Data processing method, contribution value acquisition method and related device

Abstract

A data processing method, a contribution value acquisition method and a related device. The artificial intelligence technology is used for attribution of display behaviors in a recommendation system, and the method comprises: processing a training sample by means of a first machine learning model to obtain first information, the training sample indicating at least one display behavior, and the first information indicating the probability that the at least one display behavior is interacted; processing the training sample by means of a second machine learning model to obtain second information, the second information indicating the sum of contribution values of the at least one display behavior, and the contribution value of each display behavior representing the contribution of each display behavior to the interaction of the at least one display behavior; and training the second machine learning model to the basis of a loss function, the loss function indicating the similarity between the first information and the second information, and the trained second machine learning model being used for obtaining the contribution value of each display behavior. Calculation resources consumed by calculating "the contribution value of each display behavior" are reduced.

Description

A data processing method, a method for obtaining contribution value, and related equipment

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on July 24, 2023, with application number 202310915053.5 and invention name “A data processing method, a contribution value acquisition method and related equipment”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of artificial intelligence, and in particular to a data processing method, a contribution value acquisition method, and related equipment. Background Art

[0003] Artificial Intelligence (AI) is the theory, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a branch of computer science that seeks to understand the essence of intelligence and develop new intelligent machines that can respond in a manner similar to human intelligence. AI also studies the design principles and implementation methods of various intelligent machines, enabling them to possess the capabilities of perception, reasoning, and decision-making.

[0004] Applying artificial intelligence technology to recommendation systems is a scenario-based application method. In recommendation systems, the same object can be displayed to users multiple times. Users may interact with the object during a certain display behavior of the object. In order to optimize the delivery strategy of the object, a machine learning model can be used to predict the post-click conversion rate (CVR) of each display behavior. In other words, a machine learning model can be used to predict the contribution value of each display behavior to the interaction with the object.

[0005] A currently adopted method includes: after obtaining n display behaviors of the object and some of the n display behaviors have been interacted, it is necessary to exhaustively enumerate all sets that can be composed of the aforementioned n display behaviors, and use a machine learning model to generate the probability that at least one display behavior in each set has been interacted. Then, based on the probability that at least one display behavior in each set has been interacted, the Sharpe value of each display behavior is calculated, and the Shapley value of each display behavior is determined as the contribution value of each display behavior.

[0006] However, in the process of calculating the contribution value of each display behavior, it is necessary to exhaustively enumerate all possible sets of n display behaviors and calculate the probability that at least one display behavior in each set is interacted, which results in huge computational overhead.

[0007] Summary of the Invention

[0008] The embodiments of the present application provide a data processing method, a contribution value acquisition method, and related equipment. Through a second machine learning model, the contribution value of each display behavior in at least one display behavior can be directly obtained. There is no longer a need to exhaustively enumerate all sets that can be composed of the aforementioned at least one display behavior, and then traverse the probability of at least one display behavior in each set being interacted. This greatly reduces the computing resources consumed in the calculation process of the "contribution value of each display behavior."

[0009] To solve the above technical problems, the embodiments of the present application provide the following technical solutions:

[0010] In a first aspect, an embodiment of the present application provides a data processing method that can use artificial intelligence technology to attribute display behaviors in a recommendation system. The method includes: a training device inputs a first training sample into a first machine learning model to obtain first information output by the first machine learning model, wherein the first training sample indicates at least one first display behavior, and the first information indicates the probability of at least one first display behavior being interacted; exemplarily, the first training sample includes descriptive information of each first display behavior.

[0011] The training device processes the first training sample through the second machine learning model to obtain second information, where the second information indicates the sum of the contribution values ​​of each first display behavior in at least one first display behavior, and the contribution value of each first display behavior represents the contribution of each first display behavior to the interaction of at least one first display behavior.

[0012] The training device trains the second machine learning model according to the first loss function until the first convergence condition is met, thereby obtaining a second machine learning model that has performed a training operation; wherein the first loss function indicates the similarity between the first information and the second information, and the goal of training using the first loss function includes improving the similarity between the first information corresponding to the first training sample and the second information; the second machine learning model that has performed a training operation is used to obtain a contribution value of each display behavior in at least one display behavior in the application stage.

[0013] In this implementation, a first training sample is input into a first machine learning model to obtain first information output by the first machine learning model, wherein the first training sample indicates at least one first display behavior, and the first information indicates a probability of the at least one first display behavior being interacted with. The first training sample is input into a second machine learning model to obtain second information output by the second machine learning model, wherein the second information indicates the sum of the contribution values ​​of each first display behavior in the at least one first display behavior, where the contribution value of each first display behavior represents the contribution of each first display behavior to the at least one first display behavior being interacted with. The second machine learning model is trained according to a loss function until a convergence condition is met, thereby obtaining a trained second machine learning model, wherein the loss function indicates the similarity between the first information and the second information. The trained second machine learning model can be obtained through the above scheme, and the contribution value of each display behavior in the at least one display behavior can be directly obtained through the second machine learning model. This eliminates the need to exhaustively enumerate all possible sets of the at least one display behavior and then traverse the probability of the at least one display behavior being interacted with in each set, thereby significantly reducing the computing resources consumed in the calculation of the "contribution value of each display behavior."

[0014] In one possible implementation, at least one first display behavior indicated by the first training sample may be a display behavior for the same object. For example, at least one first display behavior pointed to by the first training sample may be a display behavior for the same application. For another example, at least one display behavior pointed to by the first training sample may be a display behavior for the same news. For another example, at least one display behavior pointed to by the first training sample may be a display behavior for the same product, etc.

[0015] In one possible implementation, the target display behavior is any one of at least one first display behavior, wherein the contribution value of the target display behavior is obtained based on the first value and the second value corresponding to the target display behavior, the first value corresponding to the target display behavior represents the contribution value of the target display behavior itself, and the second value corresponding to the target display behavior represents the influence value of other first display behaviors other than the target display behavior in at least one first display behavior indicated by the first training sample on the contribution value of the target display behavior.

[0016] In this implementation, in the process of generating the contribution value of the target display behavior, the impact of other display behaviors on the contribution value of the target display behavior is also considered. The aforementioned scheme can reflect the actual situation when the user sees an object multiple times, that is, the aforementioned scheme is more consistent with the actual situation, which is conducive to obtaining a more accurate contribution value for each display behavior.

[0017] In one possible implementation, the first training sample includes the display time of each first display behavior, wherein the contribution value of other first display behaviors other than the target display behavior in at least one first display behavior indicated by the first training sample to the target display behavior is related to the first time distance, and the first time distance is the distance between the display time of the target display behavior and the display time of the other first display behaviors.

[0018] In this implementation, the longer the time interval between two advertisements seen by the user, the smaller the impact of the previous advertisement on the current advertisement will be, and the contribution value of other display behaviors to the target display behavior in this application is also related to the time distance between the two display behaviors. That is, the method for determining the contribution value of the target display behavior in this application is more consistent with the actual situation, which is conducive to improving the accuracy of the contribution value of each display behavior finally obtained.

[0019] In one possible implementation, a first parameter is used when calculating the influence value of a first display behavior whose display time is earlier than a target display behavior in at least one first display behavior on the contribution value of the target display behavior, and a second parameter is used when calculating the influence value of a first display behavior whose display time is later than the target display behavior in at least one first display behavior on the contribution value of the target display behavior, and the first parameter and the second parameter are not exactly the same.

[0020] In one possible implementation, the first training sample comes from a training data set, and the training data set contains a second training sample, wherein the second training sample is obtained based on a third training sample, and the third training sample includes a display time of at least one second display behavior, and at least one display time in the third training sample is updated to obtain the second training sample.

[0021] In this implementation, a second training sample is obtained after updating at least one display time in the third training sample. The second training sample is also placed in the training data set of the second machine learning model, so that the training data set of the second machine learning model is enhanced, that is, the training samples of the second machine learning model are enriched, which is conducive to making the training process of the second machine learning model more sufficient, so as to improve the accuracy of the information output by the trained second machine learning model.

[0022] In one possible implementation, parameter values ​​in the trained second machine learning model are used to derive a contribution value for each of at least one display behavior during the application phase. In this implementation, the training device only needs to send the value of at least one parameter in the trained second machine learning model to the execution device, which then derives the third machine learning model based on the value of the at least one parameter. This significantly reduces the communication resources consumed during the deployment of the third machine learning model and improves the convenience of the deployment process.

[0023] In one possible implementation, the second machine learning model includes a first module and a second module, the first module is used to generate a contribution value of each display behavior in at least one display behavior, and the second module is used to perform weighted summation on the contribution value of each display behavior in at least one display behavior generated by the first module to obtain a total contribution value corresponding to at least one display behavior; "the second machine learning model that has performed a training operation is used to obtain the contribution value of each display behavior in at least one display behavior" can be understood as: the first module in the second machine learning model after performing a training operation is used to obtain the contribution value of each display behavior in at least one display behavior in the application stage.

[0024] In one possible implementation, the first machine learning model is pre-trained, and the first machine learning model is any of the following: a recurrent neural network, a convolutional neural network, a fully connected neural network, or a neural network based on an attention mechanism. In this implementation, since the first machine learning model is pre-trained, that is, when the training of the second machine learning model begins, the accuracy of the first information output by the first machine learning model is relatively high, and the first information output by the first machine learning model is used to perform distillation learning on the second machine learning model, then the first machine learning model has been pre-trained, which is conducive to improving the speed of the training phase of the second machine learning model and making the information output by the trained second machine learning model more accurate; in addition, the method provided in this application is compatible with the first machine learning model using various types of neural networks, which is conducive to expanding the application scenarios of this solution and greatly improving the implementation flexibility of this solution.

[0025] In one possible implementation, the probability of at least one first display behavior being interacted with is any of the following: the probability of being clicked during the at least one first display behavior, the probability of being downloaded during the at least one first display behavior, the probability of being purchased during the at least one first display behavior, or the probability of being played during the at least one first display behavior. This implementation provides multiple specific implementation solutions for "at least one first display behavior being interacted with," expanding its application scenarios and increasing its implementation flexibility.

[0026] On the second aspect, an embodiment of the present application provides a method for obtaining contribution value, which can use artificial intelligence technology to attribute display behavior in a recommendation system. The method includes: an execution device obtains a first sample, the first sample indicates at least one display behavior, and at least one display behavior has been interacted; exemplarily, the first sample includes descriptive information of each display behavior in the aforementioned at least one display behavior.

[0027] The execution device generates a contribution value for each display behavior pointed to by the first sample through a third machine learning model. The contribution value of each display behavior represents the contribution of each display behavior to the interaction of at least one display behavior. The third machine learning model is obtained based on the second machine learning model that has performed a training operation. The first loss function used in the training operation indicates the similarity between the first information corresponding to the first training sample and the second information. The first training sample indicates at least one first display behavior. The first information is obtained through the first machine learning model. The first information indicates the probability of at least one first display behavior being interacted. The second information is obtained through the second machine learning model. The second information indicates the sum of the contribution values ​​of each first display behavior in the at least one first display behavior. The contribution value of each first display behavior represents the contribution of each first display behavior to the interaction of at least one first display behavior.

[0028] In one possible implementation, the third display behavior is any one of the at least one display behavior indicated by the first sample, wherein the contribution value of the third display behavior is obtained based on the first value and the second value corresponding to the third display behavior, the first value corresponding to the third display behavior represents the contribution value of the third display behavior itself, and the second value corresponding to the third display behavior represents the influence value of other display behaviors other than the third display behavior in the at least one display behavior indicated by the first sample on the contribution value of the third display behavior.

[0029] In one possible implementation, the first sample also includes the display time of the display behavior indicated by the first sample, wherein the contribution value of other display behaviors other than the third display behavior in at least one display behavior indicated by the first sample to the third display behavior is related to the second time distance, and the second time distance is the distance between the display time of the third display behavior and the display time of other display behaviors.

[0030] In one possible implementation, the first training sample comes from a training data set, and the training data set contains a second training sample, wherein the second training sample is obtained based on a third training sample, and the third training sample includes a display time of at least one second display behavior, and at least one display time in the third training sample is updated to obtain the second training sample.

[0031] In one possible implementation, “the third machine learning model is obtained based on the second machine learning model that has performed the training operation” can be understood as: the third machine learning model is obtained based on the value of at least one parameter in the second machine learning model that has performed the training operation.

[0032] In one possible implementation, the second machine learning model includes a first module and a second module, the first module is used to generate a contribution value of each display behavior in at least one display behavior, and the second module is used to perform weighted summation on the contribution value of each display behavior in at least one display behavior generated by the first module to obtain a total contribution value corresponding to at least one display behavior; "the third machine learning model is obtained based on the second machine learning model that has performed a training operation" can be understood as: the third machine learning model is the first module in the second machine learning model after the training operation has been performed.

[0033] The meanings of the terms in the second aspect and various possible implementation methods of the second aspect and the beneficial effects brought about can all be referred to the first aspect and will not be repeated here.

[0034] In a third aspect, an embodiment of the present application provides a data processing device that can use artificial intelligence technology to attribute display behaviors in a recommendation system. The device includes: a processing module for inputting a first training sample into a first machine learning model to obtain first information output by the first machine learning model, wherein the first training sample indicates at least one first display behavior, and the first information indicates the probability of at least one first display behavior being interacted; a processing module for processing the first training sample through a second machine learning model to obtain second information, the second information indicating the sum of the contribution values ​​of each first display behavior in at least one first display behavior, and the contribution value of each first display behavior represents the contribution of each first display behavior to the interaction of at least one first display behavior; a training module for training the second machine learning model according to a loss function until a convergence condition is met, to obtain a second machine learning model that has performed a training operation, wherein the loss function indicates the similarity between the first information and the second information, and the second machine learning model that has performed a training operation is used to obtain the contribution value of each display behavior in at least one display behavior.

[0035] In the third aspect of this application, the data processing device is also used to execute the steps performed by the training device in the first aspect and various possible implementation methods of the first aspect. The specific implementation methods, meanings of terms and beneficial effects brought about by the steps in the third aspect can all be referred to the first aspect and will not be repeated here.

[0036] In a fourth aspect, an embodiment of the present application provides a contribution value acquisition device, which can use artificial intelligence technology to attribute display behaviors in a recommendation system. The device includes: an acquisition module, used to acquire a first sample, the first sample indicates at least one display behavior, and at least one display behavior has been interacted; a generation module, used to generate a contribution value for each display behavior pointed to by the first sample through a third machine learning model, the contribution value of each display behavior represents the contribution of each display behavior to the interaction of at least one display behavior, and the third machine learning model is obtained based on a second machine learning model that has performed a training operation; wherein the loss function used in the training operation indicates the similarity between the first information and the second information corresponding to the first training sample, the first training sample indicates at least one first display behavior, the first information is obtained through the first machine learning model, the first information indicates the probability of at least one first display behavior being interacted, the second information is obtained through the second machine learning model, the second information indicates the sum of the contribution values ​​of each first display behavior in at least one first display behavior, and the contribution value of each first display behavior represents the contribution of each first display behavior to the interaction of at least one first display behavior.

[0037] In the fourth aspect of this application, the contribution value acquisition device is also used to execute the steps performed by the execution device in the second aspect and various possible implementation methods of the second aspect. The specific implementation methods of the steps in the fourth aspect, the meanings of the terms and the beneficial effects brought about can all be referred to the second aspect and will not be repeated here.

[0038] In the fifth aspect, an embodiment of the present application provides an execution device, including a processor and a memory, the processor is coupled to the memory, the memory is used to store programs; the processor is used to execute the programs in the memory, so that the execution device executes the contribution value acquisition method described in the second aspect above.

[0039] In a sixth aspect, an embodiment of the present application provides a training device, comprising a processor and a memory, wherein the processor is coupled to the memory, the memory is used to store programs, and the processor is used to execute the programs in the memory, so that the training device executes the data processing method described in the first aspect above.

[0040] In a seventh aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer executes the method described in the first or second aspect above.

[0041] In an eighth aspect, an embodiment of the present application provides a computer program product, which includes a program. When the program runs on a computer, it enables the computer to execute the method described in the first or second aspect above.

[0042] In a ninth aspect, the present application provides a chip system, which includes a processor for supporting the implementation of the functions involved in the above aspects, for example, sending or processing the data and / or information involved in the above methods. In one possible design, the chip system also includes a memory, which is used to store program instructions and data necessary for the terminal device or communication device. The chip system can be composed of a chip or can include a chip and other discrete devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] FIG1a is a schematic diagram of the structure of an artificial intelligence main framework provided in an embodiment of the present application;

[0044] FIG1b is a schematic diagram of a recommendation interface provided in an embodiment of the present application;

[0045] FIG1c is another schematic diagram of a recommendation interface provided in an embodiment of the present application;

[0046] FIG2 is a system architecture diagram of a data processing system provided in an embodiment of the present application;

[0047] FIG3 is a flow chart of a data processing method provided in an embodiment of the present application;

[0048] FIG4 is another flow chart of a data processing method according to an embodiment of the present application;

[0049] FIG5 is a schematic diagram of obtaining a second training sample based on a third training sample according to an embodiment of the present application;

[0050] FIG6 is another flow chart of a data processing method according to an embodiment of the present application;

[0051] FIG7 is a flow chart of a method for obtaining a contribution value according to an embodiment of the present application;

[0052] FIG8 is a schematic structural diagram of a data processing device provided in an embodiment of the present application;

[0053] FIG9 is a schematic structural diagram of a device for obtaining a contribution value according to an embodiment of the present application;

[0054] FIG10 is a schematic diagram of a structure of an execution device provided in an embodiment of the present application;

[0055] FIG11 is a schematic diagram of a structure of a training device provided in an embodiment of the present application;

[0056] FIG12 is a schematic structural diagram of a chip provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0058] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, and this is merely a way of distinguishing the objects of the same attributes when describing them in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0059] First, let's describe the overall workflow of an AI system. See Figure 1a, which shows a schematic diagram of the AI ​​framework. This AI framework will be explained from two perspectives: the "intelligent information chain" (horizontal axis) and the "IT value chain" (vertical axis). The "intelligent information chain" reflects the entire process from data acquisition to processing. For example, it could be the general process of intelligent information perception, intelligent information representation and formation, intelligent reasoning, intelligent decision-making, and intelligent execution and output. Throughout this process, data undergoes a condensed journey from "data-information-knowledge-wisdom." The "IT value chain," spanning the underlying infrastructure of human intelligence, information (provided and processed by technology), and the system's industrial ecosystem, reflects the value that AI brings to the information technology industry.

[0060] (1) Infrastructure

[0061] The infrastructure provides computing power for AI systems, enabling communication with the outside world and providing support through a basic platform. Communication with the outside world is achieved through sensors; computing power is provided by smart chips, which can specifically adopt hardware acceleration chips such as central processing units (CPUs), embedded neural network processing units (NPUs), graphics processing units (GPUs), application-specific integrated circuits (ASICs), or field programmable gate arrays (FPGAs); the basic platform includes related platform guarantees and support such as distributed computing frameworks and networks, and can include cloud storage and computing, and interconnected networks. For example, sensors communicate with the outside world to obtain data, which is then provided to the smart chips in the distributed computing system provided by the basic platform for calculation.

[0062] (2) Data

[0063] Data above the infrastructure layer represents data sources for AI. This data includes graphics, images, voice, and text, as well as IoT data from traditional devices. This includes business data from existing systems and sensor data such as force, displacement, liquid level, temperature, and humidity.

[0064] (3) Data processing

[0065] Data processing generally includes data training, machine learning, deep learning, search, reasoning, decision-making, etc.

[0066] Among them, machine learning and deep learning can symbolize and formalize data for intelligent information modeling, extraction, preprocessing, and training.

[0067] Reasoning refers to the process of simulating human intelligent reasoning in computers or intelligent systems, using formalized information to perform machine thinking and solve problems based on reasoning control strategies. Typical functions are search and matching.

[0068] Decision-making refers to the process of making decisions after intelligent information is reasoned, and usually provides functions such as classification, sorting, and prediction.

[0069] (4) General ability

[0070] After the data has undergone the data processing mentioned above, some general capabilities can be further formed based on the results of the data processing, such as algorithms or a general system, for example, translation, text analysis, computer vision processing, speech recognition, image recognition, etc.

[0071] (5) Smart products and industry applications

[0072] Smart products and industry applications refer to the products and applications of artificial intelligence systems in various fields. They are the encapsulation of the overall artificial intelligence solution, which productizes intelligent information decision-making and realizes practical application. Its application areas mainly include: smart terminals, smart manufacturing, smart transportation, smart homes, smart medical care, smart security, autonomous driving, smart cities, etc.

[0073] The method provided in this application can be applied to recommendation systems in various application fields. Specifically, it can be used to attribute display behaviors in the recommendation system. For example, after displaying an object to a user once or multiple times, at least one display behavior of the object can be obtained. If there is an interactive display behavior among the at least one display behavior, the at least one display behavior can be attributed to optimize the subsequent delivery strategy of the object. "Attributing display behaviors" can be understood as determining the contribution of each display behavior to the interaction of at least one display behavior, that is, assigning the credit of at least one display behavior being interacted to each display behavior.

[0074] For example, the objects displayed to the user can be any of the following: application (APP), news, audio, video, product description information, purchase links or other types of objects, etc., and the interactive behaviors performed by the user can be any of the following: click, download, purchase or other interactive behaviors, etc., which are not limited in the embodiments of this application.

[0075] Since the method provided in this application can be used in smart terminals, smart homes or other application fields, the following examples are given of application scenarios in multiple application fields of this application.

[0076] Application field 1: smart terminal field

[0077] For example, please refer to Figure 1b, which is a schematic diagram of a recommendation interface provided in an embodiment of the present application. The application store shown in Figure 1b can be deployed on a mobile phone, tablet, laptop or other smart terminal. The application store shown in Figure 1b can display multiple applications to the user. After displaying the same application (for example, application 1 in Figure 1b) to the user multiple times through the interface shown in Figure 1b, the user's installation operation of application 1 is obtained; the multiple display behaviors of application 1 can be attributed, that is, the contribution of each display behavior of application 1 to the installation of application 1 can be determined, so as to optimize the subsequent delivery strategy of application 1.

[0078] For another example, please refer to Figure 1c, which is another schematic diagram of the recommendation interface provided by the embodiment of the present application. The recommendation interface of the news shown in Figure 1c can be deployed on a mobile phone, tablet, laptop or other smart terminal. After the same news (such as News 2) is displayed to the user multiple times through the interface shown in Figure 1c, the user's click operation on News 2 is obtained; then the multiple display behaviors of News 2 can be attributed, that is, the contribution of each display behavior of News 2 to the click of News 2 can be determined, so as to optimize the subsequent delivery strategy of News 2. Recommendation interfaces for other types of objects can also be deployed on smart terminals, such as audio recommendation interfaces, commodity recommendation interfaces, etc. The examples here are only for the convenience of understanding this solution and are not used to limit this solution.

[0079] Application field 2: smart home field

[0080] For example, a display screen can be deployed on the refrigerator in a smart home, through which recipes and purchase links of the ingredients involved in the recipes are displayed to users. After the purchase link of the same ingredient (such as celery) is displayed to the user multiple times in the aforementioned manner, the user's purchase operation on the purchase link of celery is obtained. Then, the multiple display behaviors of the purchase link of celery can be attributed, that is, the contribution of each display behavior of the purchase link of celery to the user's purchase operation can be determined, so as to optimize the subsequent delivery strategy of the purchase link of celery.

[0081] It should be noted that the method provided in this application can also be applied to other application scenarios. The above examples of various application scenarios of this application are only for the convenience of understanding this solution and are not used to limit this solution.

[0082] In the various application scenarios described above, when at least one display behavior of an object is interacted with, the contribution value of each of the at least one display behavior can be determined by the method provided in this application. The "contribution value of each display behavior" represents the contribution made by each display behavior to the interaction of at least one display behavior. Before describing the method provided in this application in detail, please refer to Figure 2, which is a system architecture diagram of a data processing system provided in an embodiment of this application. In Figure 2, the data processing system 200 includes a training device 210, a database 220, an execution device 230, and a data storage system 240. The execution device 230 includes a computing module 231.

[0083] Among them, the database 220 stores a training data set. During the training phase, the training device 210 can generate a second machine learning model 201 and iteratively train the second machine learning model 201 using the training data set to obtain a second machine learning model 201 that has been trained. The second machine learning model 201 can be specifically expressed as a neural network or a non-neural network model. Specifically, please refer to Figure 3, which is a flow chart of a data processing method provided in an embodiment of the present application.

[0084] 301. The training device 210 inputs a first training sample into a first machine learning model to obtain first information output by the first machine learning model. The first training sample indicates at least one first display behavior, and the first information indicates a probability of the at least one first display behavior being interacted.

[0085] Optionally, the first machine learning model may be a machine learning model that has been trained. For example, the first information may indicate a probability of being clicked, downloaded, purchased, played, or performing other types of interactive behaviors in at least one first display behavior.

[0086] 302. The training device 210 inputs the first training sample into the second machine learning model 201 to obtain second information output by the second machine learning model 201, where the second information indicates the sum of the contribution values ​​of each first display behavior in at least one first display behavior, and the contribution value of each first display behavior represents the contribution of each first display behavior to the interaction of at least one first display behavior.

[0087] 303. The training device 210 iteratively trains the second machine learning model 201 according to the first loss function until the first convergence condition is met, thereby obtaining a second machine learning model that has performed a training operation, wherein the first loss function indicates the similarity between the first information and the second information, and the second machine learning model 201 that has performed a training operation is used to obtain a contribution value of each display behavior in at least one display behavior.

[0088] In an embodiment of the present application, the above-mentioned scheme can be used to obtain a second machine learning model 201 that has performed a training operation. The second machine learning model 201 can directly obtain the contribution value of each display behavior in at least one display behavior. There is no need to exhaustively enumerate all sets that can be composed of the aforementioned at least one display behavior, and then traverse the probability of at least one display behavior being interacted in each set, which greatly reduces the computing resources consumed in the calculation process of the "contribution value of each display behavior".

[0089] A third machine learning model is deployed in the computing module 231 of the execution device 230. The third machine learning model is derived from the trained second machine learning model 201. During the application phase, the execution device 230 may obtain a first sample, which indicates at least one display behavior, at least one of which has been interacted with. The execution device 230 generates a contribution value for each display behavior indicated by the first sample using the third machine learning model.

[0090] In some embodiments of the present application, referring to FIG. 2 , the execution device 230 and the client device may be integrated into the same device, so that the user can directly interact with the execution device 230. For example, when the client device is a desktop computer, the execution device 230 may be a module in the host CPU of the desktop computer that uses the first machine learning model to process data. The execution device 230 may also be a neural network processor (NPU) in the desktop computer, with the NPU mounted on the host CPU as a coprocessor and assigned tasks by the host CPU.

[0091] It is worth noting that Figure 2 is only a schematic diagram of the architecture of two data processing systems provided by an embodiment of the present invention, and the positional relationship between the devices, components, modules, etc. shown in the figure does not constitute any limitation. For example, in other embodiments of the present application, the execution device 230 and the client device can be independent devices, and the execution device 230 is configured with an input / output (I / O) interface to interact with the client device for data. After determining the first sample, the client device sends the first sample to the execution device 230 through the I / O interface. After the execution device 230 generates the contribution value of each display behavior pointed to by the first sample through the first machine learning model 201 in the calculation module 231, the contribution value of each display behavior pointed to by the aforementioned first sample can be returned to the client device through the I / O interface.

[0092] In combination with the above description, the following describes the specific implementation process of the training phase and application phase of the method provided in the embodiment of the present application.

[0093] 1. Training Phase

[0094] In the embodiment of the present application, the training phase describes the process of training the second machine learning model 201 by the training device 210 using the training data set in the database 220. Specifically, please refer to FIG4 , which is another flow chart of the data processing method provided in the embodiment of the present application. The data processing method provided in the embodiment of the present application may include:

[0095] 401. Input the fourth training sample into the first machine learning model to obtain first information corresponding to the fourth training sample output by the first machine learning model, the fourth training sample indicates at least one fourth display behavior, and the first information corresponding to the fourth training sample indicates the probability of the aforementioned at least one fourth display behavior being interacted.

[0096] In this embodiment of the present application, step 401 is optional. The fourth training sample refers to a training sample used when training the first machine learning model. The fourth training sample indicates at least one fourth display behavior, that is, the fourth training sample may include description information of each fourth display behavior in the at least one fourth display behavior.

[0097] Exemplarily, the aforementioned at least one fourth display behavior may be a display behavior for the same object. For example, the at least one fourth display behavior pointed to by the fourth training sample may be a display behavior for the same application; for another example, the at least one fourth display behavior pointed to by the fourth training sample may be a display behavior for the same news; for another example, the at least one fourth display behavior pointed to by the fourth training sample may be a display behavior for the same product, and so on. These examples are merely for facilitating understanding of this solution and are not intended to limit this solution.

[0098] Optionally, the at least one display behavior pointed to by the fourth training sample may include at least one display behavior of the same object to the same user; for example, the at least one display behavior may be displaying the same object to the same user at different times.

[0099] Each fourth training sample includes at least the display time of each fourth display behavior in at least one fourth display behavior; illustratively, the display time of each fourth display behavior can be the timestamp of each fourth display behavior. Optionally, the fourth training sample can also include the display location of each fourth display behavior; for example, the aforementioned display location can be home, work, on the way to work, on the way home from get off work, or other locations; for another example, the aforementioned display location can also be the address where the fourth display behavior is performed, etc., which can be determined based on actual circumstances. Alternatively, the fourth training sample can also include other descriptive information for each fourth display behavior, etc. It should be noted that the examples here are only for the convenience of understanding this solution and are not used to limit this solution.

[0100] For example, each fourth training sample can be represented as a sequence [a1, a2, ..., a N ], each element in the aforementioned sequence includes description information of a fourth display behavior, and each element in the aforementioned sequence can be represented as a vector [e1, e2,…, e N ], the aforementioned vector [e1, e2,…, e t ] can carry a timestamp of a fourth display behavior.

[0101] It should be noted that the concepts of the two terms "second training sample" and "third training sample" will be used in subsequent steps, and the meanings of the two terms "second training sample" and "third training sample" will not be introduced here for the time being.

[0102] Exemplarily, the first machine learning model can be any of the following: a recurrent neural network (RNN), a convolutional neural network (CNN), a fully connected neural network, a neural network based on an attention mechanism, or other types of neural networks, etc. The method provided in this application is compatible with the first machine learning model using various types of neural networks, which is conducive to expanding the application scenarios of this solution and greatly improving the implementation flexibility of this solution.

[0103] The first information corresponding to the fourth training sample indicates the probability of at least one fourth display behavior being interacted. For example, the first information corresponding to the fourth training sample indicates the probability of the last fourth display behavior displayed in at least one fourth display behavior being interacted. The aforementioned "interaction" may refer to any of the following: click, download, purchase, play or other types of interactive behaviors; for example, if the object targeted by the fourth display behavior is an application, the aforementioned "interaction" may be click or download; for another example, if the object targeted by the fourth display behavior is news, audio or video, the aforementioned "interaction" may be click or play; for another example, if the object targeted by the fourth display behavior is a commodity, the aforementioned "interaction" may be click or purchase, etc. The specific definition of "interaction" can be flexibly determined in combination with the actual application scenario, and is not limited in the embodiments of this application. In this implementation method, a variety of specific implementation schemes for "at least one display behavior being interacted" are provided, which expands the application scenarios of this scheme and improves the implementation flexibility of this scheme.

[0104] 402. Train the first machine learning model according to a second loss function, where the second loss function indicates a similarity between the first information corresponding to the fourth training sample and the expected information.

[0105] In this embodiment of the present application, step 402 is optional. The expected information corresponding to the fourth training sample indicates whether the at least one fourth display behavior pointed to by the fourth training sample is interacted with. In other words, the expected information corresponding to the fourth training sample indicates whether the last fourth display behavior displayed among the at least one fourth display behavior pointed to by the fourth training sample is interacted with.

[0106] After obtaining the first information corresponding to the fourth training sample, the first training device may generate a function value of a second loss function based on the first information corresponding to the fourth training sample and the expected information corresponding to the fourth training sample, perform a gradient derivative on the function value of the second loss function, and use a backpropagation algorithm to update the parameters in the first machine learning model, thereby completing one round of training of the first machine learning model. The second loss function indicates the similarity between the first information corresponding to the fourth training sample and the expected information, and the goal of iterative training using the second loss function includes improving the similarity between the first information corresponding to the fourth training sample and the expected information.

[0107] For example, the second loss function can be a cross entropy loss function, an L1 loss function, an L2 loss function, or other types of loss functions. The specific type of loss function can be flexibly determined based on the actual situation and is not limited here. To further understand this solution, an example of the second loss function is disclosed below:

[0108] Wherein, L represents the second loss function, y represents the expected information corresponding to the fourth training sample, that is, whether the last fourth display behavior displayed in the at least one fourth display behavior pointed to by the fourth training sample is interacted; y^ represents the first information corresponding to the fourth training sample output by the first machine learning model, that is, the probability that the last fourth display behavior displayed in the at least one fourth display behavior pointed to by the fourth training sample is interacted.

[0109] The first training device repeatedly performs steps 401 and 402 above to iteratively train the first machine learning model until a second convergence condition is satisfied, thereby obtaining a trained first machine learning model. For example, the second convergence condition may be satisfying a convergence condition of a second loss function, or it may be that the first machine learning model has been iteratively trained for a first number of times.

[0110] 403. Obtain a first training sample from the training data set, where the first training sample indicates at least one first display behavior.

[0111] In an embodiment of the present application, a training data set may be deployed in the second training device, and the training data set includes multiple training samples. When the second training device trains the first machine learning model, it is necessary to obtain one or more first training samples from the training data set, and the first training sample indicates at least one first display behavior; wherein the meaning of "first training sample" is similar to that of "fourth training sample", the difference being that the fourth training sample is a training sample used when training the first machine learning model, and the first training sample is a training sample used when training the second machine learning model.

[0112] The second training device and the first training device can be the same device, or the second training device and the first training device can be different devices. After obtaining the first machine learning model that has performed the training operation, the first training device can send the first machine learning model that has performed the training operation to the second training device.

[0113] In one case, a second training sample may exist among multiple training samples in a training data set, wherein the second training sample is obtained based on a third training sample, the third training sample indicates at least one second display behavior, and the third training sample includes descriptive information of at least one second display behavior, and the descriptive information of each second display behavior includes at least the display time of each second display behavior; optionally, the descriptive information of each second display behavior may also include the display location of each second display behavior, or other types of descriptive information, etc.

[0114] At least one display time in the third training sample is updated to obtain the second training sample, that is, at least one display behavior pointed to by the second training sample in the training data set may be a display behavior that has not been actually performed on the user, and the second display behavior pointed to by the third training sample in the training data set may be a display behavior that has actually been performed on the user.

[0115] It should be noted that the specific manifestations of the "second training sample" and the "third training sample" are similar to those of the "first training sample," and therefore are not further elaborated here. The "first training sample" obtained from the training dataset may be the "second training sample," meaning that at least one of the display behaviors pointed to by the first training sample may not have been actually displayed to the user; alternatively, the aforementioned "first training sample" may also be the second training sample. Similarly, the aforementioned "first training sample" may or may not be the "third training sample."

[0116] Regarding the specific implementation process of the second training device generating the second training sample based on the third training sample, for example, the second training device can obtain the third training sample from the original training data set, and each display behavior pointed to by each training sample in the original training data set can be a display behavior that has been performed on the user. Here, the third training sample is s=[(a 1， t1), (a 2， t2),...,(aN ， t N )] as an example, where a1, a2, ..., a N Represents the identification information of the N display behaviors pointed to by the third training sample, t1 represents the timestamp of a second display behavior identified by a1, t2 represents the timestamp of a second display behavior identified by a2, and so on, t N Represents a N The timestamp of the second display behavior, from t1 to t N The timestamps represented are getting later and later.

[0117] The second training device can set a1, a2, ..., a N The order of is randomly shuffled to get the shuffled sequence In order from past to future (that is, from morning to night), they are Generate a timestamp, that is, update at least one presentation time in the third training sample, and obtain the following second training sample:

[0118] The second training device repeats the above steps multiple times to generate multiple second training samples, and incorporates each second training sample into the original training data set to perform data enhancement on the original training data set to obtain an updated training data set.

[0119] It should be noted that the second training device can also use other methods to achieve "obtaining the second training sample based on the third training sample", for example, not using a1, a2, ..., a N The order of a1, a2, ..., a N The above examples are only used to prove the feasibility of this solution and are not used to limit this solution.

[0120] To understand this solution more intuitively, please refer to Figure 5, which is a schematic diagram of obtaining a second training sample based on a third training sample provided in an embodiment of the present application. As shown in Figure 5, the third training sample includes descriptive information of multiple display behaviors, and the descriptive information of each display behavior includes identification information of each display behavior and the display time (i.e., timestamp 1, timestamp 2, ..., timestamp N in Figure 5), and the time represented by "timestamp 1, timestamp 2, ..., timestamp N" is getting later and later. In order to obtain the second training sample based on the third training sample, the second training device can randomly shuffle the identification information of the N display behaviors in the third training sample to obtain the identification information of display behavior N, the identification information of display behavior 3, ..., the identification information of display behavior 2 in Figure 5.

[0121] The second training device generates a new display time for the identification information of each display behavior in the aforementioned "identification information of display behavior N, identification information of display behavior 3, ..., identification information of display behavior 2", that is, the "timestamp A, timestamp C, ..., timestamp P" in Figure 5. The time represented by the aforementioned "timestamp A, timestamp C, ..., timestamp P" is getting later and later. It should be understood that the example in Figure 5 is only for the convenience of understanding this solution and is not used to limit this solution.

[0122] In an embodiment of the present application, a second training sample is obtained after updating at least one display time in the third training sample, and the second training sample is also placed in the training data set of the second machine learning model, so that the training data set of the second machine learning model is enhanced, that is, the training samples of the second machine learning model are enriched, which is conducive to making the training process of the second machine learning model more sufficient, so as to improve the accuracy of the information output by the trained second machine learning model.

[0123] In another case, multiple training samples in the training data set may all be collected based on actual display behaviors to users, that is, at least one display behavior pointed to by each training sample in the training data set is a display behavior actually performed to users.

[0124] 404. Input the first training sample into the first machine learning model to obtain first information corresponding to the first training sample output by the first machine learning model, where the first information corresponding to the first training sample indicates a probability of at least one first display behavior being interacted with.

[0125] In the embodiment of the present application, the first information corresponding to the first training sample indicates the probability of at least one first display behavior being interacted with. That is, the first information corresponding to the first training sample indicates the probability of at least one first display behavior being interacted with. The concept of "first information corresponding to the first training sample" is similar to the concept of "first information corresponding to the fourth training sample" described above, except that "fourth training sample" is replaced by "first training sample" and "fourth display behavior" is replaced by "first display behavior." The detailed concept of "first information corresponding to the first training sample" is not repeated here.

[0126] Steps 401 and 402 are optional. If steps 401 and 402 are performed, the first machine learning model in step 404 is pre-trained. If steps 401 and 402 are not performed, step 403 may be performed directly, and the first machine learning model may be an initialized machine learning model.

[0127] 405. Process the first training sample through the second machine learning model to obtain second information, where the second information indicates the sum of the contribution values ​​of each first display behavior in at least one first display behavior, and the contribution value of each first display behavior represents the contribution of each first display behavior to the interaction of at least one first display behavior.

[0128] In an embodiment of the present application, the second training device inputs the first training sample into the second machine learning model, processes the first training sample through the second machine learning model, and obtains the second information output by the second machine learning model.

[0129] Exemplarily, the second training device processes the first training sample through the second machine learning model, which may include: the second training device first generates a contribution value for each first display behavior through the second machine learning model, and then generates the sum of the contribution values ​​of all first display behaviors in at least one first display behavior through the second machine learning model.

[0130] For ease of description, any one of the at least one first display behaviors will be referred to as a target display behavior. Optionally, the contribution value of the target display behavior can be obtained based on a first value corresponding to the target display behavior and at least one second value, where the first value corresponding to the target display behavior represents the contribution value of the target display behavior itself, and each of the at least one second value corresponding to the target display behavior represents the impact of each first display behavior other than the target display behavior in the at least one first display behavior indicated by the first training sample on the contribution value of the target display behavior.

[0131] Further optionally, the contribution value of each first display behavior other than the target display behavior in at least one first display behavior indicated by the first training sample to the target display behavior is related to the first time distance, and the first time distance is the distance between the display time of the target display behavior and the display time of the aforementioned other first display behaviors.

[0132] Optionally, a first parameter is used when calculating the influence value of the contribution value of the first display behavior whose display time is earlier than the target display behavior in at least one first display behavior to the target display behavior, and a second parameter is used when calculating the influence value of the contribution value of the first display behavior whose display time is later than the target display behavior in at least one first display behavior to the target display behavior, and the first parameter and the second parameter are not exactly the same.

[0133] For example, if the second machine learning model is specifically expressed as a neural network, the second machine learning model can be any of the following: a recurrent neural network, a convolutional neural network, a neural network based on an attention mechanism, a fully connected neural network or other types of neural networks, etc., which is not limited in the embodiments of this application.

[0134] Alternatively, the second machine learning model may be expressed as a non-neural network model. To further understand this solution, an example of the second machine learning model being expressed as a non-neural network model is disclosed below:

[0135] in, represents the second information obtained based on the first training sample, that is, the sum of the contribution values ​​of each first display behavior in the at least one first display behavior pointed to by the first training sample, and the target display behavior is the i-th first display behavior in the at least one first display behavior pointed to by the first training sample; represents the sum of the contribution values ​​of the first display behavior from the i-th to the last first display behavior in the at least one first display behavior to the i-th first display behavior. When the value of j is equal to i, it is w i，i (i.e., the impact value of the ith first display behavior on the contribution value of the ith first display behavior), which can also be understood as the contribution value of the ith first display behavior itself. represents the sum of the influence values ​​of the contribution values ​​of the 1st to the i-1th first display behaviors in the at least one first display behavior to the i-th first display behavior, t i represents the display time of the first display behavior of i, t j represents the display time of the jth first display behavior, and α and β are parameters that need to be iteratively updated during the training process of the second machine learning model. It should be understood that the example in formula (2) is only for the convenience of understanding this solution and is not used to limit this solution.

[0136] In an embodiment of the present application, in the process of generating the contribution value of the target display behavior, the impact value of other display behaviors on the contribution value of the target display behavior is also considered. The aforementioned scheme can reflect the actual situation when the user sees a certain object multiple times, that is, the aforementioned scheme is more consistent with the actual situation, which is conducive to obtaining a more accurate contribution value for each display behavior.

[0137] Since the longer the time interval between two advertisements seen by the user, the smaller the impact of the previous advertisement on the current advertisement will be, and the contribution value of other display behaviors to the target display behavior in this application is also related to the time distance between the two display behaviors, that is, the method of determining the contribution value of the target display behavior in this application is more consistent with the actual situation, which is conducive to improving the accuracy of the contribution value of each display behavior finally obtained.

[0138] 406. Train the second machine learning model according to the first loss function until the first convergence condition is met, thereby obtaining a second machine learning model that has been trained, wherein the first loss function indicates the similarity between the first information and the second information corresponding to the first training sample, and the second machine learning model that has been trained is used to obtain a contribution value of each display behavior in at least one display behavior.

[0139] In an embodiment of the present application, after obtaining the first information corresponding to the first training sample and the second information corresponding to the first training sample, the second training device can generate a function value of the first loss function based on the first information and the second information corresponding to the first training sample, derive the function value of the first loss function, and use the backpropagation algorithm to update the parameter values ​​of the second machine learning model to achieve one training of the second machine learning model.

[0140] The first loss function value indicates the similarity between the first information corresponding to the first training sample and the second information, and the goal of training using the first loss function includes improving the similarity between the first information corresponding to the first training sample and the second information. For example, the first loss function can adopt a cross-entropy loss function, an L1 loss function, an L2 loss function, or other types of loss functions, etc., which are not limited in the embodiments of the present application.

[0141] To further understand this solution, an example of the first loss function is disclosed as follows:

[0142] Among them, L a represents the first loss function, represents first information corresponding to the first training sample generated by the first machine learning model, Represents the second information corresponding to the first training sample generated by the second machine learning model. It should be understood that the example in formula (3) is only for the convenience of understanding this solution and is not used to limit this solution.

[0143] Exemplarily, in one implementation, if steps 401 and 402 are performed, the first machine learning model is pre-trained, and the second training device training the second machine learning model according to the first loss function may include: the second training device adjusting the parameters of the second machine learning model according to the first loss function while keeping the parameters of the first machine learning model unchanged. In the embodiment of the present application, since the first machine learning model is pre-trained, that is, when the training of the second machine learning model begins, the accuracy of the first information output by the first machine learning model is relatively high, and the first information output by the first machine learning model is used to perform distillation learning on the second machine learning model, then pre-training the first machine learning model is beneficial to improving the speed of the training phase of the second machine learning model, and is also beneficial to making the information output by the trained second machine learning model more accurate.

[0144] In another implementation, if steps 401 and 402 are not performed, the second training device training the second machine learning model according to the first loss function may include: the second training device training the first machine learning model and the second machine learning model according to the first loss function and the third loss function. The third loss function indicates the similarity between the first information corresponding to the first training sample and the expected information corresponding to the first training sample, and the expected information corresponding to the first training sample indicates whether at least one first display behavior pointed to by the first training sample is interacted, that is, the expected information corresponding to the first training sample indicates whether the last displayed first display behavior among the at least one first display behavior pointed to by the first training sample is interacted.

[0145] Exemplarily, the second training device generates a function value of the first loss function and a function value of the second loss function based on the first information, the second information and the expected information corresponding to the first training sample, and updates the parameters of the first machine learning model and the second machine learning model based on the back propagation algorithm according to the function value of the first loss function and the function value of the second loss function to achieve one training of the first machine learning model and the second machine learning model.

[0146] It should be noted that the meanings of the "third loss function" and the "second loss function" are similar. The difference is that the "fourth training sample" in the second loss function is replaced by the "first training sample" in the third loss function, and the "fourth display behavior" is replaced by the "first display behavior". The implementation details of the third loss function will not be repeated here.

[0147] The training device repeatedly performs the above steps to implement iterative training of the second machine learning model until the first convergence condition is met, thereby obtaining a second machine learning model that has performed the training operation.

[0148] For example, in one case, if steps 401 and 402 are performed, the first convergence condition may be that the convergence condition of the first loss function is satisfied, and / or the second machine learning model is trained a preset number of times. In another case, if steps 401 and 402 are not performed, the first convergence condition may be that the convergence conditions of the first loss function and the third loss function are satisfied, and / or the second machine learning model is trained a preset number of times.

[0149] In order to understand the present solution more intuitively, please refer to Figure 6, which is another flow chart of the data processing method provided in an embodiment of the present application. As shown in Figure 6, the first machine learning model is iteratively trained using the training samples in the original training data set until the second convergence condition is met to obtain a trained first machine learning model, and each training sample in the original training data set indicates at least one display behavior; the specific implementation process of the aforementioned steps can be referred to the description in step 401 and step 402, which will not be elaborated here.

[0150] After data enhancement is performed on the original training data set, an updated training data set is obtained. The specific implementation process of the above steps can be found in the description of step 403 above, and will not be elaborated here.

[0151] A first training sample is obtained from the updated training data set, and the first training sample is input into the trained first machine learning model and the second machine learning model respectively. The second machine learning model is iteratively trained according to the first information corresponding to the first training sample generated by the trained first machine learning model and the second information corresponding to the first training sample generated by the second machine learning model until the first convergence condition is met, thereby obtaining a second machine learning model that has been trained. The specific implementation process of the aforementioned steps can be referred to the description in steps 404 to 406, which will not be elaborated here. Among them, the second machine learning model that has been trained is used to obtain the contribution value of each display behavior in at least one display behavior in the application stage. It should be understood that the example in FIG6 is only for the convenience of understanding this solution and is not used to limit this solution.

[0152] In one case, "the second machine learning model that has been trained is used to obtain the contribution value of each display behavior in at least one display behavior" can be understood as: the value of at least one parameter in the second machine learning model that has been trained is used to obtain the contribution value of each display behavior in at least one display behavior. Taking the above formula (2) as an example, the values ​​of α and β in formula (2) are used to calculate the contribution value of each display behavior in at least one display behavior. The training device only needs to send the value of at least one parameter in the second machine learning model that has been trained to the execution device, and the execution device can obtain the third machine learning model based on the value of the aforementioned at least one parameter, which greatly reduces the communication resources consumed by the "deployment process of the third machine learning model" and improves the convenience of the "deployment process of the third machine learning model".

[0153] In another case, the second machine learning model includes a first module and a second module, the first module is used to generate a contribution value of each display behavior in at least one display behavior, and the second module is used to perform weighted summation on the contribution value of each display behavior in at least one display behavior generated by the first module to obtain a total contribution value corresponding to at least one display behavior; "the second machine learning model that has performed a training operation is used to obtain the contribution value of each display behavior in at least one display behavior" can be understood as: the first module in the second machine learning model after performing a training operation is used to obtain the contribution value of each display behavior in at least one display behavior.

[0154] 2. Application Phase

[0155] In the embodiment of the present application, the application stage describes a process in which the execution device 230 processes the first sample using the third machine learning model. Specifically, please refer to FIG7 , which is a flow chart of a method for obtaining a contribution value provided in the embodiment of the present application. The method for obtaining a contribution value provided in the embodiment of the present application may include:

[0156] 701. Obtain a first sample, where the first sample indicates at least one display behavior, and the at least one display behavior has been interacted with.

[0157] In an embodiment of the present application, the execution device may obtain a first sample, where the first sample includes description information of each display behavior in the at least one display behavior. For example, the description information of each display behavior may include the display time of each display behavior.

[0158] Optionally, the description information of each display behavior may also include the display location of each display behavior or other information, etc., which is not limited in the embodiments of this application. It should be understood that the specific expression form of the "first sample" is similar to the specific expression form of the "fourth training sample", except that the "fourth display behavior" in the fourth training sample is replaced by the "display behavior" in the first sample. The specific expression form of the first sample is not further described here.

[0159] 702. Generate a contribution value for each display behavior pointed to by the first sample through a third machine learning model, the contribution value of each display behavior represents the contribution of each display behavior to at least one display behavior being interacted with, and the third machine learning model is obtained based on a second machine learning model that has performed a training operation; wherein, the first loss function used in the aforementioned training operation indicates the similarity between the first information corresponding to the first training sample and the second information, the first training sample indicates at least one first display behavior, the first information is obtained through the first machine learning model, the first information indicates the probability of at least one first display behavior being interacted with, the second information is obtained through the second machine learning model, the second information indicates the sum of the contribution values ​​of each first display behavior in at least one first display behavior, and the contribution value of each first display behavior represents the contribution of each first display behavior to at least one first display behavior being interacted with.

[0160] In one embodiment of the present application, "the third machine learning model is obtained based on the second machine learning model that has been trained" can be understood as: the third machine learning model is obtained based on the value of at least one parameter in the second machine learning model that has been trained. By way of example, with reference to formula (2) shown in the embodiment corresponding to FIG4 , the third machine learning model can be obtained based on the values ​​of α and β in the second machine learning model that has been trained.

[0161] To further understand this solution, the formula used when processing the first sample using the third machine learning model is disclosed below:

[0162] The first sample indicates T display behaviors, w i Represents the contribution value of the i-th display behavior among the T display behaviors pointed to by the first sample, that is, it represents the contribution made by the i-th display behavior to the interaction of T display behaviors. Represents the sum of the contribution values ​​of the 1st to i-1th display behaviors to the i-th display behavior in the first sample, represents the sum of the contribution values ​​of the i-th display behavior to the T-th display behavior in the first sample. When the value of j is equal to i, it represents the contribution value of the i-th display behavior itself in the T display behaviors. irepresents the display time of the first display behavior of i, t j represents the display time of the jth first display behavior. The values ​​of α and β are obtained based on the trained second machine learning model. It should be understood that the example in formula (4) is only for the convenience of understanding this solution and is not used to limit this solution.

[0163] In another case, the second machine learning model includes a first module and a second module, the first module is used to generate a contribution value of each display behavior in at least one display behavior, and the second module is used to perform weighted summation on the contribution value of each display behavior in at least one display behavior generated by the first module to obtain a total contribution value corresponding to at least one display behavior; "the third machine learning model is obtained based on the second machine learning model that has performed a training operation" can be understood as: the third machine learning model is the first module in the second machine learning model after the training operation has been performed.

[0164] Exemplarily, after obtaining the first sample, the execution device can process the first sample through the third machine learning model to obtain the contribution value of each display behavior pointed to by the first sample; the third machine learning model is obtained based on the second machine learning model that has performed the training operation. The training process of the second machine learning model can refer to the description in the embodiment corresponding to Figure 4 above, and will not be repeated here.

[0165] In order to have a more intuitive understanding of the beneficial effects brought about by the embodiments of the present application, experiments were conducted on the Criteo dataset. The beneficial effects brought about by the present application are demonstrated below in combination with the experimental data.

[0166] Table 1

[0167] Basic, Basic+Attention, and Basic+Shapley are two existing methods for generating the contribution value of each display behavior. AUC is an evaluation metric for the "contribution value of each display behavior generated by the machine learning model." The AUC value ranges from 0.5 to 1. The closer the AUC value is to 1, the higher the accuracy of the "contribution value of each display behavior generated by the machine learning model." The above data shows that the contribution value of each display behavior obtained using the method provided in this application is the most accurate.

[0168] Based on the embodiments corresponding to Figures 1a to 7, in order to better implement the above-mentioned solutions of the embodiments of the present application, the following also provides related devices for implementing the above-mentioned solutions. Referring specifically to Figure 8, Figure 8 is a structural schematic diagram of a data processing device provided in an embodiment of the present application, wherein the data processing device 800 includes: a processing module 801, configured to input a first training sample into a first machine learning model to obtain first information output by the first machine learning model, wherein the first training sample indicates at least one first display behavior, and the first information indicates the probability of at least one first display behavior being interacted; a processing module 801, configured to process the first training sample using a second machine learning model to obtain second information, wherein the second information indicates the sum of the contribution values ​​of each first display behavior in the at least one first display behavior, and the contribution value of each first display behavior represents the contribution made by each first display behavior to the at least one first display behavior being interacted; a training module 802, configured to train the second machine learning model according to a loss function until a convergence condition is met, thereby obtaining a second machine learning model that has been trained, wherein the loss function indicates the similarity between the first information and the second information, and the second machine learning model that has been trained is used to obtain the contribution value of each display behavior in the at least one display behavior.

[0169] Optionally, the target display behavior is any one of at least one first display behavior, wherein the contribution value of the target display behavior is obtained based on the first value and the second value corresponding to the target display behavior, the first value corresponding to the target display behavior represents the contribution value of the target display behavior itself, and the second value corresponding to the target display behavior represents the influence value of other first display behaviors other than the target display behavior in at least one first display behavior indicated by the first training sample on the contribution value of the target display behavior.

[0170] Optionally, the first training sample includes the display time of the first display behavior, wherein the contribution value of other first display behaviors other than the target display behavior in at least one first display behavior indicated by the first training sample to the target display behavior is related to the first time distance, and the first time distance is the distance between the display time of the target display behavior and the display time of the other first display behaviors.

[0171] Optionally, the first training sample comes from a training data set, and there is a second training sample in the training data set, wherein the second training sample is obtained based on a third training sample, and the third training sample includes the display time of at least one second display behavior, and at least one display time in the third training sample is updated to obtain the second training sample.

[0172] Optionally, the values ​​of the parameters in the second machine learning model that has been trained are used to obtain the contribution value of each presentation behavior in at least one presentation behavior.

[0173] Optionally, the first machine learning model is pre-trained, and the first machine learning model is any one of the following: a recurrent neural network, a convolutional neural network, a fully connected neural network, or an attention-based neural network.

[0174] Optionally, the probability of at least one first display behavior being interacted is any one of the following: the probability of being clicked in at least one first display behavior, the probability of being downloaded in at least one first display behavior, the probability of being purchased in at least one first display behavior, or the probability of being played in at least one first display behavior.

[0175] It should be noted that the information interaction, execution process, etc. between the modules / units in the data processing device 800 are based on the same concept as the various method embodiments corresponding to Figures 1a to 6 in this application. For specific contents, please refer to the description in the method embodiments shown above in this application, and will not be repeated here.

[0176] Please continue to refer to Figure 9, which is a structural schematic diagram of a contribution value acquisition device provided in an embodiment of the present application. The contribution value acquisition device 900 includes: an acquisition module 901, used to acquire a first sample, the first sample indicating at least one display behavior, and at least one display behavior has been interacted; a generation module 902, used to generate a contribution value for each display behavior pointed to by the first sample through a third machine learning model, the contribution value of each display behavior represents the contribution of each display behavior to the at least one display behavior being interacted, and the third machine learning model is obtained based on a second machine learning model that has performed a training operation; wherein the loss function used in the training operation indicates the similarity between first information and second information corresponding to the first training sample, the first training sample indicates at least one first display behavior, the first information is obtained through the first machine learning model, the first information indicates the probability of the at least one first display behavior being interacted, the second information is obtained through the second machine learning model, the second information indicates the sum of the contribution values ​​of each first display behavior in the at least one first display behavior, and the contribution value of each first display behavior represents the contribution of each first display behavior to the at least one first display behavior being interacted.

[0177] Optionally, the third display behavior is any one of the at least one display behavior indicated by the first sample, wherein the contribution value of the third display behavior is obtained based on the first value and the second value corresponding to the third display behavior, the first value corresponding to the third display behavior represents the contribution value of the third display behavior itself, and the second value corresponding to the third display behavior represents the influence value of other display behaviors other than the third display behavior in the at least one display behavior indicated by the first sample on the contribution value of the third display behavior.

[0178] Optionally, the first sample also includes the display time of the display behavior indicated by the first sample, wherein the contribution value of other display behaviors other than the third display behavior in at least one display behavior indicated by the first sample to the third display behavior is related to the second time distance, and the second time distance is the distance between the display time of the third display behavior and the display time of other display behaviors.

[0179] Optionally, the first training sample comes from a training data set, and there is a second training sample in the training data set, wherein the second training sample is obtained based on a third training sample, and the third training sample includes the display time of at least one second display behavior, and at least one display time in the third training sample is updated to obtain the second training sample.

[0180] It should be noted that the information interaction, execution process, etc. between the modules / units in the contribution value acquisition device 900 are based on the same concept as the various method embodiments corresponding to Figure 7 in this application. For specific contents, please refer to the description in the method embodiment shown above in this application, and will not be repeated here.

[0181] Next, an execution device provided in an embodiment of the present application will be described. Please refer to FIG10 , which is a schematic structural diagram of an execution device provided in an embodiment of the present application. Specifically, the execution device 1000 includes: a receiver 1001, a transmitter 1002, a processor 1003, and a memory 1004 (wherein the number of processors 1003 in the execution device 1000 may be one or more, and FIG10 uses one processor as an example). The processor 1003 may include an application processor 10031 and a communication processor 10032. In some embodiments of the present application, the receiver 1001, the transmitter 1002, the processor 1003, and the memory 1004 may be connected via a bus or other means.

[0182] The memory 1004 may include a read-only memory and a random access memory, and provides instructions and data to the processor 1003. A portion of the memory 1004 may also include non-volatile random access memory (NVRAM). The memory 1004 stores processor and operation instructions, executable modules, or data structures, or subsets or extended sets thereof. The operation instructions may include various operation instructions for implementing various operations.

[0183] Processor 1003 controls the operation of the execution device. In specific applications, the various components of the execution device are coupled together via a bus system. In addition to a data bus, the bus system may also include a power bus, a control bus, and a status signal bus. However, for clarity, all bus systems are referred to as a bus system in the figure.

[0184] The methods disclosed in the above embodiments of the present application can be applied to or implemented by processor 1003. Processor 1003 can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits or software instructions in processor 1003. The above processor 1003 can be a general-purpose processor, a digital signal processor (DSP), a microprocessor, or a microcontroller, and can further include an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The processor 1003 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in conjunction with the embodiments of the present application can be directly implemented as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 1004, and processor 1003 reads the information in memory 1004 and, in conjunction with its hardware, completes the steps of the above method.

[0185] Receiver 1001 can be used to receive input digital or character information and generate signal input related to executing device-related settings and function control. Transmitter 1002 can be used to output digital or character information through the first interface. Transmitter 1002 can also be used to send instructions to the disk pack through the first interface to modify data in the disk pack. Transmitter 1002 can also include a display device such as a display screen.

[0186] In the embodiment of the present application, the processor 1003 is configured to execute the method for obtaining the contribution value executed by the execution device in the embodiment corresponding to FIG7 . It should be noted that the specific manner in which the application processor 10031 in the processor 1003 executes the aforementioned steps is based on the same concept as the various method embodiments corresponding to FIG7 in the present application, and the technical effects brought about are the same as the various method embodiments corresponding to FIG7 in the present application. For specific details, please refer to the description of the method embodiments shown above in the present application, and no further details will be given here.

[0187] The present application also provides a training device. Please refer to FIG. 11 , which is a schematic diagram of the structure of a training device provided by an embodiment of the present application. Specifically, the training device 1100 is implemented by one or more servers. The training device 1100 may vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 1122 (e.g., one or more processors), a memory 1132, and one or more storage media 1130 (e.g., one or more mass storage devices) storing application programs 1142 or data 1144. The memory 1132 and storage medium 1130 may be either ephemeral or persistent storage. The program stored in the storage medium 1130 may include one or more modules (not shown), each of which may include a series of instruction operations on the training device. Furthermore, the CPU 1122 may be configured to communicate with the storage medium 1130 to execute the series of instruction operations stored in the storage medium 1130 on the training device 1100.

[0188] The training device 1100 may also include one or more power supplies 1126, one or more wired or wireless network interfaces 1150, one or more input and output interfaces 1158, and / or one or more operating systems 1141, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0189] In the embodiment of the present application, the central processing unit 1122 is used to execute the data processing method performed by the training device in the embodiments corresponding to Figures 3 to 6, where the aforementioned training device includes the first training device and / or the second training device. It should be noted that the specific manner in which the central processing unit 1122 executes the above-mentioned steps is based on the same concept as the various method embodiments corresponding to Figures 3 to 6 of the present application, and the technical effects brought about are the same as the various method embodiments corresponding to Figures 3 to 6 of the present application. For specific details, please refer to the description of the method embodiments shown above in the present application, and will not be repeated here.

[0190] A computer-readable storage medium is also provided in an embodiment of the present application, which stores a program for signal processing. When the computer-readable storage medium is run on a computer, it enables the computer to execute the steps performed by the first training device and / or the second training device in the method described in the embodiments shown in Figures 3 to 6 above, or enables the computer to execute the steps performed by the execution device in the method described in the embodiment shown in Figure 7 above.

[0191] Also provided in an embodiment of the present application is a computer program product, which, when run on a computer, enables the computer to execute the steps performed by the first training device and / or the second training device in the method described in the embodiments shown in Figures 3 to 6 above, or enables the computer to execute the steps performed by the execution device in the method described in the embodiment shown in Figure 7 above.

[0192] The execution device and training device provided in the embodiments of the present application may specifically be a chip, and the chip includes: a processing unit and a communication unit, wherein the processing unit may be, for example, a processor, and the communication unit may be, for example, an input / output interface, a pin, or a circuit. The processing unit may execute the computer-executable instructions stored in the storage unit, so that the chip executes the data processing method described in the embodiments shown in Figures 3 to 6 above, or so that the chip executes the contribution value acquisition method described in the embodiment shown in Figure 7 above. Optionally, the storage unit is a storage unit within the chip, such as a register, a cache, etc. The storage unit may also be a storage unit located outside the chip within the wireless access device, such as a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM), etc.

[0193] Specifically, see Figure 12, which is a schematic diagram of the structure of a chip provided in an embodiment of the present application. The chip can be represented as a neural network processor NPU 120. NPU 120 is mounted on the host CPU (host CPU) as a coprocessor and assigned tasks by the host CPU. The core of the NPU is arithmetic circuit 1203, which is controlled by controller 1204 to extract matrix data from memory and perform multiplication operations.

[0194] In some implementations, the arithmetic circuit 1203 includes multiple processing units (PEs). In some implementations, the arithmetic circuit 1203 is a two-dimensional systolic array. The arithmetic circuit 1203 can also be a one-dimensional systolic array or other electronic circuitry capable of performing mathematical operations such as multiplication and addition. In some implementations, the arithmetic circuit 1203 is a general-purpose matrix processor.

[0195] For example, assume there are input matrix A, weight matrix B, and output matrix C. The arithmetic circuit retrieves the corresponding data of matrix B from weight memory 1202 and caches it on each PE in the arithmetic circuit. The arithmetic circuit retrieves the data of matrix A from input memory 1201 and performs a matrix operation on matrix B. The partial or final matrix result is stored in accumulator 1208.

[0196] Unified memory 1206 is used to store input and output data. Weight data is directly transferred to weight memory 1202 through the Direct Memory Access Controller (DMAC) 1205. Input data is also transferred to unified memory 1206 through the DMAC.

[0197] BIU stands for Bus Interface Unit, i.e., bus interface unit 1210 , which is used for interaction between the AXI bus, DMAC, and instruction fetch buffer (IFB) 1209 .

[0198] The bus interface unit 1210 (BIU) is used for the instruction fetch memory 1209 to obtain instructions from the external memory, and is also used for the storage unit access controller 1205 to obtain the original data of the input matrix A or the weight matrix B from the external memory.

[0199] DMAC is mainly used to transfer input data in the external memory DDR to the unified memory 1206 or transfer weight data to the weight memory 1202 or transfer input data to the input memory 1201.

[0200] The vector calculation unit 1207 includes multiple operation processing units. When necessary, it further processes the output of the operation circuit, such as vector multiplication, vector addition, exponential operation, logarithmic operation, size comparison, etc. It is mainly used for non-convolutional / fully connected layer network calculations in neural networks, such as batch normalization, pixel-level summation, and upsampling of feature planes.

[0201] In some implementations, the vector calculation unit 1207 can store the processed output vector to the unified memory 1206. For example, the vector calculation unit 1207 can apply a linear function and / or a nonlinear function to the output of the operation circuit 1203, such as linear interpolation of the feature plane extracted by the convolution layer, or accumulate a vector of values ​​to generate an activation value. In some implementations, the vector calculation unit 1207 generates a normalized value, a pixel-level summed value, or both. In some implementations, the processed output vector can be used as an activation input to the operation circuit 1203, for example, for use in a subsequent layer in a neural network.

[0202] An instruction fetch buffer 1209 connected to the controller 1204 is used to store instructions used by the controller 1204;

[0203] Unified memory 1206, input memory 1201, weight memory 1202, and instruction fetch memory 1209 are all on-chip memories. External memories are private to the NPU hardware architecture.

[0204] Among them, the operations of each layer in each machine learning model shown in Figures 3 to 7 can be performed by the operation circuit 1203 or the vector calculation unit 1207.

[0205] The processor mentioned in any of the above places can be a general-purpose central processing unit, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the above-mentioned first aspect method.

[0206] It should also be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided in this application, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines.

[0207] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course can also be implemented by special hardware including application-specific integrated circuits, special CPUs, special memories, special components, etc. In general, all functions performed by computer programs can be easily implemented with corresponding hardware, and the specific hardware structures used to implement the same function can also be diverse, such as analog circuits, digital circuits or special circuits, etc. However, for the present application, software program implementation is a better implementation method in most cases. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer's floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., and includes a number of instructions to enable a computer device (which can be a personal computer, training equipment, or network equipment, etc.) to execute the methods described in each embodiment of the present application.

[0208] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0209] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, a computer, a training device or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website, a computer, a training device or a data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

Claims

1. A data processing method, characterized in that: The method comprises: Inputting a first training sample into a first machine learning model to obtain first information output by the first machine learning model, wherein the first training sample indicates at least one first display behavior, and the first information indicates a probability of the at least one first display behavior being interacted with; Processing the first training sample by a second machine learning model to obtain second information, wherein the second information indicates the sum of contribution values ​​of each first display behavior in the at least one first display behavior, and the contribution value of each first display behavior represents the contribution of each first display behavior to the at least one first display behavior being interacted with; The second machine learning model is trained according to the loss function until a convergence condition is met, thereby obtaining the second machine learning model that has performed a training operation, wherein the loss function indicates the similarity between the first information and the second information, and the second machine learning model that has performed a training operation is used to obtain a contribution value of each display behavior in at least one display behavior.

2. The method according to claim 1, characterized in that: The target display behavior is any one of the at least one first display behavior, wherein the contribution value of the target display behavior is obtained based on a first value and a second value corresponding to the target display behavior, the first value corresponding to the target display behavior represents the contribution value of the target display behavior itself, and the second value corresponding to the target display behavior represents the influence value of other first display behaviors other than the target display behavior in at least one first display behavior indicated by the first training sample on the contribution value of the target display behavior.

3. The method according to claim 2, characterized in that The first training sample includes the display time of the first display behavior, wherein a contribution value of other first display behaviors other than the target display behavior in at least one first display behavior indicated by the first training sample to the target display behavior is related to a first time distance, and the first time distance is the distance between the display time of the target display behavior and the display time of the other first display behaviors.

4. The method according to any one of claims 1 to 3, characterized in that: The first training sample is derived from a training data set, and there is a second training sample in the training data set, wherein the second training sample is obtained based on a third training sample, and the third training sample includes a display time of at least one second display behavior, and at least one display time in the third training sample is updated to obtain the second training sample.

5. The method according to any one of claims 1 to 3, characterized in that: The values ​​of the parameters in the second machine learning model that has performed the training operation are used to obtain the contribution value of each display behavior in at least one display behavior.

6. The method according to any one of claims 1 to 3, characterized in that: The first machine learning model is pre-trained, and the first machine learning model is any one of the following: a recurrent neural network, a convolutional neural network, a fully connected neural network, or an attention-based neural network.

7. The method according to any one of claims 1 to 3, characterized in that: The probability of the at least one first display behavior being interacted with is any one of the following: the probability of being clicked in the at least one first display behavior, the probability of being downloaded in the at least one first display behavior, the probability of being purchased in the at least one first display behavior, or the probability of being played in the at least one first display behavior.

8. A method for obtaining a contribution value, characterized in that: The method comprises: Acquire a first sample, where the first sample indicates at least one display behavior, and the at least one display behavior has been interacted with; generating a contribution value of each display behavior pointed to by the first sample through a third machine learning model, wherein the contribution value of each display behavior represents a contribution made by each display behavior to the at least one display behavior being interacted with, and the third machine learning model is obtained based on the second machine learning model that has performed a training operation; Among them, the loss function adopted in the training operation indicates the similarity between the first information and the second information corresponding to the first training sample, the first training sample indicates at least one first display behavior, the first information is obtained through the first machine learning model, the first information indicates the probability of the at least one first display behavior being interacted, and the second information is obtained through the second machine learning model, the second information indicates the sum of the contribution values ​​of each first display behavior in the at least one first display behavior, and the contribution value of each first display behavior represents the contribution of each first display behavior to the interaction of the at least one first display behavior.

9. The method according to claim 8, characterized in that The third display behavior is any one of the at least one display behavior indicated by the first sample, wherein the contribution value of the third display behavior is obtained based on the first value and the second value corresponding to the third display behavior, the first value corresponding to the third display behavior represents the contribution value of the third display behavior itself, and the second value corresponding to the third display behavior represents the influence value of other display behaviors other than the third display behavior in the at least one display behavior indicated by the first sample on the contribution value of the third display behavior.

10. The method according to claim 9, characterized in that The first sample also includes the display time of the display behavior indicated by the first sample, wherein the contribution value of other display behaviors other than the third display behavior in at least one display behavior indicated by the first sample to the third display behavior is related to the second time distance, and the second time distance is the distance between the display time of the third display behavior and the display time of the other display behaviors.

11. The method according to any one of claims 8 to 10, characterized in that: The first training sample is derived from a training data set, and there is a second training sample in the training data set, wherein the second training sample is obtained based on a third training sample, and the third training sample includes a display time of at least one second display behavior, and at least one display time in the third training sample is updated to obtain the second training sample.

12. A data processing device, characterized in that: The device comprises: a processing module, configured to input a first training sample into a first machine learning model to obtain first information output by the first machine learning model, wherein the first training sample indicates at least one first display behavior, and the first information indicates a probability of the at least one first display behavior being interacted with; The processing module is configured to process the first training sample through a second machine learning model to obtain second information, wherein the second information indicates the sum of contribution values ​​of each first display behavior in the at least one first display behavior, and the contribution value of each first display behavior represents the contribution of each first display behavior to the at least one first display behavior being interacted with; A training module is used to train the second machine learning model according to a loss function until a convergence condition is met, thereby obtaining the second machine learning model that has performed a training operation, wherein the loss function indicates the similarity between the first information and the second information, and the second machine learning model that has performed a training operation is used to obtain a contribution value of each display behavior in at least one display behavior.

13. The device according to claim 12, characterized in that The target display behavior is any one of the at least one first display behavior, wherein the contribution value of the target display behavior is obtained based on a first value and a second value corresponding to the target display behavior, the first value corresponding to the target display behavior represents the contribution value of the target display behavior itself, and the second value corresponding to the target display behavior represents the influence value of other first display behaviors other than the target display behavior in at least one first display behavior indicated by the first training sample on the contribution value of the target display behavior.

14. The device according to claim 13, characterized in that The first training sample includes the display time of the first display behavior, wherein a contribution value of other first display behaviors other than the target display behavior in at least one first display behavior indicated by the first training sample to the target display behavior is related to a first time distance, and the first time distance is the distance between the display time of the target display behavior and the display time of the other first display behaviors.

15. The device according to any one of claims 12 to 14, characterized in that The first training sample is derived from a training data set, and there is a second training sample in the training data set, wherein the second training sample is obtained based on a third training sample, and the third training sample includes a display time of at least one second display behavior, and at least one display time in the third training sample is updated to obtain the second training sample.

16. The device according to any one of claims 12 to 14, characterized in that The values ​​of the parameters in the second machine learning model that has performed the training operation are used to obtain the contribution value of each display behavior in at least one display behavior.

17. The device according to any one of claims 12 to 14, characterized in that The first machine learning model is pre-trained, and the first machine learning model is any one of the following: a recurrent neural network, a convolutional neural network, a fully connected neural network, or an attention-based neural network.

18. The device according to any one of claims 12 to 14, characterized in that The probability of the at least one first display behavior being interacted with is any one of the following: the probability of being clicked in the at least one first display behavior, the probability of being downloaded in the at least one first display behavior, the probability of being purchased in the at least one first display behavior, or the probability of being played in the at least one first display behavior.

19. A contribution value acquisition device, characterized in that: The device comprises: An acquisition module, configured to acquire a first sample, wherein the first sample indicates at least one display behavior, and the at least one display behavior has been interacted with; a generating module, configured to generate a contribution value of each display behavior pointed to by the first sample through a third machine learning model, wherein the contribution value of each display behavior represents a contribution made by each display behavior to the at least one display behavior being interacted with, and the third machine learning model is obtained based on the second machine learning model that has performed a training operation; Among them, the loss function adopted in the training operation indicates the similarity between the first information and the second information corresponding to the first training sample, the first training sample indicates at least one first display behavior, the first information is obtained through the first machine learning model, the first information indicates the probability of the at least one first display behavior being interacted, and the second information is obtained through the second machine learning model, the second information indicates the sum of the contribution values ​​of each first display behavior in the at least one first display behavior, and the contribution value of each first display behavior represents the contribution of each first display behavior to the interaction of the at least one first display behavior.

20. The device according to claim 19, characterized in that The third display behavior is any one of the at least one display behavior indicated by the first sample, wherein the contribution value of the third display behavior is obtained based on the first value and the second value corresponding to the third display behavior, the first value corresponding to the third display behavior represents the contribution value of the third display behavior itself, and the second value corresponding to the third display behavior represents the influence value of other display behaviors other than the third display behavior in the at least one display behavior indicated by the first sample on the contribution value of the third display behavior.

21. The device according to claim 20, characterized in that The first sample also includes the display time of the display behavior indicated by the first sample, wherein the contribution value of other display behaviors other than the third display behavior in at least one display behavior indicated by the first sample to the third display behavior is related to the second time distance, and the second time distance is the distance between the display time of the third display behavior and the display time of the other display behaviors.

22. The device according to any one of claims 19 to 21, characterized in that The first training sample is derived from a training data set, and there is a second training sample in the training data set, wherein the second training sample is obtained based on a third training sample, and the third training sample includes a display time of at least one second display behavior, and at least one display time in the third training sample is updated to obtain the second training sample.

23. An execution device, characterized in that: comprising a processor and a memory, the processor being coupled to the memory, The memory is used to store programs; The processor is configured to execute the program in the memory so that the execution device executes the method according to any one of claims 8 to 11.

24. A training device, characterized in that comprising a processor and a memory, the processor being coupled to the memory, The memory is used to store programs; The processor is used to execute the program in the memory so that the training device performs the method as claimed in any one of claims 1 to 7.

25. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, and when the program is executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 11.

26. A computer program product, characterized in that The computer program product comprises a program, and when the program is run on a computer, the computer is caused to execute the method according to any one of claims 1 to 11.

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