Multimedia information revenue forecasting methods, devices, electronic equipment and storage media

Abstract

This invention provides a method, apparatus, and electronic device for predicting the revenue of multimedia information. The method includes: determining a second multimedia information revenue prediction result corresponding to the target multimedia information based on historical data of the target multimedia information; determining a weight adjustment coefficient and a cost slack coefficient corresponding to the target multimedia information, wherein the cost slack coefficient is used to adjust the overall cost of the target multimedia information; and determining the revenue prediction result of the target multimedia information based on the first multimedia information revenue prediction result, the second multimedia information revenue prediction result, the weight adjustment coefficient, and the cost slack coefficient. Therefore, the multimedia information revenue prediction method can recommend multimedia information in the usage environment to different users, while enhancing the accuracy and relevance of multimedia information revenue prediction, effectively improving the quality of multimedia information recommendations, and enhancing the user experience.

Description

Technical Field

[0001] This invention relates to information processing technology, and more particularly to a method, apparatus, and electronic device for estimating the revenue of multimedia information. Background Technology

[0002] Predicting click-through rates and conversion rates for advertisements requires advertising prediction models such as machine learning or deep learning. In related technologies, the optimized online advertising prediction model and the relevant features used to evaluate the advertisements need to be evaluated offline before the model goes live. However, this evaluation cannot guarantee that the return on investment and total cost of advertising will meet expectations. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method, apparatus, electronic device, and storage medium for estimating multimedia information revenue. The technical solution of the embodiments of the present invention is implemented as follows:

[0004] This invention provides a method for estimating the revenue from multimedia information, including:

[0005] Acquire target multimedia information in the environment to predict the benefits of multimedia information acquisition;

[0006] Based on the historical data of the target multimedia information, the estimated revenue result of the first multimedia information corresponding to the target multimedia information is determined;

[0007] Based on the historical data of the target multimedia information, the estimated revenue of the second multimedia information corresponding to the target multimedia information is determined;

[0008] Determine the weight adjustment coefficient and cost slack coefficient corresponding to the target multimedia information, wherein the cost slack coefficient is used to adjust the overall cost of the target multimedia information;

[0009] Based on the first multimedia information revenue forecast result, the second multimedia information revenue forecast result, the weight adjustment coefficient, and the cost slack coefficient, the revenue forecast result of the target multimedia information is determined.

[0010] This invention also provides a multimedia information revenue estimation device, comprising:

[0011] The information transmission module is used to acquire target multimedia information in the multimedia information revenue prediction environment;

[0012] The information processing module is used to determine the estimated revenue result of the first multimedia information corresponding to the target multimedia information based on the historical data of the target multimedia information.

[0013] The information processing module is used to determine the estimated revenue result of the second multimedia information corresponding to the target multimedia information based on the historical data of the target multimedia information.

[0014] The information processing module is used to determine the weight adjustment coefficient and cost slack coefficient corresponding to the target multimedia information, wherein the cost slack coefficient is used to adjust the overall cost of the target multimedia information;

[0015] The information processing module is used to determine the revenue forecast result of the target multimedia information based on the revenue forecast result of the first multimedia information, the revenue forecast result of the second multimedia information, the weight adjustment coefficient, and the cost tightness coefficient.

[0016] In the above scheme,

[0017] The information processing module is used to obtain a first click probability parameter and a first conversion probability parameter of the target multimedia information based on the historical data of the target multimedia information.

[0018] The information processing module is used to determine the target conversion cost parameter that matches the target multimedia information;

[0019] The information processing module is used to determine the estimated revenue result of the first multimedia information corresponding to the target multimedia information based on the product of the first click probability parameter, the first conversion probability parameter and the target conversion cost parameter.

[0020] In the above scheme,

[0021] The information processing module is used to obtain a second click probability parameter and a second conversion probability parameter of the target multimedia information based on the historical data of the target multimedia information.

[0022] The information processing module is used to calculate the estimated cost parameters that match the target multimedia information;

[0023] The information processing module is used to obtain the target investment return rate estimate parameters that match the target multimedia information;

[0024] The information processing module is used to determine the estimated revenue result of the second multimedia information corresponding to the target multimedia information based on the second click probability parameter, the second conversion probability parameter, the estimated payment value parameter, and the estimated target return on investment value parameter.

[0025] In the above scheme,

[0026] The information processing module is used to acquire crowd tags that match the target multimedia information, wherein the crowd tags include at least one of the following:

[0027] Paid user tags and interest-based user tags;

[0028] The information processing module is used to determine the payment rate and user revenue parameters that match the target multimedia information based on the audience tags.

[0029] The information processing module is used to calculate the estimated payment parameters that match the target multimedia information based on the payment rate and user revenue parameters that match the target multimedia information.

[0030] In the above scheme,

[0031] The information processing module is used to determine the first value range and step size parameter of the weight adjustment coefficient based on the historical data of the target multimedia information.

[0032] The information processing module is used to perform traversal processing on the first value range of the weight adjustment coefficient according to the step size parameter of the weight adjustment coefficient to obtain the second value range of the weight adjustment coefficient.

[0033] The information processing module is used to obtain the initial weight adjustment coefficient within the second value range of the weight adjustment coefficient;

[0034] The information processing module is used to determine the first value range and step size parameter of the cost tightness coefficient based on the historical data of the target multimedia information.

[0035] The information processing module is used to perform traversal processing on the first value range of the cost tightness coefficient according to the step size parameter of the cost tightness coefficient, so as to obtain the second value range of the cost tightness coefficient.

[0036] The information processing module is used to obtain the initial cost tightness coefficient within the second value range of the cost tightness coefficient.

[0037] In the above scheme,

[0038] The information processing module is used to adjust the initial weight adjustment coefficient according to the target conversion cost parameter and the target return on investment estimate parameter to obtain the weight adjustment coefficient corresponding to the target multimedia information.

[0039] The information processing module is used to adjust the initial cost tightness coefficient according to the target conversion cost parameter and the target return on investment estimate parameter, so as to obtain the cost tightness coefficient corresponding to the target multimedia information.

[0040] In the above scheme,

[0041] The information processing module is used to monitor the exposure of the target multimedia information based on the revenue forecast results of the target multimedia information, and determine the actual target conversion cost parameter and the actual target return on investment parameter.

[0042] The information processing module is used to dynamically adjust the weight adjustment coefficient and the cost tightness coefficient based on the actual target conversion cost parameter and the actual target return on investment parameter.

[0043] In the above scheme,

[0044] The information processing module is configured to reduce the weighting adjustment coefficient when the actual target conversion cost parameter is less than the target conversion cost parameter and the actual target return on investment parameter is less than the estimated target return on investment parameter; or,

[0045] The information processing module is configured to increase the weighting adjustment coefficient when the actual target conversion cost parameter is greater than or equal to the target conversion cost parameter, and the actual target return on investment parameter is greater than or equal to the estimated target return on investment parameter; or

[0046] The information processing module is configured to increase the cost slack coefficient when the actual target conversion cost parameter is less than the target conversion cost parameter, and the actual target return on investment parameter is greater than or equal to the estimated target return on investment parameter; or,

[0047] The information processing module is used to reduce the cost tightness coefficient when the actual target conversion cost parameter is greater than or equal to the target conversion cost parameter, and the actual target return on investment parameter is less than the estimated target return on investment parameter.

[0048] In the above scheme,

[0049] The information processing module is used to obtain the target user's historical browsing information;

[0050] The information processing module is used to determine the multimedia information exposure history corresponding to the historical browsing information based on the target user's historical browsing information;

[0051] The information processing module is used to dynamically adjust the playback strategy of the multimedia information based on the exposure history of the multimedia information corresponding to the historical browsing information and the estimated revenue of the multimedia information.

[0052] In the above scheme,

[0053] The information processing module is used to dynamically adjust the exposure rate corresponding to the multimedia information; or

[0054] The information processing module is used to adjust the exposure channel corresponding to the multimedia information; or

[0055] The information processing module is used to adjust the exposure position corresponding to the multimedia information.

[0056] In the above scheme,

[0057] The information processing module is used to determine the type of environment for multimedia information revenue estimation;

[0058] The information processing module is used to determine the category of multimedia information to be played based on the type of the multimedia information revenue estimation environment.

[0059] The information processing module is used to respond to the category of the multimedia information to be played, trigger a matching multimedia information data source, so as to adjust the multimedia information to be played by a multimedia information data source that matches the category of the multimedia information to be played.

[0060] In the above scheme,

[0061] The information processing module is used to monitor the exposure parameters of the short video when the target multimedia information is embedded advertising information in the short video;

[0062] The information processing module is used to send the exposure parameters of the short video during playback to the monitoring server, so that the monitoring server can monitor the exposure of the short video and the conversion of embedded advertising information.

[0063] The information processing module is used to use the exposure parameters of short video playback and the click-through rate and conversion rate of embedded advertising information stored on the monitoring server as the data source for the playback effect parameters of multimedia information.

[0064] This invention also provides an electronic device, the electronic device comprising:

[0065] Memory, used to store executable instructions;

[0066] The processor, when executing executable instructions stored in the memory, implements the aforementioned multimedia information revenue estimation method.

[0067] This invention also provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, implement the aforementioned multimedia information revenue estimation method.

[0068] The embodiments of the present invention have the following beneficial effects:

[0069] This invention acquires target multimedia information within a multimedia information revenue prediction environment; based on historical data of the target multimedia information, determines a first multimedia information revenue prediction result corresponding to the target multimedia information; based on the historical data of the target multimedia information, determines a second multimedia information revenue prediction result corresponding to the target multimedia information; determines a weight adjustment coefficient and a cost slack coefficient corresponding to the target multimedia information, wherein the cost slack coefficient is used to adjust the overall cost of the target multimedia information; and based on the first multimedia information revenue prediction result, the second multimedia information revenue prediction result, the weight adjustment coefficient, and the cost slack coefficient, determines the revenue prediction result of the target multimedia information. This enhances the accuracy and relevance of multimedia information revenue prediction, ensures optimal ROI for multimedia information, effectively improves the accuracy of judging the revenue prediction results of multimedia information during the delivery process, and enhances the user experience for advertisers. Attached Figure Description

[0070] Figure 1 A schematic diagram illustrating a use case of the multimedia information revenue estimation method provided in this embodiment of the invention;

[0071] Figure 2 A schematic diagram of the composition structure of the multimedia information revenue estimation device provided in an embodiment of the present invention;

[0072] Figure 3 A schematic diagram of an optional process for estimating the revenue of multimedia information provided in an embodiment of the present invention;

[0073] Figure 4A This is a schematic diagram of the crowd tagging process in an embodiment of the present invention;

[0074] Figure 4B This is a schematic diagram of an optional multimedia information playback window in an embodiment of the present invention;

[0075] Figure 5 A schematic diagram of an optional process for estimating the revenue of multimedia information provided in an embodiment of the present invention;

[0076] Figure 6 A schematic diagram of an optional process for estimating the revenue of multimedia information provided in an embodiment of the present invention;

[0077] Figure 7 This is a schematic diagram illustrating an optional multimedia information recommendation in an embodiment of the present invention;

[0078] Figure 8This is an optional flowchart illustrating the multimedia information revenue estimation method provided in this embodiment of the invention. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0080] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0081] In the implementation of this application, the collection and processing of relevant data should strictly comply with the requirements of relevant laws and regulations, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the personal information subject.

[0082] Before providing a further detailed description of the embodiments of the present invention, the nouns and terms involved in the embodiments of the present invention will be explained, and the nouns and terms involved in the embodiments of the present invention shall be interpreted as follows.

[0083] 1) In response to, used to indicate the conditions or states on which the operation performed depends. When the conditions or states on which it depends are met, one or more operations performed may be performed in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations are performed.

[0084] 2) Based on, used to indicate the conditions or states on which the operation is performed. When the conditions or states on which the operation is performed are met, one or more operations can be performed in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations are performed.

[0085] 3) "Product placement" involves embedding product or brand information into video content in the form of a physical object, an image, or a video clip to leave a brand impression on the audience and achieve marketing objectives. The "advertisement" is presented as multimedia information, including but not limited to images, text, video, and audio. Product placement is hidden within and integrated with the medium, while the advertising information is carefully encoded using non-advertising methods, allowing the audience to unconsciously perceive the product and brand information, thus receiving the advertising stimulus. Because the audience receives the advertisement in a non-confrontational state, this advertising effect cannot be achieved by ordinary advertising.

[0086] 4) Terminals, including but not limited to: ordinary terminals and dedicated terminals, wherein the ordinary terminals maintain a long connection and / or a short connection with the transmission channel, and the dedicated terminals maintain a long connection with the transmission channel.

[0087] 5) CPM: Cost Per Mille, meaning the price is based on the number of impressions. For example, WeChat Moments video ads in Shanghai are charged at ¥180 per thousand impressions, regardless of whether you click on the ad. This is obviously most beneficial to the media in the short term because it doesn't care whether the impression is effective. However, when advertisers find that the subsequent conversion rate of the ad is consistently poor, they will inevitably reduce their spending on that media.

[0088] 6) Click-through rate (CTR): The probability that a user will click on an ad after it is displayed. Estimating the CTR is usually an important module for ad optimization.

[0089] 7) CVR: Conversion Rate. It is a metric for measuring the effectiveness of CPA (Cost Per Action) advertising. Simply put, it is the conversion rate from a user clicking on an ad to becoming an active, registered, or even paying user.

[0090] For example, if ad A links to the download page for application A, and a user sees and clicks ad A, is redirected to the download page, downloads application A, and then installs application A on their device, then this constitutes a valid activation of application A, and the CVR for this click is 1. However, if the user clicks ad A, is redirected to the download page, but does not download application A and exits the download page, then this is not a valid activation of application A, and the CVR for this click is 0. The same principle applies to registered users and paid users.

[0091] In this invention, embodiments can be implemented using cloud technology. Cloud technology refers to a hosting technology that unifies hardware, software, and network resources within a wide area network (WAN) or local area network (LAN) to achieve data computation, storage, processing, and sharing. It can also be understood as a general term for network technologies, information technologies, integration technologies, management platform technologies, and application technologies based on cloud computing business models. The backend services of network systems require substantial computing and storage resources, such as video websites, image websites, and many portal websites; therefore, cloud technology needs cloud computing as its support.

[0092] It's important to note that cloud computing is a computing model that distributes computing tasks across a resource pool comprised of numerous computers, enabling various application systems to access computing power, storage space, and information services as needed. The network providing these resources is called the "cloud." From the user's perspective, resources in the "cloud" are infinitely scalable, readily available, and can be used on demand, expanded at any time, and paid for based on usage. As the foundational providers of cloud computing capabilities, they establish cloud resource pool platforms, often referred to as cloud platforms or Infrastructure as a Service (IaaS). These platforms deploy various types of virtual resources within the resource pool for external customers to choose from. The cloud resource pool primarily includes: computing devices (which can be virtualized machines containing operating systems), storage devices, and network devices.

[0093] Figure 1 This is a schematic diagram illustrating a use case of the multimedia information revenue estimation method provided in this embodiment of the invention. (See attached diagram.) Figure 1 The terminals (including terminals 10-1 and 10-2) are equipped with corresponding clients capable of playing embedded multimedia information. The terminals connect to server 200 via network 300, which can be a wide area network (WAN), a local area network (LAN), or a combination of both. Data transmission is achieved using a wireless link. The multimedia information includes, but is not limited to, videos, images, GIF animations, and advertising information. The types of multimedia information obtained by the terminals (including terminals 10-1 and 10-2) from the corresponding server 200 via network 300 can be the same or different. For example, the terminals (including terminals 10-1 and 10-2) can obtain video advertisements or image advertisements placed by advertisers from the corresponding server 200 via network 300. The specific types are not limited in this application. Server 200 can store different multimedia information, including advertising multimedia information in different dynamic formats, such as GIF, MP4, and MOV.

[0094] During the process of the terminal (terminal 10-1 and / or terminal 10-2) obtaining and displaying the corresponding service with embedded multimedia information from the server 200 via the network 300, the user can perform different operations on the multimedia information displayed in the multimedia information playback window through the terminal (terminal 10-1 and / or terminal 10-2), resulting in different user behaviors. For example, when the multimedia information is a video advertisement, the user can share and / or like the displayed short video while watching the information, or click on it. When the multimedia information is a dynamic GIF advertisement, during the exposure of the advertisement through the terminal (terminal 10-1 and / or terminal 10-2), the user can forward and / or comment on the advertisement, or jump to the corresponding product purchase link page through the GIF advertisement.

[0095] As an example, when server 200 determines which multimedia information to recommend for playback to user's terminal 10-1 or 10-2, it needs to adjust the multimedia information to be played in a timely manner. For example, it may replace any multimedia information in the set of multimedia information to be played to adapt to the viewing needs of different target users. For instance, it may obtain the target multimedia information in the multimedia information revenue prediction environment; determine the first multimedia information revenue prediction result corresponding to the target multimedia information based on the historical data of the target multimedia information; determine the second multimedia information revenue prediction result corresponding to the target multimedia information based on the historical data of the target multimedia information; determine the weight adjustment coefficient and cost slack coefficient corresponding to the target multimedia information, wherein the cost slack coefficient is used to adjust the overall cost of the target multimedia information; and determine the revenue prediction result of the target multimedia information based on the first multimedia information revenue prediction result, the second multimedia information revenue prediction result, the weight adjustment coefficient, and the cost slack coefficient.

[0096] The multimedia information revenue estimation method provided in this application is based on artificial intelligence (AI). AI is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. In other words, AI is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can react in a way similar to human intelligence. AI studies the design principles and implementation methods of various intelligent machines, enabling them to have perception, reasoning, and decision-making functions.

[0097] Artificial intelligence (AI) is a comprehensive discipline encompassing a wide range of fields, including both hardware and software technologies. Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies primarily include computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0098] In the embodiments of this application, the main artificial intelligence software technologies involved include the aforementioned speech processing technologies and machine learning. For example, it may involve Automatic Speech Recognition (ASR) technology in speech technology, including speech signal preprocessing, speech signal frequency analyzing, speech signal feature extraction, speech signal feature matching / recognition, and speech training.

[0099] For example, this could involve machine learning (ML), a multidisciplinary field encompassing probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers can simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to endow computers with intelligence; its applications span all areas of artificial intelligence. Machine learning typically includes techniques such as deep learning, which includes artificial neural networks, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep neural networks (DNNs).

[0100] It is understood that the multimedia information revenue estimation method and voice processing provided in this application can be applied to intelligent devices. Intelligent devices can be any device with information display function, such as smart terminals, smart home devices (such as smart speakers, smart washing machines, etc.), smart wearable devices (such as smartwatches), in-vehicle intelligent central control systems (which display multimedia information to users through applets that perform different tasks), or AI intelligent medical devices (which display treatment cases by showing multimedia information), etc.

[0101] The structure of the multimedia information revenue estimation device according to an embodiment of the present invention will be described in detail below. The multimedia information revenue estimation device can be implemented in various forms, such as a dedicated terminal with multimedia information revenue estimation processing function, or a server equipped with multimedia information revenue estimation processing function, for example, the preceding... Figure 1 Server 200. Figure 2 This is a schematic diagram of the composition structure of the multimedia information revenue estimation device provided in an embodiment of the present invention. It can be understood that... Figure 2 This is merely an exemplary structure of the multimedia information revenue estimation device, not the entire structure; it can be implemented as needed. Figure 2 The structure shown may be part or all of the structure.

[0102] The multimedia information revenue estimation device provided in this embodiment of the invention includes: at least one processor 201, a memory 202, a user interface 203, and at least one network interface 204. The various components in the multimedia information revenue estimation device are coupled together via a bus system 205. It can be understood that the bus system 205 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 205 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 2 The general labeled all buses as Bus System 205.

[0103] The user interface 203 may include a monitor, keyboard, mouse, trackball, click wheel, buttons, touchpad, or touch screen.

[0104] It is understood that memory 202 can be volatile memory or non-volatile memory, or both. In this embodiment of the invention, memory 202 is capable of storing data to support the operation of a terminal (such as 10-1). Examples of this data include any computer programs used to operate on the terminal (such as 10-1), such as operating systems and applications. The operating system includes various system programs, such as the framework layer, core library layer, driver layer, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications.

[0105] In some embodiments, the multimedia information revenue estimation device provided in this invention can be implemented using a combination of hardware and software. For example, the multimedia information revenue estimation device provided in this invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the training method of the multimedia information revenue estimation method provided in this invention. For instance, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0106] As an example of the multimedia information revenue estimation device provided in this embodiment of the invention, which is implemented using a combination of hardware and software, the multimedia information revenue estimation device provided in this embodiment of the invention can be directly embodied as a combination of software modules executed by processor 201. The software modules can be located in a storage medium, which is located in memory 202. Processor 201 reads the executable instructions included in the software modules in memory 202 and, in combination with necessary hardware (e.g., including processor 201 and other components connected to bus 205), completes the training method of the multimedia information revenue estimation method provided in this embodiment of the invention.

[0107] As an example, processor 201 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., wherein the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0108] As an example of the hardware implementation of the multimedia information revenue estimation device provided in this embodiment of the invention, the device provided in this embodiment of the invention can be directly executed by a processor 201 in the form of a hardware decoding processor. For example, it can be executed by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components to implement the training method of the multimedia information revenue estimation method provided in this embodiment of the invention.

[0109] In this embodiment of the invention, the memory 202 is used to store various types of data to support the operation of the multimedia information revenue prediction device. Examples of such data include: any executable instructions for operation on the multimedia information revenue prediction device, such as executable instructions that can be included in the executable instructions, and a program implementing the training method of the multimedia information revenue prediction method of this embodiment of the invention.

[0110] In other embodiments, the multimedia information revenue estimation device provided in this invention can be implemented in software. Figure 2 A multimedia information revenue estimation device stored in memory 202 is shown. This device can be software in the form of programs and plugins, and includes a series of modules. As an example of a program stored in memory 202, it may include the multimedia information revenue estimation device, which includes the following software modules:

[0111] Information transmission module 2081 and information processing module 2082. When the software modules in the multimedia information revenue prediction device are read into RAM and executed by processor 201, the training method of the multimedia information revenue prediction method provided in this embodiment of the invention will be implemented. The functions of each software module in the multimedia information revenue prediction device include:

[0112] Information transmission module 2081 is used to acquire target multimedia information in the multimedia information revenue prediction environment;

[0113] Information processing module 2082 is used to determine the first multimedia information revenue prediction result corresponding to the target multimedia information based on the historical data of the target multimedia information;

[0114] The information processing module 2082 is used to determine the estimated revenue result of the second multimedia information corresponding to the target multimedia information based on the historical data of the target multimedia information.

[0115] The information processing module 2082 is used to determine the weight adjustment coefficient and cost tightness coefficient corresponding to the target multimedia information, wherein the cost tightness coefficient is used to adjust the overall cost of the target multimedia information;

[0116] The information processing module 2082 is used to determine the revenue forecast result of the target multimedia information based on the revenue forecast result of the first multimedia information, the revenue forecast result of the second multimedia information, the weight adjustment coefficient, and the cost tightness coefficient.

[0117] according to Figure 2 The electronic device shown, in one aspect of this application, also provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. The processor of the electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform various embodiments and combinations of embodiments provided in the various optional implementations of the multimedia information revenue estimation method described above.

[0118] Combination Figure 2 The multimedia information revenue estimation device shown illustrates the multimedia information revenue estimation method provided in this embodiment of the invention. See also: Figure 3 , Figure 3 This is an optional flowchart illustrating the multimedia information revenue estimation method provided in this embodiment of the invention. It can be understood that... Figure 3 The steps shown can be performed by various electronic devices that run the multimedia information revenue prediction device, such as a dedicated terminal, server, or server cluster equipped with the multimedia information revenue prediction device. The dedicated terminal equipped with the multimedia information revenue prediction device can be a preceding... Figure 2 The illustrated embodiment is an electronic device with a multimedia information revenue estimation device. The following section addresses... Figure 3 The steps shown are explained.

[0119] Step 301: The multimedia information revenue estimation device receives the multimedia information revenue estimation request sent by the terminal.

[0120] Step 302: The multimedia information revenue estimation device responds to the multimedia information revenue estimation request and acquires the target multimedia information in the multimedia information revenue estimation environment.

[0121] In some embodiments of the present invention, various user behaviors matched with the corresponding client can be collected through different program components. This involves effectively extracting raw logs of user behavior data, such as the user's device ID (user account), multimedia information type, multimedia information browsing duration, and multimedia information browsing completeness parameters. The user's historical click behavior and corresponding information browsing duration are recorded through a subscription service and stored in Redis. When a user request arrives, the online recommendation system retrieves the corresponding user's historical click behavior to determine the historical data of the target object.

[0122] Step 303: The multimedia information revenue estimation device determines the first multimedia information revenue estimation result corresponding to the target multimedia information based on the historical data of the target multimedia information.

[0123] In some embodiments of the present invention, determining the estimated revenue result of the first multimedia information corresponding to the target multimedia information can be achieved in the following ways:

[0124] Based on historical data of the target multimedia information, a first click probability parameter and a first conversion probability parameter are obtained; a target conversion cost parameter matching the target multimedia information is determined; and a first multimedia information revenue prediction result corresponding to the target multimedia information is determined based on the product of the first click probability parameter, the first conversion probability parameter, and the target conversion cost parameter. Specifically, accumulative performance indicators (such as impressions, clicks, etc.) are directly accumulated, while non-accumulative performance indicators (such as click-through rate (CTR), conversion rate (CVR), etc.) are accumulated according to their calculation factors based on the indicator calculation method (e.g., conversion rate can be accumulated by adding clicks and conversions separately). In the dynamic adjustment strategy framework, the checkpoint component in the Spark Streaming framework records the cumulative performance value of the previous period, avoiding duplicate calculations through incremental accumulation. Referring to Formula 1, the first multimedia information revenue prediction result can be expressed as:

[0125] Formula 1

[0126] oCPX is a bidding mechanism that allows advertisers to bid based on conversion cost. For example, if a game advertiser wants to optimize app activation, after setting the activation cost in the ad delivery system, the algorithm will automatically filter valuable audiences based on past conversion data and the advertiser's bids. It will increase bids for people with high activation probability to win ad exposure, and decrease bids for people with low activation probability to reduce ad exposure and reduce ad waste.

[0127] When the first multimedia message is an oCPX-type ad that the advertiser wants to place, the advertiser will set a target bid (TargetCPA). When the target bid is reached, the target will be activated and the ad message will begin to be displayed. The advertising operator's advertising system mainly optimizes the cost achievement of this ad, that is, the cost fluctuates within a small range above and below the target price set by the advertiser. Cost = Ad Cost (ad_cost) / Number of Activations. Therefore, in the advertising bidding system, the value estimation of this type of ad is based on its activation probability and the revenue per activation. When advertisers place ads, there can be various types of bidding mechanisms. The multimedia message revenue estimation method provided in this application can estimate the revenue of oCPX-type ads, so as to enable advertisers to obtain a better advertising experience.

[0128] When calculating the revenue forecast for the first multimedia information, historical conversion rates and click-through rates can be obtained from the historical data of ad playback stored in the ad server. For example, in the historical data, the revenue forecast for game ads exposed during the loading process of WeChat game mini programs can be predicted. Based on the prediction results, the game ads to be played during the loading process of WeChat game mini programs can be adjusted so that game users can see richer game ads and have a better viewing experience while loading WeChat game mini programs, and also enable advertisers to have a better ad placement experience.

[0129] Step 304: The multimedia information revenue estimation device determines the second multimedia information revenue estimation result corresponding to the target multimedia information based on the historical data of the target multimedia information.

[0130] In some embodiments of the present invention, determining the estimated revenue result of the second multimedia information corresponding to the target multimedia information can be achieved in the following ways:

[0131] Based on historical data of the target multimedia information, a second click probability parameter and a second conversion probability parameter are obtained; a paid estimate parameter matching the target multimedia information is calculated; a target return on investment estimate parameter matching the target multimedia information is obtained; and based on the second click probability parameter, the second conversion probability parameter, the paid estimate parameter, and the target return on investment estimate parameter, a second multimedia information revenue estimate result corresponding to the target multimedia information is determined. (Referring to...) Figure 4A , Figure 4AThis is a schematic diagram of the audience tagging process in an embodiment of the present invention. Specifically, when calculating the estimated payment value parameters matching the target multimedia information, audience tags matching the target multimedia information can be obtained first. Based on the audience tags, the payment rate and user revenue parameters matching the target multimedia information are determined. Based on the payment rate and user revenue parameters matching the target multimedia information, the estimated payment value parameters matching the target multimedia information are calculated. Specifically, when multimedia information is played in a live video streaming scenario (e.g., advertising is placed during a live video stream), the terminal can upload monitored advertising playback content and playback effects to the application programming interface in real time through the client. This monitored content can include user-related information and monitoring content. Determining the payment rate and user revenue parameters matching the target multimedia information allows for better monitoring of the return on investment of the target multimedia information. The estimated revenue result for the second multimedia information can be referenced in Formula 2.

[0132] Formula 2

[0133] Among them, reference Figure 4B , Figure 4B This is a schematic diagram of an optional multimedia information playback window in an embodiment of the present invention. When multimedia information is played in a live video scenario (e.g., advertising is placed in a live video), the content for different users is different and their points of interest are also different. The advertising information that users with different user tags pay attention to may include: (1) technology advertising, (2) sports advertising, (3) automobile advertising, (4) art advertising, and (5) public service advertising. Each type of advertising includes text information and images, and users can obtain a richer advertising browsing experience. For the same advertisement placed by the advertiser, users will only click on the advertisements they are interested in to browse or pay to purchase the products in the advertisement information.

[0134] Therefore, when calculating the revenue forecast for the second multimedia information, it is first necessary to calculate the estimated payment parameters that match the target multimedia information. Taking the placement of advertisements during short video playback as an example, the terminal can upload the monitored advertisement playback content and playback effect to the application programming interface in real time through the client, such as... Figure 4BAs shown, advertisements placed by different advertisers can be displayed in advertisement display positions 401 and 402, and the video operator can monitor the display of the advertisement information. The monitoring results can include user-related information and the monitoring content of the advertisement information. For example, the user-related information can be user identifier, terminal IP address, terminal operator, and network type, etc. The monitoring content can include video quality data when the live content is generated and video quality data when the live content is viewed. For example, the monitoring content can also be the "attempt" and "revenue" of advertisement playback in the previous embodiment. During the process of resolving the real server address of the live video, the multiple terminals can send content acquisition requests to the node devices of the target service provider in the content delivery network through the API to obtain the specific URL (Uniform Resource Locator). The Locator (Uniform Resource Locator) is used to locate web pages. Therefore, during the monitoring of the playback process of advertising information, it is possible to monitor user purchase or payment behavior. Specifically, in some embodiments of the present invention, when the multimedia information played in the multimedia information playback window is a video advertisement, a trigger operation can be received for the multimedia information playback window; in response to the trigger operation, the business processing interface is exposed and redirected to the product display interface indicated by the multimedia information. Thus, users can more conveniently purchase the products exposed in the multimedia information playback window. The determined audience tags include at least one of the following: paying audience tags, interest-based audience tags; based on the audience tags, the payment rate and user revenue parameters matching the target multimedia information are determined. It should be noted that, due to the various playback environments of advertising information... Therefore, when monitoring the playback effect of advertising information, sufficient front-end conversion rate data (including pCTR and pCVR) can be used without altering the calculated paid subscription estimate parameters that match the target multimedia information. However, due to insufficient data returned by advertisers (some data is withheld for confidentiality reasons) and the influence of data distribution differences and cross-factors, direct pLTV estimation can have significant deviations when segmenting audiences or ads. Therefore, audience tag information can be directly applied to the estimation of paid subscription rate and paid ARPU through direct connection, and the tag features can be strongly correlated with the output results through memory methods. This allows for accurate estimation of paid subscription estimate parameters that match the target multimedia information, enabling more precise ad targeting and allowing advertisers to achieve better ad delivery results without increasing costs.

[0135] Step 305: The multimedia information revenue estimation device determines the weight adjustment coefficient and cost slack coefficient corresponding to the target multimedia information, wherein the cost slack coefficient is used to adjust the overall cost of the target multimedia information.

[0136] Step 306: The multimedia information revenue estimation device determines the revenue estimation result of the target multimedia information based on the first multimedia information revenue estimation result, the second multimedia information revenue estimation result, the weight adjustment coefficient, and the cost tightness coefficient.

[0137] The revenue forecast for the target multimedia information is based on Formula 3:

[0138] Formula 3

[0139] in, The weighting coefficient is used to adjust the weights between ecpm1 and ecpm2, and m is the tightness coefficient for adjusting the overall advertising cost. When m=1, When =1, it degenerates into the original oCPX advertising value estimation method.

[0140] The following explanation continues with Formula 3, please refer to... Figure 5 , Figure 5 This is an optional flowchart illustrating the multimedia information revenue estimation method provided in this embodiment of the invention. It can be understood that... Figure 5 The steps shown can be performed by various electronic devices operating a multimedia information revenue estimation device, and specifically include the following steps:

[0141] Step 501: Based on the historical data of the target multimedia information, determine the first value range and step size parameter of the weight adjustment coefficient.

[0142] Step 502: Based on the step size parameter of the weight adjustment coefficient, perform traversal processing within the first value range of the weight adjustment coefficient to obtain the second value range of the weight adjustment coefficient.

[0143] Step 503: Obtain the initial weight adjustment coefficient within the second value range of the weight adjustment coefficient.

[0144] Wherein, referring to formula 3, the coefficient ( When estimating the initial value of m, we can use historical data of the target multimedia information. For advertisements with existing exposure and return data, we collect all exposure and return data as a sample set A, and then perform data binning with two different step sizes. The first set α has an initial value of [0, 1] and a step size of 0.05. The second set m has a value range of [0.7, 13] and a step size of 0.02. We bin the values ​​of the two coefficients according to the above step sizes to obtain sets I and J, and double-traverse the values ​​of each bucket for the two parameters. The data binning process provided in this embodiment, also known as data binning, is a data preprocessing method in the field of machine learning, used to observe the statistical distribution characteristics of data, discretize data, and remove outlier data. Data binning can group continuous feature values ​​into a smaller number of "buckets", thereby achieving the effect of discretizing continuous values. In the field of machine learning, data binning is used to preprocess some continuous features, making them discretized. Multimedia information revenue prediction methods based on discretized features are more stable. Furthermore, discretized features can be cross-referenced to improve their expressive power and avoid overfitting defects in the processing results of multimedia information revenue prediction methods.

[0145] Step 504: Based on the historical data of the target multimedia information, determine the first value range and step size parameter of the cost tightness coefficient.

[0146] Step 505: Based on the step size parameter of the cost tightness coefficient, perform traversal processing within the first value range of the cost tightness coefficient to obtain the second value range of the cost tightness coefficient.

[0147] Step 506: Obtain the initial cost tightness coefficient within the second range of the cost tightness coefficient.

[0148] Based on the descriptions in steps 503 to 506, the data in set A is selected and discarded. For a single exposure of the target multimedia information, a ∈ set A, assuming the charge for this exposure is first_ecpm (the fee paid by the advertiser to the video operator for each ad exposure), an estimate is made using formula 3. The value of the advertisement under the given parameters is ecpm_new. If ecpm_new > first_ecpm, the exposure of the advertisement information is retained; otherwise, the exposure of the advertisement information is discarded. Thus, by comparing ecpm_new and first_ecpm, invalid data in the historical data of the target multimedia information that cannot accurately reflect the advertising value can be eliminated, enabling advertisers to obtain more accurate revenue prediction results for multimedia information.

[0149] In some embodiments of the present invention, the initial weight adjustment coefficient can be adjusted according to the target conversion cost parameter and the target return on investment (ROI) estimate parameter to obtain the weight adjustment coefficient corresponding to the target multimedia information; the initial cost slack coefficient can be adjusted according to the target conversion cost parameter and the target ROI estimate parameter to obtain the cost slack coefficient corresponding to the target multimedia information. Wherein, obtaining ,Right now The exposure set A under the i-th bucket and the j-th bucket of m ij The total cost of advertising information within this set is as follows: and the LTV (Lifetime Value) of the collection ij The number of conversions under this set (action) ij Therefore, the cost of each conversion in the average cost can be calculated as follows: The ratio of returns to inputs is: .

[0150] Finally, in some embodiments of the present invention, to avoid the impact of the deviation between the data and the target CPA on the revenue prediction results of multimedia information, it is also necessary to verify the data of each group bucket when determining the initial values ​​of the coefficients of α and m. Specifically, for all i∈I and j∈J, when the deviation of CPA(i,j) is greater than ±20% of the target CPA, the group bucket is discarded; when the ROI(i,j) is less than the target ROI - 20%, the group bucket is abandoned. The coefficients of α and m under the bucket with the largest cost(i,j)*LTV(i,j) are selected as the initial values.

[0151] Continuing with Formula 3, when adjusting the parameters in Formula 3, the exposure of the target multimedia information can be monitored based on the revenue forecast results of the target multimedia information to determine the actual target conversion cost parameter and the actual target return on investment parameter. Based on the actual target conversion cost parameter and the actual target return on investment parameter, the weighting adjustment coefficient and cost slack coefficient can be dynamically adjusted to make the multimedia information revenue forecasting method provided in this application more suitable for advertisers' needs. (Refer to...) Figure 6 , Figure 6 This is an optional flowchart illustrating the multimedia information revenue estimation method provided in this embodiment of the invention. It can be understood that... Figure 6 The steps shown can be performed by various electronic devices operating a multimedia information revenue estimation device, and specifically include the following steps:

[0152] Step 601: When the actual target conversion cost parameter is less than the target conversion cost parameter, and the actual target return on investment parameter is less than the estimated target return on investment parameter, reduce the weight adjustment coefficient.

[0153] Step 602: When the actual target conversion cost parameter is greater than or equal to the target conversion cost parameter, and the actual target return on investment parameter is greater than or equal to the estimated target return on investment parameter, increase the weight adjustment coefficient.

[0154] Step 603: When the actual target conversion cost parameter is less than the target conversion cost parameter, and the actual target return on investment parameter is greater than or equal to the estimated target return on investment parameter, increase the cost slack coefficient.

[0155] Step 604: When the actual target conversion cost parameter is greater than or equal to the target conversion cost parameter, and the actual target return on investment parameter is less than the estimated target return on investment parameter, reduce the cost tightness coefficient.

[0156] In some embodiments of the present invention, the historical browsing information of the target user can also be obtained; based on the historical browsing information of the target user, the exposure history of multimedia information corresponding to the historical browsing information is determined; based on the exposure history of multimedia information corresponding to the historical browsing information and the multimedia information revenue prediction result, the playback strategy of the multimedia information is dynamically adjusted. See also Figure 7 , Figure 7This is a schematic diagram of an optional multimedia information recommendation in an embodiment of the present invention. When different advertisements from the same advertiser are displayed in different ad display positions, the time-sensitive short video ad information included in different resource groups can be played sequentially in the time-sensitive short video playback window. When all time-sensitive short video playback areas in the display interface are occupied by the advertiser, after the ad information playback ends, the same advertiser's ad information can be displayed in a loop in the time-sensitive short video playback window of the ad information display interface. Simultaneously, when the advertiser's time-sensitive short video is a video ad, the same advertiser's video ad information can be displayed in a loop, and the audio volume carried by the video can be adjusted to the maximum to prompt the user to watch the played video ad. Replacing ad A with ad B allocates more playback bandwidth to ad B, resulting in a better viewing experience for the user. Specifically, based on the traffic parameters and iterative experimental parameters matched to the ad playback strategy, the ad playback strategy can be dynamically adjusted to increase ad exposure. In some embodiments of this invention, the exposure channel of ad A can be changed from the current short video playback client to the contact status information of the instant messaging client. Of course, when adjusting the exposure position of ad A, it can be changed from a Moments ad in the instant messaging client to a splash screen ad, to conform to different dynamically adjusted playback strategies. This allows time-sensitive short videos to be recommended to different users in a short period of time, achieving better video recommendation results. Figure 7 For example, if it is determined that male target user 1 has shared an ad with feature B in their browsing history, the playback strategy can be dynamically adjusted to replace the current ad with other ad information containing feature B (such as an ad or short video containing feature B). Similarly, if it is determined that female target user 2 has clicked on an ad with feature X in their browsing history, the playback strategy can be dynamically adjusted to replace ad A with other ad information (such as an ad or short video ad link containing feature X) to match the user's usage habits and provide a better user experience.

[0157] In some embodiments of the present invention, when the target multimedia information is embedded advertising information in a short video, the exposure parameters of the short video are monitored; the exposure parameters during short video playback are sent to a monitoring server, so that the monitoring server can monitor the exposure of the short video and the conversion of the embedded advertising information; the exposure parameters during short video playback and the click-through rate and conversion rate of the embedded advertising information stored by the monitoring server are used as the data source for the playback effect parameters of the multimedia information. This enables comprehensive monitoring of advertising information.

[0158] To better illustrate the multimedia information revenue estimation method provided in this application, the following example of inserting advertising information during video playback will be used to illustrate the reference provided in this application. Figure 8 , Figure 8 This is an optional flowchart illustrating the multimedia information revenue estimation method provided in this embodiment of the invention. It can be understood that... Figure 8 The steps shown can be performed by various electronic devices that operate the multimedia information revenue forecasting device, combined with Figure 1 As shown, when the multimedia information is an advertisement, server 200 sends the corresponding advertising information to terminals (terminal 10-1 and / or terminal 10-2) via network 300 to enable users of terminals (terminal 10-1 and / or terminal 10-2) to browse this multimedia information. In this process, feed advertising, due to its advantages such as being embedded in the information stream and allowing for unlimited scrolling with content, has become the mainstream form of mobile advertising. However, in the process of advertising placement, the advertiser is the initiator of the advertising campaign, a merchant selling or promoting their products and services online. Any merchant promoting or selling their products or services can act as an advertiser. The advertiser publishes the advertising campaign and pays the website owner according to the total number of marketing effects and the unit price per effect stipulated in the advertising campaign completed by the advertising platform. Advertisers can set optimization goals in advertising placement, such as mobile application downloads, activations, or payments, as well as the conversion costs they are willing to pay for the optimization goals. The advertising platform automatically bids when exposure opportunities exist, making the actual conversion cost of the advertisement close to the advertiser's expected conversion cost.

[0159] When an exposure opportunity exists, the advertising platform automatically bids for multiple ads and determines their order based on their bids, thus deciding whether an ad receives the exposure. This process also affects whether the actual conversion cost after the ad receives exposure matches the advertiser's expected conversion cost.

[0160] The multimedia information revenue estimation method provided in this application specifically includes the following steps:

[0161] Step 801: Determine the target ads that the advertiser is placing, as well as the historical playback data of the target ads.

[0162] Among them, it is possible to Figure 7 In the playback environment shown, the playback data of the target advertisement from 00:00 on January 1st to 00:00 on January 10th is obtained as the historical playback data of the target advertisement. Real-time playback data for any time period can also be obtained according to the advertiser's purchase instructions, so as to facilitate the advertiser's monitoring of the revenue prediction results of multimedia information.

[0163] Step 802: Based on the product of the first click probability parameter, the first conversion probability parameter, and the target conversion cost parameter in the historical playback data, determine the first advertising revenue prediction result ecmpm1 corresponding to the target multimedia information.

[0164] Step 803: Determine the paid audience tags and interest-based audience tags of the target ad audience, and determine the payment rate and user revenue parameters based on the paid audience tags and interest-based audience tags.

[0165] For any target ad audience, the tag information can be derived from the target ad audience's feature data. The feature data of the target ad audience refers to information related to the delivery effect of multimedia data in the relevant information of the target ad audience. For example, multimedia data is a certain type of multimedia data, and the feature data of the audience may include, but is not limited to, the audience's age, gender, and some information related to this type of multimedia data. For example, for game ads, this information may include the target ad audience's gaming age, the types of games the target ad audience is interested in, and the target ad audience's consumption information during the game (such as payment information for purchasing game items).

[0166] Step 804: Based on the second click probability parameter, second conversion probability parameter, paid estimate parameter, and target return on investment estimate parameter in the historical playback data, determine the second revenue estimate result ecmpm2 corresponding to the advertising information.

[0167] Step 805: Determine the revenue forecast result of the advertising information based on the first revenue forecast result, the second revenue forecast result, the weight adjustment coefficient, and the cost tightness coefficient.

[0168] When calculating the revenue forecast for advertising information using Formula 3 above, the revenue forecast for the target advertisement is as follows: At the same time, the cost and ROI of the advertisement can meet expectations, and advertisers can also accurately obtain the expected return on advertising information, so as to increase the advertising expenditure in a timely manner and obtain higher expected return on advertising information.

[0169] Step 806: Inform the advertiser of the estimated revenue from the advertising information.

[0170] Step 807: Receive adjustment notifications from advertisers and dynamically adjust the playback strategy for advertising information.

[0171] like Figure 7As shown, during dynamic adjustment, advertisements from different resource groups can be played sequentially in the multimedia information playback window. When all multimedia information playback areas in the display interface are occupied by the advertiser, the same advertiser's advertisements can be displayed in a loop in the multimedia information playback window of the advertisement display interface after the advertisement playback ends. Simultaneously, when the advertiser's multimedia information is a video advertisement, the same advertiser's video advertisement can be displayed in a loop, with the audio volume of the video adjusted to maximum to prompt the user to watch the playing video advertisement. Therefore, by dynamically adjusting the advertisement playback strategy based on traffic parameters matched with the multimedia information playback strategy and iterative experimental parameters, the advertiser's total cost per mille (ecpm) can be reduced, resulting in better advertisement playback performance.

[0172] Beneficial technical effects:

[0173] This invention acquires target multimedia information within a multimedia information revenue prediction environment; based on historical data of the target multimedia information, determines a first multimedia information revenue prediction result corresponding to the target multimedia information; based on the historical data of the target multimedia information, determines a second multimedia information revenue prediction result corresponding to the target multimedia information; determines a weight adjustment coefficient and a cost slack coefficient corresponding to the target multimedia information, wherein the cost slack coefficient is used to adjust the overall cost of the target multimedia information; and based on the first multimedia information revenue prediction result, the second multimedia information revenue prediction result, the weight adjustment coefficient, and the cost slack coefficient, determines the revenue prediction result of the target multimedia information. This enhances the accuracy and relevance of multimedia information revenue prediction, ensures optimal ROI for multimedia information, effectively improves the multimedia information delivery process, and enhances the user experience.

[0174] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for estimating the revenue of multimedia information, characterized by, The method includes: Acquire target multimedia information in the environment to predict the benefits of multimedia information acquisition; Based on the historical data of the target multimedia information, the estimated revenue result of the first multimedia information corresponding to the target multimedia information is determined; Based on the historical data of the target multimedia information, the estimated revenue of the second multimedia information corresponding to the target multimedia information is determined; Based on the historical data of the target multimedia information, determine the first value range and step size parameter of the weight adjustment coefficient corresponding to the target multimedia information; Based on the step size parameter of the weight adjustment coefficient, a traversal process is performed within the first value range of the weight adjustment coefficient to obtain the second value range of the weight adjustment coefficient; wherein, the traversal process includes collecting all exposure and reflow data of the first multimedia information with existing exposure and reflow data as a sample set, and performing data bucketing processing on the sample set with two different step sizes to discretize the continuous feature values. Based on historical data of the target multimedia information, a first value range and step size parameter of the cost tightness coefficient corresponding to the target multimedia information are determined, wherein the cost tightness coefficient is used to adjust the overall cost of the target multimedia information; Based on the step size parameter of the cost tightness coefficient, the second value range of the cost tightness coefficient is obtained by traversing the first value range of the cost tightness coefficient. The initial weight adjustment coefficient in the second value range of the weight adjustment coefficient and the initial cost slack coefficient in the second value range of the cost slack coefficient are adjusted to obtain the weight adjustment coefficient and cost slack coefficient corresponding to the target multimedia information. Based on the first multimedia information revenue forecast result, the second multimedia information revenue forecast result, the weight adjustment coefficient, and the cost tightness coefficient, the revenue forecast result of the target multimedia information is determined.

2. The method of claim 1, wherein, The step of determining the estimated revenue result of the first multimedia information corresponding to the target multimedia information based on historical data of the target multimedia information includes: Based on the historical data of the target multimedia information, the first click probability parameter and the first conversion probability parameter of the target multimedia information are obtained. Determine the target conversion cost parameters that match the target multimedia information; Based on the product of the first click probability parameter, the first conversion probability parameter, and the target conversion cost parameter, the estimated revenue result of the first multimedia information corresponding to the target multimedia information is determined.

3. The method of claim 1, wherein, The step of determining the estimated revenue result of the second multimedia information corresponding to the target multimedia information based on historical data of the target multimedia information includes: Based on the historical data of the target multimedia information, a second click probability parameter and a second conversion probability parameter of the target multimedia information are obtained. Calculate the estimated cost parameters that match the target multimedia information; Obtain the target return on investment (ROI) estimate parameters that match the target multimedia information; Based on the second click probability parameter, the second conversion probability parameter, the paid estimate parameter, and the target return on investment estimate parameter, the estimated revenue result of the second multimedia information corresponding to the target multimedia information is determined.

4. The method of claim 3, wherein, The calculation of the estimated payment parameters that match the target multimedia information includes: Obtain crowd tags that match the target multimedia information, wherein the crowd tags include at least one of the following: Paid user tags and interest-based user tags; Based on the aforementioned audience tags, determine the payment rate and user revenue parameters that match the target multimedia information; Based on the payment rate and user revenue parameters that match the target multimedia information, calculate the estimated payment parameters that match the target multimedia information.

5. The method according to claim 1, characterized in that, The method further includes: Based on the target conversion cost parameter and the target return on investment estimate parameter, the initial weight adjustment coefficient is adjusted to obtain the weight adjustment coefficient corresponding to the target multimedia information; Based on the target conversion cost parameters and the target return on investment estimate parameters, the initial cost tightness coefficient is adjusted to obtain the cost tightness coefficient corresponding to the target multimedia information.

6. The method according to claim 1, characterized in that, The method further includes: Based on the revenue forecast of the target multimedia information, the exposure of the target multimedia information is monitored to determine the actual target conversion cost parameter and the actual target return on investment parameter. The weighting adjustment coefficient and cost slack coefficient are dynamically adjusted based on the actual target conversion cost parameter and the actual target return on investment parameter.

7. The method according to claim 6, characterized in that, The step of dynamically adjusting the weighting adjustment coefficient and cost slack coefficient based on the actual target conversion cost parameter and the actual target return on investment parameter includes: When the actual target conversion cost parameter is less than the target conversion cost parameter, and the actual target return on investment parameter is less than the estimated target return on investment parameter, the weighting adjustment coefficient is reduced; or, When the actual target conversion cost parameter is greater than or equal to the target conversion cost parameter, and the actual target return on investment parameter is greater than or equal to the estimated target return on investment parameter, the weighting adjustment coefficient is increased; or When the actual target conversion cost parameter is less than the target conversion cost parameter, and the actual target return on investment parameter is greater than or equal to the estimated target return on investment parameter, the cost slack coefficient is increased; or, When the actual target conversion cost parameter is greater than or equal to the target conversion cost parameter, and the actual target return on investment parameter is less than the estimated target return on investment parameter, the cost tightness coefficient is reduced.

8. The method according to claim 1, characterized in that, The method further includes: Obtain the target user's browsing history; Based on the target user's historical browsing information, determine the multimedia information exposure history corresponding to the historical browsing information; Based on the exposure history of the multimedia information and the estimated revenue of the multimedia information corresponding to the historical browsing information, the playback strategy of the multimedia information is dynamically adjusted.

9. The method according to claim 8, characterized in that, The dynamic adjustment of the multimedia information playback strategy based on the multimedia information exposure history and multimedia information revenue prediction results corresponding to the historical browsing information includes: The exposure rate corresponding to the multimedia information is dynamically adjusted; or Adjust the exposure channels corresponding to the multimedia information; or The exposure position corresponding to the multimedia information is adjusted.

10. The method according to claim 1, characterized in that, The method further includes: Determine the type of environment for multimedia information revenue forecasting; Based on the type of the multimedia information revenue prediction environment, determine the category of multimedia information to be played; In response to the category of the multimedia information to be played, a matching multimedia information data source is triggered to adjust the multimedia information to be played by using a multimedia information data source that matches the category of the multimedia information to be played.

11. The method according to claim 1, characterized in that, The method further includes: When the target multimedia information is embedded advertising information in a short video, monitor the exposure parameters of the short video; The exposure parameters of the short video during playback are sent to the monitoring server so that the monitoring server can monitor the exposure of the short video and the conversion of embedded advertising information. The exposure parameters of short video playback and the click-through rate and conversion rate of embedded advertising information stored on the monitoring server are used as the data source for the playback effect parameters of multimedia information.

12. A multimedia information revenue estimation device, characterized in that, The device includes: The information transmission module is used to acquire target multimedia information in the multimedia information revenue prediction environment; The information processing module is used to determine the estimated revenue result of the first multimedia information corresponding to the target multimedia information based on the historical data of the target multimedia information. The information processing module is used to determine the estimated revenue result of the second multimedia information corresponding to the target multimedia information based on the historical data of the target multimedia information. The information processing module is used to determine, based on historical data of the target multimedia information, a first value range and step size parameter of the weight adjustment coefficient corresponding to the target multimedia information; according to the step size parameter of the weight adjustment coefficient, iterates through the first value range of the weight adjustment coefficient to obtain a second value range of the weight adjustment coefficient; wherein, the traversal process includes, for the first multimedia information with existing exposure and reflow data, collecting all exposure and reflow data of the first multimedia information as a sample set, and performing data bucketing processing on the sample set with two sets of different step sizes to discretize the continuous feature values; based on the... Based on historical data of the target multimedia information, a first value range and step size parameter of the cost slackness coefficient corresponding to the target multimedia information are determined, wherein the cost slackness coefficient is used to adjust the overall cost of the target multimedia information; according to the step size parameter of the cost slackness coefficient, the first value range of the cost slackness coefficient is traversed to obtain a second value range of the cost slackness coefficient; the initial weight adjustment coefficient in the second value range of the weight adjustment coefficient and the initial cost slackness coefficient in the second value range of the cost slackness coefficient are adjusted to obtain the weight adjustment coefficient and cost slackness coefficient corresponding to the target multimedia information; The information processing module is used to determine the revenue forecast result of the target multimedia information based on the revenue forecast result of the first multimedia information, the revenue forecast result of the second multimedia information, the weight adjustment coefficient, and the cost tightness coefficient.

13. The apparatus as claimed in claim 12, characterized in that, The information processing module is also used for: Based on historical data of the target multimedia information, obtain the first click probability parameter and the first conversion probability parameter of the target multimedia information; determine the target conversion cost parameter that matches the target multimedia information. Based on the product of the first click probability parameter, the first conversion probability parameter, and the target conversion cost parameter, the estimated revenue result of the first multimedia information corresponding to the target multimedia information is determined.

14. The apparatus as claimed in claim 12, characterized in that, The information processing module is also used for: Based on historical data of the target multimedia information, a second click probability parameter and a second conversion probability parameter of the target multimedia information are obtained; a paid estimate parameter matching the target multimedia information is calculated; a target return on investment estimate parameter matching the target multimedia information is obtained; and based on the second click probability parameter, the second conversion probability parameter, the paid estimate parameter, and the target return on investment estimate parameter, a second multimedia information revenue estimate result corresponding to the target multimedia information is determined.

15. The apparatus as claimed in claim 14, characterized in that, The information processing module is also used for: Obtain audience tags that match the target multimedia information, wherein the audience tags include at least one of the following: paid audience tags and interest-based audience tags; based on the audience tags, determine the payment rate and user revenue parameters that match the target multimedia information; and calculate the estimated payment value parameters that match the target multimedia information based on the payment rate and user revenue parameters that match the target multimedia information.

16. The apparatus as claimed in claim 12, characterized in that, The information processing module is also used for: Based on the target conversion cost parameter and the target return on investment estimate parameter, the initial weight adjustment coefficient is adjusted to obtain the weight adjustment coefficient corresponding to the target multimedia information; based on the target conversion cost parameter and the target return on investment estimate parameter, the initial cost slack coefficient is adjusted to obtain the cost slack coefficient corresponding to the target multimedia information.

17. The apparatus as claimed in claim 12, characterized in that, The information processing module is also used for: Based on the revenue forecast of the target multimedia information, the exposure of the target multimedia information is monitored to determine the actual target conversion cost parameter and the actual target return on investment parameter. The weighting adjustment coefficient and cost slack coefficient are dynamically adjusted based on the actual target conversion cost parameter and the actual target return on investment parameter.

18. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; A processor, when executing executable instructions stored in the memory, implements the multimedia information revenue estimation method according to any one of claims 1 to 11.

19. A computer-readable storage medium storing executable instructions, characterized in that, When the executable instructions are executed by the processor, they implement the multimedia information revenue estimation method according to any one of claims 1 to 11.

20. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the multimedia information revenue estimation method according to any one of claims 1 to 11.

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