CN116993410A

CN116993410A - Method, device and computing equipment for determining media content delivery strategy

Info

Publication number: CN116993410A
Application number: CN202211233490.0A
Authority: CN
Inventors: 秦嘉
Original assignee: Tencent Technology Shanghai Co Ltd
Current assignee: Tencent Technology Shanghai Co Ltd
Priority date: 2022-10-10
Filing date: 2022-10-10
Publication date: 2023-11-03

Abstract

There is provided a method of determining a delivery policy for media content, comprising: acquiring historical data of each object in a plurality of objects, wherein the historical data comprises profit resources of the objects and media content consumption resources aiming at the objects; obtaining a first factor for each object based on the revenue resources of the respective object, the first factor characterizing the importance of delivering media content for the object with the revenue resources taken into account separately; obtaining a second factor for each object based on the media content consumption resources of each of the plurality of objects, the second factor characterizing the importance of delivering media content for the object with the media content consumption resources taken into account alone; and determining a media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor. Therefore, the efficiency of determining the media content delivery strategy aiming at the object can be improved, and the scientificity and the accuracy of determining the media content delivery strategy aiming at the object can be improved.

Description

Method, device and computing equipment for determining media content delivery strategy

Technical Field

The present application relates to the field of artificial intelligence technologies, and in particular, to a method, an apparatus, a computing device, and a storage medium for determining a delivery policy of media content.

Background

With the rapid development of internet technology, online consumption plays an increasingly important role in people's daily lives. In order to promote a product for sale on the internet, related media content (such as video, audio, images, text) is often delivered to the public or users based on the production of the product, so as to achieve the effect of promoting.

In the related art, a worker typically relies on information and experience collected by itself to determine a delivery strategy for media content. However, there are large differences in information mastered by different staff and the experience relied upon may be limited. Experience-based media content delivery strategy determination is easy to rely on personal perception judgment, and scientific programmed delivery strategy guidance is lacking. In addition, too much reliance on manpower can lead to lower efficiency and omission of information, and accurate and timely media content delivery strategies are difficult to obtain.

Disclosure of Invention

In view of this, an embodiment of the present application provides a method for determining a delivery policy of media content, where the method includes: acquiring historical data of each object in a plurality of objects, wherein the historical data comprises profit resources of the objects and media content consumption resources aiming at the objects; obtaining a first factor for each object based on a revenue resource for each of the plurality of objects, the first factor characterizing an importance of delivery of media content for the object with the revenue resource taken into account alone; obtaining a second factor for each object based on media content consumption resources of each object of the plurality of objects, the second factor characterizing importance of delivery of media content for the object with the media content consumption resources taken into account alone; and determining a media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor.

Another embodiment of the present application provides an apparatus for determining a delivery policy for media content, the apparatus comprising: a history data acquisition module configured to acquire history data for each of a plurality of objects, the history data including revenue resources for the object and media content consumption resources for the object; a first factor determination module configured to obtain a first factor for each object based on a revenue resource for each object of the plurality of objects, the first factor characterizing an importance of delivery of media content for the object with the revenue resource taken into account alone; a second factor determination module configured to obtain a second factor for each object based on media content consumption resources of each object of the plurality of objects, the second factor characterizing importance of delivery of media content for the object with the media content consumption resources taken into account alone; and a content delivery policy determination module configured to determine a media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor.

Another embodiment of the application provides a computing device comprising a memory configured to store computer-executable instructions; a processor configured to perform the method according to any of the preceding method embodiments when the computer executable instructions are executed by the processor.

Another embodiment of the application provides a computer-readable storage medium storing computer-executable instructions that, when executed, perform a method as in any of the preceding method embodiments.

Another embodiment of the application provides a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements a method according to any of the preceding method embodiments.

By utilizing the method for determining the media content delivery strategy provided by the embodiment of the application, the media content delivery strategy can be comprehensively determined for the plurality of objects by a system, so that the efficiency of determining the media content delivery strategy for the objects can be greatly improved, and the scientificity and the accuracy of determining the media content delivery strategy for the objects can be improved.

These and other advantages of the present application will become apparent from and elucidated with reference to the embodiments described hereinafter.

Drawings

Embodiments of the application will now be described in more detail and with reference to the accompanying drawings, in which:

FIG. 1 illustrates an example implementation environment for a method of determining a delivery policy for media content according to one embodiment of the application;

FIG. 2 illustrates steps in a method of determining a delivery policy for media content according to one embodiment of the application;

FIG. 3 illustrates the steps involved in determining a first factor for each object according to one embodiment of the application;

FIG. 4 illustrates the steps involved in determining a first factor for each object according to another embodiment of the present application;

FIG. 5 illustrates the steps involved in determining a second weight for calculating a first factor in accordance with another embodiment of the present application;

FIG. 6 illustrates the steps involved in determining a second factor for each object according to another embodiment of the present application;

FIG. 7a illustrates steps involved in determining a media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor in a method of determining a delivery policy for media content according to one embodiment of the application;

FIG. 7b illustrates steps involved in determining a media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor in a method of determining a delivery policy for media content according to another embodiment of the application;

FIG. 8 illustrates steps in a method of determining a delivery policy for media content according to another embodiment of the application;

FIG. 9 schematically illustrates a three-dimensional coordinate space constructed for respective objects based on first, second, and third factors in an embodiment of the application;

FIG. 10 illustrates steps in a method of determining a delivery policy for media content according to yet another embodiment of the application;

FIG. 11 schematically illustrates an application scenario of a method for determining a delivery policy for media content according to an embodiment of the present application;

FIG. 12 illustrates a block diagram of an apparatus for determining a delivery policy for media content provided in accordance with an embodiment of the present application; and

FIG. 13 illustrates an example system including an example computing device that represents one or more systems and/or devices in which the various methods or apparatus described herein may be implemented.

Detailed Description

The following description provides specific details of various embodiments of the application so that those skilled in the art may fully understand and practice the various embodiments of the application. It is understood that the inventive arrangements may be practiced without some of these details. In some instances, well known structures or functions have not been shown or described in detail to avoid unnecessarily obscuring the description of the embodiments of the application. The terminology used in the present application should be understood in its broadest reasonable manner even though it is being used in conjunction with a particular embodiment of the present application.

The terminology used in the present application should be understood in its broadest reasonable manner even though it is being used in conjunction with a particular embodiment of the present application.

Artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use the knowledge to obtain optimal results. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision.

The artificial intelligence technology is a comprehensive subject, and relates to the technology with wide fields, namely the technology with a hardware level and the technology with a software level. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning, automatic driving, intelligent transportation, automatic control and other directions.

Machine Learning (ML) is a multi-domain interdisciplinary, involving multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, etc. It is specially studied how a computer simulates or implements learning behavior of a human to acquire new knowledge or skills, and reorganizes existing knowledge structures to continuously improve own performance. Machine learning is the core of artificial intelligence, a fundamental approach to letting computers have intelligence, which is applied throughout various areas of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, confidence networks, reinforcement learning, transfer learning, induction learning, teaching learning, and the like.

Reference herein to "media content" refers to information that may be conveyed through a medium, e.g., the media content may include text, images, video, audio, links, etc., i.e., the media content may be presented in any suitable form, including sound, still pictures, or a piece of video, etc. Reference herein to an "object" refers to any item that is legitimately tradable.

The embodiment of the application provides a method for determining a delivery strategy of media content. FIG. 1 illustrates an exemplary implementation environment for determining a delivery policy for media content according to some embodiments of the application. As shown in fig. 1, various types of terminals (e.g., mobile phones, desktop computers, tablet computers, notebook computers, and palm computers) communicate with a server through a network. The server may be, for example, an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, a content distribution network, basic cloud computing services such as big data and an artificial intelligence platform. The terminals and the server can be directly or indirectly connected through wired or wireless communication, and the server can also be node equipment in a blockchain network.

The respective steps in the method of determining a delivery policy of media content, which will be described in the embodiments below, may be all performed in a server or may be performed by a terminal. Alternatively, part of the steps in the method of determining the delivery policy of the media content are performed by the server and another part of the steps are performed by the terminal. That is, there is no limitation herein as to which steps in the method of determining the delivery policy of the media content are performed by the server and which steps are performed by the terminal. For simplicity, the following description is made in detail with respect to a method for determining a delivery policy of media content by a server.

Fig. 2 illustrates a flow chart of a method of determining a delivery policy for media content according to an embodiment of the application. As shown in fig. 2, according to one embodiment of the present application, a method of determining a delivery policy for media content includes: 210. acquiring historical data of each object in a plurality of objects, wherein the historical data comprises profit resources of the objects and media content consumption resources of the objects; 220. obtaining a first factor for each object based on a revenue resource for each of the plurality of objects, the first factor characterizing the importance of delivering media content for the object with the revenue resource taken into account alone; 230. obtaining a second factor for each object based on media content consumption resources of each object of the plurality of objects, the second factor characterizing importance of media content delivery for the object with the media content consumption resources taken into account alone; and 240 determining a media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor.

As previously mentioned, references herein to an "object" refer to any legal tradeable commodity. Accordingly, the historical data referred to in step 210 refers to data related to the merchandise over a past period of time, and the historical data may include revenue resources and media content consumption resources for each of the plurality of objects. As referred to herein, a revenue resource refers to sales data generated from selling a commodity over a period of time, and may include sales of the commodity over a past period of time, changes in sales over a past period of time, and the like. The media content consumption resource referred to herein refers to a fee consumed for promoting a certain commodity to make media content delivery to the commodity in a past period of time. In step 210, historical data of a plurality of objects may be obtained, and each of the plurality of objects may belong to a different class or may belong to the same class.

In step 220, a first factor for each object is obtained based on the revenue resources for each of the plurality of objects, the first factor characterizing the importance of delivering media content for the object with the revenue resources taken into account alone. For example, in some embodiments, the first factor for each object may reflect the benefit resource of that object relative to the benefit resources of other objects in the plurality of objects. If the revenue resource of a certain object has a larger advantage over the revenue resources of other objects, indicating that the market share of the object is relatively larger, the media content for the object may be delivered or the delivery strength of the media content for the object may be increased to maintain or expand the revenue resource of the object, i.e. the first factor of the object indicates that the importance of delivering the media content for the object is higher with the revenue resource taken into account alone.

In step 230, a second factor for each object is obtained based on the media content consumption resources of each object of the plurality of objects, the second factor characterizing the importance of delivering media content for the object with the media content consumption resources taken into account alone. For example, the second factor for each object may reflect the relative size between the media content consumption resources of that object and the media content consumption resources of other objects for which it is necessary to deliver or further enhance the delivery strength of the media content for objects for which the media content consumption resources are relatively small or even zero, and accordingly, the second factor for these objects indicates that the importance of delivering the media content for these objects is high with the media content consumption resources taken into account alone.

In step 240, a media content delivery policy for each of the plurality of objects is determined based at least on the first and second factors obtained in steps 220 and 230 described above. On the basis of obtaining the first factor and the second factor for each of the plurality of objects, different media content delivery policies for each object may be output. For example, for some objects, if its first factor indicates that the importance of delivering media content for those objects is higher with the revenue resource taken into account alone, and its second factor also indicates that the importance of delivering media content for those objects is higher with the media content consumption resource taken into account alone, information (e.g., merchandise name, merchandise producer, etc.) for those objects may be presented to the staff via the display interface, such that the delivery of media content for those objects may be initiated or enhanced. Alternatively, the ranking of importance aspects of delivering the media content for each of the plurality of objects may be obtained based on the first factor and the second factor of each of the plurality of objects, and the ranking of importance aspects may be presented through the display interface, so that the delivery policy of the media content may be systematically and comprehensively determined for the plurality of objects. In this way, the efficiency of determining the media content delivery strategy for the object can be greatly improved, and the scientificity and accuracy of determining the media content delivery strategy for the object can be improved.

According to some embodiments of the application, the revenue resources of the objects include sales of the objects over a first historical period of time, and FIG. 3 illustrates an example of a method of obtaining a first factor for each object based on the revenue resources of each object. As shown in fig. 3, the step 220 of obtaining a first factor for each object based on the revenue resources of each object of the plurality of objects includes: 310. determining a maximum sales and a minimum sales of each of the plurality of objects in each sales within the first historical period, respectively; 320. determining a first difference between sales and the minimum sales for each of the plurality of objects over the first historical period of time, and a second difference between the maximum sales and minimum sales; and 330, determining a ratio of the first difference and the second difference to obtain a first weight as the first factor.

For example, sales of N subjects over the last period of time (e.g., 7 days) may be collected separately, with sales of N subjects over the last 7 days being V1, V2 … … Vn, respectively. On the basis of this, the maximum sales Vmax and the minimum sales Vmin among sales of the N objects, respectively, in the past 7 days can be determined. Next, a first difference between the sales Vi and the minimum sales Vmin and a second difference between the maximum sales Vmax and the minimum sales Vmin for each of the plurality of objects over the past 7 days may be calculated. Finally, a ratio of the first difference and the second difference may be calculated to obtain a first weight W1 as a first factor F1. That is, the first factor F1 of each object can be expressed as:

In some embodiments, a larger value of the first factor F1 means that the importance of delivering media content to the object to which the first factor corresponds is higher.

Alternatively, in another embodiment, the revenue resources of the objects include sales of the objects over a first historical period of time and sales increments over a second historical period of time, and FIG. 4 illustrates another example of a method of obtaining a first factor for each object based on the revenue resources of each object. As shown in fig. 4, the step 220 of obtaining a first factor for each object based on the revenue resources of each object of the plurality of objects includes: 410. determining a maximum sales and a minimum sales of each of the plurality of objects in each sales within the first historical period, respectively; 420. determining a first difference between sales and the minimum sales for each of the plurality of objects over the first historical period of time, and a second difference between the maximum sales and minimum sales; 430. determining a ratio of the first difference to the second difference to obtain a first weight; 440. determining a similarity between each of the plurality of objects and a reference object to obtain a second weight, the reference object being the object of the plurality of objects that has the greatest sales increment in the second historical period of time; and 450 determining a sum of the first weight and the second weight as the first factor.

Steps 410 to 430 in this embodiment are similar to steps 310 to 330 in the previous embodiments to obtain the first weight W1 described above. In step 440 of this embodiment, a similarity between each of the plurality of objects and a reference object, which is the object of the plurality of objects that has the greatest sales increase over the second historical period of time, is determined to obtain a second weight. The second history period mentioned here may be identical to the first history period described above, or the second history period may partially overlap or not overlap with the first history period at all. For example, the first historical period may be 7 days in the past, while the second historical period may be 14 days in the past. The above-described reference object may be determined based on sales increments of N objects over the second history period (e.g., an increment of sales of each object at the end time of the second history period relative to sales at the start time of the second history period). For each of the N objects including the reference object, M object features of each object may be acquired, and the M object features may include, for example, a price zone of the object, an applicable crowd (e.g., old people, children, etc.), an applicable season, an object class (e.g., food, cosmetics, etc.), an applicable scene (outdoor, indoor, etc.), an applicable region (beijing, shanghai, jetsu, etc.). The second weight may be obtained by determining a similarity between each object and the reference object based on the object feature of each object and the reference object feature of the reference object.

As shown in fig. 5, in some embodiments, the step 440 of determining the similarity between each of the plurality of objects and the reference object to obtain the second weight may include the steps of: 510. acquiring at least one object feature of each object in the plurality of objects; 520. determining at least one feature similarity between each of the at least one object feature of each object and a corresponding reference object feature of the reference object, respectively; and 530, obtaining the second weight based on the at least one feature similarity.

The second weight W2 is further illustrated below by way of example. Some of the above-described object features of the individual objects may be represented mathematically as continuous values, while others may be represented as discrete values. For example, the price range, the age of the applicable crowd, and the like may each be represented as a continuous value, while the use season, the object class, the applicable scene, and the like may be represented as a discrete value. Feature similarities between each object feature of the object and the corresponding reference object feature of the reference object may be calculated separately and the second weight W2 may be obtained based on these feature similarities.

In some embodiments, for an object feature that may appear as a continuous value, the feature similarity between the object feature and the corresponding reference object feature of the reference object may be expressed as:

,

where Tref represents the numerical range of the reference object feature of the reference object (e.g., price range), T represents the numerical range of the object feature of the other object corresponding to the reference object feature (e.g., price range), and feature similarity between the object feature and the reference object feature corresponding to the reference object is the ratio of the intersection between the numerical range of the reference object feature and the numerical range of the object feature of the other object to the numerical range of the reference object feature. And for an object feature that may appear as a discrete value, the feature similarity between the object feature and the corresponding reference object feature of the reference object may be expressed as:

that is, if a certain object feature of a certain object is identical to an object feature corresponding to a reference object (for example, the applicable seasons of the certain object and the reference object are winter), the feature similarity between the object feature and the reference object feature corresponding to the reference object is 1, otherwise, the feature similarity is 0.

On the basis of obtaining the feature similarity between the respective object feature of each object and the reference object feature corresponding to the reference object, the similarity between each object and the reference object may be determined to obtain the second weight W2.

In some embodiments, the second weight W2 may be a feature similarity between the respective object feature of each object and the reference object feature corresponding to the reference objectIs a weighted average of (c). For example, the second weight W2 may be expressed as

Wherein the method comprises the steps ofFor the feature similarity between the ith object feature of M object features of each object and the reference object feature corresponding to the reference object,/the object feature is a part of the M object features of each object>Is a weighting coefficient for the ith object feature of the M object features.

Further, in step 450 of the above embodiment, the sum of the first weight W1 and the second weight W2 is determined as the first factor F1, that is, f1=w1+w2. In this embodiment, in determining the first factor described above for each object, not only sales of each object in the first history period is utilized, but also sales of each object in the second history period are taken into account, whereby the obtained first factor can be made to more accurately characterize importance of delivery of media content to the object. For example, the neglect of objects with small sales but large sales increments in media content delivery can be avoided, and the efficiency of determining media content delivery strategies for the objects and the accuracy of determining media content delivery strategies are further improved.

FIG. 6 illustrates a specific example of obtaining a second factor for each object based on the media content consumption resources of each object of the plurality of objects. As shown in fig. 6, obtaining a second factor for each object based on the media content consumption resources of each object of the plurality of objects may include: 610. determining a consumption resource maximum value and a consumption resource minimum value in the media content consumption resources of each object in the plurality of objects in a third historical time period respectively; 620. determining a third difference between the media content consumption resource and the consumption resource maximum and a fourth difference between the consumption resource maximum and the consumption resource minimum for each of the plurality of objects over a third historical period; 630. a ratio between the third difference and the fourth difference is determined to obtain a second factor. The third history period here may be the same as or overlap with the aforementioned first history period or second history period, or the third history period may be earlier than the first history period or second history period. For example, the media content consumption resources A1, A2 … … An of N objects in the third history period may be collected, so that the consumption resource maximum value Amax and the consumption resource minimum value Amin in the media content consumption resources of the respective objects in the third history period may be determined, respectively, and then the second factor F2 of each object may be expressed as:

In this embodiment, the second factor F2 is a non-positive number, and the smaller the second factor F2, the higher the importance of delivering the media content to the object to which the second factor corresponds.

In some embodiments, as shown in fig. 7a, step 240, above, determining the media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor, comprises: 710. determining at least one first target object from the plurality of objects based on the first and second factors for the respective object, the first factor for the first target object being greater than a first threshold and the second factor for the first target object being less than a second threshold; and 720, increasing delivery of media content for the at least one first target object. In this embodiment, at least one first target object is determined from the plurality of objects based on the aforementioned first and second factors of the respective objects, the first factor of the first target object being greater than the first threshold meaning that the first target object already has a larger market size or the market size increases faster, the importance of the first target object for the delivery of media content from the perspective of revenue resources being higher, the second factor of the first target object being smaller than the second threshold meaning that the first target object is currently relatively lower in media content consumption resources, the importance of the first target object for the delivery of media content from the perspective of media content consumption resources being higher, and therefore, in combination with the aforementioned first and second factors, it can be determined that the delivery of media content for the at least one first target object is increased. The first threshold or the second threshold described above may be set according to actual needs, and in one embodiment, the first threshold is the median in the first factor for each object and the second threshold is the median in the second factor for each object. Alternatively, the first threshold is an average of the first factors for each subject and the second threshold is an average of the second factors for each subject.

According to one embodiment of the present application, as shown in fig. 7b, the step 240 of determining the media content delivery policy for each of the plurality of objects based on at least the first factor and the second factor further comprises: 710', determining an absolute value of a product of the first factor and the second factor for each of the at least one first target object to obtain a media content delivery priority for each first target object, at which time the above-described step 720-increasing delivery of media content for the at least one first target object may include the step 720': and increasing the delivery of the media content for each first target object in the at least one first target object according to the media content delivery priority of each first target object. In some embodiments, the first factor is a non-negative number, the larger the first factor means that the importance of delivering the media content to the object to which the first factor corresponds is higher, the second factor is a non-positive number, and the smaller the second factor means that the importance of delivering the media content to the object to which the second factor corresponds is higher. Thus, the absolute value of the product of the first factor and the second factor for each first target object may characterize the media content delivery priority for each first target object, the larger the absolute value of the product, meaning the higher the priority of media content delivery for the respective first target object. Therefore, the media content delivery of each first target object in the at least one first target object can be increased according to the media content delivery priority of each first target object, so that resources for media content resource delivery are more efficiently utilized.

Fig. 8 illustrates the steps involved in a method of determining a delivery policy for media content provided in accordance with another embodiment of the present application. As shown in FIG. 8, this embodiment includes steps 810-860. In step 810, historical data for each of a plurality of objects is obtained, the historical data including revenue resources for the object, media content consumption resources for the object, and a cost-return for delivery of media content for each object; steps 820 and 830 are identical to steps 220 and 230 in the previous embodiment. Obtaining a third factor for each object based on the cost-return of each object of the plurality of objects, the third factor characterizing the importance of adjusting the delivery strategy for the media content of the object with the cost-return taken into account alone, in step 840, determining at least one second target object from the plurality of objects according to the first, second, and third factors of each object, the third factor of the second target object being less than a third threshold, and the first factor of the second target object being greater than a first threshold or the second factor of the second target object being less than a second threshold; and in step 860, adjusting a media content delivery policy for the at least one second target object to increase a cost-return rate of the at least one second target object.

The cost-per-return rate for each object mentioned in this embodiment may characterize the contribution of the delivery of media content to the revenue resource of that object. In some embodiments, the cost-per-object rate of return may be calculated as a ratio of revenue resources obtained based on the delivery of the media content (e.g., an order amount obtained based on the delivery of the media content) to media content consumption resources for the object. The third factor for each object may be determined as the difference between the cost-return for that object and the reference cost-return. The reference cost-return rate may be a predetermined expected cost-return rate, or may be a median or average cost-return rate of the cost-return rates of the respective objects in the plurality of objects, or may be a maximum value of the cost-return rates of the respective objects in the plurality of objects. Accordingly, the smaller the third factor for each object, the greater the difference between the cost-return rate for that object and the reference cost-return rate, meaning the greater the importance of adjusting the delivery policy of the media content for that object. Based on the obtained first factor, second factor and third factor determined by each object, the second target object can be automatically obtained, and the related information of the second target object can be presented to the user through the display interface, so that the media content delivery strategy can be adjusted for the second target object, and the scientificity and accuracy of the media content delivery strategy can be further optimized.

Thus, the above-described embodiments of the present application actually determine a respective media content delivery policy for each object based on three different dimensions of the first factor, the second factor, and the third factor. With only the first and second factors, a two-dimensional coordinate system for each object may be constructed, each object may be categorized into one of four quadrants defined by the two-dimensional coordinate system, while a three-dimensional coordinate system for each object may be constructed based on the first, second, and third factors, fig. 9 schematically illustrates the three-dimensional coordinate system, and objects categorized into different ones of the four quadrants defined by the two-dimensional coordinate system may have different third factors. Fig. 9 shows four different objects 1, 2, 3 and 4, and the objects are represented by dots of different areas with different third factors.

In some embodiments, step 240, above, determining the media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor, further comprises: determining absolute values of products of the first factor, the second factor, and the third factor for each of the at least one second target object to obtain media content delivery policy adjustment priorities for each second target object, and correspondingly, step 860-adjusting the media content delivery policy for the at least one second target object to increase the cost-return of the at least one second target object includes: and adjusting the media content delivery strategy of the at least one second target object according to the media content delivery strategy adjustment priority of each second target object. In some embodiments, the larger the absolute value of the product of the first factor, the second factor, and the third factor, meaning the higher the media content delivery policy adjustment priority of the corresponding second target object. The media content delivery strategy adjustment priority ordering of the plurality of second target objects can be presented to the user through the display interface, so that the system can comprehensively adjust the delivery strategy of the media content for the plurality of second target objects, and the efficiency of adjusting the media content delivery strategy for the objects is greatly improved.

According to some embodiments of the application, the adjusting the media delivery content policy for the at least one second target object at step 860 includes: acquiring the click rate of each second target object in the at least one second target object; and adjusting the material of the media content for a second target object with the click rate lower than the click rate threshold. Alternatively, in other embodiments, the step 860 of adjusting the media delivery content policy for the at least one second target object includes: obtaining the conversion rate of each second target object in the at least one second target object; and adjusting the target crowd of the media content delivery for a second target object with the conversion rate lower than the conversion rate threshold value. The click rate referred to herein refers to a ratio of an amount of click of a certain object by a user on the internet platform to a presentation amount of media content delivered for the object, and the conversion referred to herein refers to a ratio of an amount of conversion of a certain object by a user to an amount of click. The conversion of a certain object by the user means that the user completes the registration operation to become a registered user or pays out user assets according to the guidance of the media content. Click rate and conversion rate may be obtained based on a Deep learning model using feature vectors of the respective objects, examples of which include, but are not limited to Deep cross and ESMM, and the like.

In some embodiments, the material of the media content corresponding to the object with the higher click rate may be obtained from the plurality of objects, and the material of the media content corresponding to the second target object with the click rate lower than the click rate threshold may be adjusted by referring to the material of the media content corresponding to the object with the higher click rate. In other embodiments, adjusting the media delivery content policy for the at least one second target object may further include outputting additional hint information regarding adjusting the media delivery content policy, e.g., prompting the user to check whether the current media content and the corresponding object are bound, and prompting the user to complete the binding between the media content and the object if the current media content is not bound to the corresponding object.

FIG. 10 outlines a method of determining a delivery policy for media content according to one embodiment of the application. As shown in fig. 10, at step 1001, historical data for each of a plurality of objects may be first obtained, the historical data including revenue resources, media content consumption resources, and cost-return for each object. On this basis, a first factor for each object may be obtained based on the revenue resources of the respective object, a second factor for each object may be obtained based on the media content consumption resources of the respective object, and a third factor for each object may be obtained based on the cost-return of the respective object, as shown in steps 1002, 1003, and 1004 of fig. 10. At step 1005, at least one first target object may be determined from the plurality of objects according to the first and second factors of the respective objects, and further, at step 1007, a media content delivery priority for the respective first target objects may be obtained according to an absolute value of a product of the first and second factors of the respective first target objects, whereby delivery of the media content may be increased for the respective first objects according to the media content delivery priorities of the respective first target objects. Further, as shown in step 1006 of FIG. 10, based on the obtained first, second, and third factors for each object, at least one second target object may be determined from the plurality of objects, and a delivery policy of the media content may be adjusted for the second target object. For example, the click rate and conversion rate of each second target object may be obtained in steps 1008 and 1009, respectively, the material of the media content is adjusted for the second target object whose click rate is lower than the click rate threshold, and the target crowd of the media content delivery is adjusted for the second target object whose conversion rate is lower than the conversion rate threshold, as shown in steps 1011 and 1012 in fig. 10.

As described above, each step in the method for determining a delivery policy of media content described in the embodiments herein may be performed in the server, or may be performed by the terminal. Alternatively, part of the steps in the method of determining the delivery policy of the media content are performed by the server and another part of the steps are performed by the terminal. In some embodiments, the results obtained by performing the method of determining a delivery policy for media content described in the above embodiments may be obtained or received separately on different terminals. As shown in fig. 11, a user may limit the plurality of objects described above to be in the same industry by entering industry information at step 1101, and may further select a specific object within the industry, as shown at step 1102 in fig. 11. The server or terminal then calculates the first factor, the second factor, and the third factor for the selected individual objects and determines the first target object and the second target object, as shown in step 1103 in fig. 11. In some embodiments, the related information of the first target object and the second target object may be received or displayed at different terminals. For example, as shown in fig. 11, the relevant information of the first target object, for example, brand information, producer information, and the like of the first target object is received through the terminal 1 and the terminal 2. That is, the terminal 1 and the terminal 2 output the related information of the first target object as shown in steps 1104, 1105 in fig. 11. Further, in step 1107, the outputted information of the first target object may also be checked periodically in combination with the actual media content delivery practice. In step 1106, the terminal 3 may receive or output the related information of the second target object and the adjustment information of the media content delivery policy for the second target object, for example, adjust the material of the media content, adjust the target crowd of the media content delivery, and so on.

Another embodiment of the present application provides an apparatus for determining a delivery policy for media content, as shown in fig. 12, the apparatus comprising: a history data acquisition module 1200a configured to acquire history data for each of a plurality of objects, the history data including revenue resources for the object and media content consumption resources for the object; a first factor determination module 1200b configured to obtain a first factor for each object based on a revenue resource for each of the plurality of objects, the first factor characterizing an importance of delivery of media content for the object with the revenue resource taken into account alone; a second factor determination module 1200c configured to obtain a second factor for each object based on media content consumption resources of each object of the plurality of objects, the second factor characterizing an importance of delivery of media content for the object with the media content consumption resources considered separately; and a media content delivery policy determination module 1200d configured to determine a media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor.

Further embodiments of the present application provide a computing device comprising: a memory configured to store computer-executable instructions; a processor configured to perform the steps of the method according to any of the preceding embodiments when the computer executable instructions are executed by the processor.

In particular, the method of the methods described above with reference to the flowcharts may be implemented as a computer program. For example, an embodiment of the present application provides a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing at least one step of the method of determining a delivery policy for media content described in the above embodiment.

Another embodiment of the present application provides one or more computer-readable storage media having stored thereon computer-readable instructions that, when executed, implement methods of determining a delivery policy for media content according to some embodiments of the present application. The various steps of the method of determining a delivery policy for media content may be converted by programming into computer readable instructions for storage in a computer readable storage medium. When such a computer-readable storage medium is read or accessed by a computing device or computer, the computer-readable instructions therein are executed by a processor on the computing device or computer to implement a method of determining a delivery policy for media content.

Fig. 13 illustrates an example system 1300 that includes an example computing device 1310 in one or more systems and/or devices that represent aspects in which the embodiments described herein may be implemented. Computing device 1310 may be, for example, a server of a service provider, a device associated with a server, a system-on-chip, and/or any other suitable computing device or computing system. The apparatus 1200 for determining a delivery policy for media content described above with reference to fig. 12 may take the form of a computing device 1310. Alternatively, the apparatus 1200 that determines the delivery policy of the media content may be implemented as a computer program in the form of an application 1316.

The example computing device 1310, as illustrated in fig. 13, includes a processing system 1311, one or more computer-readable media 1312, and one or more I/O interfaces 1313 communicatively coupled to each other. Although not shown, computing device 1310 may also include a system bus or other data and command transfer system that couples the various components to one another. A system bus may include any one or combination of different bus structures, such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and/or a processor or local bus that utilizes any of a variety of bus architectures.

The processing system 1311 is representative of functionality to perform one or more operations using hardware. Thus, the processing system 1311 is illustrated as including hardware elements 1314 that may be configured as processors, functional blocks, and the like. This may include implementation in hardware as application specific integrated circuits or other logic devices formed using one or more semiconductors. The hardware element 1314 is not limited by the materials from which it is formed or the processing mechanisms employed therein. For example, the processor may be comprised of semiconductor(s) and/or transistors (e.g., electronic Integrated Circuits (ICs)). In such a context, the processor-executable instructions may be electronically-executable instructions.

Computer-readable media 1312 is illustrated as including memory/storage 1315. Memory/storage 1315 represents memory/storage capacity associated with one or more computer-readable media. The memory/storage 1315 may include volatile media (such as Random Access Memory (RAM)) and/or nonvolatile media (such as Read Only Memory (ROM), flash memory, optical disks, magnetic disks, and so forth). The memory/storage 1315 may include fixed media (e.g., RAM, ROM, a fixed hard drive, etc.) and removable media (e.g., flash memory, a removable hard drive, an optical disk, and so forth). The computer-readable medium 1312 may be configured in a variety of other ways as described further below. One or more I/O interfaces 1313 represent functionality that allows a user to input commands and information to computing device 1310 using various input devices, and optionally also allows information to be presented to the user and/or other components or devices using various output devices. Examples of input devices include keyboards, cursor control devices (e.g., mice), microphones (e.g., for voice input), scanners, touch functions (e.g., capacitive or other sensors configured to detect physical touches), cameras (e.g., motion that does not involve touches may be detected as gestures using visible or invisible wavelengths such as infrared frequencies), and so forth. Examples of output devices include a display device (e.g., a display or projector), speakers, a printer, a network card, a haptic response device, and so forth. Accordingly, computing device 1310 may be configured in a variety of ways as described further below to support user interaction.

Computing device 1310 also includes applications 1316. The application 1316 may be, for example, a software instance of the apparatus 1200 for determining a delivery policy for media content described with reference to fig. 12, and implement the techniques described herein in combination with other elements in the computing device 1310.

Various techniques may be described herein in the general context of software hardware elements or program modules. Generally, these modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The terms "module," "functionality," and "component" as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of computing platforms having a variety of processors.

An implementation of the described modules and techniques may be stored on or transmitted across some form of computer readable media. Computer-readable media can include a variety of media that are accessible by computing device 1310. By way of example, and not limitation, computer readable media may comprise "computer readable storage media" and "computer readable signal media".

"computer-readable storage medium" refers to a medium and/or device that can permanently store information and/or a tangible storage device, as opposed to a mere signal transmission, carrier wave, or signal itself. Thus, computer-readable storage media refers to non-signal bearing media. Computer-readable storage media include hardware such as volatile and nonvolatile, removable and non-removable media and/or storage devices implemented in methods or techniques suitable for storage of information such as computer-readable instructions, data structures, program modules, logic elements/circuits or other data. Examples of a computer-readable storage medium may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical storage, hard disk, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other storage devices, tangible media, or articles of manufacture adapted to store the desired information and which may be accessed by a computer.

"computer-readable signal medium" refers to a signal bearing medium configured to hardware, such as to send instructions to computing device 1310 via a network. Signal media may typically be embodied in computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, data signal, or other transport mechanism. Signal media also include any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.

As previously described, the hardware elements 1314 and computer-readable media 1312 represent instructions, modules, programmable device logic, and/or fixed device logic implemented in hardware that may be used in some embodiments to implement at least some aspects of the techniques described herein. The hardware elements may include integrated circuits or components of a system on a chip, application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs), complex Programmable Logic Devices (CPLDs), and other implementations in silicon or other hardware devices. In this context, the hardware elements may be implemented as processing devices that perform program tasks defined by instructions, modules, and/or logic embodied by the hardware elements, as well as hardware devices that store instructions for execution, such as the previously described computer-readable storage media.

Combinations of the foregoing may also be used to implement the various techniques and modules described herein. Accordingly, software, hardware, or program modules, and other program modules may be implemented as one or more instructions and/or logic embodied on some form of computer-readable storage medium and/or by one or more hardware elements 1314. Computing device 1310 may be configured to implement specific instructions and/or functions corresponding to software and/or hardware modules. Thus, for example, by using the computer-readable storage medium of the processing system and/or the hardware element 1314, a module may be implemented at least in part in hardware as a module executable by the computing device 1310 as software. The instructions and/or functions may be executable/operable by one or more articles of manufacture (e.g., one or more computing devices 1310 and/or processing systems 1311) to implement the techniques, modules, and examples described herein.

In various implementations, computing device 1310 may take a variety of different configurations. For example, computing device 1310 may be implemented as a computer-like device including a personal computer, desktop computer, multi-screen computer, laptop computer, netbook, and the like. Computing device 1310 may also be implemented as a mobile appliance-like device that includes mobile devices such as mobile phones, portable music players, portable gaming devices, tablet computers, multi-screen computers, and the like. Computing device 1310 may also be implemented as a television-like device that includes devices having or connected to generally larger screens in casual viewing environments. Such devices include televisions, set-top boxes, gaming machines, and the like.

The techniques described herein may be supported by these various configurations of computing device 1310 and are not limited to the specific examples of techniques described herein. The functionality may also be implemented in whole or in part on the "cloud" 1320 using a distributed system, such as by platform 1322 as described below. Cloud 1320 includes and/or represents platform 1322 for resource 1324. Platform 1322 abstracts underlying functionality of hardware (e.g., servers) and software resources of cloud 1320. Resources 1324 may include other applications and/or data that may be used when executing computer processing on servers remote from computing device 1310. Resources 1324 may also include services provided over the internet and/or over subscriber networks such as cellular or Wi-Fi networks.

Platform 1322 may abstract resources and functionality to connect computing device 1310 with other computing devices. Platform 1322 may also be used to abstract a hierarchy of resources to provide a corresponding level of hierarchy of requirements encountered for resources 1324 implemented via platform 1322. Thus, in an interconnected device embodiment, implementation of the functionality described herein may be distributed throughout the system 1300. For example, the functionality may be implemented in part on computing device 1310 and by platform 1322 that abstracts the functionality of cloud 1320.

It will be appreciated that for clarity, embodiments of the application have been described with reference to different functional units. However, it will be apparent that the functionality of each functional unit may be implemented in a single unit, in a plurality of units or as part of other functional units without departing from the application. For example, functionality illustrated to be performed by a single unit may be performed by multiple different units. Thus, references to specific functional units are only to be seen as references to suitable units for providing the described functionality rather than indicative of a strict logical or physical structure or organization. Thus, the application may be implemented in a single unit or may be physically and functionally distributed between different units and circuits.

It will be understood that, although the terms first, second, third, etc. may be used herein to describe various devices, elements, components or sections, these devices, elements, components or sections should not be limited by these terms. These terms are only used to distinguish one device, element, component, or section from another device, element, component, or section.

Although the present application has been described in connection with some embodiments, it is not intended to be limited to the specific form set forth herein. Rather, the scope of the application is limited only by the appended claims. Additionally, although individual features may be included in different claims, these may possibly be advantageously combined, and the inclusion in different claims does not imply that a combination of features is not feasible and/or advantageous. The order of features in the claims does not imply any specific order in which the features must be worked. Furthermore, in the claims, the word "comprising" does not exclude other elements, and the term "a" or "an" does not exclude a plurality.

Claims

1. A method of determining a delivery policy for media content, comprising:

acquiring historical data of each object in a plurality of objects, wherein the historical data comprises profit resources of the objects and media content consumption resources aiming at the objects;

Obtaining a first factor for each object based on a revenue resource for each of the plurality of objects, the first factor characterizing an importance of delivery of media content for the object with the revenue resource taken into account alone;

obtaining a second factor for each object based on media content consumption resources of each object of the plurality of objects, the second factor characterizing importance of delivery of media content for the object with the media content consumption resources taken into account alone; and

a media content delivery policy for each of the plurality of objects is determined based at least on the first factor and the second factor.

2. The method of claim 1, wherein the revenue resource comprises sales of the object over a first historical period of time, the obtaining a first factor for each object based on revenue resources of individual objects of the plurality of objects comprising:

determining a maximum sales and a minimum sales of each of the plurality of objects in each sales within the first historical period, respectively;

determining a first difference between sales and the minimum sales for each of the plurality of objects over the first historical period of time, and a second difference between the maximum sales and minimum sales; and

A ratio of the first difference and the second difference is determined to obtain a first weight as the first factor.

3. The method of claim 1, wherein the revenue resource comprises sales of the object over a first historical period of time and sales of the object over a second historical period of time, the obtaining a first factor for each object based on the revenue resource for each object of the plurality of objects comprising:

determining a first difference between sales and the minimum sales for each of the plurality of objects over the first historical period of time, and a second difference between the maximum sales and minimum sales;

determining a ratio of the first difference to the second difference, thereby obtaining a first weight;

determining a similarity between each of the plurality of objects and a reference object to obtain a second weight, the reference object being the object of the plurality of objects that has the greatest sales increment in the second historical period of time; and

Determining a sum of the first weight and the second weight as the first factor.

4. The method of claim 3, wherein determining a similarity between each of the plurality of objects and a reference object to obtain a second weight comprises:

acquiring at least one object feature of each object in the plurality of objects;

determining a similarity between each of the at least one object feature of each object and a corresponding reference object feature of the reference object to obtain at least one feature similarity; and

the second weight is obtained based on the at least one feature similarity.

5. The method of claim 3, wherein obtaining a second factor for each object based on the media content consumption resources of the respective object of the plurality of objects comprises:

determining a consumption resource maximum value and a consumption resource minimum value in the media content consumption resources of each object in the plurality of objects in a third historical time period respectively;

determining a third difference between the media content consumption resource and the consumption resource maximum and a fourth difference between the consumption resource maximum and the consumption resource minimum for each of the plurality of objects over the third historical period; and

Determining a ratio between the third difference and the fourth difference to obtain the second factor.

6. The method of claim 1, wherein determining a media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor comprises:

determining at least one first target object from the plurality of objects based on the first and second factors for the respective object, the first factor for the first target object being greater than a first threshold and the second factor for the first target object being less than a second threshold; and

the delivery of the media content is increased for the at least one first target object.

7. The method of claim 6, wherein determining a media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor further comprises:

determining an absolute value of a product of a first factor and a second factor for each of the at least one first target object, thereby obtaining a media content delivery priority for each first target object,

wherein increasing delivery of media content for the at least one first target object comprises:

And adding the delivery of the media content to each first target object in the at least one first target object according to the media content delivery priority of each first target object.

8. The method of claim 1, wherein the historical data further comprises a cost-return for media content delivery for each of the plurality of objects, the method further comprising:

obtaining a third factor for each object based on the cost-return of each object of the plurality of objects, the third factor characterizing the importance of making adjustments to the delivery policy of the media content for the object,

wherein determining media content delivery policies for respective ones of the plurality of objects based at least on the first factor and the second factor comprises:

determining at least one second target object from the plurality of objects according to the first, second and third factors of the respective objects, the third factor of the second target object being less than a third threshold, and at least one of the first and second factors of the second target object being greater than a respective first or second threshold; and

Adjusting a media delivery content policy for the at least one second target object to increase a cost-return of the at least one second target object.

9. The method of claim 8, wherein determining a media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor further comprises:

determining an absolute value of a product of the first factor, the second factor, and the third factor for each of the at least one second target object, thereby obtaining a media content delivery policy adjustment priority for each second target object,

wherein adjusting the media delivery content policy for the at least one second target object comprises:

and adjusting the media content delivery strategy of the at least one second target object according to the media content delivery strategy adjustment priority of each second target object.

10. The method of claim 8, wherein adjusting a media delivery content policy for the at least one second target object comprises:

acquiring the click rate of each second target object in the at least one second target object;

and adjusting the material of the media content for a second target object with the click rate lower than the click rate threshold.

11. The method of claim 8, wherein adjusting a media delivery content policy for the at least one second target object comprises:

obtaining the conversion rate of each second target object in the at least one second target object;

and adjusting the target crowd of the media content delivery aiming at a second target object with the conversion rate lower than the conversion rate threshold value.

12. An apparatus for determining a delivery policy for media content, comprising:

a history data acquisition module configured to acquire history data for each of a plurality of objects, the history data including revenue resources for the object and media content consumption resources for the object;

a first factor determination module configured to obtain a first factor for each object based on a revenue resource for each object of the plurality of objects, the first factor characterizing an importance of delivery of media content for the object with the revenue resource taken into account alone;

a second factor determination module configured to obtain a second factor for each object based on media content consumption resources of each object of the plurality of objects, the second factor characterizing importance of delivery of media content for the object with the media content consumption resources taken into account alone; and

A volumetric content delivery policy determination module configured to determine a media content delivery policy for each of the plurality of objects based at least on the first factor and the second factor.

13. A computing device, comprising

A memory configured to store computer-executable instructions;

a processor configured to perform the method of any of claims 1-11 when the computer executable instructions are executed by the processor.

14. A computer readable storage medium storing computer executable instructions which, when executed, perform the method of any one of claims 1-11.

15. A computer program product comprising a computer program, wherein the computer program when executed by a processor implements the steps of the method of any of claims 1-11.