CN112446717A - Advertisement putting method and device - Google Patents
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- CN112446717A CN112446717A CN201910798337.4A CN201910798337A CN112446717A CN 112446717 A CN112446717 A CN 112446717A CN 201910798337 A CN201910798337 A CN 201910798337A CN 112446717 A CN112446717 A CN 112446717A
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- G06Q30/00—Commerce
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Abstract
The application provides an advertisement putting method and an advertisement putting device, wherein the method comprises the following steps: receiving an advertisement putting request of a website, wherein the advertisement putting request comprises: identification of the website and website related information; the website related information comprises: website traffic attributes, user access behaviors and resource utilization indexes of release; determining the flow value of the website according to the relevant information of the website; determining each candidate advertisement and the click rate estimation probability of each candidate advertisement according to the flow value of the website and the related information of the website; the method can be used for delivering the advertisements according to the flow with different values, and the advertisement delivery efficiency is improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to an advertisement delivery method and apparatus.
Background
The existing advertisement delivery system is mainly used for delivering advertisements based on search terms or user interests, flow value is not considered, the same advertisements can be delivered aiming at flows with different values, the advertisements are difficult to deliver aiming at the flows with different values, and advertisement delivery efficiency is reduced.
Disclosure of Invention
The present application aims to solve at least one of the above technical problems to a certain extent.
Therefore, a first objective of the present application is to provide an advertisement delivery method, which can deliver advertisements for traffic of different values, thereby improving advertisement delivery efficiency.
A second object of the present application is to provide an advertisement delivery device.
A third object of the present application is to propose another advertisement delivery device.
A fourth object of the present application is to propose a computer readable storage medium.
A fifth object of the present application is to propose a computer program product.
To achieve the above object, an embodiment of a first aspect of the present application provides an advertisement delivery method, including: receiving an advertisement putting request of a website, wherein the advertisement putting request comprises: identification of the website and website related information; the website related information comprises: website traffic attributes, user access behaviors and resource utilization indexes of release; determining the flow value of the website according to the relevant information of the website; determining each candidate advertisement and the click rate estimation probability of each candidate advertisement according to the flow value of the website and the related information of the website; and determining the advertisements to be delivered according to the Click-Through-Rate (CTR for short) estimated probability and the bids of the candidate advertisements.
According to the advertisement delivery method, the advertisement delivery request of the website is received, and the advertisement delivery request comprises the following steps: identification of the website and website related information; the website related information comprises: website traffic attributes, user access behaviors and resource utilization indexes of release; determining the flow value of the website according to the relevant information of the website; determining each candidate advertisement and the click rate estimation probability of each candidate advertisement according to the flow value of the website and the related information of the website; and determining the advertisement to be delivered according to the click rate estimated probability and the bid of each candidate advertisement. The method can be used for delivering advertisements according to the flow with different values, and the advertisement delivery efficiency is improved.
In order to achieve the above object, an embodiment of a second aspect of the present application provides an advertisement delivery device, including: a receiving module, configured to receive an advertisement delivery request of a website, where the advertisement delivery request includes: identification of the website and website related information; the website related information comprises: website traffic attributes, user access behaviors and resource utilization indexes of release; the first determining module is used for determining the flow value of the website according to the relevant information of the website; the second determination module is used for determining each candidate advertisement and the click rate estimation probability of each candidate advertisement according to the flow value of the website and the related information of the website; and the third determining module is used for determining the advertisements to be released according to the click rate estimated probability and the bids of the candidate advertisements.
The advertisement delivery device of the embodiment of the application receives an advertisement delivery request of a website, and the advertisement delivery request comprises: identification of the website and website related information; the website related information comprises: website traffic attributes, user access behaviors and resource utilization indexes of release; determining the flow value of the website according to the relevant information of the website; determining each candidate advertisement and the click rate estimation probability of each candidate advertisement according to the flow value of the website and the related information of the website; and determining the advertisement to be delivered according to the click rate estimated probability and the bid of each candidate advertisement. The device can realize that the advertisement can be put in aiming at the flow of different values, has improved advertisement putting efficiency.
To achieve the above object, an embodiment of a third aspect of the present application provides another advertisement delivery apparatus, including: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the advertisement delivery method as described above when executing the program.
To achieve the above object, a fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the advertisement delivery method as described above.
To achieve the above object, an embodiment of a fifth aspect of the present application provides a computer program product, where when executed by an instruction processor of the computer program product, the method for advertisement delivery is implemented as described above.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow diagram of a method of advertisement delivery, according to one embodiment of the present application;
FIG. 2 is a flow diagram of a method of advertisement delivery, according to another embodiment of the present application;
FIG. 3 is a schematic structural diagram of a click rate model according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an advertising device according to an embodiment of the present application;
FIG. 5 is a schematic structural diagram of an advertisement delivery device according to another embodiment of the present application;
FIG. 6 is a schematic structural diagram of an advertisement delivery device according to yet another embodiment of the present application;
fig. 7 is a schematic structural diagram of another advertisement delivery device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
An advertisement delivery method and apparatus according to an embodiment of the present application are described below with reference to the drawings. It should be noted that the execution subject of the advertisement delivery method in the embodiment of the present application is an advertisement delivery device, and the advertisement delivery device may specifically be an intelligent advertisement delivery system.
Fig. 1 is a flowchart illustrating an advertisement delivery method according to an embodiment of the present application. As shown in fig. 1, the advertisement delivery method includes the following steps:
Specifically, in the advertisement delivery process, the advertisement delivery device may receive an advertisement delivery request of a website, where the advertisement delivery request may include, but is not limited to, an identifier of the website and website related information, where the identifier of the website may be used to uniquely identify the website, and the advertisement delivery device may determine a source of the advertisement delivery request according to the identifier; the relevant information of the website may include, but is not limited to, website traffic attributes, user access behavior, and delivered resource utilization index. The website traffic attributes may include, but are not limited to, a traffic type, a website page uniform resource locator and its content, a channel to which the website page belongs, an advertisement position in the website page, and the like. In the embodiment of the present application, the traffic type is search traffic, feed (required) traffic, or content-oriented traffic. For example: baidu search traffic, Baidu feed traffic, Baidu network alliance traffic. The user access behavior may be an access behavior of the user to a website.
And 102, determining the flow value of the website according to the related information of the website.
As an example, the traffic value of the website can be determined according to the traffic attribute of the website, the user access behavior and the utilization index of the delivered resources. The traffic value of the website may include, but is not limited to, an advertisement click-through rate, a purchase rate, etc. of the website.
In the embodiment of the application, the advertisement delivery device can count the number of users accessing the website, the access behaviors of the users in the website and the delivery resource utilization index, and determine the total click rate or purchase rate of the historically delivered advertisements of the website. The access behavior of the user in the website may include, but is not limited to, a new user in the website, a return visit of the user, a purchasing behavior of the user, and the like. The projected resource utilization index may be, but is not limited to, projected resource utilization, and may be calculated by the following formula, for example: the delivered resource utilization index is the utilized delivered resource/total delivered resource.
And 103, determining each candidate advertisement and the click rate estimation probability of each candidate advertisement according to the flow value of the website and the related information of the website.
Further, the flow value of the website is determined according to the related information of the website. And then, determining each candidate advertisement and the click rate estimation probability of each candidate advertisement according to the flow value of the website and the related information of the website. .
In the embodiment of the application, the network branches to be used and the candidate advertisements in the click rate model can be determined according to the flow value of the website, the flow attribute of the website and the utilization index of the delivered resources; and inputting the advertisement information and the user access behavior of each candidate advertisement into the network branch to be used in the click rate model to obtain the click rate estimation probability of each candidate advertisement. Reference may be made specifically to the description of the subsequent embodiments.
And step 104, determining the advertisements to be released according to the click rate estimated probability and the bids of the candidate advertisements.
Further, the click-through rate estimated probability of each candidate advertisement is obtained, and then the advertisement to be delivered can be determined according to the click-through rate estimated probability and the bid price of each candidate advertisement.
Optionally, sorting the candidate advertisements in an ascending order according to the product of the click rate estimated probability and the bid price; and determining the candidate advertisement ranked at the top as the advertisement to be delivered.
That is, in order to improve the advertisement delivery efficiency, the click-through rate estimated probability of each candidate advertisement may be multiplied by the preset bid of each corresponding candidate advertisement and ranked, and the candidate advertisement ranked before is determined as the advertisement to be delivered. Wherein, each candidate advertisement preset bid can be set in advance according to each click of the website.
According to the advertisement delivery method, the advertisement delivery request of the website is received, and the advertisement delivery request comprises the following steps: identification of the website and website related information; the website related information comprises: website traffic attributes, user access behaviors and resource utilization indexes of release; determining the flow value of the website according to the relevant information of the website; determining each candidate advertisement and the click rate estimation probability of each candidate advertisement according to the flow value of the website and the related information of the website; and determining the advertisement to be delivered according to the click rate estimated probability and the bid of each candidate advertisement. The method can be used for delivering advertisements according to the flow with different values, and the advertisement delivery efficiency is improved.
In this embodiment of the present application, the network branches to be used and the candidate advertisements in the click rate model are determined according to the traffic value of the website, the website traffic attribute, and the delivered resource utilization index, and optionally, as shown in fig. 2, the specific steps of determining the network branches to be used and the candidate advertisements in the click rate model according to the traffic value of the website, the website traffic attribute, and the delivered resource utilization index may be as follows:
In order to further save related resources, improve advertisement delivery efficiency and ensure maximization of advertisement delivery yield, when determining the network branches to be used in the click rate model and each candidate advertisement, it may be determined whether the advertisement delivery request needs to be terminated.
It should be understood that, in the embodiment of the present application, the advertisement delivery device may first obtain the value threshold corresponding to the traffic type in the advertisement delivery request, where the value thresholds corresponding to different traffic types are different. Then, comparing the flow value of the website with the corresponding value threshold, and if the flow value of the website is smaller than the corresponding value threshold, indicating that the advertisement delivery income of the website is smaller, terminating the advertisement delivery request; if the flow value of the website is larger than or equal to the corresponding value threshold value, the advertisement putting income of the website is larger, and the advertisement putting can be continued.
Step 203, when it is determined that the advertisement putting request is not terminated, determining the network branches to be used in the click rate model and each candidate advertisement according to the value threshold, the website traffic attribute and the putting resource utilization index.
Further, when it is determined not to terminate the advertisement placement request, the network branches to be used in the click rate model and the candidate advertisements can be determined according to the value threshold, the website traffic attribute and the placement resource utilization index.
Optionally, advertisement related information of the website is determined according to the value threshold, the website traffic attribute, and the utilization index of the delivered resource, where the advertisement related information includes: intention branch combination, candidate truncation number and advertisement creative style combination; and selecting each candidate advertisement according to the advertisement related information.
In the embodiment of the application, the advertisement delivery device can generate advertisement related information of a website by using an offline pre-trained reinforcement learning model according to a value threshold value corresponding to a traffic type, a website traffic attribute and a delivery resource utilization index, and then can perform retrieval according to the advertisement related information, so that each candidate advertisement is selected. The offline pre-trained reinforcement learning model can be, but is not limited to, a pre-trained deep network model, and the advertisement related information can include, but is not limited to, an intention branch combination, a candidate truncation number, and an advertisement creative style combination. The intent branch combination may be, for example: search intent, user interest intent, trending intent, industry intent, and the like. The ad creative style combination may be a presentation element of an ad, such as: the title, picture, album, summary, etc. of the advertisement.
In order to further improve the advertisement delivery efficiency, each candidate advertisement is selected according to the advertisement related information, and then the advertisement information and the user access behavior of each candidate advertisement can be input into the network branch to be used in the click rate model to obtain the click rate estimation probability of each candidate advertisement.
Before inputting the advertisement information and the user access behavior of each candidate advertisement into the network branches to be used in the click rate model and obtaining the click rate pre-estimated probability of each candidate advertisement, the click rate model can be obtained firstly and can comprise a plurality of network branches; thereafter, training data is acquired, the training data including: the advertisement information, the user access behavior and the click rate prediction probability of the advertisement sample; in order to achieve elastic calculation of the click rate estimation probability of the advertisement, the click rate model can be set based on a multilayer loss function, and training data are adopted to train the click rate model until the value of the preset loss function is smaller than a preset value. The loss function can be calculated and determined according to the output of each network branch and the click rate estimation probability of the advertisement sample. The predetermined loss function value less than the predetermined value may be a sum of values of loss functions of respective layers less than the predetermined value.
For example, as shown in fig. 3, the network branch structure in the click rate model is shown in the figure, when the network branch in the click rate model is selected, when 4096 network branches +256 network branches are selected, the loss function is the loss function 1; when 4096 network branch +2048 network branch +256 network branch is selected, the loss function at this time is loss function 2; when 4096 network branch +2048 network branch +1024 network branch +256 network branch is selected, the loss function at this time is loss function 3; when 4096 network branch +2048 network branch +1024 network branch +512 network branch +256 network branch is selected, the loss function at this time is loss function 4; the loss function of the entire network branching structure is a combination of loss functions 1, 2, 3, and 4, and the value of the loss function of the network branching structure is the value of loss function 1 + the value of loss function 2 + the value of loss function 3 + the value of loss function 4.
In the embodiment of the application, the network branches to be used and the candidate advertisements in the click rate model can be determined according to the flow value of the website, the flow attribute of the website and the utilization index of the delivered resources; and inputting the advertisement information and the user access behavior of each candidate advertisement into the network branch to be used in the click rate model to obtain the click rate estimation probability of each candidate advertisement. Therefore, related resources can be further saved, the advertisement putting efficiency is improved, and the advertisement putting profit maximization is ensured.
Corresponding to the advertisement delivery methods provided by the above embodiments, an embodiment of the present application further provides an advertisement delivery device, and since the advertisement delivery device provided by the embodiment of the present application corresponds to the advertisement delivery methods provided by the above embodiments, the implementation manner of the foregoing advertisement delivery method is also applicable to the advertisement delivery device provided by the embodiment, and is not described in detail in the embodiment. Fig. 4 is a schematic structural diagram of an advertisement delivery device according to an embodiment of the present application. As shown in fig. 4, the advertisement delivery apparatus includes: a receiving module 410, a first determining module 420, a second determining module 430, and a third determining module 440.
The receiving module 410 is configured to receive an advertisement delivery request of a website, where the advertisement delivery request includes: identification of the website and website related information; the website related information comprises: website traffic attributes, user access behaviors and resource utilization indexes of release; a first determining module 420, configured to determine a traffic value of the website according to the website related information; the second determining module 430 is configured to determine each candidate advertisement and a click rate pre-estimated probability of each candidate advertisement according to the traffic value of the website and the related information of the website; and a third determining module 440, configured to determine the advertisement to be delivered according to the click-through rate estimated probability and the bid price of each candidate advertisement.
As a possible implementation manner of the embodiment of the present application, as shown in fig. 5, on the basis of fig. 4, the second determining module 430 includes: a determination unit 431 and an input unit 432.
The determining unit 431 is configured to determine, according to the traffic value of the website, the website traffic attribute, and the delivery resource utilization index, a network branch to be used in the click rate model and each candidate advertisement; the input unit 432 is configured to input the advertisement information and the user access behavior of each candidate advertisement into a network branch to be used in the click rate model, so as to obtain the click rate estimation probability of each candidate advertisement.
As a possible implementation manner of the embodiment of the present application, the website traffic attribute includes: a traffic type; the determining unit 431 is specifically configured to obtain a value threshold corresponding to the traffic type; determining whether to terminate the advertisement putting request according to the flow value and the value threshold of the website; and when the advertisement putting request is determined not to be terminated, determining the network branches to be used in the click rate model and each candidate advertisement according to the value threshold, the website traffic attribute and the putting resource utilization index.
As a possible implementation manner of the embodiment of the present application, a method for determining each candidate advertisement includes determining advertisement related information of a website according to a value threshold, a website traffic attribute, and a utilization index of delivered resources, where the advertisement related information includes: intention branch combination, candidate truncation number and advertisement creative style combination; and selecting each candidate advertisement according to the advertisement related information.
As a possible implementation manner of the embodiment of the present application, the determining unit 431 is specifically further configured to determine whether the flow value is smaller than a value threshold; if the flow value is less than the value threshold, determining to terminate the advertisement putting request; and if the flow value is larger than or equal to the value threshold value, determining not to terminate the advertisement putting request.
As a possible implementation manner of the embodiment of the present application, the third determining module 440 is specifically configured to sort the candidate advertisements in an ascending order according to a product of the estimated probability of click rate and the bid price; and determining the candidate advertisement ranked at the top as the advertisement to be delivered.
As a possible implementation manner of the embodiment of the present application, as shown in fig. 6, on the basis of fig. 4, the advertisement delivery apparatus further includes: an acquisition module 450 and a training module 460.
The obtaining module 450 is configured to obtain a click rate model, where the click rate model includes a plurality of network branches; the obtaining module 450 is further configured to obtain training data, where the training data includes: the advertisement information, the user access behavior and the click rate prediction probability of the advertisement sample; the training module 460 is configured to train the click rate model using the training data until the value of the predetermined loss function is smaller than a predetermined value.
As a possible implementation manner of the embodiment of the application, the loss function is determined by calculation according to the output of each network branch and the click rate estimation probability of the advertisement sample.
The advertisement delivery device of the embodiment of the application receives an advertisement delivery request of a website, and the advertisement delivery request comprises: identification of the website and website related information; the website related information comprises: website traffic attributes, user access behaviors and resource utilization indexes of release; determining the flow value of the website according to the relevant information of the website; determining each candidate advertisement and the click rate estimation probability of each candidate advertisement according to the flow value of the website and the related information of the website; and determining the advertisement to be delivered according to the click rate estimated probability and the bid of each candidate advertisement. The device can realize that the advertisement can be put in aiming at the flow of different values, has improved advertisement putting efficiency.
In order to implement the foregoing embodiment, an embodiment of the present application further provides another advertisement delivery device, and fig. 7 is a schematic structural diagram of another advertisement delivery device provided in the embodiment of the present application. This advertisement puts in device includes: memory 1001, processor 1002, and computer programs stored on memory 1001 and executable on processor 1002.
The processor 1002, when executing the program, implements the advertisement delivery method provided in the above-described embodiments.
Further, the advertisement delivery device further includes:
a communication interface 1003 for communicating between the memory 1001 and the processor 1002.
A memory 1001 for storing computer programs that may be run on the processor 1002.
The processor 1002 is configured to implement the advertisement delivery method according to the foregoing embodiment when executing the program.
If the memory 1001, the processor 1002, and the communication interface 1003 are implemented independently, the communication interface 1003, the memory 1001, and the processor 1002 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 7, but this is not intended to represent only one bus or type of bus.
Optionally, in a specific implementation, if the memory 1001, the processor 1002, and the communication interface 1003 are integrated on one chip, the memory 1001, the processor 1002, and the communication interface 1003 may complete communication with each other through an internal interface.
The processor 1002 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
In order to implement the above embodiments, the present application also provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the advertisement delivery method as described above.
In order to implement the above embodiments, the present application also provides a computer program product, which when executed by an instruction processor in the computer program product, implements the advertisement delivery method as described above.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (19)
1. An advertisement delivery method, comprising:
receiving an advertisement delivery request of a website, wherein the advertisement delivery request comprises: identification of the website and website related information; the website related information comprises: website traffic attributes, user access behaviors and resource utilization indexes of release;
determining the flow value of the website according to the relevant information of the website;
determining each candidate advertisement and the click rate estimation probability of each candidate advertisement according to the flow value of the website and the related information of the website;
and determining the advertisement to be delivered according to the click rate estimated probability and the bid of each candidate advertisement.
2. The method of claim 1, wherein determining each candidate advertisement and the estimated probability of click-through rate of each candidate advertisement according to the traffic value of the website and the website related information comprises:
determining network branches to be used and each candidate advertisement in a click rate model according to the flow value of the website, the website flow attribute and the delivered resource utilization index;
and inputting the advertisement information of each candidate advertisement and the user access behavior into the network branch to be used in the click rate model to obtain the click rate estimated probability of each candidate advertisement.
3. The method of claim 2, wherein the website traffic attributes comprise: a traffic type;
the determining the network branches to be used and the candidate advertisements in the click rate model according to the flow value of the website, the website flow attribute and the delivered resource utilization index comprises the following steps:
obtaining a value threshold corresponding to the flow type;
determining whether to terminate the advertisement putting request according to the flow value of the website and the value threshold;
and when the advertisement putting request is determined not to be terminated, determining the network branches to be used in the click rate model and each candidate advertisement according to the value threshold, the website traffic attribute and the putting resource utilization index.
4. The method of claim 3, wherein each candidate advertisement is determined by,
determining advertisement related information of the website according to the value threshold, the website traffic attribute and the delivered resource utilization index, wherein the advertisement related information comprises: intention branch combination, candidate truncation number and advertisement creative style combination;
and selecting each candidate advertisement according to the advertisement related information.
5. The method of claim 3, wherein determining whether to terminate the advertisement placement request based on the traffic value of the website and the value threshold comprises:
judging whether the flow value is smaller than the value threshold value;
if the flow value is less than the value threshold, determining to terminate the advertisement putting request;
and if the flow value is larger than or equal to the value threshold value, determining not to terminate the advertisement putting request.
6. The method of claim 1, wherein determining the advertisement to be delivered according to the estimated click-through rate probability and the bid price of each candidate advertisement comprises:
sequencing the candidate advertisements in an ascending order according to the product of the click rate estimated probability and the bid price;
and determining the candidate advertisement ranked at the top as the advertisement to be delivered.
7. The method of claim 2, wherein before inputting the advertisement information and the user access behavior of each candidate advertisement into the network branch to be used in the click-through rate model and obtaining the estimated probability of click-through rate of each candidate advertisement, the method further comprises:
obtaining a click rate model, wherein the click rate model comprises a plurality of network branches;
obtaining training data, the training data comprising: the advertisement information, the user access behavior and the click rate prediction probability of the advertisement sample;
and training the click rate model by adopting the training data until the value of a preset loss function is smaller than a preset value.
8. The method of claim 7, wherein the loss function is computationally determined based on the output of each network branch and the estimated probability of click-through rate of the advertisement sample.
9. An advertisement delivery device, comprising:
a receiving module, configured to receive an advertisement delivery request of a website, where the advertisement delivery request includes: identification of the website and website related information; the website related information comprises: website traffic attributes, user access behaviors and resource utilization indexes of release;
the first determining module is used for determining the flow value of the website according to the relevant information of the website;
the second determination module is used for determining each candidate advertisement and the click rate estimation probability of each candidate advertisement according to the flow value of the website and the related information of the website;
and the third determining module is used for determining the advertisements to be released according to the click rate estimated probability and the bids of the candidate advertisements.
10. The apparatus of claim 9, wherein the second determining module comprises: a determination unit and an input unit;
the determining unit is used for determining the network branches to be used and each candidate advertisement in the click rate model according to the flow value of the website, the website flow attribute and the delivery resource utilization index;
and the input unit is used for inputting the advertisement information and the user access behavior of each candidate advertisement into the network branch to be used in the click rate model to obtain the click rate estimated probability of each candidate advertisement.
11. The apparatus of claim 10, wherein the website traffic attributes comprise: a traffic type;
the determination unit is specifically configured to,
obtaining a value threshold corresponding to the flow type;
determining whether to terminate the advertisement putting request according to the flow value of the website and the value threshold;
and when the advertisement putting request is determined not to be terminated, determining the network branches to be used in the click rate model and each candidate advertisement according to the value threshold, the website traffic attribute and the putting resource utilization index.
12. The apparatus of claim 11, wherein each candidate advertisement is determined by,
determining advertisement related information of the website according to the value threshold, the website traffic attribute and the delivered resource utilization index, wherein the advertisement related information comprises: intention branch combination, candidate truncation number and advertisement creative style combination;
and selecting each candidate advertisement according to the advertisement related information.
13. The apparatus according to claim 11, characterized in that the determination unit is further specifically configured to,
judging whether the flow value is smaller than the value threshold value;
if the flow value is less than the value threshold, determining to terminate the advertisement putting request;
and if the flow value is larger than or equal to the value threshold value, determining not to terminate the advertisement putting request.
14. The apparatus of claim 9, wherein the third determination module is specifically configured to,
sequencing the candidate advertisements in an ascending order according to the product of the click rate estimated probability and the bid price;
and determining the candidate advertisement ranked at the top as the advertisement to be delivered.
15. The apparatus of claim 10, further comprising: an acquisition module and a training module;
the obtaining module is used for obtaining a click rate model, and the click rate model comprises a plurality of network branches;
the obtaining module is further configured to obtain training data, where the training data includes: the advertisement information, the user access behavior and the click rate prediction probability of the advertisement sample;
and the training module is used for training the click rate model by adopting the training data until the value of a preset loss function is smaller than a preset value.
16. The apparatus of claim 15, wherein the loss function is computationally determined based on the output of each network branch and a click-through probability estimate for an advertisement sample.
17. An advertisement delivery device, comprising:
memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program implements the advertisement delivery method according to any of claims 1-8.
18. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the advertisement delivery method according to any one of claims 1 to 8.
19. A computer program product implementing the method of advertisement delivery according to any of claims 1-8 when executed by an instruction processor in the computer program product.
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