Method, apparatus, device and medium for determining quantity of requisite resources for placement of media item

Abstract

To provide a method for determining the quantity of requisite resources for placing a media item.SOLUTION: A method includes: extracting feature information of a target media item from relevant data of the target media item; acquiring predicted values of the quantity of requisite resources for a plurality of placements in competitive placement of the target media item, using a prediction model based at least on the feature information, the predicted values of the quantity of requisite resources for the plurality of placements respectively corresponding to a plurality of predetermined probabilities of the target media item being placed; determining the quantity of requisite resources for placing the target media item from the predicted values of the quantity of requisite resources for the plurality of placements, based on a plurality of placement efficiency measures associated with the predicted values of the quantity of requisite resources. Accordingly, the quantity of requisite resources for placement at various probabilities may be predicted, and then the quantity of requisite resources for placement with higher placement efficiency measures may be selected, thereby improving placement efficiency of the media item.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] FIELD OF THE DISCLOSURE Exemplary implementations of the present invention relate generally to the field of computer technology, and more particularly to methods, apparatus, devices, and computer-readable storage media for determining the amount of resources required for the delivery of a media item. [Background technology]

[0002] The Internet provides access to a wide variety of objects. For example, various applications, products, audio, video, and other data can be accessed via the Internet. The accessible data also includes specific media items related to each of the above-mentioned items, including, for example, advertisements. An object provider with an object can offer a distribution of the media item to a media distributor. The distribution of a media item can be competitive. Successful distribution depends on the amount of resources, also called bidding, required for the distribution of that media item. Summary of the Invention

[0003] In a first aspect of the present invention, there is provided a method for determining an amount of resources required for delivery of a media item, the method comprising: extracting characteristic information of a target media item from related data of the target media item; utilizing a prediction model to obtain a predicted amount of resources required for a plurality of delivery(s) during competitive delivery of the target media item based on at least the characteristic information, the predicted amount of resources required for the plurality of delivery(s) respectively corresponding to a plurality of predetermined probabilities that the target media item will be delivered; and determining an amount of resources required for delivery of the target media item from the predicted amount of resources required for the plurality of delivery(s) based on a plurality of delivery efficiency measures respectively associated with the predicted amount of resources required for the plurality of delivery(s).

[0004] In a second aspect of the present invention, there is provided an apparatus for determining an amount of resources required for delivery of a media item, the apparatus comprising: an extraction module for extracting characteristic information of a target media item from related data of the target media item, an obtaining module for obtaining a predicted value of an amount of resources required for a plurality of delivery during competitive delivery of the target media item utilizing a prediction model based on at least the characteristic information, the predicted values ​​of the amount of resources required for the plurality of delivery respectively corresponding to a plurality of predetermined probabilities that the target media item will be delivered, and a determination module for determining an amount of resources required for the delivery of the target media item from the predicted values ​​of the amount of resources required for the plurality of delivery based on a plurality of delivery efficiency measures respectively associated with the predicted values ​​of the amount of resources required for the plurality of delivery.

[0005] In a third aspect of the invention, there is provided an electronic device comprising at least one processing unit and at least one memory coupled to the at least one processing unit and adapted to store instructions to be executed by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the electronic device to perform a method according to the first aspect of the invention.

[0006] In a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to implement the method of the first aspect of the present invention.

[0007] It should be understood that the contents described in the summary of the present invention are not intended to limit the main or important features of the embodiments of the present invention, and are not intended to limit the scope of the present invention. Other features of the present invention will be easily understood from the following description. [Brief description of the drawings]

[0008] The above and other features, advantages, and aspects of the embodiments of the present invention will become more apparent from the following detailed description taken in conjunction with the drawings, in which the same or similar symbols indicate the same or similar elements. [Figure 1] 1 illustrates a block diagram of an environment for distributing media items according to one exemplary implementation of the present invention. [Diagram 2] 1 shows a block diagram for determining the amount of resources required for delivery of a media item according to some implementations of the present invention. [Diagram 3] 1 illustrates a block diagram of media item feature information according to some implementations of the present invention. [Figure 4] FIG. 2 shows a block diagram of a relationship between a predictive model and multiple predetermined probabilities according to some implementations of the present invention. [Diagram 5] FIG. 1 illustrates a block diagram of a predictive model according to some implementations of the present invention. [Figure 6] 1 shows a flowchart of a method for determining the amount of resources required for delivery of a media item according to some implementations of the present invention. [Figure 7] 1 illustrates a block diagram of an apparatus for determining the amount of resources required for delivery of a media item according to some implementations of the present invention. [Figure 8] 1 shows a block diagram of a device in which multiple implementations of the present invention can be practiced. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, the embodiments of the present invention will be described in more detail with reference to the drawings. Although the drawings show specific embodiments of the present invention, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, but rather, these embodiments are provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes and are not used to limit the protection scope of the present invention.

[0010] In the description of the embodiments of the present invention, the term "comprises" and similar terms are open-ended inclusions of "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one implementation" or "the implementation" should be understood as "at least one implementation". The term "some implementations" should be understood as "at least some implementations". Other explicit and implicit definitions may be included below. The term "model" as used herein may indicate an association relationship between each data. For example, the above-mentioned association relationship may be obtained based on various technical solutions currently known and / or developed in the future.

[0011] It is understood that any data related to the technical solution (including but not limited to the data itself, the acquisition of the data, or the use of the data) should comply with the corresponding laws and regulations and related specified requirements.

[0012] It is understood that before using the technical solutions disclosed in each embodiment of the present invention, the types, scope of use, usage scenarios, etc. of personal information related to the present invention should be notified to users in an appropriate manner in accordance with relevant laws and regulations, and consent from users should be obtained.

[0013] For example, in response to receiving an unsolicited request from a user, presenting information is sent to the user to explicitly present to the user that the requested operation requires the acquisition and use of the user's personal information, so that the user can independently choose whether or not to provide the personal information to software or hardware, such as an electronic device, application, server, or storage medium, that performs the operation of the technical solution of the present invention, based on the presenting information.

[0014] As an optional, non-limiting implementation, the method of transmitting the presentation to the user in response to receiving the user's unsolicited request may be, for example, by utilizing a pop-up window in which the presentation may be displayed in the form of text and which may further include a selection control for the user to "agree" or "not agree" to providing the personal information to the electronic device.

[0015] It will be appreciated that the notification and user authorization process described above is merely a general outline and is not intended to limit the implementation of the present invention, and other means that comply with relevant laws and regulations may be applied to the implementation of the present invention.

[0016] The term "in response to" means that a corresponding event occurs or a condition is satisfied. It is understood that there is not necessarily a strong relationship between the timing sequence of execution of subsequent actions performed in response to the event or condition and the time of the event occurring or the condition being satisfied. In certain cases, the subsequent action may be performed immediately upon the occurrence of the event or the satisfaction of the condition, and in other cases, the subsequent action may be performed a period of time after the event occurs or the condition is satisfied.

[0017] Example Environment An overview according to one exemplary implementation of the present invention will be described with reference to FIG. 1. FIG. 1 illustrates a block diagram of an environment 100 for distributing media items according to one exemplary implementation of the present invention. As illustrated in FIG. 1, one or more media providers can use a media management system 120 to manage media provided on a media distribution platform 110. One or more terminal devices 130-1, 130-2, 130-3, etc. (collectively or individually referred to as terminal devices 130 for ease of description) can be associated with the media distribution platform 110 and access various media provided on the media distribution platform 110 based on, for example, corresponding audiences 132-1, 132-2, 132-3, etc. (collectively or individually referred to as audiences 132 for ease of description). By way of example, the media distribution platform 110 can be an application, a website, a netpage, and other accessible platform. The terminal devices 130 can have an application installed for accessing the media distribution platform 110 or can access the media distribution platform 110 in any suitable manner.

[0018] The media management system 120 may be configured to deliver (e.g., provide or display at terminal device 130) one or more particular media items associated with one or more objects to an audience group based on a corresponding strategy. The delivered media items may include, for example, one or more media items 142-1, 142-2, ..., 142-M (collectively or individually referred to as media items 142 for ease of explanation) in media database 140.

[0019] As used herein, an object may include various recommendable recommendation items, which may include, for example, applications, entity goods, virtual goods, audio / video media, etc. As used herein, a media item refers to media that is exhibited to recommend a corresponding object. Examples of media items may include advertisements. As used herein, an audience group may include one or more audience members, such as audience 132. An audience member may be any potential consumer of an object, such as a user, a group, an organization, an entity, etc.

[0020] In some implementations, the media management system 120 can distribute corresponding media items on the media distribution platform 110 based on requests from object providers 150-1, 150-2, 150-3, etc. (collectively or individually referred to as object providers 150 for ease of description). In some implementations, the media management system 120 can deliver media items 142 to corresponding audiences 132 on the media distribution platform 110 based on requests from at least each object provider 150-1, 150-2, 150-3, etc. (collectively or individually referred to as object providers 150 for ease of description). In an advertisement delivery scenario, the object provider 150 may also be referred to as an advertiser. In some implementations, the object provider may also pay a fee to the media provider based on the exhibition and subsequent conversion of the media item, etc.

[0021] In some implementations, the media management system 120 can select media items to display on a particular terminal device 130 at a media distribution opportunity (e.g., at a particular time and location) of the media distribution platform 110 based on the bidding results. For example, the media management system 120 can receive a unit price (bId) from the object provider 150. In some implementations, the media management system 120 can distribute the media distribution opportunity to the highest bidder, which means that the corresponding media item can be successfully competed for distribution. The unit price may refer to the amount of resources (e.g., the amount of resources required) expended to compete for the distribution of a particular media item at a particular media distribution opportunity. The successful distribution of a media item at a certain cost is called a send, and the cost is called the unit price (rank bId) of the current distribution or send.

[0022] In the environment 100, the terminal device 130 may be any kind of mobile, fixed, or portable terminal, including a mobile cell phone, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a flat-panel computer, a media computer, a multimedia flat-panel, a personal communication system (PCS) device, a personal navigation device, a personal digital assistant (PDA), a personal navigation device, assistant (PDA), an audio / video player, a digital camera / camcorder, a positioning device, a television receiver, a radio receiver, an e-book device, a gaming device, or any combination of the foregoing, including accessories and peripherals of these devices or any combination thereof. In some implementations, the terminal device 130 may also support any kind of interface to a user (such as "wearable" circuitry). The media management system 120 may be, for example, any kind of computing system / server capable of providing computing functionality, including, but not limited to, a mainframe, an edge computing node, a computing device in a cloud environment, and the like. It should be understood that the structure and functionality of each element in the environment 100 is described for illustrative purposes only and does not imply any limitation to the scope of the invention.

[0023] In competitive distribution, multiple object providers 150 can provide the amount of resources required for the distribution of each self-media item, i.e., bid according to the amount of resources required for the distribution. The media management system 120 can, for example, select the highest bid from among the bids and distribute the related media item 142 of the object provider 150 to the media distribution platform 110. A higher bid may increase the chance of winning, but a bid that is too high may increase the burden on the object provider 150. A bid that is too low may cause the object provider to lose the opportunity to exhibit the media item. In this case, it is expected that the amount of resources required for distribution will be more rationally determined and a bid will be made according to the amount of resources required for the distribution.

[0024] Overview of determining the amount of resources required for delivery To at least partially address the shortcomings of the prior art, a method for determining the amount of resources required for the delivery of a media item is proposed according to one exemplary implementation of the present invention. An overview according to one exemplary implementation of the present invention is described with reference to FIG. 2, which shows a block diagram 200 for determining the amount of resources required for the delivery of a media item according to some implementations of the present invention. FIG. 2 illustrates a process for determining the amount of resources required for the delivery of a target media item 210 to be delivered, where the target media item 210 is delivered to promote a target object depicted in the target media item 210. For example, the target media item 210 may be an advertisement for promoting a particular application software, etc.

[0025] Specifically, the characteristic information 212 of the target media item 210 can be extracted from various related data of the target media item 210. According to an exemplary implementation of the present invention, a prediction model 220 can be pre-established, and based on at least the characteristic information 212, the prediction model 220 can be used to determine a predicted value of the amount of resources required for multiple deliveries during competitive delivery of the target media item 210. Here, the predicted values ​​of the amount of resources required for multiple deliveries output by the prediction model 220 may respectively correspond to multiple predetermined probabilities that the target media item 210 can be successfully delivered.

[0026] Here, the prediction model 220 can provide a prediction of the amount of resources required for delivery associated with a number of (e.g., n) predetermined probabilities. The multiple predicted probabilities can be denoted, for example, as F1(i), where 0≦I≦n-1, and the multiple corresponding predictions of the amount of resources required for delivery can be denoted, for example, as B1(i), where 0≦I≦n-1). In this case, I can represent the I-th position, F1(i) can represent the I-th predetermined probability, and B1(i) can represent the prediction of the amount of resources required for delivery for the I-th predetermined probability. For example, the multiple predetermined probabilities can be represented in the form of quantiles, for example, the predetermined probability 240 can represent that there is a 5% probability of winning according to the corresponding prediction value 230, ..., the predetermined probability 242 can represent that there is a 95% probability of winning according to the corresponding prediction value 232.

[0027] Further, the amount of resources required for the delivery of the target media item can be determined 260 from the predicted amount of resources required for the multiple delivery based on a plurality of delivery efficiency measures 250, ..., and 252, each of which is associated with a predicted amount of resources required for the multiple delivery. Here, the delivery efficiency measure may be indicative of, for example, a revenue that may be generated by delivering the target media item, and the predicted amount of resources required for the multiple delivery corresponding to the highest or higher delivery efficiency measure from the predicted amount of resources required for the multiple delivery can be selected as the amount of resources required for the final delivery (i.e., bid). This can improve the delivery efficiency of the media item, for example, to expand the impact of the target media item and allow a larger audience to view the delivered media item.

[0028] A detailed process for determining the amount of resources required for delivery Having provided an overview according to one exemplary implementation of the present invention, more details of determining the amount of resources required for the delivery of a target media item are provided below. In the context of the present invention, historical data during the historical delivery process is utilized as a basis to generate a predictive model. It should be understood that although the media items involved in each historical delivery process are not completely identical, there may be certain commonalities between these media items, and therefore using the common aspects as a basis makes it easier to extract a general basis for determining the amount of resources required for the delivery of a media item.

[0029] The term "model" as used herein refers to a model that can learn the association relationship between corresponding inputs and outputs from training data, thereby generating a corresponding output for a given input after training is completed. The generation of the model can be based on machine learning techniques. Deep learning is a machine learning algorithm that uses multiple layers of processing units to process inputs and provide a corresponding output. A neural network model is an example based on a deep learning model. In this specification, a "model" may also be referred to as a "machine learning model", "learning model", "machine learning network", or "learning network", and these terms are used interchangeably in this specification.

[0030] A "neural network" is a machine learning network based on deep learning. A neural network can process an input and provide a corresponding output, and typically includes an input layer, an output layer, and one or more hidden layers between the input layer and the output layer. Neural networks used in deep learning applications typically include many hidden layers, which increases the depth of the network. Each layer of a neural network is connected sequentially such that the output of the previous layer is provided as the input to the subsequent layer, the input layer receives the input of the neural network, and the output of the output layer serves as the final output of the neural network. Each layer of a neural network includes one or more nodes (also called processing nodes or neurons), and each node processes the input from the previous layer.

[0031] Generally, machine learning is broadly divided into three stages: training, testing, and application (also called inference). In the training stage, a large amount of training data is used to train a given model, and parameter values ​​are continuously and iteratively updated until the model can obtain consistent inferences from the training data that meet the desired goal. Through training, it can be considered that the model can learn input-to-output associations (also called input-to-output mappings) from the training data. The parameter values ​​of the trained model are determined. In the testing stage, the performance of the model is determined by applying test inputs to the trained model and testing whether the model can provide the correct output. The testing stage is fused to the training stage. In the application stage, the trained model is used to process actual model inputs to determine the corresponding model output based on the parameter values ​​obtained in training.

[0032] According to one exemplary implementation of the present invention, the target media item 210 may have associated data from which characteristic information 212 may be extracted. The characteristic information 212 may include, for example, at least one of the following: a type of the target media item, a platform on which the target media item is distributed, an operating system of the platform on which the target media item is distributed, a client device of the platform on which the target media item is distributed, and a region to which the target media item is distributed.

[0033] FIG. 3 illustrates a block diagram 300 of the media item characteristic information 212 according to some implementations of the present invention. As illustrated in FIG. 3, type 311 can indicate a type of the target media item, which may include, but is not limited to, application, product, audio, video, and the like. Platform 312 can indicate a platform to which the target media item is delivered, which may include, but is not limited to, a social network platform, a news push platform, a music platform, a video platform, a short video platform, a game platform, and the like. Operating system 313 can indicate an operating system of a platform to which the target media item is delivered, which may include, but is not limited to, an Android system, an IOS system, and various operating systems of desktop computers, and the like. Client device 314 can indicate a client device of a platform to which the target media item is delivered, which includes, but is not limited to, a desktop computing device, a mobile device, and the like. Region 315 can indicate a region to which the target media item is delivered, such as Region I, Region II, and the like.

[0034] Using an exemplary implementation of the present invention, the feature information 212 may describe multiple aspects of the content of the target media item 210 in detail, which facilitates the representation of various information related to different distribution scenarios, thus improving the accuracy of the predictive model 220. Here, for example, the feature information 212 may be represented in the form of features (e.g., embedded), which are not visible from the outside in an encoded form and therefore do not reveal potentially sensitive information.

[0035] According to one exemplary implementation of the present invention, the feature information 212 may be used as input to a prediction model 220 to determine a forecast of the amount of resources required for a number of deliveries corresponding to a number of predetermined probabilities, respectively. More details of the prediction model 220 are described with reference to Figure 4, which shows a block diagram 400 of the relationship between the prediction model and a number of predetermined probabilities according to some implementations of the present invention.

[0036] As shown in Fig. 4, the abscissa represents a number of probabilities of winning the competitive broadcast, and the number of predetermined probabilities of the predictive model 220 may be expressed, for example, in the form of quantiles. Also, by way of example, quantiles may be determined at intervals of 5% (or other intervals), where the number of predetermined probabilities may be indicated, for example, as 5%, 10%, 15%, 20%, ..., 95%, etc. Alternatively and / or additionally, quantiles may be determined at intervals of 10%, where the number of predetermined probabilities may be indicated, for example, as 10%, 20%, ..., 90%, etc. The smaller the interval, the higher the accuracy represented by the predictive model 220, but the higher the amount of calculation involved. According to one exemplary implementation of the present invention, a balance between accuracy and amount of calculation is performed, so that a suitable interval is determined.

[0037] According to one exemplary implementation of the present invention, in the process of determining a predicted value of the amount of resources required for the multiple deliveries, a prediction model 220 can be utilized to determine a distribution of the amount of resources required for the multiple deliveries, each associated with a number of predefined probabilities, based at least on the characteristic information 212. Continuing with the above example, if the prediction model 220 includes N predefined probabilities, the distribution may be represented in the form of an n-dimensional array. For example,

number

[0038] Exemplary implementations of the present invention can be used to accurately and efficiently describe the relationship between the amount of resources required for each stream and the corresponding probability of winning, thereby improving the accuracy of the prediction model, and can determine predicted values ​​of the amount of resources required for multiple streams based on the distributions of the amount of resources required for each stream.

[0039] It should be understood that the distribution of the amount of resources required for the distribution here may be obtained, for example, based on historical data during the historical distribution process. It should be understood that the historical data may include different scenarios, and the above distribution may be determined based on the different scenarios. According to one exemplary implementation of the present invention, related bid data of a particular historical distribution may be obtained. For example, a particular media management system 120 receives multiple bids from multiple object providers, respectively, in which case the highest bid will win, and the media management system 120 distributes the media item of the highest bidder in the media distribution platform 110.

[0040] In one scenario, the media management system 120 transmits feedback bid data (e.g., the smallest winning bid (mInbId)) to each object provider. In this case, we refer to this scenario as a "scenario with feedback transmission" and the mInbId can represent an objective bidding format. If the object provider wins, the mInbId represents the second highest bid amount among all object provider unit prices, and if the object provider loses, the mInbId represents the winning bid amount. When feedback transmitted bid data is obtained, these historical bid data can be used to obtain the distribution of the amount of resources required for delivery as described above.

[0041] In another scenario, the media management system 120 does not feedback transmit the bid data to each object provider. In this case, this scenario is called "scenario without feedback transmission". When the mInbId is not feedback transmitted, only the data of the related send-show rate (SSR) of the media item is obtained. Here, the SSR data represents the rate at which the media item is sent and displayed. However, the actual display process of the media item is Send-Bid-Win-Show. However, the SSR data does not represent the influence of the bidding process, and may be influenced by the bidding strategy, resulting in bias. In this case, the influence of the bidding process needs to be considered in order to eliminate the potential bias of the SSR data.

[0042] According to one exemplary implementation of the present invention, the proposed prediction model 220 can implement a bias correction method for the bidding style estimation model based on mInbId post-validation by comprehensively considering both the above-mentioned scenarios with and without feedback transmission, so that even in the scenario without feedback transmission, the amount of resources required for delivery can be better predicted by utilizing mInbId information of other similar cases, so that a more accurate bidding style estimation can be obtained even in the scenario without feedback transmission.

[0043] 5 illustrates a block diagram 500 of a prediction model according to some implementations of the present invention. As illustrated in FIG. 5, the prediction model 220 can receive feature information 212 of a target media item 210 and can obtain a posterior validation distribution 542 associated with the feature information 212 from a database 540 for storing historical bidding data. Furthermore, a first network 510 and a second network 520 can be utilized to process the feature information and the corresponding posterior validation distribution 542 to determine a specific distribution associated with a plurality of predetermined probabilities.

[0044] While FIG. 5 illustrates database 540 for storing historical data as being located within predictive model 220, it should be understood that alternatively and / or additionally, database 540 may be located external to predictive model 220, such as in any location accessible to predictive model 220, so long as post-validation distribution 542 of historical bid data can be obtained from database 540.

[0045] According to one exemplary implementation of the present invention, the prediction model 220 may include a first network 510 that can describe a relational relationship between a posterior validation distribution 542 of the amount of resources required for each predefined probability winning related delivery and the characteristics of the media items in the historical bids. In other words, the first network 510 can describe a first relational relationship between the amount of resources (i.e., historical unit price) required for the first reference delivery in a first reference competitive delivery (i.e., historical bid) to deliver the first reference media item (i.e., historical media item) and the first reference characteristic information (i.e., historical media item characteristics) of the first reference media item.

[0046] According to one exemplary implementation of the present invention, in the process of determining a distribution of resource amounts required for a plurality of deliveries associated with a plurality of predetermined probabilities, a first network 510 can be utilized to determine a first distribution of resource amounts required for a plurality of deliveries associated with a plurality of predetermined probabilities, thereby allowing the prediction model 220 to fully take into account the post-validation distribution of true historical bid data, and thus utilize the distribution trends of these true bid data to calibrate potential prediction biases.

[0047] According to one exemplary implementation of the present invention, the above-mentioned association relationship can be determined based on a statistical method, for example, a distribution of the amount of resources required for each delivery corresponding to each winning probability can be obtained from a large amount of historical bidding data based on a statistical method. Alternatively and / or additionally, the above-mentioned association relationship can be expressed based on a machine learning model. When the association relationship has already been obtained, the feature data of the target media item can be used to determine a distribution of the amount of resources required for delivery based on statistics (also referred to as a first distribution).

[0048] Assuming that the feature information of the target media item is (application, social network platform, Android system, mobile device, region I), we can obtain Value1=(0.21, 0.43, 0.65, ...). Alternatively and / or additionally, if the historical bidding data in the database 540 is different or if the feature data of the target media item is different, the distribution of the amount of resources required for delivery may have other values. Also, for example, assuming that the feature information of the target media item is (product, social network platform, IOS system, mobile device, region II), we can obtain Value1=(0.20, 0.45, 0.60, ...).

[0049] According to one exemplary implementation of the present invention, a predicted amount of resources required for delivery at different quantiles can be determined based on the post-validation distribution. Specifically, in advertising scenarios, ECPM (Expected Cost Per Mille) is usually used as a value measure for a target media item. For example, the ECPM of a target media item can be calculated by the following formula:

number

[0050] In the context of the present invention, a bid cpa_bId from an object provider may be received, and a bid may be made on a predicted amount of resources required for delivery (e.g., represented as bId_prIce) in one or more third-party media distribution platforms in a bidding manner, thereby improving the exposure rate of the media item and expanding the impact of the media item. In this case, it is expected that the relationship between the predicted amount of resources required for delivery and the value measure of the target media item satisfies a predetermined constraint tac. In the context of the present invention, the constraint tac may be expressed, for example, as a predetermined percentage (usually tac<1). For example, the predicted amount of resources required for delivery may be limited based on the following formula:

number

[0051] According to one exemplary implementation of the present invention, a distribution of the amount of resources required for delivery associated with each given probability is

number

number

number

[0052] According to one exemplary implementation of the present invention, the prediction model 220 may include a second network 520, which may represent related knowledge extracted from the true SSR in the historical distribution. Specifically, the second network 520 may describe a second related relationship between the display of the second reference media item (i.e., the historical SSR data) in the second reference competitive distribution (i.e., the historical bidding) of the distributed second reference media item (i.e., the historical media item) and the second reference feature information (i.e., the features of the historical media item) of the second reference media item.

[0053] Here, the second network 520 may be trained based on historical truth data. Specifically, initial parameters of the second network 520 may be obtained and updated in various manners now known and / or developed in the future to minimize the difference between the output result of the second network 520 and the corresponding truth data. Furthermore, once the second network 520 is obtained, the second network 520 may be used to determine a second distribution of resource amounts required for multiple deliveries, each of which is associated with a plurality of predetermined probabilities. Using the exemplary implementation of the present invention, knowledge of multiple aspects of media item delivery may be extracted from the truth exhibition data during the historical delivery process, and further assist in the subsequent bidding process.

[0054] Similar to the various operational steps of the first network 510 described above, a second distribution of amounts of resources required for the multiple deliveries associated with each predetermined probability determined based on the SSR data can be determined.

number

number

number

[0055] According to one exemplary implementation of the present invention, a plurality of delivery efficiency measures can be determined based on a predicted amount of resources required for a plurality of deliveries, a value measure of the target media item, and a plurality of predetermined probabilities. Specifically, the plurality of delivery efficiency measures can be determined based on the following formula:

number

number

[0056] With the use of exemplary implementations of the present invention, the complex steps of determining the delivery efficiency measure can be transformed into a simple mathematical process, which allows the delivery efficiency measure resulting from the bid with each given probability to be determined in a simple and effective manner based on mathematical operations.

[0057] According to an exemplary implementation of the present invention, the control parameters in the above formula can be determined based on various methods. For example, the above-mentioned control parameters can be determined based on the Proportional Integral Differential Control (PID) technique to adjust multiple delivery efficiency measures. The above-mentioned control parameters can be set based on the general principle of the PID control algorithm, in which case multiple delivery efficiency measures can be determined based on the following formula:

number

number

[0058] According to one exemplary implementation of the present invention, the above formula can be utilized to determine a delivery efficiency measure associated with each predetermined probability. Furthermore, the magnitude of each delivery efficiency measure can be compared to further select a predicted value of the amount of resources required for delivery of a larger delivery efficiency measure (e.g., a higher profit). In this case, when it is determined that a first delivery efficiency measure of the plurality of delivery efficiency measures is higher than a second delivery efficiency measure, a first predicted value corresponding to the first delivery efficiency measure can be selected from the predicted values ​​of the amount of resources required for the plurality of deliveries, i.e., a predicted value corresponding to a higher profit.

[0059] Specifically, it is possible to determine each delivery efficiency measure corresponding to a probability of 5%, 10%, 15%, etc. Each delivery efficiency measure can be compared, and the amount of resources required for delivery corresponding to the largest delivery efficiency measure can be selected. For example, the amount of resources B required for delivery corresponding to max[profit1(i)] and max[profit2(i)] can be selected as 1,max and B. 2,max , respectively, and then a final bid for the target media item can be determined.

[0060] According to one exemplary implementation of the present invention, the above-mentioned Equations 9.1 and 9.2 can be summed to determine the probability of obtaining the largest delivery efficiency measure, and then the amount of resources required for delivery corresponding to the posterior validation distribution and the amount of resources required for delivery based on SSR data can be obtained, respectively.

[0061] According to one exemplary implementation of the present invention, a predicted amount of resources required for multiple deliveries can be determined based on both the first network 510 and the second network 520. Specifically, the weights of the first network 510 and the second network 520 can be determined based on the posterior validation distribution and the importance of the SSR data, respectively, and a predicted amount of resources required for multiple deliveries can be determined based on the weighting of the first distribution and the second distribution. Assuming that the weights of the two networks are coef and (1-coef), respectively, then a predicted amount of resources required for final deliveries associated with multiple predetermined probabilities can be determined based on the following formula:

number

[0062] Exemplary implementations of the present invention can be used to predict the amount of resources required for delivery with various probabilities, and can select as bids the amount of resources required to generate a delivery with a higher delivery efficiency measure, thereby improving the delivery efficiency of a media item, for example, expanding the impact of a target media item and allowing a larger audience to view the delivered media item. Additionally, a post-validation distribution of historical bid data can be used to correct for the problem that the SSR data does not contain objective bid information, thereby enabling the prediction model 220 to provide a more accurate prediction of the amount of resources required for delivery.

[0063] Example Process 6 illustrates a flowchart of a method 600 for determining an amount of resources required for delivery of a media item according to some implementations of the present invention. At block 610, feature information of a target media item is extracted from related data of the target media item. At block 620, based on at least the feature information, a prediction model is utilized to obtain a predicted value of an amount of resources required for a plurality of deliveries during competitive delivery of the target media item, the predicted value of the amount of resources required for the plurality of deliveries respectively corresponding to a plurality of predetermined probabilities that the target media item will be delivered. At block 630, an amount of resources required for delivery of the target media item is determined from the predicted value of the amount of resources required for the plurality of deliveries based on a plurality of delivery efficiency measures respectively associated with the predicted value of the amount of resources required for the plurality of deliveries.

[0064] According to one exemplary implementation of the present invention, obtaining a predicted value of an amount of resources required for the multiple delivery includes: determining, based at least on the characteristic information, a distribution of amounts of resources required for the multiple delivery, each associated with a plurality of predetermined probabilities, utilizing a prediction model; and determining a predicted value of an amount of resources required for the multiple delivery based on each of the distributions of amounts of resources required for the multiple delivery.

[0065] According to one exemplary implementation of the present invention, the predictive model includes a first network describing a first association relationship between an amount of resources required for a first reference delivery during a first reference competitive delivery of a first reference media item and first reference feature information of the first reference media item, and determining a distribution of the amount of resources required for multiple delivery respectively associated with multiple predetermined probabilities includes determining a first distribution of the amount of resources required for the multiple delivery associated with the multiple predetermined probabilities utilizing the first network.

[0066] According to one exemplary implementation of the present invention, the predictive model includes a second network describing a second association relationship between the exhibition of the second reference media item during a second reference competitive distribution of the second reference media item and second reference feature information of the second reference media item, and determining a distribution of amounts of resources required for the multiple distributions each associated with a plurality of predetermined probabilities includes determining a second distribution of amounts of resources required for the multiple distributions each associated with a plurality of predetermined probabilities utilizing the second network.

[0067] According to one exemplary implementation of the present invention, obtaining a predicted value of an amount of resources required for the multiple distributions includes determining a predicted value of an amount of resources required for the multiple distributions based on the first distribution and the second distribution.

[0068] According to one exemplary implementation of the present invention, a plurality of delivery efficiency measures are determined based on a predicted amount of resources required for the plurality of deliveries, a value measure of the target media item, and a plurality of predetermined probabilities.

[0069] According to one exemplary implementation of the present invention, the method further includes adjusting the plurality of delivery efficiency measures based on the proportional-integral-derivative control parameters.

[0070] According to one exemplary implementation of the present invention, the relationship between the predicted amount of resources required for multiple deliveries and the value metric of the target media items satisfies a predefined constraint.

[0071] According to one exemplary implementation of the present invention, a value measure for a target media item is determined based on the amount of resources required to deliver the target media item, a predicted click-through rate for the target media item, and a predicted conversion rate for the target media item.

[0072] According to one exemplary implementation of the present invention, determining the amount of resources required for delivery based on the plurality of delivery efficiency measures includes, in response to determining that a first delivery efficiency measure of the plurality of delivery efficiency measures is higher than a second delivery efficiency measure, selecting a first predicted value corresponding to the first delivery efficiency measure from the plurality of predicted values ​​of the amount of resources required for delivery.

[0073] According to one exemplary implementation of the present invention, the characteristic information of the target media item includes at least one of the type of the target media item, the platform on which the target media item is delivered, the operating system of the platform on which the target media item is delivered, the client device of the platform on which the target media item is delivered, and the region on which the target media item is delivered.

[0074] Exemplary Apparatus and Devices 7 shows a block diagram of an apparatus 700 for determining an amount of resources required for delivery of a media item according to some implementations of the present invention, the apparatus including: an extraction module 710 for extracting characteristic information of a target media item from related data of the target media item; an acquisition module 720 for obtaining a predicted value of an amount of resources required for a plurality of deliveries during competitive delivery of the target media item utilizing a prediction model based on at least the characteristic information, the predicted values ​​of the amount of resources required for the plurality of deliveries respectively corresponding to a plurality of predetermined probabilities that the target media item will be delivered; and a determination module 730 for determining an amount of resources required for delivery of the target media item from the predicted values ​​of the amount of resources required for the plurality of deliveries based on a plurality of delivery efficiency measures respectively associated with the predicted values ​​of the amount of resources required for the plurality of deliveries.

[0075] According to one exemplary implementation of the present invention, the obtaining module comprises: a distribution determination module for determining a distribution of amounts of resources required for a plurality of delivery, each associated with a plurality of predetermined probabilities utilizing a prediction model based on at least the feature information; and a prediction value obtaining module for determining a prediction value of an amount of resources required for a plurality of delivery, each based on the distribution of the amount of resources required for the plurality of delivery.

[0076] According to one exemplary implementation of the present invention, the predictive model includes a first network describing a first association relationship between an amount of resources required for a first reference delivery during a first reference competitive delivery of a first reference media item and first reference feature information of the first reference media item, and a distribution determination module determines a first distribution of the amount of resources required for multiple deliveries associated with multiple predetermined probabilities using the first network.

[0077] According to one exemplary implementation of the present invention, the predictive model includes a second network describing a second association relationship between the exhibition of the second reference media item during a second reference competitive distribution of the second reference media item and second reference feature information of the second reference media item, and a distribution determination module determines a distribution of the amount of resources required for the multiple distributions each associated with a plurality of predetermined probabilities, utilizing the second network to determine a second distribution of the amount of resources required for the multiple distributions each associated with the plurality of predetermined probabilities.

[0078] According to one exemplary implementation of the present invention, the predicted value determining module further determines a predicted value of an amount of resources required for the multiple distributions based on the first distribution and the second distribution.

[0079] According to one exemplary implementation of the present invention, a plurality of delivery efficiency measures are determined based on a predicted amount of resources required for the plurality of deliveries, a value measure of the target media item, and a plurality of predetermined probabilities.

[0080] According to one exemplary implementation of the present invention, the apparatus adjusts a number of delivery efficiency measures based on proportional-integral-derivative control parameters.

[0081] According to one exemplary implementation of the present invention, the relationship between the predicted amount of resources required for multiple deliveries and the value metric of the target media items satisfies a predefined constraint.

[0082] According to one exemplary implementation of the present invention, a value measure for a target media item is determined based on the amount of resources required to deliver the target media item, a predicted click-through rate for the target media item, and a predicted conversion rate for the target media item.

[0083] According to one exemplary implementation of the present invention, the determination module includes a selection module for selecting a first predicted value corresponding to the first delivery efficiency measure from the predicted values ​​of the amount of resources required for the multiple deliveries in response to determining that a first delivery efficiency measure among the multiple delivery efficiency measures is higher than a second delivery efficiency measure.

[0084] According to one exemplary implementation of the present invention, the characteristic information of the target media item includes at least one of the type of the target media item, the platform on which the target media item is delivered, the operating system of the platform on which the target media item is delivered, the client device of the platform on which the target media item is delivered, and the region on which the target media item is delivered.

[0085] 8 illustrates a block diagram of a device 800 capable of implementing multiple implementations of the present invention. It should be understood that the computing device 800 illustrated in FIG. 8 is merely exemplary and does not constitute any limitation on the functionality and scope of the implementations described herein. The computing device 800 illustrated in FIG. 8 is capable of implementing the methods described above.

[0086] As shown in Fig. 8, the computing device 800 is in the form of a general-purpose electronic device. The components of the computing device 800 may include, but are not limited to, one or more processors or processing units 810, memory 820, storage device 830, one or more communication units 840, one or more input devices 850, and one or more output devices 860. The processing unit 810 may be a real or virtual processor and may perform various processes based on programs stored in the memory 820. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel, thereby improving the parallel processing capabilities of the computing device 800.

[0087] Computing device 800 typically includes a number of computer storage media. Such media may be any obtainable media accessible by computing device 800, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 820 may be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory), or some combination thereof. Storage device 830 may be removable or non-removable media and may include machine-readable media, such as a flash memory drive, a magnetic disk, or any other media, that may be used to store information and / or data (e.g., training data for training) and accessible within computing device 800.

[0088] The computing device 800 may further include other removable / non-removable, volatile / non-volatile storage media. Although not shown in FIG. 8, a magnetic disk drive for reading from or writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a path (not shown) by one or more data media interfaces. The memory 820 includes a computer program product 825 having one or more program modules that are configured to perform various methods or operations of various implementations of the invention.

[0089] The communication unit 840 implements communication with other computing devices over a communication medium. Additionally, the functionality of the components of the computing device 800 may be implemented as a single computing cluster or multiple computing machines that can communicate over a communication connection. Thus, the computing device 800 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or other network nodes.

[0090] The input device 850 may be one or more input devices such as a mouse, a keyboard, a trackball, etc. The output device 860 may be one or more output devices such as a display, a speaker, a printer, etc. The computing device 800 may further communicate, as necessary, with one or more external devices (not shown) such as a storage device, a display device, etc. via the communication unit 840, one or more devices that allow a user to interact with the computing device 800, or the computing device 800 communicates with any device (net card, modem, etc.) that communicates with one or more other computing devices. Such communication may be performed via an input / output (I / O) interface (not shown).

[0091] According to an exemplary implementation of the present invention, a computer-readable storage medium is provided having one or more computer instructions stored thereon, the one or more computer instructions being executed by a processor to implement the above-mentioned method. According to an exemplary implementation of the present invention, a computer program product is further provided, the computer program product being tangibly stored on a non-transitory computer-readable medium and including computer-executable instructions executed by a processor to implement the above-mentioned method. An exemplary implementation of the present invention provides a computer program product having a computer program stored therein, the computer program product implementing the above-mentioned method when the program is executed by a processor.

[0092] Aspects of the present invention have been described herein with reference to flowchart and / or block diagrams of methods, apparatus (systems) and computer program products implemented by the present invention. It will be understood that each box in the flowchart and / or block diagrams, and combinations of boxes in the flowchart and / or block diagrams, can be implemented by computer readable program instructions.

[0093] These computer readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to generate a machine such that, when the instructions are executed by a processing unit of the computer or other programmable data processing apparatus, they generate an apparatus for implementing the functions / operations specified in one or more boxes in the flowcharts and / or block diagrams. These computer readable program instructions may be stored on a computer readable storage medium such that the instructions cause the computer, programmable data processing apparatus, and / or other device to operate in a particular manner such that the computer readable medium on which the instructions are stored constitutes an article of manufacture including instructions that implement each aspect of the functions / operations specified in one or more boxes in the flowcharts and / or block diagrams.

[0094] Loading the computer readable program instructions into a computer, other programmable data processing apparatus, or other device causes the computer, other programmable data processing apparatus, or other device to perform a series of operational steps to produce a computer-implemented process, such that the instructions executing on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in one or more boxes in the flowcharts and / or block diagrams.

[0095] The flowcharts and block diagrams in the figures illustrate possible architectures, functions, and operations of some possible systems, methods, and computer program products according to the present invention. In this regard, each box in the flowcharts or block diagrams may represent a module, program fragment, or part of instructions, which includes one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions depicted in the boxes may occur in a different order than depicted in the figures. For example, two consecutive boxes may in fact be executed substantially in parallel, or may be executed in reverse order depending on the functionality involved. It should also be noted that each box in the block diagrams and / or flowcharts, and combinations of boxes in the block diagrams and / or flowcharts, may be implemented by a special purpose hardware-based system that performs the specified functions or operations, or by a combination of special purpose hardware and computer instructions.

[0096] Although each implementation of the present invention has been described above, the above description is illustrative, not exhaustive, and is not limited to each disclosed implementation. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of each described implementation. The selection of terms used in this specification is intended to best interpret the principles, practical applications, or improvements to technology in the marketplace of each implementation, or to enable those skilled in the art to understand each implementation disclosed in this specification.

Claims

1. extracting feature information of the target media item from related data of the target media item; obtaining a prediction value of an amount of resources required for a plurality of streams during a competitive stream of the target media item utilizing a prediction model based on at least the characteristic information, the prediction value of the amount of resources required for the plurality of streams corresponding to a plurality of predetermined probabilities that the target media item will be streamed, respectively; determining an amount of resources required for delivery of the target media item from the predicted amounts of resources required for the plurality of deliveries based on a plurality of delivery efficiency measures respectively associated with the predicted amounts of resources required for the plurality of deliveries. A method for determining an amount of resources required for delivery of a media item.

2. Obtaining a predicted value of a resource amount required for the plurality of deliveries determining a distribution of resource amounts required for a plurality of deliveries, each of which is associated with a plurality of predetermined probabilities, based on at least the characteristic information and utilizing the prediction model; determining a predicted value of an amount of resources required for the plurality of distributions based on a distribution of an amount of resources required for each of the plurality of distributions; The method of claim 1.

3. The prediction model includes a first network that describes a first association relationship between a resource amount required for a first reference distribution during a first reference competitive distribution of a first reference media item and first reference characteristic information of the first reference media item; determining a distribution of amounts of resources required for a plurality of deliveries associated with the plurality of predetermined probabilities respectively includes determining a first distribution of amounts of resources required for a plurality of deliveries associated with the plurality of predetermined probabilities using the first network. The method of claim 2.

4. the predictive model includes a second network, the second network describing a second association relationship between a display of the second reference media item during a second reference competitive distribution of the second reference media item and second reference characteristic information of the second reference media item; determining a distribution of amounts of resources required for a plurality of deliveries each associated with a plurality of predetermined probabilities includes determining a second distribution of amounts of resources required for a plurality of deliveries each associated with a plurality of predetermined probabilities using the second network. The method according to claim 3.

5. obtaining a predicted value of an amount of resources required for the plurality of deliveries includes determining a predicted value of an amount of resources required for the plurality of deliveries based on the first distribution and the second distribution; The method according to claim 4.

6. the plurality of delivery efficiency measures are determined based on a predicted amount of resources required for the plurality of deliveries, a value measure for the target media item, and the plurality of predetermined probabilities. The method of claim 1.

7. adjusting the plurality of delivery efficiency measures based on proportional-integral-derivative control parameters. The method according to claim 6.

8. a relationship between the predicted amount of resources required for the plurality of deliveries and the value measure of the target media item satisfies a predetermined constraint; The method according to claim 6.

9. a value measure for the target media item is determined based on an amount of resources required for delivery of the target media item, a predicted click-through rate for the target media item, and a predicted conversion rate for the target media item; The method of claim 1.

10. determining an amount of resources required for the delivery based on the plurality of delivery efficiency measures includes, in response to a determination that a first delivery efficiency measure among the plurality of delivery efficiency measures is higher than a second delivery efficiency measure, selecting a first predicted value corresponding to the first delivery efficiency measure from among the plurality of predicted values ​​of the amount of resources required for the delivery. The method of claim 1.

11. The characteristic information of the target media item includes at least one of a type of the target media item, a platform on which the target media item is distributed, an operating system of the platform on which the target media item is distributed, a client device of the platform on which the target media item is distributed, and a region on which the target media item is distributed. The method of claim 1.

12. an extraction module for extracting feature information of the target media item from related data of the target media item; an acquisition module for acquiring a prediction value of a resource amount required for a plurality of deliveries during the competitive delivery of the target media item using a prediction model based on at least the characteristic information, the prediction value of the resource amount required for the plurality of deliveries respectively corresponding to a plurality of predetermined probabilities that the target media item will be delivered; a determination module for determining an amount of resources required for the delivery of the target media item from the predicted amounts of resources required for the plurality of deliveries based on a plurality of delivery efficiency measures respectively associated with the predicted amounts of resources required for the plurality of deliveries; An apparatus for determining an amount of resources required for delivery of a media item.

13. 1. An electronic device comprising: At least one processing unit; and at least one memory coupled to said at least one processing unit and adapted to store instructions to be executed by said at least one processing unit, said instructions, when executed by said at least one processing unit, causing said electronic device to perform the method according to any one of claims 1 to 11. Electronic devices.

14. A computer program is stored which, when executed by a processor, causes the processor to implement the method according to any one of claims 1 to 11. A computer-readable storage medium.

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