CN112215643A - Preloading system and method based on historical advertising price - Google Patents
Preloading system and method based on historical advertising price Download PDFInfo
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- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0273—Determination of fees for advertising
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0273—Determination of fees for advertising
- G06Q30/0275—Auctions
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- H—ELECTRICITY
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Abstract
The advertisement historical price based preloading system comprises a triggering module, a receiving module, a sequencing module and a sending module; the trigger module is positioned at the client and used for identifying client behavior data and sending the client behavior data to the server, wherein the client behavior data comprises a preposed page loading state and an advertisement preloading state; the receiving module is located at a server and used for receiving the client behavior data sent by the triggering module; the ordering module is positioned at the server end and used for periodically communicating with a plurality of advertisement putting platforms, ordering the advertisements of the advertisement putting platforms according to bids, obtaining advertisement ordering and obtaining advertisement content; and the sending module is positioned at the server side and used for sending the advertisement with the highest bid in the advertisement sequencing discharged by the sequencing module to the client side and preloading the advertisement according to the content sent by the client side request. The system solves the problems that the advertisement preloading request time is too long and the response of the front page is influenced.
Description
Technical Field
The invention relates to the technical field of advertisements of the Internet, in particular to a preloading system and a preloading method based on historical prices of advertisements.
Background
With the wide application of the internet, the internet advertisement is increasingly favored by advertisers. The loading of advertisements in websites or applications can increase the response time of websites or applications, so more and more websites or applications adopt the way of preloading advertisements to advance the loading of advertisements. However, the existing preloading technology mainly adopts a Head Bidding scheme for initiating inquiry requests to a plurality of advertisement platforms, which ensures that the bids of the advertisements are the highest, but because the preloading and the website or the application program are communicated with the server at the same time, the information transmission amount is increased, and the resources occupied by the preloading of the advertisements also prolong the response time of the website or the application program on the page, so that the response time of the website or the application program is prolonged.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a preloading system and a preloading method based on historical advertising prices, which can solve the problems that the advertisement preloading request time is too long and the response of a front page is influenced.
In order to solve the technical problem, the invention provides a preloading system based on historical advertising prices, which comprises a triggering module, a receiving module, a sorting module and a sending module.
The trigger module is positioned at the client and used for identifying client behavior data and sending the client behavior data to the server, wherein the client behavior data comprises a preposed page loading state and an advertisement preloading state.
The receiving module is located at the server side and used for receiving the client side behavior data sent by the triggering module.
The sequencing module is positioned at the server end and used for periodically communicating with the plurality of advertisement putting platforms, sequencing the advertisements of the plurality of advertisement putting platforms according to bids, obtaining advertisement sequencing and obtaining advertisement content.
And the sending module is positioned at the server side and used for sending the advertisement with the highest bid in the advertisement sequencing discharged by the sequencing module to the client side and preloading the advertisement according to the content sent by the client side request.
As an improvement of the scheme, the preloading system based on the historical advertising price further comprises an adjusting module, wherein the adjusting module is located at the server side and comprises the adjusting module.
And the feedback unit is used for receiving the advertisement preloading state sent by the trigger module after the sending module sends the advertisement.
And the updating unit is used for eliminating the advertisements which fail to be loaded in the advertisement sequencing, sending the advertisements with the highest bid in the current advertisement sequencing to the client, and preloading the advertisements according to the sending content requested by the client.
In the technical scheme, the adjusting module is optimized aiming at the condition of advertisement preloading failure, so that when advertisement preloading failure with the highest bid is failed, advertisements with the second highest bid are still preloaded.
As an improvement of the above solution, the adjusting module continues to feed back until feedback that the preloading is successful is received.
In the above technical solution, the continuous feedback of the adjustment module means that the feedback unit continuously receives and judges the advertisement preloading state until receiving a feedback that the advertisement preloading is successful. And as long as the feedback of the adjusting module is that the advertisement preloading fails, the updating unit removes the sent advertisements in the advertisement sequencing, sends the advertisements with the highest bid in the rest advertisement sequencing to the client, and performs the advertisement preloading according to the sending content requested by the client.
As an improvement of the scheme, the sorting module comprises.
And the request unit is used for periodically requesting and storing the advertising bids and the advertising contents from a plurality of the advertising platforms.
And the arranging unit is used for arranging the advertisements of the plurality of advertisement putting platforms according to the order of the bids obtained by the requesting unit.
In the technical scheme, the sequencing module stores the advertisement bids and the advertisement contents, so that when the system receives the advertisement request of the website or the program within a period of time, the system does not need to request the advertisement putting platform, the data transmission quantity is saved, the advertisement request time is greatly shortened, and the response speed of the website or the program is improved.
As an improvement of the above solution, the bid height of the ranking unit refers to weighted prices of a plurality of historical times, wherein the historical times are all times when the requesting unit requests advertising bids from the advertising platforms.
In the technical scheme, the advertisements of the advertisement delivery platforms are sequenced by adopting the weighted price, so that the average price of different advertisement delivery platforms can be reflected, the variation of advertisement delivery is reduced, and the influence of frequent variation of the price on the frequent loading of a website or a program is reduced.
As an improvement of the above, the shorter the history time of the ranking unit, the larger the weight value thereof.
In the above technical solution, the shorter the history time is, the closer the bidding time is to the current time. And giving it higher weight may make the weighted price more sensitive to bid variations for individual ad serving platforms.
As an improvement of the above scheme, the sending module sends the information that the loading of the front page is completed after receiving the information.
In the technical scheme, the advertisement preloading is carried out after the loading of the front page is finished, so that the response of the front page is not influenced, and the use experience of a user on the whole website or program is improved.
Correspondingly, the invention also provides a preloading method based on the historical price of the advertisement, which comprises the following steps.
A. And identifying client behavior data by using the trigger module and sending the client behavior data to a server, wherein the client behavior data comprises a front page loading state and an advertisement preloading state.
In the technical scheme, various state changes of the client are identified through the trigger module, so that the system can operate the client in a targeted manner. The client refers to a website or a program.
B. And receiving the client behavior data sent by the triggering module by using the receiving module.
In the above technical solution, the receiving module located at the server side is configured to receive the client behavior data sent by the triggering module located at the client. The received client behavior data is the basis for the subsequent steps.
C. The sequencing module is used for periodically communicating with a plurality of advertisement putting platforms, sequencing the advertisements of the advertisement putting platforms according to bids, obtaining advertisement sequencing and obtaining advertisement content.
In the technical scheme, the sequencing module is communicated with a plurality of advertisement putting platforms in a fixed period. The fixed period is the same as the period of the advertisement delivery platform with the slowest data updating in the plurality of advertisement delivery platforms.
D. And sending the advertisement with the highest bid in the advertisement sequencing to the client by using the sending module, sending the advertisement to the client according to the client request, and preloading the advertisement according to the content sent by the client request.
In the technical scheme, the advertisement with the highest bid is sent to the client, and the advertisement preloading is carried out according to the sending content requested by the client, so that the advertisement putting can obtain higher income.
As an improvement of the scheme, the preloading method based on the historical advertising price further comprises the step.
E. And receiving the advertisement preloading state sent by the triggering module after the sending module sends the advertisement by using the feedback unit.
F. And eliminating the advertisements which fail to be loaded in the advertisement sequencing by using the updating unit, sending the advertisements with the highest bid in the current advertisement sequencing to the client, and preloading the advertisements according to the sending content requested by the client.
In the technical scheme, whether the preloading of the sent advertisement is successful or not is judged by identifying the preloading state, and if the preloading of the sent advertisement is unsuccessful, the highest bid person in the unsent advertisements is sent, so that the success of the preloading of the advertisement is ensured.
The implementation of the invention has the following beneficial effects:
the invention periodically obtains quoted prices from a plurality of advertisement putting platforms, and directly returns the advertisement content with the highest bid price when the system receives the advertisement preloading request, thereby greatly shortening the delay and instability caused by the response of the plurality of advertisement putting platforms during the advertisement preloading. The invention periodically obtains quotations from a plurality of advertisement putting platforms, and because the quotation change of each advertisement is small, the invention can still keep the accuracy of the price for a long time, thereby playing the role of taking the price accuracy and the response efficiency into consideration.
Drawings
Fig. 1 is a schematic structural diagram of a preloading system based on historical advertising prices according to a first embodiment of the present invention.
FIG. 2 is a flow chart of an adjustment module of the present invention.
Fig. 3 is a schematic structural diagram of a preloading system based on historical advertising prices according to a second embodiment of the present invention.
Fig. 4 is a flowchart of a preloading method based on historical prices of advertisements according to a first embodiment of the present invention.
Fig. 5 is a flowchart of a preloading method based on historical prices of advertisements according to a second embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
In a first embodiment of the present invention, as shown in fig. 1, a preloading system based on historical prices of advertisements is provided, which includes a triggering module 100, a receiving module 200, a sorting module 300, and a sending module 400.
And the triggering module 100 is located at the client and is used for identifying client behavior data and sending the client behavior data to the server, wherein the client behavior data comprises a preposed page loading state and an advertisement preloading state.
Specifically, the trigger module 100 identifies various click or dotting events for the user, which may vary from website to website or program to program. For a website, the trigger module 100 identifies operations including opening the website, clicking on a link on the website, downloading, closing a browser page, and so on. For the program, the trigger module 100 identifies the APP, clicks a text or a picture in the APP, downloads, returns, exits, and the like. And setting the previous page of the page where the advertisement is positioned as a front page. The front page can be a content in a website or a program, or an interface outside the website or the program triggered by user operation. The trigger module 100 determines the preloading opportunity by identifying the front page, and further sends a preloading request to the server. In addition, the trigger module can judge the preloading state and send information containing the success or failure of preloading to the server side for subsequent actions.
And the receiving module 200 is located at the server side and is used for receiving the client behavior data sent by the triggering module.
Specifically, the receiving module 200 receives various behavior data of the client sent by the triggering module 100, including information of pre-page loading and advertisement preloading state information, and sends related information to other modules. The plurality of trigger modules 100 corresponds to one receiving module 200.
The sorting module 300 is located on the server side and comprises.
A requesting unit 301, configured to periodically request and store advertising bids and advertising contents from a plurality of the advertising platforms.
An arranging unit 302, configured to arrange the advertisements of the multiple advertisement delivery platforms according to the order of the bids obtained by the requesting unit.
Specifically, when the sorting module 300 receives the front page loading information sent by the receiving module 200 for the first time, the requesting unit 301 requests an advertisement bid for each advertisement delivery platform. After the request unit 301 obtains bids of each advertisement delivery platform, the ranking unit 302 ranks the bids from high to low to generate an advertisement ranking. The ranking unit 302 ranks the prices at the plurality of historical times. For example, the bid price of an advertisement delivery platform obtained by the requesting unit 301 is 6.5 yuan, the bid price of a day before is 6.1 yuan, the bid price of a day before is 5.5 yuan, and the bid price of a day before is 7.2 yuan, and the ranking unit 302 assigns weight values of 0.7, 0.18, 0.09, and 0.03 respectively according to the time interval, and calculates the weighted price of 6.359 yuan. When the weight value of the price in each time period is calculated, the current calculation is determined according to the current time of the acquired price and the current bidding request period. The weight values are rounded to two decimal places. The closer the bid time is to the current, the greater its weight. In the foregoing calculation, the current bid request period is calculated in 6 hours.
If the front page loading information of the ad slot is received within a later period of time, the ordering module 300 does not request the bid from each ad serving platform by the requesting unit 301, but adopts the previous bid, and directly takes out the advertisement with the highest price from the arranging unit 302. The period of time referred to herein is a fixed period of time, typically one of 12 hours, 6 hours, 5 hours, and 3 hours. The fixed period of time is determined by the interval time of each advertisement delivery platform. The interval time of updating the bid price on the API interface of each advertisement putting platform is different, namely 3 hours or 6 hours, and the advertisement putting platforms are updated in real time. For example, if the interval time of the longest update bid among the five advertisement delivery platforms is 6 hours, the system is set to have a request frequency of a period of 6 hours, and no more bid of the advertisement slot is requested from each advertisement delivery platform within 6 hours after the initial bid request, and the ranking unit 302 does not calculate the weighted bid of each advertisement delivery platform again within 6 hours according to the change of time, but uses the first calculation result when triggering the current request as the only bid in the period. It should be noted that although the system sets the request frequency with a period of 6 hours, it does not mean that each advertisement putting platform is queried again 6 hours after the initial query, but means that after the 6 hours limit is reached, the advertisement putting platform is requested again for advertisement bidding when the preloading request of the advertisement slot is received again. The requesting unit 301 downloads and stores the advertisement contents of the two advertisement delivery platforms with the highest weighted bids according to the advertisement ranking obtained by the ranking unit 302. The advertisement space refers to a specific advertisement display space, that is, a display position of an advertisement on an information page directly seen by a webpage or an APP or other terminal users.
And the sending module 400 is located at the server side and is used for sending the advertisement with the highest bid in the advertisement ranking discharged by the ranking module to the client side and preloading the advertisement according to the content sent by the client side request.
Specifically, the sending module 400 sends the advertisement content of the advertisement serving platform with the highest weighted bid, which is discharged by the arranging unit 302, to the client for preloading. For example, in the advertisement ranking listed by the ranking unit 302, if the advertisement delivery platform a bids the highest, the advertisement provided by the advertisement delivery platform a is sent to the client, and the advertisement is preloaded according to the sending content requested by the client. The sending module 400 sends the information that the loading of the front page is completed after receiving the information. Therefore, overlong time and even breakdown during loading of the front page can be effectively avoided.
The second embodiment of the present invention as shown in fig. 2 is different from the first embodiment, and further includes an adjusting module 500, where the adjusting module 500 is located at the server side and includes.
The feedback unit 501 is configured to receive the advertisement preloading status sent by the trigger module 100 after the sending module 400 sends the advertisement.
An updating unit 502, configured to eliminate the advertisement that fails to be loaded in the advertisement ranking, and send the advertisement content with the highest bid in the current advertisement ranking to the sending module 400 for preloading.
In particular, the adaptation module 500 is used to ensure that the advertising content is successfully preloaded. As shown in fig. 3, after receiving the advertisement preloading state information, the feedback unit 501 determines whether preloading is successful. If the judgment result is yes, no operation is carried out; if the judgment result is no, the updating unit 502 feeds back the feedback result to the arranging unit 302. Ranking unit 302 removes the failed loaded advertisements from the advertisement rankings to generate new advertisement rankings. The request unit 301 requests the advertisement content from the advertisement delivery platform according to the new advertisement ranking, and if the advertisement content required to be requested in the new advertisement ranking is already stored, the request unit does not need to request the advertisement content again. The update unit 502 obtains the advertising content of the highest bid in the new advertising ranking and sends it to the sending module 400. The adjustment module 500 continues the feedback until feedback that the preloading was successful is received.
Correspondingly, as shown in fig. 4, the invention further provides a preloading method based on the historical prices of the advertisements, which comprises the following steps.
S100, identifying client behavior data by using the trigger module and sending the client behavior data to a server, wherein the client behavior data comprises a preposed page loading state and an advertisement preloading state.
Specifically, the pre-page loading and advertisement preloading states triggered and obtained in the step are information sources and judgment bases of subsequent steps.
S200, the receiving module is used for receiving the client behavior data sent by the triggering module.
Specifically, the client behavior data received in this step includes the pre-page load and the advertisement preload status. And the step also sends the client behavior data to other modules for processing.
S300, the sequencing module is used for periodically communicating with a plurality of advertisement putting platforms, sequencing the advertisements of the advertisement putting platforms according to bids, obtaining advertisement sequencing and obtaining advertisement content.
Specifically, this step obtains the content of the advertisement with the highest bid among the plurality of advertisement delivery platforms, where the bid refers to a weighted price. In a period, the sequencing module is no longer in communication with the advertisement delivery platform, so in a period, when the advertisement preloading request is received again, the step becomes:
and sequencing the advertisements of the plurality of advertisement putting platforms according to bids by using a sequencing module, obtaining advertisement sequencing and obtaining advertisement content.
S400, the sending module is used for sending the advertisement with the highest bid in the advertisement sequencing to the client, and preloading the advertisement according to the sending content requested by the client.
Specifically, the highest-priced advertisement obtained in the previous step is sent to the client for preloading, so that the highest bidding price of the advertisement is guaranteed, the minimum data volume transmitted before the client is guaranteed, and the advertisement income and the user experience are both considered.
As another embodiment of the above method, as shown in fig. 5, unlike the above method, a step is further included.
S500, the feedback unit is used for receiving the advertisement preloading state sent by the trigger module after the sending module sends the advertisement.
Specifically, this step is used to determine whether the advertisement content sent in the previous step was successfully preloaded. If the loading is successful, no subsequent steps are required; if the loading fails, the next step is performed. This step may be performed once or multiple times for the same ad preload request.
S600, the updating unit is used for eliminating the advertisements which are failed to be loaded in the advertisement sequencing, sending the advertisements with the highest bid in the current advertisement sequencing to the client, and preloading the advertisements according to the sending content requested by the client.
Specifically, the advertisement which fails to be loaded is directly removed from the advertisement sequence in the step, so that the advertisements in the new advertisement sequence are all unpinned advertisements. The advertisement with the highest price is screened from the unpopulated advertisements, and the relatively high price of the advertisement can still be guaranteed.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (9)
1. The preloading system based on the historical prices of the advertisements is characterized in that: the device comprises a triggering module, a receiving module, a sorting module and a sending module;
the trigger module is positioned at the client and used for identifying client behavior data and sending the client behavior data to the server, wherein the client behavior data comprises a preposed page loading state and an advertisement preloading state;
the receiving module is located at a server and used for receiving the client behavior data sent by the triggering module;
the ordering module is positioned at the server end and used for periodically communicating with a plurality of advertisement putting platforms, ordering the advertisements of the advertisement putting platforms according to bids, obtaining advertisement ordering and obtaining advertisement content;
and the sending module is positioned at the server side and used for sending the advertisement with the highest bid in the advertisement sequencing discharged by the sequencing module to the client side and preloading the advertisement according to the content sent by the client side request.
2. The preloading system based on the historical prices of advertisements according to claim 1, further comprising an adjusting module, wherein the adjusting module is located at the server side and comprises:
the feedback unit is used for receiving the advertisement preloading state sent by the trigger module after the sending module sends the advertisement;
and the updating unit is used for eliminating the advertisements which fail to be loaded in the advertisement sequencing, sending the advertisements with the highest bid in the current advertisement sequencing to the client, and preloading the advertisements according to the sending content requested by the client.
3. The advertisement historical price based preloading system of claim 2, wherein the adjusting module continues to feed back until feedback that the preloading succeeded is received.
4. The advertisement historical price based preloading system of claim 1, wherein the ranking module comprises:
a request unit, configured to periodically request and store advertising bids and advertising content from a plurality of the advertising platforms;
and the arranging unit is used for arranging the advertisements of the plurality of advertisement putting platforms according to the order of the bids obtained by the requesting unit.
5. The advertisement historical price based preloading system of claim 4, wherein the bid high and low of the ranking unit refer to weighted prices of a plurality of historical times, wherein the historical times are all times when the requesting unit requests advertisement bids from the plurality of advertisement delivery platforms.
6. The advertisement-history-price-based preloading system according to claim 5, wherein the weight value of the ranking unit is increased as the history time is shorter.
7. The advertisement historical price based preloading system of claim 1, wherein the sending module sends after receiving the information that the loading of the front page is completed.
8. The preloading method based on the historical price of the advertisement is characterized by comprising the following steps:
A. identifying and sending client behavior data to a server using the trigger module of any of claims 1-7, the client behavior data comprising a front page load and an advertisement preload status;
B. receiving the client behavior data sent by the trigger module using the receiving module of any one of claims 1-7;
C. periodically communicating with a plurality of advertising platforms using a ranking module according to any of claims 1-7, ranking the advertisements of the plurality of advertising platforms by bid, obtaining an advertisement ranking, and obtaining advertisement content;
D. the sending module of any of claims 1-7 is used to send the most bidding advertisement in the advertisement ranking to the client, and the advertisement preloading is performed according to the client request sending content.
9. The advertisement historical price based preloading method according to claim 8, further comprising the steps of:
E. receiving, using the feedback unit of claim 2, an advertisement preload status sent by the trigger module after the sending module sends an advertisement;
F. the updating unit of claim 2 is used for eliminating the advertisements which fail to be loaded in the advertisement sorting, sending the advertisements with the highest bid in the current advertisement sorting to the client, and preloading the advertisements according to the sending content requested by the client.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113487367A (en) * | 2021-07-28 | 2021-10-08 | 陈倩 | Advertisement position prediction system based on cloud computing |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070038508A1 (en) * | 2005-08-10 | 2007-02-15 | Microsoft Corporation | Normalized click-through advertisement pricing |
| US20090164298A1 (en) * | 2007-12-21 | 2009-06-25 | Yahoo! | System and Method for Market Reserve Price Modeling in Online Auctions with Advanced Match |
| US20120130798A1 (en) * | 2010-11-23 | 2012-05-24 | Cooley Robert W | Model sequencing for managing advertising pricing |
| US8527333B1 (en) * | 2009-09-21 | 2013-09-03 | Google Inc. | System and method of selecting advertising |
| US20150058113A1 (en) * | 2013-08-23 | 2015-02-26 | Yahoo! Inc. | Dwell time based advertising |
| EP3159842A1 (en) * | 2015-10-23 | 2017-04-26 | Intowow Innovation Limited B.V.I. | Decentralized advertisement system and method |
| CN108256882A (en) * | 2016-12-28 | 2018-07-06 | 北京奇虎科技有限公司 | A kind of advertisement display method, device and equipment |
| CN108711069A (en) * | 2018-05-03 | 2018-10-26 | 泰康保险集团股份有限公司 | price estimation method and device, storage medium and electronic equipment |
| CN108726205A (en) * | 2018-07-04 | 2018-11-02 | 武汉过控科技有限公司 | A kind of continuous, uniform, the accurate charging gear of powder |
| CN109191207A (en) * | 2018-09-05 | 2019-01-11 | 北京三快在线科技有限公司 | Determine the method and device of advertisement Recommended Price |
| CN109636488A (en) * | 2019-01-17 | 2019-04-16 | 珠海天燕科技有限公司 | Advertisement placement method and device |
| US10565622B1 (en) * | 2015-03-24 | 2020-02-18 | Amazon Technologies, Inc. | Optimization of real-time probabilistic model evaluation for online advertising |
-
2020
- 2020-10-12 CN CN202011083730.4A patent/CN112215643A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070038508A1 (en) * | 2005-08-10 | 2007-02-15 | Microsoft Corporation | Normalized click-through advertisement pricing |
| US20090164298A1 (en) * | 2007-12-21 | 2009-06-25 | Yahoo! | System and Method for Market Reserve Price Modeling in Online Auctions with Advanced Match |
| US8527333B1 (en) * | 2009-09-21 | 2013-09-03 | Google Inc. | System and method of selecting advertising |
| US20120130798A1 (en) * | 2010-11-23 | 2012-05-24 | Cooley Robert W | Model sequencing for managing advertising pricing |
| US20150058113A1 (en) * | 2013-08-23 | 2015-02-26 | Yahoo! Inc. | Dwell time based advertising |
| US10565622B1 (en) * | 2015-03-24 | 2020-02-18 | Amazon Technologies, Inc. | Optimization of real-time probabilistic model evaluation for online advertising |
| EP3159842A1 (en) * | 2015-10-23 | 2017-04-26 | Intowow Innovation Limited B.V.I. | Decentralized advertisement system and method |
| CN108256882A (en) * | 2016-12-28 | 2018-07-06 | 北京奇虎科技有限公司 | A kind of advertisement display method, device and equipment |
| CN108711069A (en) * | 2018-05-03 | 2018-10-26 | 泰康保险集团股份有限公司 | price estimation method and device, storage medium and electronic equipment |
| CN108726205A (en) * | 2018-07-04 | 2018-11-02 | 武汉过控科技有限公司 | A kind of continuous, uniform, the accurate charging gear of powder |
| CN109191207A (en) * | 2018-09-05 | 2019-01-11 | 北京三快在线科技有限公司 | Determine the method and device of advertisement Recommended Price |
| CN109636488A (en) * | 2019-01-17 | 2019-04-16 | 珠海天燕科技有限公司 | Advertisement placement method and device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113487367A (en) * | 2021-07-28 | 2021-10-08 | 陈倩 | Advertisement position prediction system based on cloud computing |
| CN113487367B (en) * | 2021-07-28 | 2023-12-26 | 深圳优优互联网络科技有限公司 | Advertisement position prediction system based on cloud computing |
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