment 4 of the present application, after the flow rate pushing condition is displayed, the target flow rate and the price of the target flow rate that need to be compared are determined according to the flow rate pushing condition, and are displayed, the recommendation result corresponding to the flow rate pushing condition is displayed, the price of the target flow rate is obtained and displayed according to the flow rate pushing condition, and then the target flow rate is compared based on the price of the target flow rate, and the comparison result of the target flow rate is obtained and displayed, so that the purpose of comparing the flow rate and the price is achieved.

Therefore, the scheme of the embodiment 4 provided by the application solves the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of the user.

Example 5

According to an embodiment of the present application, there is also provided an advertisement bidding apparatus for implementing the above advertisement bidding method, as shown in fig. 12, the apparatus 1200 includes: an obtaining module 1202, a determining module 1204, a generating module 1206, and an aligning module 1208.

The obtaining module 1202 is configured to obtain a traffic pushing condition; the determining module 1204 is configured to determine, according to the traffic pushing condition, a target traffic to be compared and a price of the target traffic; the generating module 1206 is configured to generate a recommendation result corresponding to the traffic pushing condition; the comparison module 1208 is configured to compare the target traffic based on the traffic pushing condition to obtain a comparison result of the target traffic.

Specifically, in the field of internet advertisement promotion, the target traffic may be a traffic in which an advertiser participates in bidding for advertisement delivery of the advertiser, and the price of the target traffic may be a bidding price provided by the advertiser for bidding on the target traffic. The method can provide the advertisement result for the advertiser to pre-estimate (namely the recommendation result) according to the information input by the advertiser, thereby helping the advertiser to pre-judge the rationality of the popularization parameter setting, reducing the trial and error cost, improving the operation experience of the advertiser, bringing beneficial effects, and simultaneously having differences with other schemes on the flow and front-end display. After the recommendation result is generated, the advertiser can estimate through viewing the delivery result provided by the system, and if the place which is not expected is found, the flow pushing condition can be updated; if no unexpected places are found to exist, advertising bids can be placed based on this information.

It should be noted here that the obtaining module 1202, the determining module 104, the generating module 1206 and the comparing module 1208 correspond to steps S22 to S28 in embodiment 1, and the four modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 1. It should be noted that the above modules may be operated in the computer terminal 10 provided in embodiment 1 as a part of the apparatus.

According to the scheme provided by the foregoing embodiment 5 of the present application, after the traffic pushing condition is obtained, the target traffic and the price of the target traffic that need to be compared can be determined according to the traffic pushing condition, a recommendation result corresponding to the traffic pushing condition is further generated, and the target traffic is compared based on the traffic pushing condition to obtain a comparison result of the target traffic, so as to achieve the purpose of comparing the traffic price.

Therefore, the scheme of the embodiment 5 provided by the application solves the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of the user.

In the above embodiments of the present application, the generating module includes: the acquisition unit is used for acquiring a history comparison log, wherein the history comparison log comprises: a plurality of attribute features regarding traffic, maximum price, and number of clicks; the first processing unit is used for comparing the log according to the traffic pushing condition and the history to obtain a first traffic set and a second traffic set, wherein the first traffic set comprises the traffic set successfully matched with the traffic attribute parameters, and the second traffic set comprises the traffic set successfully matched with the price condition in the first traffic set; the generating unit is used for generating a recommendation result, wherein the recommendation result comprises: the value distribution and price distribution of the first traffic set, and the estimated number of impressions, clicks and consumption of the second traffic set.

In the above embodiments of the present application, the first processing unit includes: the matching submodule is used for matching a plurality of attribute characteristics about the flow with the flow attribute parameters; the first obtaining submodule is used for obtaining the flow with the attribute characteristics successfully matched with the flow attribute parameters to obtain a first flow set; the first processing submodule is used for obtaining the price of each flow in the first flow set according to the price condition; a comparison submodule for comparing the highest price of each flow in the first set of flows with the price of each flow; and the second obtaining submodule is used for obtaining the flow with the highest price smaller than the price to obtain a second flow set.

In the above embodiments of the present application, the generating unit includes: the second processing submodule is used for obtaining the value score and the price of each flow in the first flow set according to the value scoring condition and the price condition; the first statistic submodule is used for carrying out statistics on the value score of each flow in the first flow set to obtain value distribution; the second statistical submodule is used for carrying out statistics on the price of each flow in the first flow set to obtain price distribution; the third statistical submodule is used for carrying out statistics on each flow in the second flow set to obtain the display times; the fourth counting submodule is used for counting the click number of each flow in the second flow set to obtain the click number; and the fifth counting submodule is used for counting the highest price of each flow in the second flow set to obtain the consumption amount.

In the above embodiment of the present application, the apparatus further includes: the processing module is used for adjusting the flow pushing conditions and/or acquiring input limiting rules to obtain new flow pushing conditions; and the comparison module is used for comparing the target flow based on the new flow pushing condition to obtain a comparison result.

In the above embodiments of the present application, the comparing module includes: the second processing unit is used for carrying out grading processing on the target flow according to the value grading condition to obtain a value score of the target flow; the third processing unit is used for obtaining the price of the target flow according to the price condition and the value score of the target flow; and the fourth processing unit is used for comparing the target flow based on the price of the target flow to obtain a comparison result.

Example 6

According to an embodiment of the present application, there is also provided an advertisement bidding apparatus for implementing the above advertisement bidding method, as shown in fig. 13, the apparatus 1300 includes: a first display module 1302, a second display module 1304, a third display module 1306, and a fourth display module 1308.

The first display module 1302 is configured to display a traffic pushing condition; the second display module 1304 is configured to display a target flow and a price of the target flow, where the target flow and the price of the target flow are determined according to a flow pushing condition; the third display module 1306 is configured to display a recommendation result corresponding to the traffic pushing condition; the fourth display module 1308 is configured to display a comparison result of the target traffic, where the comparison result is obtained by comparing the target traffic based on the traffic pushing condition.

It should be noted that the first display module 1302, the second display module 1304, the third display module 1306 and the fourth display module 1308 correspond to steps S92 to S98 in embodiment 2, and the four modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 2. It should be noted that the above modules may be operated in the computer terminal 10 provided in embodiment 1 as a part of the apparatus.

According to the scheme provided by the foregoing embodiment 6 of the present application, after the flow rate pushing condition is displayed, the target flow rate and the price of the target flow rate that need to be compared are determined according to the flow rate pushing condition, and are displayed, the recommendation result corresponding to the flow rate pushing condition is displayed, and the target flow rate is compared based on the flow rate pushing condition, so as to obtain the comparison result of the target flow rate, and is displayed, thereby achieving the purpose of comparing the flow rate price.

Therefore, the scheme of the embodiment 6 provided by the application solves the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of the user.

In the above embodiment of the present application, the apparatus further includes: the fifth display module is used for displaying a new flow pushing condition, wherein the new flow pushing condition is obtained by adjusting the flow pushing condition and/or acquiring a limiting rule under the condition that the recommendation result does not meet the preset condition; the fourth display module is further configured to display a comparison result, where the comparison result is obtained by comparing the target traffic based on the new traffic pushing condition.

Example 7

According to an embodiment of the present application, there is also provided an advertisement bidding apparatus for implementing the above advertisement bidding method, as shown in fig. 14, the apparatus 1400 includes: an acquisition module 1402, a determination module 1404, a generation module 1406, a processing module 1408, and an alignment module 1410.

The obtaining module 1402 is configured to obtain a traffic pushing condition; the determining module 1404 is configured to determine a target traffic to be compared and a price of the target traffic according to the traffic pushing condition; the generating module 1406 is configured to generate a recommendation result corresponding to the traffic pushing condition; the processing module 1408 is configured to obtain a price of the target flow according to the flow pushing condition; the comparison module 1410 is configured to compare the target flow based on the price of the target flow, so as to obtain a comparison result of the target flow.

It should be noted here that the acquiring module 1402, the determining module 1404, the generating module 1406, the processing module 1408 and the comparing module 1410 correspond to steps S102 to S1010 in embodiment 3, and the five modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 3. It should be noted that the above modules may be operated in the computer terminal 10 provided in embodiment 1 as a part of the apparatus.

According to the scheme provided by the foregoing embodiment 7 of the present application, after the flow pushing condition is obtained, the target flow and the price of the target flow that need to be compared may be determined according to the flow pushing condition, a recommendation result corresponding to the flow pushing condition is further generated, the price of the target flow is obtained according to the flow pushing condition, and the target flow is compared based on the price of the target flow, so as to obtain a comparison result of the target flow, thereby achieving the purpose of comparing the flow prices.

Therefore, the scheme of the embodiment 7 provided by the application solves the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of the user.

In the above embodiment of the present application, the processing module is further configured to adjust the traffic pushing condition, and/or obtain an input limiting rule to obtain a new traffic pushing condition, and obtain the price of the target traffic according to the new traffic pushing condition.

In the above embodiments of the present application, the processing module includes: the second processing unit is used for carrying out grading processing on the target flow according to the value grading condition to obtain a value score of the target flow; and the third processing unit is used for obtaining the price of the target flow according to the price condition and the value score of the target flow.

Example 8

According to an embodiment of the present application, there is also provided an advertisement bidding apparatus for implementing the advertisement bidding method, as shown in fig. 15, the apparatus 1500 includes: a first display module 1502, a second display module 1504, a third display module 1506, a fourth display module 1508, and a fifth display module 1510.

The first display module 1502 is configured to display a traffic pushing condition; the second display module 1504 is configured to display a target flow and a price of the target flow that need to be compared, where the target flow and the price of the target flow are determined according to a flow pushing condition; the second display module 1506 is configured to display a recommendation result corresponding to the traffic pushing condition; the third display module 1508 is configured to display a price of the target flow rate, wherein the price of the target flow rate is obtained according to the flow pushing condition; the fourth display module 1510 is configured to display a comparison result of the target flow, where the comparison result is obtained by comparing the target flow based on the price of the target flow.

It should be noted here that the first display module 1502, the second display module 1504, the third display module 1506, the fourth display module 1508 and the fifth display module 1510 correspond to steps S112 to S1110 in embodiment 4, and the five modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in embodiment 4. It should be noted that the above modules may be operated in the computer terminal 10 provided in embodiment 1 as a part of the apparatus.

According to the scheme provided by the foregoing embodiment 8 of the present application, after the flow rate pushing condition is displayed, the target flow rate and the price of the target flow rate that need to be compared are determined according to the flow rate pushing condition, and are displayed, the recommendation result corresponding to the flow rate pushing condition is displayed, the price of the target flow rate is obtained and displayed according to the flow rate pushing condition, and then the target flow rate is compared based on the price of the target flow rate, and the comparison result of the target flow rate is obtained and displayed, so that the purpose of comparing the flow rate and the price is achieved.

Therefore, the scheme of the embodiment 8 provided by the application solves the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of the user.

Example 9

According to the embodiment of the application, an advertisement bidding system is further provided.

Fig. 16 is a schematic diagram of an advertisement bidding system according to an embodiment of the present application, as shown in fig. 16, the system including: a first setup module 162, a second setup module 164, a forecast module 166, and a bid module 168.

The first setting module 162 is configured to obtain a traffic pushing condition; the second setting module 164 is configured to determine a target flow to be compared and a price of the target flow according to the flow pushing condition; the estimation module 166 is configured to generate a recommendation result corresponding to the traffic pushing condition; the bidding module 168 is configured to compare the target traffic based on the traffic pushing condition to obtain a comparison result of the target traffic.

Specifically, as shown in fig. 4, the first setting module may be a basic promotion parameter setting module, the second setting module may be a bidding strategy setting module, the forecast module may be a delivery result forecast module, and the bidding module may be an online bidding module.

According to the scheme provided by the foregoing embodiment 9 of the present application, after the traffic pushing condition is obtained, the target traffic and the price of the target traffic that need to be compared may be determined according to the traffic pushing condition, a recommendation result corresponding to the traffic pushing condition is further generated, and the target traffic is compared based on the traffic pushing condition to obtain a comparison result of the target traffic, so as to achieve the purpose of comparing the traffic price.

Therefore, the scheme of the embodiment 9 provided by the application solves the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of the user.

Example 10

There is also provided, according to an embodiment of the present application, an advertisement bidding system, including:

a processor. And

a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: acquiring a flow pushing condition, wherein the flow pushing condition is used for determining a target flow to be compared and the price of the target flow; generating a recommendation result corresponding to the traffic pushing condition; and comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.

According to the scheme provided by the foregoing embodiment 10 of the present application, after the traffic pushing condition is obtained, the target traffic and the price of the target traffic that need to be compared may be determined according to the traffic pushing condition, a recommendation result corresponding to the traffic pushing condition is further generated, and the target traffic is compared based on the traffic pushing condition to obtain a comparison result of the target traffic, so as to achieve the purpose of comparing the traffic price.

Therefore, the scheme of the embodiment 10 provided by the present application solves the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of the user.

Example 11

The embodiment of the application can provide a computer terminal, and the computer terminal can be any one computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal may also be replaced with a terminal device such as a mobile terminal.

Optionally, in this embodiment, the computer terminal may be located in at least one network device of a plurality of network devices of a computer network.

In this embodiment, the computer terminal may execute program codes of the following steps in the advertisement bidding method: acquiring a flow pushing condition; determining the target flow to be compared and the price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the traffic pushing condition; and comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.

Alternatively, fig. 17 is a block diagram of a computer terminal according to an embodiment of the present application. As shown in fig. 17, the computer terminal a may include: one or more processors 1702 (only one of which is shown), and a memory 1704.

The memory may be used to store software programs and modules, such as program instructions/modules corresponding to the advertisement bidding method and apparatus in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, so as to implement the above-mentioned advertisement bidding method. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory remotely located from the processor, and these remote memories may be connected to terminal a through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring a flow pushing condition; determining the target flow to be compared and the price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the traffic pushing condition; and comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.

Optionally, the processor may further execute the program code of the following steps: the flow pushing conditions comprise: the flow attribute parameters are used for determining target flow, the value scoring conditions are used for determining value scores of the target flow, and the price conditions are used for representing the mapping relation between the value scores of the target flow and the prices of the target flow.

Optionally, the processor may further execute the program code of the following steps: obtaining a history comparison log, wherein the history comparison log comprises: a plurality of attribute features regarding traffic, maximum price, and number of clicks; comparing the log according to the traffic pushing condition and the history to obtain a first traffic set and a second traffic set, wherein the first traffic set comprises the traffic set successfully matched with the traffic attribute parameters, and the second traffic set comprises the traffic set successfully matched with the price condition in the first traffic set; generating a recommendation result, wherein the recommendation result comprises: the value distribution and price distribution of the first traffic set, and the estimated number of impressions, clicks and consumption of the second traffic set.

Optionally, the processor may further execute the program code of the following steps: matching a plurality of attribute features about the flow with the flow attribute parameters; obtaining the flow with successfully matched attribute characteristics and flow attribute parameters to obtain a first flow set; obtaining the price of each flow in the first flow set according to the price condition; comparing the highest price for each flow in the first set of flows to the price for each flow; and obtaining the flow with the highest price less than the price to obtain a second flow set.

Optionally, the processor may further execute the program code of the following steps: obtaining a value score and a price of each flow in the first flow set according to the value scoring condition and the price condition; counting the value score of each flow in the first flow set to obtain value distribution; counting the price of each flow in the first flow set to obtain price distribution; counting each flow in the second flow set to obtain the display times; counting the number of clicks of each flow in the second flow set to obtain the number of clicks; and counting the highest price of each flow in the second flow set to obtain the consumption amount.

Optionally, the processor may further execute the program code of the following steps: the attribute characteristic about the traffic includes at least one of: pushing time, region, directional crowd and resource position of the flow.

Optionally, the processor may further execute the program code of the following steps: the mapping relationship includes at least one of: linear, exponential, power-exponential, logarithmic.

Optionally, the processor may further execute the program code of the following steps: the flow property parameter comprises at least one of the following: pushing time, region, oriented crowd, resource location, creativity and total budget; the value scoring conditions include: scoring dimensions and scoring methods; the price conditions include: price conditions and custom price conditions in the condition template.

Optionally, the processor may further execute the program code of the following steps: the scoring method comprises the following steps: and (4) grading the model.

Optionally, the processor may further execute the program code of the following steps: the scoring model includes at least one of: a linear weighted sum model, a model trained based on user data.

Optionally, the processor may further execute the program code of the following steps: after generating a recommendation result corresponding to the traffic pushing condition, adjusting the traffic pushing condition to obtain a new traffic pushing condition; and comparing the target flow based on the new flow pushing condition to obtain a comparison result.

Optionally, the processor may further execute the program code of the following steps: grading the target flow according to the value grading condition to obtain a value score of the target flow; obtaining the price of the target flow according to the price condition and the value score of the target flow; and comparing the target flow based on the price of the target flow to obtain a comparison result.

By adopting the embodiment of the application, after the flow pushing condition is obtained, the target flow to be compared and the price of the target flow can be determined according to the flow pushing condition, the recommendation result corresponding to the flow pushing condition is further generated, the target flow is compared based on the flow pushing condition, the comparison result of the target flow is obtained, and therefore the purpose of comparing the flow price is achieved.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: displaying the flow pushing condition; displaying the price of the target flow to be compared, wherein the target flow and the target flow price are determined according to the flow pushing condition; displaying a recommendation result corresponding to the flow pushing condition; and displaying a comparison result of the target flow, wherein the comparison result is obtained by comparing the target flow based on the flow pushing condition.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring a flow pushing condition; determining the target flow to be compared and the price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the traffic pushing condition; obtaining the price of the target flow according to the flow pushing condition; and comparing the target flow based on the price of the target flow to obtain a comparison result of the target flow.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: displaying the flow pushing condition; displaying a target flow to be compared and a target flow price, wherein the target flow and the target flow price are determined according to the flow pushing condition; displaying the price of the target flow, wherein the price of the target flow is obtained according to the flow pushing condition; and displaying a comparison result of the target flow, wherein the comparison result is obtained by comparing the target flow based on the price of the target flow.

It will be understood by those skilled in the art that the structure shown in fig. 17 is only an illustration, and the computer terminal may also be a terminal device such as a smart phone (e.g., AndroID phone, iOS phone, etc.), a tablet computer, a palmtop computer, a Mobile Internet Device (MID), a PAD, etc. Fig. 17 is a diagram illustrating the structure of the electronic device. For example, the computer terminal a may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in fig. 17, or have a different configuration than shown in fig. 17.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

Example 12

Embodiments of the present application also provide a storage medium. Optionally, in this embodiment, the storage medium may be configured to store program codes executed by the advertisement bidding method provided in the first embodiment.

Optionally, in this embodiment, the storage medium may be located in any one of computer terminals in a computer terminal group in a computer network, or in any one of mobile terminals in a mobile terminal group.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: acquiring a flow pushing condition; determining the target flow to be compared and the price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the traffic pushing condition; and comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.

Optionally, the storage medium is further arranged to store program code for performing the steps of: the flow pushing conditions comprise: the flow attribute parameters are used for determining target flow, the value scoring conditions are used for determining value scores of the target flow, and the price conditions are used for representing the mapping relation between the value scores of the target flow and the prices of the target flow.

Optionally, the storage medium is further arranged to store program code for performing the steps of: obtaining a history comparison log, wherein the history comparison log comprises: a plurality of attribute features regarding traffic, maximum price, and number of clicks; comparing the log according to the traffic pushing condition and the history to obtain a first traffic set and a second traffic set, wherein the first traffic set comprises the traffic set successfully matched with the traffic attribute parameters, and the second traffic set comprises the traffic set successfully matched with the price condition in the first traffic set; generating a recommendation result, wherein the recommendation result comprises: the value distribution and price distribution of the first traffic set, and the estimated number of impressions, clicks and consumption of the second traffic set.

Optionally, the storage medium is further arranged to store program code for performing the steps of: matching a plurality of attribute features about the flow with the flow attribute parameters; obtaining the flow with successfully matched attribute characteristics and flow attribute parameters to obtain a first flow set; obtaining the price of each flow in the first flow set according to the price condition; comparing the highest price for each flow in the first set of flows to the price for each flow; and obtaining the flow with the highest price less than the price to obtain a second flow set.

Optionally, the storage medium is further arranged to store program code for performing the steps of: obtaining a value score and a price of each flow in the first flow set according to the value scoring condition and the price condition; counting the value score of each flow in the first flow set to obtain value distribution; counting the price of each flow in the first flow set to obtain price distribution; counting each flow in the second flow set to obtain the display times; counting the number of clicks of each flow in the second flow set to obtain the number of clicks; and counting the highest price of each flow in the second flow set to obtain the consumption amount.

Optionally, the storage medium is further arranged to store program code for performing the steps of: the attribute characteristic about the traffic includes at least one of: pushing time, region, directional crowd and resource position of the flow.

Optionally, the storage medium is further arranged to store program code for performing the steps of: the mapping relationship includes at least one of: linear, exponential, power-exponential, logarithmic.

Optionally, the storage medium is further arranged to store program code for performing the steps of: the flow property parameter comprises at least one of the following: pushing time, region, oriented crowd, resource location, creativity and total budget; the value scoring conditions include: scoring dimensions and scoring methods; the price conditions include: price conditions and custom price conditions in the condition template.

Optionally, the storage medium is further arranged to store program code for performing the steps of: the scoring method comprises the following steps: and (4) grading the model.

Optionally, the storage medium is further arranged to store program code for performing the steps of: after generating a recommendation result corresponding to the traffic pushing condition, adjusting the traffic pushing condition to obtain a new traffic pushing condition; and comparing the target flow based on the new flow pushing condition to obtain a comparison result.

Optionally, the storage medium is further arranged to store program code for performing the steps of: grading the target flow according to the value grading condition to obtain a value score of the target flow; obtaining the price of the target flow according to the price condition and the value score of the target flow; and comparing the target flow based on the price of the target flow to obtain a comparison result.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: displaying the flow pushing condition; displaying the price of the target flow to be compared, wherein the target flow and the target flow price are determined according to the flow pushing condition; displaying a recommendation result corresponding to the flow pushing condition; and displaying a comparison result of the target flow, wherein the comparison result is obtained by comparing the target flow based on the flow pushing condition.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: acquiring a flow pushing condition; determining the target flow to be compared and the price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the traffic pushing condition; obtaining the price of the target flow according to the flow pushing condition; and comparing the target flow based on the price of the target flow to obtain a comparison result of the target flow.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: displaying the flow pushing condition; displaying a target flow to be compared and a target flow price, wherein the target flow and the target flow price are determined according to the flow pushing condition; displaying the price of the target flow, wherein the price of the target flow is obtained according to the flow pushing condition; and displaying a comparison result of the target flow, wherein the comparison result is obtained by comparing the target flow based on the price of the target flow.

The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.

In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims

1. An advertisement bidding method, comprising:

acquiring a flow pushing condition;

determining target flow needing to be compared and the price of the target flow according to the flow pushing condition;

generating a recommendation result corresponding to the traffic pushing condition;

and comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.

2. The method of claim 1, wherein the traffic push condition comprises: the flow attribute parameters are used for determining the target flow, the value scoring conditions are used for determining the value score of the target flow, and the price conditions are used for representing the mapping relation between the value score of the target flow and the price of the target flow.

3. The method of claim 2, wherein generating recommendations corresponding to the traffic pushing conditions comprises:

obtaining a history comparison log, wherein the history comparison log comprises: a plurality of attribute features regarding traffic, maximum price, and number of clicks;

obtaining a first traffic set and a second traffic set according to the traffic pushing condition and the history comparison log, wherein the first traffic set comprises the traffic set successfully matched with the traffic attribute parameters, and the second traffic set comprises the traffic set successfully matched with the price condition in the first traffic set;

generating the recommendation result, wherein the recommendation result comprises: the value distribution and the price distribution of the first traffic set, and the estimated display times, clicks and consumption amount of the second traffic set.

4. The method of claim 3, wherein obtaining a first traffic set and a second traffic set according to the traffic pushing condition and the history comparison log comprises:

matching the plurality of attribute features about the traffic with the traffic attribute parameters;

obtaining the flow with successfully matched attribute characteristics and the flow attribute parameters to obtain the first flow set;

obtaining the price of each flow in the first flow set according to the price condition;

comparing a highest price for each flow in the first set of flows to the price for said each flow;

and obtaining the flow with the highest price smaller than the price to obtain the second flow set.

5. The method of claim 3, wherein generating the recommendation comprises:

obtaining a value score and a price of each flow in the first flow set according to the value scoring condition and the price condition;

counting the value score of each flow in the first flow set to obtain the value distribution;

counting the price of each flow in the first flow set to obtain the price distribution;

counting each flow in the second flow set to obtain the display times;

counting the number of clicks of each flow in the second flow set to obtain the number of clicks;

and counting the highest price of each flow in the second flow set to obtain the consumption amount.

6. The method of claim 3, wherein the traffic-related attribute comprises at least one of: the pushing time, the region, the directional crowd and the resource position of the flow.

7. The method of claim 2, wherein the mapping relationship comprises at least one of: linear, exponential, power-exponential, logarithmic.

8. The method of claim 2, wherein the traffic attribute parameters comprise at least one of: pushing time, region, oriented crowd, resource location, creativity and total budget; the value scoring conditions include: scoring dimensions and scoring methods; the price conditions include: price conditions and custom price conditions in the condition template.

9. The method of claim 8, wherein the scoring method comprises: and (4) grading the model.

10. The method of claim 9, wherein the scoring model is at least one of: a linear weighted sum model, a model trained based on user data.

11. The method of claim 2, wherein after generating the recommendation corresponding to the traffic pushing condition, the method further comprises:

adjusting the flow pushing conditions to obtain new flow pushing conditions;

and comparing the target flow based on the new flow pushing condition to obtain the comparison result.

12. The method according to any one of claims 2 to 11, wherein comparing the target traffic based on the traffic pushing condition to obtain a comparison result of the target traffic includes:

grading the target traffic according to the value grading condition to obtain a value of the target traffic;

obtaining the price of the target flow according to the price condition and the value score of the target flow;

and comparing the target flow based on the price of the target flow to obtain the comparison result.

13. An advertisement bidding method, comprising:

acquiring a flow pushing condition;

determining the target flow to be compared and the price of the target flow according to the flow pushing condition;

obtaining the price of the target flow according to the flow pushing condition;

and comparing the target flow based on the price of the target flow to obtain a comparison result of the target flow.

14. The method of claim 13, wherein the traffic push condition comprises: the flow attribute parameters are used for determining the target flow, the value scoring conditions are used for determining the value score of the target flow, and the price conditions are used for representing the mapping relation between the value score of the target flow and the price of the target flow.

15. The method of claim 14, wherein generating recommendations corresponding to the traffic pushing conditions comprises:

16. The method of claim 14, wherein after generating the recommendation corresponding to the traffic pushing condition, the method further comprises:

adjusting the flow pushing conditions to obtain new flow pushing conditions;

17. The method according to any one of claims 14 to 16, wherein obtaining the price of the target flow according to the flow pushing condition comprises:

and obtaining the price of the target flow according to the price condition and the value score of the target flow.

18. An advertisement bidding system, comprising:

a processor; and

a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: acquiring a flow pushing condition, wherein the flow pushing condition is used for determining a target flow to be compared and the price of the target flow; generating a recommendation result corresponding to the traffic pushing condition; and comparing the target flow based on the flow pushing condition under the condition that the recommendation result meets a preset condition to obtain a comparison result of the target flow.

19. An advertisement bidding system, comprising:

the first setting module is used for acquiring flow pushing conditions;

the second setting module is used for determining the target flow to be compared and the price of the target flow according to the flow pushing condition;

the pre-estimation module is used for generating a recommendation result corresponding to the flow pushing condition;

and the bidding module is used for comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.