CN110766427B

CN110766427B - Advertisement bidding method and system

Info

Publication number: CN110766427B
Application number: CN201810828790.0A
Authority: CN
Inventors: 李莉; 王志勇; 张小洵; 盖坤
Original assignee: Alibaba East China Co ltd
Current assignee: Alibaba East China Co ltd
Priority date: 2018-07-25
Filing date: 2018-07-25
Publication date: 2023-07-21
Anticipated expiration: 2038-07-25
Also published as: CN110766427A

Abstract

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

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 advertising popularization, the current mainstream internet traffic pushing mode can be RTB display advertising, an advertiser firstly sets a price for an audience desiring to reach, and a platform decides which advertiser's creative is displayed for the traffic in a mode based on price auction. Therefore, the setting of the price is very critical for advertisers, and the price not only determines the quantity of the acquired flow, but also influences the final advertisement delivery effect.

The flow attributes on the internet vary widely and are of great value to advertisers. However, in the conventional RTB exhibition advertisement system, the advertiser can only make a unified bid on a coarse granularity (of the targeted audience) and cannot make a real-time bid according to the quality/value difference of each flow, so that the advertisement effect of the advertiser is not ideal. Subsequently, some internet platforms have introduced a price adjustment method for a reference price by adjusting the coefficients, and different adjustment coefficients can be set for traffic attributes such as different devices/time periods/regions/page contents to change the bid (for example, floating-90% -900% on the basis of the reference price).

At present, all large advertisement DSPs develop towards programming and intellectualization, the intelligent bidding function basically becomes the standard of an advertisement platform, the market application range of intelligent bidding is very wide, and the commercial value is remarkable. Similar intelligent bidding product functions are also provided by large DSPs in the industry.

However, the existing intelligent bidding products still have the following defects: the current intelligent bidding tool has single standard for evaluating the flow value, is usually an index of short-term commercial interests such as click rate, conversion rate and the like, does not support the customization of the flow value by advertisers, and cannot meet the personalized requirements of the advertisers in different marketing scenes; the bidding strategy of the current intelligent bidding tool is opaque, the link of how the value of the flow 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, no effective solution is proposed 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 one aspect of the embodiments of the present application, there is provided an advertisement bidding method, including: acquiring a flow pushing condition; determining a target flow and a price of the target flow to be compared according to the flow pushing conditions; generating a recommendation result corresponding to the flow 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 the target flow and the price of the target flow to be compared, wherein the target flow and the price of the target flow are determined according to the flow pushing conditions; 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; the method comprises the steps of determining target flow and price of the target flow to be compared according to a flow pushing condition; generating a recommendation result corresponding to the flow pushing condition; obtaining the price of the target flow according to the flow pushing conditions; 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 price comparison method of flow, 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, where the program controls a device in which the storage medium is located to perform the following steps when running: acquiring a flow pushing condition; determining a target flow to be compared and a price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the flow 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 for running a program, wherein the program executes the following steps: acquiring a flow pushing condition; determining a target flow to be compared and a price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the flow 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 system, including: a processor; and a memory, coupled to the processor, for providing instructions to the processor for processing the steps of: acquiring a flow pushing condition; determining a target flow to be compared and a price of the target flow according to the flow pushing condition; generating a recommendation result corresponding to the flow 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 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 estimating module is used for generating a recommended 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 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 recommended result corresponding to the flow pushing condition is further generated, the target flow is compared based on the flow pushing condition, and the comparison result of the target flow is obtained, so that the purpose of flow price comparison is achieved.

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

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

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 embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:

FIG. 1 is a block diagram of the hardware architecture 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 an advertisement bidding method 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 alternative promotion setup phase according to an embodiment of the present application;

FIG. 6 is an interactive schematic diagram of an alternative flow value score setting in accordance with an embodiment of the present application;

FIG. 7 is an interactive schematic diagram of an alternative delivery forecast presentation in accordance with an embodiment of the present application;

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

FIG. 9 is a flow chart of an advertisement bidding method according to embodiment 2 of the present application;

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

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

FIG. 12 is a schematic diagram of an advertisement bidding device according to embodiment 5 of the present application;

FIG. 13 is a schematic diagram of an advertisement bidding device according to embodiment 6 of the present application;

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

FIG. 15 is a schematic diagram 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 present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise 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, partial terms or terminology appearing in describing embodiments of the present application are applicable to the following explanation:

RTB: real Time Bidding, real-time bidding.

DSP: the Demand-side platform is used for serving advertisers and helping the advertisers to put advertisements on the Internet or mobile Internet.

DMP: data Management Platform, a data management platform.

CTR: click Through Rate, click rate.

CVR: conversion Rate, click Conversion Rate.

CPC: cost Per Click, charge Per Click.

CPM: cost Per mill, thousands of presentation 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

In accordance with embodiments of the present application, there is also provided an embodiment of an advertisement bidding method, it being noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system, such as a set of computer executable instructions, and, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.

The method embodiment provided in the first embodiment of the present application may be executed in a mobile terminal, a computer terminal or a similar computing device. Fig. 1 shows a block diagram of a hardware architecture 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 microprocessor MCU, a programmable logic device FPGA, etc. processing means), a memory 104 for storing data, and a transmission means 106 for communication functions. In addition, the method may further include: 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 supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the electronic device described above. 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 circuits described above may be referred to generally herein as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry 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 present application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination to 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 embodiments of the present application, and the processor 102 executes the software programs and modules stored in the memory 104, thereby performing various functional applications and data processing, that is, implementing the advertisement bidding method described above. 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 means 106 is arranged to receive or transmit data via a network. The 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 (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.

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 herein that in some embodiments, the computer device (or mobile device) shown in FIG. 1 described above has a touch display (also referred to as a "touch screen" or "touch display"). In some embodiments, the computer device (or mobile device) shown in fig. 1 above has a Graphical User Interface (GUI) with which a user may interact with by touching finger contacts and/or gestures on a touch-sensitive surface, where the human-machine interaction functionality optionally includes the following interactions: executable instructions for performing the above-described human-machine interaction functions, such as creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving electronic mail, talking interfaces, playing digital video, playing digital music, and/or web browsing, are configured/stored in a computer program product or readable storage medium executable by one or more processors.

In the above-described operating environment, the present application provides an advertisement bidding method as shown in fig. 2. Fig. 2 is a flow chart 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:

Step S22, obtaining flow pushing conditions.

Optionally, the traffic pushing conditions may include: the system comprises a flow attribute parameter, a value scoring condition and a price condition, wherein the flow attribute parameter is used for determining a target flow, the value scoring condition is used for determining a value score of the target flow, and the price condition is used for representing a mapping relation between the value score of the target flow and the price of the target flow.

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

Specifically, in the field of internet advertising popularization, the flow attribute parameter may be a basic popularization parameter set by an advertiser; advertisers may formulate bidding strategies based on advertising demand, including traffic value assessment criteria (i.e., the value scoring conditions described above) and bid functions (i.e., the price conditions described above), e.g., the bid functions may be linear functions, exponential functions, power exponential functions, logarithmic functions, and the like.

Further, the flow attribute parameters include at least one of: pushing time, region, directional crowd, resource position, creative and total budget; the value scoring conditions include: scoring dimensions and scoring methods; the price conditions include: price conditions in the condition templates and custom price conditions.

Specifically, in the field of internet advertising promotion, the flow attribute parameters may include: the advertisement delivery time period, region, directional crowd, resource position, creative, total budget and the like are not limited to this, and can also comprise other popularization parameters; the traffic value evaluation criteria may include a scoring dimension and a scoring method, where the scoring dimension may refer to a feature that an advertiser evaluates the value of each traffic, and various features related to the traffic that can be obtained or processed by the advertisement platform may be provided to the advertiser as the scoring dimension, for example, a region, an age, an educational degree, and other mouth attributes of the traffic, consumption habits such as consumption frequency, consumption amount, and other consumption habits, trends, and other shopping preferences such as brands; scoring methods may refer to specific score calculation means at a given scoring dimension. The scoring method can be implemented in various ways, for example, a calculation method of weighted summation based on custom scoring dimension weights can be adopted, and a method of training a scoring model based on advertiser uploading labeling sample data can also be adopted. The price condition may be a bid function that obtains a corresponding price based on a value score of the traffic.

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

Specifically, in the field of internet advertising, in general, the bid functions are monotonically increasing functions of the traffic value scores, i.e., the higher the traffic value score, the higher the corresponding bid. Assuming that the value score of the flow ranges from 0 to 1, the bid range (highest/lowest value) is determined given the form of the bid function F (X). Thus, the bid function contains information about both the functional form and the bid range.

There are a number of implementations in the setting of the bid function. For example, to ease the input burden of the advertiser, the platform may provide several commonly used function types as alternative templates (linear, logarithmic, idempotent, etc.), and after the advertiser selects a function type and gives a bid range, the platform automatically calculates the function. For example, as shown in fig. 3, the advertiser may choose a linear function, where the bid range is 3-10 yuan, and the bid function F (X) =7x+3.

Another way is for the advertiser to enter the function formula by itself. For example, as shown in FIG. 3, to make the advertiser's interactive experience more intuitive and friendly, the platform may present previews of information of the function, such as bid ranges, function curves, etc., to assist the advertiser in making modifications adjustments.

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

Specifically, in the field of internet advertising, the target traffic may be traffic in which an advertiser participates in bidding for advertisement pushing of the advertiser itself, and the price of the target traffic may be an auction price provided by the advertiser for competing the target traffic.

Step S26, generating a recommendation result corresponding to the traffic pushing condition.

Specifically, in the field of internet advertisement popularization, prediction of a delivery result (namely the recommended result) can be provided for an advertiser according to information input by the advertiser, so that the advertiser can be helped to pre-judge the rationality of popularization 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 method is 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 a comparison result of the target flow.

Specifically, in the field of internet advertising popularization, after generating a recommendation result, an advertiser can estimate through looking at a delivery result provided by a system, and if a place which does not meet expectations is found, the traffic pushing condition can be updated; if no unexpected places are found, advertising bidding can be performed based on this information.

In an alternative scheme, the bid of the target flow can be obtained by calculation according to the information input by the advertiser, real-time bidding is carried out on the target flow, and then, the winning advertiser (namely, the comparison result is obtained) is judged according to the bidding mechanism and the settlement mode of the platform, and the winning advertiser is charged.

A preferred embodiment of the present application will be described with reference to fig. 4 to 7 by taking the field of internet advertising as an example. Fig. 4 is an optional advertisement bidding system architecture diagram according to an embodiment of the present application, fig. 5 is a flowchart of an optional promotion setting stage according to an embodiment of the present application, fig. 6 is an interactive schematic diagram of an optional traffic value score setting according to an embodiment of the present application, and fig. 7 is an interactive schematic diagram of an optional delivery estimation result display according to an embodiment of the present application.

As shown in fig. 4, the system may include: the system comprises a basic popularization parameter setting module, a bidding strategy setting module, a throwing result estimating module and four online bidding modules. The advertiser can perform basic promotion parameter setting through a basic promotion parameter setting module, and specifically can comprise orientation, resource position, creative, total budget and the like. Advertisers can formulate bidding strategies through a bidding strategy setting module, can set flow value evaluation standards through a flow value evaluation standard setting module, and can set bidding functions through a bidding function setting module. The advertiser can check the corresponding estimated delivery result through the estimated delivery result module, and the method specifically comprises the steps of calculating the estimated delivery result through the bid simulation and result statistics module, displaying the estimated delivery result to the advertiser through the estimated delivery result display module, judging whether a place which does not accord with the expectation exists or not through the advertiser, and adding limiting rules through the rule limiting module when the advertiser finds that the place which does not accord with the expectation exists, and completing the popularization and setting stage. The online bidding module can perform real-time bidding on the traffic of the advertiser participating in the bidding, and specifically, the online bidding module can score the traffic according to the traffic value evaluation standard set in the popularization and setting stage to obtain the value score of the traffic, further calculate the corresponding bid according to the bidding function set in the popularization and setting stage through the traffic score mapping bidding calculation module, and finally perform real-time bidding on the traffic according to the calculated bid through the real-time bidding module to obtain the final bidding result.

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

step S51, the advertiser performs basic popularization parameter setting including a delivery period, a region, a directional crowd, a resource position, a creative, a total budget and the like.

Step S52, the advertiser formulates a bidding strategy including traffic value evaluation criteria and bid functions.

Alternatively, the traffic value evaluation criteria may include two factors, a scoring dimension and a scoring method.

For example, as shown in FIG. 6, advertisers may enter traffic value scoring criteria, including two scoring methods, respectively: the method comprises the steps of self-defining scoring dimension weight, linearly weighting and solving, uploading labeling sample data, training a scoring model, and selecting any scoring method by an advertiser according to the needs. For example, when an advertiser selects a first scoring method, the advertiser may select a scoring dimension and set a corresponding weight; when the advertiser selects the second scoring method, the annotation sample data may be uploaded by clicking the upload button and selecting the scoring dimension. After the advertiser completes the setup, the next button may be clicked to begin setting up the bid function.

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

Step S53, according to the input information of the advertiser in the step, the system provides the estimated delivery result, including crowd price distribution, bid distribution, estimated display/click/consumption and the like, in combination with the history bid log.

For example, as shown in fig. 7, pre-delivery prediction may be performed according to the setting of the advertiser, to specifically obtain value score distribution prediction, bid distribution prediction, and delivery effect prediction. The value score distribution forecast and the bid distribution forecast can display flow distribution corresponding to different value score intervals or bid intervals in the form of a histogram. The impression estimate may display the estimated number of available impressions, number of clicks, and amount consumed.

And S54, the advertiser modifies or further limits the popularization setting or bidding strategy according to the estimated putting result of the step, and the effective putting is started after confirmation.

Alternatively, the advertiser may review the impression prediction provided by the system and return to make modifications if it is found that there are places that are not expected. In addition to returning to modifying promotional settings or bidding strategies, the qualifications may be further increased.

For example, as shown in FIG. 7, advertisers find low value traffic less than 0.3 points to be relatively high and wish to consume a limited budget at high value traffic, at which point a qualifying rule may be added to qualify traffic for a impression value point >0.3 points. After adding the qualifying rule, the advertiser may manually click on the update button to trigger the update of the prediction. Further, by clicking OK, the putting button is confirmed to start to put in effect, the bid of the flow of the participation of the advertiser in bidding is calculated in real time, and real-time bidding is carried out on the flow, so that a final bidding result is obtained.

After the flow pushing conditions are obtained, the solution provided in the above embodiment 1 of the present application may determine the target flow and the price of the target flow to be compared according to the flow pushing conditions, further generate a recommended result corresponding to the flow pushing conditions, and compare the target flow based on the flow pushing conditions to obtain a comparison result of the target flow, thereby achieving the purpose of flow price comparison.

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

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

step S262, a history comparison log is obtained, wherein the history comparison log comprises: a plurality of attribute characteristics, highest price and number of clicks for the flow.

Optionally, the attribute features for the flow may include at least one of: push time, region, directional crowd and resource position of flow.

In particular, in the field of internet advertising, the above-described historical alignment log may be a bid log of one or more days in history, assuming that the daily access traffic is relatively stable. In the embodiment of the application, taking the bid log of a collected historical day as an example, the historical bid log may include a directional tag carried by the traffic, a highest bid, an estimated pctr for the advertiser, and a characteristic attribute of the traffic.

Step S264, a first flow set and a second flow set are obtained according to the flow pushing conditions and the history comparison log, wherein the first flow set is a flow set which comprises successful matching of flow attribute parameters, and the second flow set comprises a flow set which comprises successful matching of price conditions in the first flow set.

In an alternative scheme, in the field of internet advertising popularization, all biddable traffic sets (namely the first traffic set) meeting the advertising popularization setting conditions can be screened out from the bidding candidate set according to the information of traffic time periods, regions, orientations, resource positions and the like, and further the biddable traffic sets (the second traffic set) can be obtained according to advertiser bids.

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

In an alternative scheme, in the field of internet advertising popularization, each parameter bid flow in a biddable flow set can be traversed, medium distribution and bid distribution of all parameter bid flows are calculated according to a bid strategy set by an advertiser, and the medium distribution and bid distribution are displayed in a chart mode such as a histogram and a bar chart. For all flows in the set of biddable flows, the delivery effect information is obtained by statistics, including the display number (the total number of bidded flows), the click number (the sum of estimated pctr of bidded flows), and the consumption amount (the sum of the highest bids of bidded flows), as shown in fig. 7.

In the above embodiment of the present application, step S264, according to the traffic pushing condition and the history comparison log, obtains a first traffic set and a second traffic set, including:

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

Step S2642, obtaining the flow of which the attribute characteristics are successfully matched with the flow attribute parameters, and obtaining a first flow set.

In an alternative scheme, in the field of internet advertisement popularization, all biddable traffic sets meeting advertisement popularization setting conditions can be screened from a bid candidate set according to traffic time period, region, orientation, resource position and other information.

Step S2644, obtaining the price of each flow in the first flow set according to the price condition.

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

Step S2646 compares the highest price per flow in the first set of flows to the price per flow.

And step S2648, obtaining the flow with the highest price less than the price, and obtaining a second flow set.

In an alternative scheme, in the field of internet advertising popularization, for each biddable traffic, it may be determined whether the advertiser bid for that traffic is higher than the highest bid of other advertisers, that is, whether the calculated bid is greater than the highest bid for that traffic in the historical bid log, and if so, the traffic is considered to be a biddable traffic.

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

step S2661, according to the value scoring condition and the price condition, the value score and the price of each flow in the first flow set are obtained.

Step S2662, counting the value scores of each flow in the first flow set to obtain value distribution.

In step S2663, the price of each flow in the first flow set is counted to obtain a price distribution.

In an alternative scheme, in the field of internet advertising popularization, each parameter bid flow in a biddable flow set can be traversed, value scores and corresponding bids of each flow are calculated according to a bid strategy set by an advertiser, and distribution of the value scores and the bids of all the parameter bid flows is counted.

Step S2664, counting each flow in the second flow set to obtain the display times.

Step S2665, counting the number of clicks of each flow in the second flow set to obtain the number of clicks.

In step S2666, the highest price of each flow in the second flow set is counted to obtain the consumption amount.

In an alternative scheme, in the field of internet advertising popularization, the information of the throwing effect can be counted aiming at all the flow capable of bidding, wherein the information comprises the estimated display number, the estimated click number and the estimated consumption amount.

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

step S210, adjusting the flow pushing conditions and/or acquiring input limiting rules to obtain new flow pushing conditions.

In an alternative scheme, in the field of internet advertising promotion, an advertiser views a delivery estimation result provided by a system, and if a place which does not meet expectations exists, the advertiser can return to be modified. For example, finding that the estimated consumption is higher than the set budget, the overall budget for the impression may be increased back; the bid distribution is found to be too concentrated, the bid differentiation of the high quality and poor quality traffic is not high, and the modified bid function is returned.

In addition to returning to modifying promotional settings or bidding strategies, the qualifications may be further increased. For example, as shown in fig. 7, a flow rate of >0.3 minutes is defined for the delivery value score. In addition to the value score, the bidding range may be further defined. After adding the qualifying rule, the advertiser may manually click on the update button to trigger the update of the prediction.

The addition of the constraint may be regarded as updating the bid function. For example, a value score of >0.3 is defined, and a range of values corresponding to F (X) is defined as x.epsilon. (0.3,1).

And 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 advertising popularization, after the advertiser sets up, the bid of the target traffic can be calculated in real time according to traffic pushing conditions set by the advertiser, and real-time bidding is carried out on the traffic.

In the above embodiment, step S28 of comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow includes:

and step S282, scoring the target flow according to the value scoring condition to obtain the value score of the target flow.

In an alternative scheme, in the field of internet advertising popularization, for an access flow, according to information such as a time period, a region, a direction, a resource position and the like of the flow, the access flow is matched with a popularization condition set by an advertiser, and the matched flow is the flow of participation of the advertiser in bidding. At this time, the attribute characteristics of the traffic are obtained simultaneously based on the traffic value evaluation criteria established in advance by the advertiser, and the traffic is scored (generally normalized to a section, e.g., 0 to 1).

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 sets a bid function of F (X) =7x+3 (with the proviso that xe (0.3,1)) assuming a value score of 0.2 for traffic 1 and 0.6 for traffic 2, the corresponding bid is shown in table 1 below.

TABLE 1

Traffic ID	Flow value score	Bidding (Yuan)
			1	0.2	0
2	0.6	7.2

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

In an alternative, in the field of Internet advertising, the calculated bid may be used to bid on the traffic in real time. Subsequently, the winning advertiser is judged according to the bidding mechanism and the settlement mode of the platform, and is charged, and the method belongs to a general flow for displaying advertisement bidding, and is not described in detail in the application.

A preferred embodiment of the present application will be described with reference to fig. 8 by taking the field of internet advertising as an example. FIG. 8 is a flow chart of an alternative flow 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 steps of:

step S81, for the traffic of the advertiser participating in bidding, calculating the value score of the traffic in real time according to the traffic value evaluation standard formulated by the advertiser.

And step S82, mapping the value score of the flow into a bid according to a bid function formulated by the advertiser.

And step S83, real-time bidding is carried out on the flow by using the calculated bid in the step.

Two embodiments of scoring methods are illustrated below in connection with FIG. 6, including specific implementations of how advertisers enter on the interface during the advertisement placement phase, and how the real-time bidding phase scores for each traffic volume.

For embodiments based on custom scoring dimension weights, linear weighted summation.

In the advertisement setting stage, a scoring dimension candidate set provided by an advertiser browsing platform selects a plurality of scoring dimensions from the scoring dimension candidate set, and the promotion purpose of the advertiser A at this time is assumed to be to promote a high-end automobile which is newly promoted by a certain brand, so that the promotion value of different flows is distinguished by the brand preference, the consumption level and the education degree; 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 in the scoring dimension, and then carrying out linear weighted average according to the weight of the scoring dimension:wherein Score _i A value component representing the ith flow rate, f _ij Representing the eigenvalue of the flow i in the j-th dimension of the score, w _j Representing the j-th dimension of scoring (normalized) weight, assuming that the scores for traffic 1, traffic 2 over the 3 selected scoring dimensions by the advertiser are shown in Table 2, the final value score is 0.56, 0.42, respectively.

TABLE 2

Flow Id	Brand preference	Consumption hierarchy	Educational level	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 scoring models are trained for advertisers to upload annotation sample data.

The advertisement setting stage comprises a labeling sample collection uploading stage and a model training stage, wherein in the labeling sample collection uploading stage, in the brand/store operation process of an advertiser, user ID information (ID of identification identities such as website user names, telephones and mailboxes) can be collected, the batch 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 the format specified by a platform.

For example, the advertiser a accumulates more than 3 ten thousand member information in an online store, and the advertiser processes a consumer lifetime Value LTV (total Life cycle Value, collectively referred to as Life Time Value) according to the consumption information of the group of members online, normalizes the Value to a Value of 0 to 1, and then uses the Value as a Value score of the member. Advertiser A wishes to identify the value of online traffic (corresponding value scores are generally higher for traffic similar to that of high-value members in the feature dimension) by the data and model capabilities of the platform, taking the data of offline store members as a template. The advertiser may select its own scoring dimension as a feature of model training.

In the model training stage, carrying out dirty data filtering, format unification and the like pretreatment on data files submitted by advertisers; assuming that a database (or a third party DMP) of the advertisement platform stores a user characteristic table processed in advance, as shown in a table 3, searching and obtaining a characteristic value of an advertiser labeling sample ID on a selected characteristic dimension in a user ID matching mode to generate a training sample; the marked sample is used for training a scoring model, and the common machine learning methods such as GBDT, LR, deep learning and the like can be adopted, so that a plurality of existing algorithms exist in the industry and academia.

TABLE 3 Table 3

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

It should be noted that, in general, a period of calculation time is required for model training, so unlike the first method, the score of the flow cannot be immediately used effectively, and the model training needs to be started after waiting.

Through the scheme, in the advertisement popularization setting stage, an advertiser establishes a bid strategy (comprising a flow value evaluation standard and a bid function) according to popularization requirements, and meanwhile, the system outputs a throwing estimated result according to the input of the advertiser and historical bid information so as to assist the advertiser in judging the rationality of strategy setting and further adjusting; in the real-time bidding stage, calculating the value score of each flow according to the flow value evaluation standard set by the advertiser and the attribute of the flow, mapping the value score into a bid based on a bid function, and performing real-time bidding on the flow by using the price. The intelligent bidding system can better meet the personalized requirements of advertisers on intelligent bidding, and meanwhile, the advertisers are helped to reduce trial-and-error cost through result estimation, so that the intelligent bidding system has better usability. Meanwhile, for the platform, the intelligent bidding method has better expandability and universality, and can be deployed (searching advertisements, brand advertisements and the like) on various advertisement platforms.

It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.

From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method described in the embodiments of the present application.

Example 2

According to the embodiment of the application, an embodiment of an advertisement bidding method is also provided.

Fig. 9 is a flow chart 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:

step S92, displaying the traffic pushing condition.

Step S94, displaying the target flow rate and the price of the target flow rate to be compared, wherein the price of the target flow rate and the price of the target flow rate are determined according to the flow pushing conditions.

Step S96, displaying the recommended result corresponding to the traffic pushing condition.

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.

After the flow pushing conditions are displayed, the solution provided in the above embodiment 2 can compare the target flow and the price of the target flow according to the needs determined by the flow pushing conditions, display the recommended result corresponding to the flow pushing conditions, compare the target flow based on the flow pushing conditions, obtain the comparison result of the target flow, and display the comparison result, thereby achieving the purpose of flow price comparison.

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

In the above embodiment of the present application, the recommended results include: the value distribution and the price distribution of the first flow set, and the estimated display times, the number of clicks and the consumption amount of the second flow set, wherein the first flow set comprises a flow set successfully matched with the flow attribute parameters, the second flow set comprises a flow set successfully matched with the price condition in the first flow set, the first flow set and the second flow set are obtained according to the flow pushing conditions and the acquired history comparison log, and the history comparison log comprises: attribute characteristics, highest price, and number of clicks for multiple flows.

In an alternative scheme, in the field of internet advertising popularization, all biddable traffic sets (namely the first traffic set) meeting the advertising popularization setting conditions can be screened out from the bidding candidate set according to the information of traffic time periods, regions, orientations, resource positions and the like, and further the biddable traffic sets (the second traffic set) can be obtained according to advertiser bids. The method can traverse each parameter bid flow in the biddable flow set, calculate and obtain medium distribution and bid distribution of all the parameter bid flows according to the bid strategy set by an advertiser, and display the medium distribution and bid distribution in the form of a histogram, a bar graph and the like. For all flows in the set of biddable flows, the delivery effect information is obtained by statistics, including the display number (the total number of bidded flows), the click number (the sum of estimated pctr of bidded flows), and the consumption amount (the sum of the highest bids of bidded flows), as shown in fig. 7.

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

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

Step S912, displaying a comparison result, where the comparison result is obtained by performing a comparison process on the target flow based on the new flow pushing condition.

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

In an alternative scheme, in the field of internet advertising popularization, for an access flow, according to information such as a time period, a region, a direction, a resource position and the like of the flow, the access flow is matched with a popularization condition set by an advertiser, and the matched flow is the flow of participation of the advertiser in bidding. At this time, the attribute characteristics of the traffic are obtained simultaneously based on the traffic value evaluation criteria established in advance by the advertiser, and the traffic is scored (generally normalized to a section, e.g., 0 to 1). For example, the advertiser formulated bid function is F (X) =7xX+3 (while the constraint is xE (0.3,1)), assuming a value score of 0.2 for flow 1, a value score of 0.6 for flow 2, and the corresponding bid as shown in Table 1.

Example 3

Fig. 10 is a flow chart 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, obtaining flow pushing conditions.

Step S104, determining the target flow and the price of the target flow to be compared according to the flow pushing conditions.

Step S106, generating a recommendation result corresponding to the flow pushing condition.

Step S108, obtaining the price of the target flow according to the flow pushing conditions.

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

After the flow pushing conditions are obtained, the solution provided in the above embodiment 3 of the present application may determine the target flow and the price of the target flow to be compared according to the flow pushing conditions, further generate a recommended result corresponding to the flow pushing conditions, obtain the price of the target flow according to the flow pushing conditions, and compare the target flow based on the price of the target flow to obtain the comparison result of the target flow, thereby achieving the purpose of flow price comparison.

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

In the above embodiment of the present application, step S106 generates a recommendation result corresponding to the traffic pushing condition, including:

step S1062, obtaining a history comparison log, where the history comparison log includes: a plurality of attribute characteristics, highest price and number of clicks for the flow.

Step S1064, obtaining a first flow set and a second flow set according to the flow pushing condition and the history comparison log, wherein the first flow set comprises a flow set successfully matched with the flow attribute parameter, and the second flow set comprises a flow set successfully matched with the price condition in the first flow set.

Step S1066, generating a recommendation result, where the recommendation result includes: the value distribution and the price distribution of the first flow set, and the estimated display times, the number of clicks, and the consumption amount of the second flow set.

The implementation manners of the above steps S1062 to S1066 are the same as those of the steps S262 to S266 in embodiment 1, the specific implementation manner of the step S1064 is the same as those of the steps S2640 to S2648 in embodiment 1, and the specific implementation manner of the step S1066 is the same as those of the steps S2661 to S2666 in embodiment 1, and will not be described here again.

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

step S1012, adjusting the traffic pushing condition, and/or obtaining the input limit rule, to obtain a new traffic pushing condition.

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

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

In the above embodiment of the present application, step S108, according to the traffic pushing condition, obtains the price of the target traffic, includes:

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

And 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 those of the steps S282 to S284 in the embodiment 1, and are not described herein.

Example 4

Fig. 11 is a flow chart 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.

Step S114, displaying the target flow rate and the price of the target flow rate to be compared, wherein the price of the target flow rate and the price of the target flow rate are determined according to the flow pushing conditions.

Step S116, displaying the recommended result corresponding to the traffic pushing condition.

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.

After the flow pushing conditions are displayed, the solution provided in the above embodiment 4 may be used to compare the target flow and the price of the target flow according to the flow pushing conditions, display the recommended result corresponding to the flow pushing conditions, obtain the price of the target flow according to the flow pushing conditions, display the price of the target flow, and then compare the target flow based on the price of the target flow, obtain the comparison result of the target flow and display the comparison result of the target flow, thereby achieving the purpose of flow price comparison.

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

Example 5

There is further provided, according to an embodiment of the present application, an advertisement bidding apparatus for implementing the above-mentioned advertisement bidding method, as shown in fig. 12, the apparatus 1200 includes: an acquisition module 1202, a determination module 1204, a generation module 1206, and a comparison module 1208.

The acquiring module 1202 is configured to acquire a traffic pushing condition; the determining module 1204 is configured to determine a target flow to be compared and a price of the target flow according to a flow pushing condition; the generating module 1206 is configured to generate a recommendation result corresponding to the traffic pushing condition; the comparison module 1208 is configured to perform comparison processing on the target flow based on the flow pushing condition, so as to obtain a comparison result of the target flow.

Specifically, in the field of internet advertising, the target traffic may be traffic in which an advertiser participates in bidding for advertisement pushing of the advertiser itself, and the price of the target traffic may be an auction price provided by the advertiser for competing the target traffic. The method can provide the estimated putting result (namely the recommended result) for the advertiser according to the information input by the advertiser, thereby helping the advertiser to prejudge the rationality of the popularization parameter setting, reducing the trial-and-error cost, improving the operation experience of the advertiser, bringing beneficial effects, and being different from other schemes in flow and front-end display. After generating the recommendation result, the advertiser can estimate the delivery result provided by the viewing system, and if finding that a place which does not accord with the expectation exists, the advertiser can update the flow pushing condition; if no unexpected places are found, advertising bidding can be performed based on this information.

Here, the acquisition module 1202, the determination module 104, the generation module 1206, and the comparison module 1208 correspond to steps S22 to S28 in embodiment 1, and the four modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 1. It should be noted that the above-described module may be operated as a part of the apparatus in the computer terminal 10 provided in embodiment 1.

After the flow pushing conditions are obtained, the solution provided in the above embodiment 5 of the present application may determine the target flow and the price of the target flow to be compared according to the flow pushing conditions, further generate a recommended result corresponding to the flow pushing conditions, and compare the target flow based on the flow pushing conditions to obtain a comparison result of the target flow, thereby achieving the purpose of flow price comparison.

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

In the above embodiment of the present application, the generating module includes: an obtaining unit, configured to obtain a history comparison log, where the history comparison log includes: a plurality of attribute features, highest price, and number of clicks for the flow; the first processing unit is used for obtaining a first flow set and a second flow set according to the flow pushing conditions and the history comparison log, wherein the first flow set comprises a flow set successfully matched with the flow attribute parameters, and the second flow set comprises a flow set successfully matched with the price conditions in the first flow set; the generation unit is used for generating a recommendation result, wherein the recommendation result comprises the following steps: the value distribution and the price distribution of the first flow set, and the estimated display times, the number of clicks, and the consumption amount of the second flow set.

In the above embodiments of the present application, the first processing unit includes: the matching sub-module is used for matching a plurality of attribute characteristics related to the flow with flow attribute parameters; the first acquisition sub-module is used for acquiring the flow of which the attribute characteristics are 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 sub-module for comparing the highest price of each flow in the first set of flows with the price of each flow; and the second acquisition sub-module is used for acquiring the flow with the highest price smaller than the price to obtain a second flow set.

In the above embodiment 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 statistics sub-module is used for counting the value scores of each flow in the first flow set to obtain value distribution; the second statistics sub-module is used for counting the price of each flow in the first flow set to obtain price distribution; the third statistics sub-module is used for counting each flow in the second flow set to obtain the display times; the fourth statistics sub-module is used for counting the click number of each flow in the second flow set to obtain the click number; and the fifth statistics sub-module is used for counting the highest price of each flow in the second flow set to obtain the consumption amount.

In the above embodiments 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 embodiment of the present application, the comparison module includes: the second processing unit is used for scoring the target flow according to the value scoring condition to obtain the 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

There is further provided, according to an embodiment of the present application, an advertisement bidding apparatus for implementing the above-mentioned 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 rate and a price of the target flow rate that need to be compared, where the target flow rate and the price of the target flow rate 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 flow, where the comparison result is obtained by comparing the target flow based on the flow pushing condition.

Here, 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 examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 2. It should be noted that the above-described module may be operated as a part of the apparatus in the computer terminal 10 provided in embodiment 1.

After the flow pushing conditions are displayed, the solution provided in the above embodiment 6 can compare the target flow and the price of the target flow according to the needs determined by the flow pushing conditions, display the recommended result corresponding to the flow pushing conditions, compare the target flow based on the flow pushing conditions, obtain the comparison result of the target flow, and display the comparison result, thereby achieving the purpose of flow price comparison.

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

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

Example 7

There is further provided, according to an embodiment of the present application, an advertisement bidding apparatus for implementing the above-mentioned 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 acquiring module 1402 is configured to acquire a traffic pushing condition; the determining module 1404 is configured to determine a target flow to be compared and a price of the target flow according to the flow 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 traffic according to the traffic pushing condition; the comparison module 1410 is configured to perform a comparison process on the target flow based on the price of the target flow, to obtain a comparison result of the target flow.

It should be noted that, the above-mentioned obtaining module 1402, determining module 1404, generating module 1406, processing module 1408 and comparing module 1410 correspond to steps S102 to S1010 in embodiment 3, and the five modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 3 above. It should be noted that the above-described module may be operated as a part of the apparatus in the computer terminal 10 provided in embodiment 1.

After the flow pushing conditions are obtained, the solution provided in the above embodiment 7 of the present application may determine the target flow and the price of the target flow to be compared according to the flow pushing conditions, further generate a recommended result corresponding to the flow pushing conditions, obtain the price of the target flow according to the flow pushing conditions, and compare the target flow based on the price of the target flow to obtain the comparison result of the target flow, thereby achieving the purpose of flow price comparison.

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

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

There is further provided, according to an embodiment of the present application, an advertisement bidding apparatus for implementing the above-mentioned 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 rate and a price of the target flow rate that need to be compared, where the target flow rate and the price of the target flow rate 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 traffic, where the price of the target traffic is obtained according to a traffic 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.

Here, 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 examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 4. It should be noted that the above-described module may be operated as a part of the apparatus in the computer terminal 10 provided in embodiment 1.

After the flow pushing conditions are displayed, the solution provided in the above embodiment 8 can be used for comparing the target flow and the price of the target flow according to the needs determined by the flow pushing conditions, displaying the recommended result corresponding to the flow pushing conditions, obtaining and displaying the price of the target flow according to the flow pushing conditions, and then comparing the target flow based on the price of the target flow to obtain and display the comparison result of the target flow, thereby realizing the purpose of flow price comparison.

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

Example 9

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

FIG. 16 is a schematic diagram of an advertisement bidding system, as shown in FIG. 16, according to an embodiment of the present application, comprising: a first setting module 162, a second setting module 164, a pre-estimation module 166, and a bidding 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 rate to be compared and a price of the target flow rate according to the flow rate pushing condition; the pre-estimation module 166 is configured to generate a recommendation result corresponding to the traffic pushing condition; the bidding module 168 is configured to perform a comparison process on the target traffic based on the traffic pushing condition, so as to obtain a comparison result of the target traffic.

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

In the solution provided in embodiment 9, after the flow pushing condition is obtained, the target flow to be compared and the price of the target flow may be determined according to the flow pushing condition, further a recommended result corresponding to the flow pushing condition is generated, and the target flow is compared based on the flow pushing condition to obtain a comparison result of the target flow, so as to achieve the purpose of flow price comparison.

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

Example 10

According to an embodiment 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 steps of: acquiring a flow pushing condition, wherein the flow pushing condition is used for determining a target flow to be compared and a price of the target flow; generating a recommendation result corresponding to the flow pushing condition; and comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.

In the solution provided in the foregoing embodiment 10 of the present application, after the flow pushing condition is obtained, the target flow to be compared and the price of the target flow may be determined according to the flow pushing condition, further a recommended result corresponding to the flow pushing condition is generated, and the target flow is compared based on the flow pushing condition, so as to obtain a comparison result of the target flow, thereby achieving the purpose of flow price comparison.

Therefore, the technical problem that the advertisement bidding method in the prior art cannot meet the personalized requirements of the user is solved by the scheme of the embodiment 10.

Example 11

Embodiments of the present application may provide a computer terminal, which may be any one of a group of computer terminals. Alternatively, in the present embodiment, the above-described computer terminal may be replaced with a terminal device such as a mobile terminal.

Alternatively, in this embodiment, the above-mentioned computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

In this embodiment, the above-mentioned computer terminal may execute the program code for the following steps in the advertisement bidding method: acquiring a flow pushing condition; determining a target flow and a price of the target flow to be compared according to the flow pushing conditions; generating a recommendation result corresponding to the flow 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 (only one is shown) processors 1702 and 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 the software programs and modules stored in the memory, thereby executing various functional applications and data processing, that is, implementing the advertisement bidding method described above. 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 with respect to the processor, which 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 may call the information and the application program stored in the memory through the transmission device to perform the following steps: acquiring a flow pushing condition; determining a target flow and a price of the target flow to be compared according to the flow pushing conditions; generating a recommendation result corresponding to the flow pushing condition; and comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow.

Optionally, the above processor may further execute program code for: the traffic pushing conditions include: the system comprises a flow attribute parameter, a value scoring condition and a price condition, wherein the flow attribute parameter is used for determining a target flow, the value scoring condition is used for determining a value score of the target flow, and the price condition is used for representing a mapping relation between the value score of the target flow and the price of the target flow.

Optionally, the above processor may further execute program code for: obtaining a history comparison log, wherein the history comparison log comprises: a plurality of attribute features, highest price, and number of clicks for the flow; obtaining a first flow set and a second flow set according to the flow pushing conditions and the history comparison log, wherein the first flow set comprises a flow set successfully matched with the flow attribute parameters, and the second flow set comprises a flow set successfully matched with the price conditions in the first flow set; generating a recommendation result, wherein the recommendation result comprises: the value distribution and the price distribution of the first flow set, and the estimated display times, the number of clicks, and the consumption amount of the second flow set.

Optionally, the above processor may further execute program code for: matching a plurality of attribute features related to the flow with flow attribute parameters; acquiring the flow of which the attribute characteristics and the flow attribute parameters are successfully matched, and obtaining 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 smaller than the price, and obtaining a second flow set.

Optionally, the above processor may further execute program code for: according to the value scoring condition and the price condition, obtaining the value score and the price of each flow in the first flow set; counting the value scores 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 click number of each flow in the second flow set to obtain the click number; and counting the highest price of each flow in the second flow set to obtain the consumption amount.

Optionally, the above processor may further execute program code for: the attribute characteristics for the flow include at least one of: push time, region, directional crowd and resource position of flow.

Optionally, the above processor may further execute program code for: the mapping relationship includes at least one of: linear relationship, exponential relationship, power exponent relationship, logarithmic relationship.

Optionally, the above processor may further execute program code for: the flow attribute parameters include at least one of: pushing time, region, directional crowd, resource position, creative and total budget; the value scoring conditions include: scoring dimensions and scoring methods; the price conditions include: price conditions in the condition templates and custom price conditions.

Optionally, the above processor may further execute program code for: the scoring method comprises the following steps: and (5) a scoring model.

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

Optionally, the above processor may further execute program code for: after generating a recommended result corresponding to the flow pushing condition, adjusting the flow pushing condition to obtain a new flow pushing condition; and comparing the target flow based on the new flow pushing condition to obtain a comparison result.

Optionally, the above processor may further execute program code for: scoring the target flow according to the value scoring condition to obtain the 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.

After the flow pushing conditions are obtained, the target flow and the price of the target flow to be compared can be determined according to the flow pushing conditions, the recommended result corresponding to the flow pushing conditions is further generated, the target flow is compared based on the flow pushing conditions, and the comparison result of the target flow is obtained, so that the purpose of flow price comparison is achieved.

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

The processor may call the information and the application program stored in the memory through the transmission device to perform the following steps: displaying the flow pushing condition; displaying the target flow and the price of the target flow to be compared, wherein the target flow and the price of the target flow are determined according to the flow pushing conditions; 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 appreciated by those skilled in the art that the configuration shown in fig. 17 is merely illustrative, and the computer terminal may be a smart phone (e.g., androID phone, iOS phone, etc.), a tablet computer, a palm computer, and a terminal device such as a mobile internet device (Mobile Internet Devices, MID), a PAD, etc. Fig. 17 is not limited to the structure of the electronic device. For example, the computer terminal a may also include more or fewer components (such as a network interface, a display device, etc.) than shown in fig. 17, or have a different configuration than shown in fig. 17.

Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program for instructing a terminal device to execute in association with hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.

Example 12

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

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

Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: acquiring a flow pushing condition; determining a target flow and a price of the target flow to be compared according to the flow pushing conditions; generating a recommendation result corresponding to the flow 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 traffic pushing conditions include: the system comprises a flow attribute parameter, a value scoring condition and a price condition, wherein the flow attribute parameter is used for determining a target flow, the value scoring condition is used for determining a value score of the target flow, and the price condition is used for representing a mapping relation between the value score of the target flow and the price 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, highest price, and number of clicks for the flow; obtaining a first flow set and a second flow set according to the flow pushing conditions and the history comparison log, wherein the first flow set comprises a flow set successfully matched with the flow attribute parameters, and the second flow set comprises a flow set successfully matched with the price conditions in the first flow set; generating a recommendation result, wherein the recommendation result comprises: the value distribution and the price distribution of the first flow set, and the estimated display times, the number of clicks, and the consumption amount of the second flow set.

Optionally, the storage medium is further arranged to store program code for performing the steps of: matching a plurality of attribute features related to the flow with flow attribute parameters; acquiring the flow of which the attribute characteristics and the flow attribute parameters are successfully matched, and obtaining 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 smaller than the price, and obtaining a second flow set.

Optionally, the storage medium is further arranged to store program code for performing the steps of: according to the value scoring condition and the price condition, obtaining the value score and the price of each flow in the first flow set; counting the value scores 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 click number of each flow in the second flow set to obtain the click number; 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 characteristics for the flow include at least one of: push time, region, directional crowd and resource position of 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 relationship, exponential relationship, power exponent relationship, logarithmic relationship.

Optionally, the storage medium is further arranged to store program code for performing the steps of: the flow attribute parameters include at least one of: pushing time, region, directional crowd, resource position, creative and total budget; the value scoring conditions include: scoring dimensions and scoring methods; the price conditions include: price conditions in the condition templates and custom price conditions.

Optionally, the storage medium is further arranged to store program code for performing the steps of: the scoring method comprises the following steps: and (5) a scoring model.

Optionally, the storage medium is further arranged to store program code for performing the steps of: after generating a recommended result corresponding to the flow pushing condition, adjusting the flow pushing condition to obtain a new flow 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: scoring the target flow according to the value scoring condition to obtain the 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.

Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: 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.

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

Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: displaying the flow pushing condition; displaying the target flow and the price of the target flow to be compared, wherein the target flow and the price of the target flow are determined according to the flow pushing conditions; 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 foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.

In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the division of the units, is merely a logical function division, and may be implemented in another manner, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.

The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods described in 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, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.

Claims

1. An advertisement bidding method, comprising:

acquiring a flow pushing condition;

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

generating a recommendation result corresponding to the flow pushing condition;

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

wherein, the flow pushing conditions include: a flow attribute parameter, a value scoring condition and a price condition, wherein the flow attribute parameter is used for determining the target flow, the value scoring condition is used for determining a value score of the target flow, and the price condition is used for representing a mapping relation between the value score of the target flow and the price of the target flow;

generating a recommendation result corresponding to the traffic pushing condition, including: obtaining a history comparison log, wherein the history comparison log comprises: a plurality of attribute features, highest price, and number of clicks for the flow; obtaining a first flow set and a second flow set according to the flow pushing condition and the history comparison log, wherein the first flow set comprises a flow set successfully matched with the flow attribute parameter, and the second flow set comprises a flow set successfully matched with the price condition in the first flow set; generating the recommendation result, wherein the recommendation result comprises: the value distribution and the price distribution of the first flow set, and the estimated display times, the number of clicks and the consumption amount of the second flow set.

2. The method of claim 1, wherein obtaining a first traffic set and a second traffic set according to the traffic push condition and the history comparison log comprises:

matching the plurality of flow-related attribute features with the flow attribute parameters;

acquiring the flow of which the attribute characteristics are successfully matched with the flow attribute parameters, and obtaining the 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 said first set of flows to said price for each flow;

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

3. The method of claim 1, wherein generating the recommendation comprises:

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

counting the value scores 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 click number of each flow in the second flow set to obtain the click number;

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

4. The method of claim 1, wherein the flow-related attribute characteristics include at least one of: the push time, region, directional crowd and resource position of the flow.

5. The method of claim 1, wherein the mapping relationship comprises at least one of: linear relationship, exponential relationship, power exponent relationship, logarithmic relationship.

6. The method of claim 1, wherein the flow attribute parameters include at least one of: pushing time, region, directional crowd, resource position, creative and total budget; the value scoring conditions include: scoring dimensions and scoring methods; the price conditions include: price conditions in the condition templates and custom price conditions.

7. The method of claim 6, wherein the scoring method comprises: and (5) a scoring model.

8. The method of claim 7, wherein the scoring model is at least one of: a linear weighted sum model, a model trained based on user data.

9. The method of claim 1, 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.

10. The method according to any one of claims 1 to 9, wherein the comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow, includes:

scoring the target flow according to the value scoring 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 the comparison result.

11. An advertisement bidding method, comprising:

acquiring a flow pushing condition;

generating a recommendation result corresponding to the flow pushing condition;

Obtaining the price of the target flow according to the flow pushing conditions;

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

12. The method of claim 11, 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;

13. The method according to any one of claims 11 to 12, wherein deriving the price of the target traffic according to the traffic pushing conditions comprises:

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

14. An advertisement bidding system, comprising:

a processor; and

a memory, coupled to the processor, for providing instructions to the processor to process 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 a price of the target flow; generating a recommendation result corresponding to the flow pushing condition; under the condition that the recommended result meets a preset condition, comparing the target flow based on the flow pushing condition to obtain a comparison result of the target flow;

the memory is also for: obtaining a history comparison log, wherein the history comparison log comprises: a plurality of attribute features, highest price, and number of clicks for the flow; obtaining a first flow set and a second flow set according to the flow pushing condition and the history comparison log, wherein the first flow set comprises a flow set successfully matched with the flow attribute parameter, and the second flow set comprises a flow set successfully matched with the price condition in the first flow set; generating the recommendation result, wherein the recommendation result comprises: the value distribution and the price distribution of the first flow set, and the estimated display times, the number of clicks and the consumption amount of the second flow set.

15. An advertisement bidding system, comprising:

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

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

the estimating module is used for generating a recommendation result corresponding to the flow pushing condition;

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;

the estimation module is further configured to: obtaining a history comparison log, wherein the history comparison log comprises: a plurality of attribute features, highest price, and number of clicks for the flow; obtaining a first flow set and a second flow set according to the flow pushing condition and the history comparison log, wherein the first flow set comprises a flow set successfully matched with the flow attribute parameter, and the second flow set comprises a flow set successfully matched with the price condition in the first flow set; generating the recommendation result, wherein the recommendation result comprises:

The value distribution and the price distribution of the first flow set, and the estimated display times, the number of clicks and the consumption amount of the second flow set.