Data processing method and related apparatus

Abstract

A data processing method, which can be applied to the field of artificial intelligence. The method comprises: acquiring first attribute information and at least one calibration feature of a first user and a first article, the calibration feature indicating at least one of a recommendation scenario, an article, or an article provider; acquiring a calibration coefficient corresponding to each calibration feature; on the basis of the first attribute information, by means of a recommendation model, obtaining a first recommendation score of recommending the first article to the first user; and adjusting the first recommendation score on the basis of the calibration coefficient. According to the present application, corresponding calibration coefficients are set for different calibration dimensions, and calibration coefficients for calibration dimensions related to the current data to be recommended are used to adjust the output of models, so that the recommendation effect and precision of the models can be improved.

Description

A data processing method and related device

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on October 31, 2023, with application number 202311437873.4 and application name “A data processing method and related device”, 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 and related devices. Background Art

[0003] Artificial intelligence (AI) refers to the theories, methods, technologies, 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] Accurately estimating user behavior is crucial in recommendation models. For example, when estimating the click-through rate (CTR) of ads, the model needs to accurately estimate the probability of a user clicking on an ad or news item, thereby guiding the platform to perform personalized ranking based on user interests. In typical online ad recommendation systems, the model's estimated scores influence the ranking and bidding mechanisms. Ideally, the score distribution estimated by the recommendation model should align with the probability of actual feedback. For example, if the model estimates an average CTR of 5%, then the expectation is that 5% of ad impressions will result in a click. However, in reality, the model's estimated scores may differ from the actual observed values ​​in online data. This may be due to inherent bias in the model or variations in the data distribution. Overestimation or underestimation of the model's scores can lead to wasted impression opportunities, excessive costs for advertisers, and even online operational failures. Therefore, model calibration methods are often used to post-process the model's estimated scores, ensuring that the score distribution of the recommendation model's estimates aligns with the probability of actual feedback.

[0005] Existing techniques use post-processing to correct the recommendation model's predictions based on recent logs collected online, ensuring that the model's estimated probability distribution aligns with actual user feedback. However, because these existing methods calibrate the model's global output using a single calibration coefficient, the calibration effect is poor.

[0006] Summary of the Invention

[0007] In a first aspect, the present application provides a data processing method, comprising: obtaining first attribute information of a first user and a first item and at least one calibration feature; the calibration feature indicates at least one of a recommended scenario, an item, or a provider of an item; obtaining a calibration coefficient corresponding to each of the calibration features; based on the first attribute information, obtaining a first recommendation score for recommending the first item to the first user through a recommendation model; and adjusting the first recommendation score based on the calibration coefficient.

[0008] Calibration features are features that influence the recommendation score. For example, these features can include the item ID, the recommended layout, the item provider's (e.g., advertiser's) ID, the recommendation platform, and the recommendation scenario. The recommendation scenario can be contextual (e.g., time, region, etc.). Different calibration features can lead to different data distributions, thus affecting the recommendation score and results.

[0009] In an embodiment of the present application, by setting corresponding calibration coefficients for different calibration dimensions and adjusting the output of the model using the calibration coefficients of the calibration dimensions related to the data currently to be recommended, the recommendation effect and accuracy of the model can be improved.

[0010] In one possible implementation, the method further includes:

[0011] Obtain a second recommendation score for recommending the second item to the second user, and an actual interaction score of the second user with the second item; the second recommendation score is obtained through the recommendation model, and the second attribute information corresponds to a target calibration feature; the target standard feature is one of the at least one calibration feature; calculate a first degree of deviation between the actual interaction score and the second recommendation score through a binomial confidence interval calculation method; the calibration coefficient corresponding to the target calibration feature is generated based on the degree of deviation.

[0012] The confidence level of the positive sample proportion (that is, the calibration coefficient) is calculated through the confidence interval of the binomial distribution. The model is calibrated with different intensities according to the size of the confidence level. Even if the confidence level of the accumulated data in a shorter time window is used, a good calibration effect can be obtained.

[0013] In one possible implementation, the method further includes: mapping the first deviation degree to obtain a second deviation degree using a target mapping method, wherein the target mapping method is a monotonically increasing function; and calculating a third recommendation score obtained by deviating the actual interaction score by the second deviation degree using a binomial confidence interval calculation method;

[0014] A calibration coefficient corresponding to the target calibration feature is obtained according to a difference between the third recommendation score and the second recommendation score.

[0015] In one possible implementation, obtaining the calibration coefficient corresponding to the target calibration feature based on the difference between the third recommended score and the second recommended score includes: using the ratio of the third recommended score to the second recommended score as the calibration coefficient corresponding to the target calibration feature.

[0016] In a possible implementation, the confidence interval is a Wilson interval.

[0017] In one possible implementation, the first attribute information corresponds to multiple calibration features, and adjusting the first recommendation score based on the calibration coefficients includes: fusing multiple calibration coefficients corresponding to the multiple calibration features to obtain fused calibration coefficients; and adjusting the first recommendation score based on the fused calibration coefficients.

[0018] In a second aspect, the present application provides a data processing device, comprising:

[0019] an acquisition module, configured to acquire first attribute information of a first user and a first item and at least one calibration feature; the calibration feature indicating at least one of a recommended scene, an item, or a provider of the item;

[0020] Obtaining a calibration coefficient corresponding to each of the calibration features;

[0021] A processing module, configured to obtain, based on the first attribute information and using a recommendation model, a first recommendation score for recommending the first item to the first user;

[0022] The first recommendation score is adjusted according to the calibration factor.

[0023] In a possible implementation, the acquisition module is further configured to:

[0024] Obtaining a second recommendation score for recommending the second item to the second user and an actual interaction score of the second user with the second item, wherein the second recommendation score is obtained by the recommendation model, the second attribute information corresponds to a target calibration feature, and the target standard feature is one of the at least one calibration feature;

[0025] The processing module is also used to

[0026] A first degree of deviation between the actual interaction score and the second recommendation score is calculated using a binomial confidence interval calculation method; and a calibration coefficient corresponding to the target calibration feature is generated based on the degree of deviation.

[0027] In a possible implementation, the processing module is further configured to:

[0028] Mapping the first deviation degree by a target mapping device to obtain a second deviation degree; the target mapping device is a monotonically increasing function;

[0029] Calculating, by a binomial confidence interval calculation device, a third recommendation score obtained after the actual interaction score deviates by the second deviation degree;

[0030] A calibration coefficient corresponding to the target calibration feature is obtained according to a difference between the third recommendation score and the second recommendation score.

[0031] In a possible implementation, the processing module is specifically configured to:

[0032] A ratio of the third recommendation score to the second recommendation score is used as a calibration coefficient corresponding to the target calibration feature.

[0033] In a possible implementation, the confidence interval is a Wilson interval.

[0034] In a possible implementation, the processing module is specifically configured to:

[0035] fusing a plurality of calibration coefficients corresponding to the plurality of calibration features to obtain fused calibration coefficients;

[0036] The first recommendation score is adjusted according to the fused calibration coefficient.

[0037] In a third aspect, an embodiment of the present application provides a data processing device, which may include a memory, a processor, and a bus system, wherein the memory is used to store programs, and the processor is used to execute the programs in the memory to perform any optional method as described in the first aspect above.

[0038] In a fourth 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 above-mentioned first aspect and any optional method.

[0039] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising code, which, when executed, is used to implement the above-mentioned first aspect and any optional method.

[0040] In a sixth aspect, the present application provides a chip system comprising a processor configured to support a data processing device in implementing the functions described in the aforementioned aspects, such as transmitting or processing data or information described in the aforementioned methods. In one possible design, the chip system further comprises a memory configured to store program instructions and data necessary for executing or training the device. The chip system may consist solely of a chip or may include a chip and other discrete components. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic diagram of the structure of the artificial intelligence main framework;

[0042] FIG2 is a schematic diagram of a system architecture provided in an embodiment of the present application;

[0043] FIG3 is a schematic diagram of a system architecture provided in an embodiment of the present application;

[0044] FIG4 is a schematic diagram of a recommendation scenario provided in an embodiment of the present application;

[0045] FIG5 is a flow chart of a data processing method provided in an embodiment of the present application;

[0046] Figure 6 is a schematic diagram of an application architecture;

[0047] FIG7 is a schematic structural diagram of a data processing device provided in an embodiment of the present application;

[0048] FIG8 is a schematic diagram of an execution device provided in an embodiment of the present application;

[0049] FIG9 is a schematic diagram of a training device provided in an embodiment of the present application;

[0050] FIG10 is a schematic diagram of a chip provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following describes the embodiments of the present invention in conjunction with the accompanying drawings. The terms used in the embodiments of the present invention are only used to explain the specific embodiments of the present invention, and are not intended to limit the present invention.

[0052] 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.

[0053] 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.

[0054] First, let's describe the overall workflow of an AI system. See Figure 1, which shows a schematic diagram of the AI ​​framework. This 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.

[0055] (1) Infrastructure

[0056] Infrastructure provides computing power for AI systems, enabling communication with the outside world and supporting this through a foundational platform. External communication occurs through sensors; computing power is provided by intelligent chips (CPUs, NPUs, GPUs, ASICs, FPGAs, and other hardware accelerators). The foundational platform includes a distributed computing framework and network-related platform guarantees and support, including cloud storage and computing, and interconnected networks. For example, sensors communicate with the outside world to acquire data, which is then fed into the intelligent chips within the distributed computing system provided by the foundational platform for computation.

[0057] (2) Data

[0058] 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.

[0059] (3) Data processing

[0060] Data processing generally includes data training, machine learning, deep learning, search, reasoning, decision-making, etc.

[0061] Among them, machine learning and deep learning can symbolize and formalize data for intelligent information modeling, extraction, preprocessing, and training.

[0062] 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.

[0063] Decision-making refers to the process of making decisions after intelligent information is reasoned, and usually provides functions such as classification, sorting, and prediction.

[0064] (4) General ability

[0065] 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.

[0066] (5) Smart products and industry applications

[0067] 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 transportation, smart medical care, autonomous driving, smart cities, etc.

[0068] The embodiments of the present application can be applied to the field of information recommendation, which includes but is not limited to scenarios involving e-commerce product recommendations, search engine result recommendations, application market recommendations, music recommendations, video recommendations, etc. The recommended items in various application scenarios can also be called "objects" to facilitate subsequent descriptions, that is, in different recommendation scenarios, the recommended object can be an APP, or a video, or music, or a certain product (such as the presentation interface of an online shopping platform, which will display different products for presentation according to different users, which can actually be presented through the recommendation results of a recommendation model). These recommendation scenarios usually involve user behavior log collection, log data preprocessing (for example, quantization, sampling, etc.), sample set training to obtain a recommendation model, and analysis and processing of the objects involved in the scenario corresponding to the training sample items (such as APP, music, etc.) according to the recommendation model. For example, the samples selected in the recommendation model training link come from the user's operation behavior on the recommended APP in the mobile application market. The recommendation model trained thereby is applicable to the above-mentioned mobile APP application market, or can be used for the APP application market of other types of terminals to recommend terminal APPs. The recommendation model will ultimately calculate the recommendation probability or score of each object to be recommended. The recommendation system selects the recommendation results based on certain selection rules, such as sorting them by recommendation probability or score, and presents them to users through corresponding applications or terminal devices. Users operate on the objects in the recommendation results to generate user behavior logs, etc.

[0069] Referring to Figure 4, during the recommendation process, when a user interacts with the recommendation system, a recommendation request is triggered. The recommendation system inputs this request and its associated feature information into the deployed recommendation model, which then predicts the user's click-through rate for all candidate items. The candidate items are then sorted in descending order based on the predicted click-through rates and displayed in sequential order at different locations as the recommended results for the user. Users browse the displayed items and perform user actions, such as browsing, clicking, and downloading. These user actions are logged as training data, and the offline training module periodically updates the recommendation model parameters to improve the model's recommendation effectiveness.

[0070] For example, when a user opens a mobile app market, the app market's recommendation module is triggered. This module predicts the user's likelihood of downloading each candidate app based on historical download and click history, app features, and contextual information such as time and location. Based on these predictions, the app market displays apps in descending order of likelihood, increasing the probability of download. Specifically, apps more likely to be downloaded are ranked higher, while apps less likely to be downloaded are ranked lower. User behavior is also logged, and the prediction model parameters are trained and updated through an offline training module.

[0071] For example, in applications related to lifelong companions, based on the user's historical data in video, music, news and other domains, various models and algorithms can be used to imitate the human brain mechanism to build a cognitive brain and establish a user lifelong learning system framework. Lifelong companions can record past events of users based on system data and application data, understand users' current intentions, predict users' future actions or behaviors, and ultimately realize intelligent services. In the current first phase, based on music apps, video apps, browser apps, etc., user behavior data (including end-side text messages, photos, email events, etc.) is obtained. On the one hand, a user portrait system is built, and on the other hand, learning and memory modules based on user information filtering, association analysis, cross-domain recommendations, causal reasoning, etc. are implemented to build a user's personal knowledge graph.

[0072] Next, the application architecture of the embodiment of the present application is introduced.

[0073] Referring to FIG2 , an embodiment of the present invention provides a recommendation system architecture 200. The data acquisition device 260 is used to collect samples. A training sample can be composed of multiple feature information (or described as attribute information, such as user attributes and item attributes). Feature information can be of multiple types, specifically including user feature information, object feature information, and label features. User feature information is used to characterize user characteristics, such as gender, age, occupation, hobbies, etc. Object feature information is used to characterize the characteristics of the object pushed to the user. Different recommendation systems correspond to different objects, and the types of features that need to be extracted from different objects are also different. For example, the object features extracted from the training samples of the APP market can be the name (identification), type, size, etc. of the APP. ; The object features mentioned in the training samples of e-commerce apps can be the name of the product, the category it belongs to, the price range, etc.; the label feature is used to indicate whether the sample is a positive example or a negative example. Usually, the label feature of the sample can be obtained through the user's operation information on the recommended object. The sample in which the user operates the recommended object is a positive example, and the sample in which the user does not operate the recommended object or only browses the recommended object is a negative example. For example, when the user clicks, downloads, or purchases the recommended object, the label feature is 1, indicating that the sample is a positive example. If the user does not perform any operation on the recommended object, the label feature is 0, indicating that the sample is a negative example. After collection, the sample can be stored in the database 230. Some or all of the feature information in the sample in the database 230 can also be directly obtained from the client device 240, such as user feature information, user operation information on the object (used to determine the type identification), object feature information (such as object identification), etc. The training device 220 obtains a model parameter matrix based on the sample training in the database 230 for generating a recommendation model 201 (such as the feature extraction network and neural network in the embodiment of the present application). The following will describe in more detail how the training device 220 trains to obtain the model parameter matrix used to generate the recommendation model 201. The recommendation model 201 can be used to evaluate a large number of objects to obtain the scores of each object to be recommended. Furthermore, a specified or preset number of objects can be recommended from the evaluation results of a large number of objects. The calculation module 211 obtains the recommendation results based on the evaluation results of the recommendation model 201 and recommends them to the client device through the I / O interface 212.

[0074] In an embodiment of the present application, the training device 220 can select positive and negative samples from the sample set in the database 230 and add them to the training set, and then use the recommendation model to train the samples in the training set to obtain a trained recommendation model; the implementation details of the calculation module 211 can refer to the detailed description of the method embodiment shown in Figure 5.

[0075] After the training device 220 obtains the model parameter matrix based on sample training and uses it to construct the recommendation model 201, the recommendation model 201 is sent to the execution device 210, or the model parameter matrix is ​​directly sent to the execution device 210, and a recommendation model is constructed in the execution device 210 for making recommendations for the corresponding system. For example, the recommendation model obtained based on video-related sample training can be used to recommend videos to users on video websites or APPs, and the recommendation model obtained based on APP-related sample training can be used to recommend APPs to users in the application market.

[0076] Execution device 210 is equipped with an I / O interface 212 for data exchange with external devices. Execution device 210 can obtain user characteristic information, such as user ID, user identity, gender, occupation, and hobbies, from client device 240 via I / O interface 212. This information can also be obtained from a system database. Recommendation model 201 recommends target objects to the user based on the user characteristic information and the characteristic information of the recommended objects. Execution device 210 can be located in a cloud server or in a user client.

[0077] The execution device 210 can call data, code, etc. in the data storage system 250, and can also store output data in the data storage system 250. The data storage system 250 can be set in the execution device 210, can be set independently, or can be set in other network entities, and can be one or more.

[0078] The calculation module 211 uses the recommendation model 201 to process the user characteristic information and the characteristic information of the object to be recommended. For example, the calculation module 211 uses the recommendation model 201 to analyze and process the user characteristic information and the characteristic information of the object to be recommended, so as to obtain the score of the object to be recommended, and sort the objects to be recommended according to the score, among which the objects with higher rankings will be recommended to the client device 240.

[0079] Finally, the I / O interface 212 returns the recommendation result to the client device 240 and presents it to the user.

[0080] More deeply, the training device 220 can generate corresponding recommendation models 201 based on different sample feature information for different goals to provide users with better results.

[0081] It is worth noting that Figure 2 is only a schematic diagram of a system architecture provided by an embodiment of the present invention. The positional relationship between the devices, components, modules, etc. shown in the figure does not constitute any limitation. For example, in Figure 2, the data storage system 250 is an external memory relative to the execution device 210. In other cases, the data storage system 250 can also be placed in the execution device 210.

[0082] In an embodiment of the present application, the training device 220, the execution device 210, and the client device 240 can be three different physical devices respectively. It is also possible that the training device 220 and the execution device 210 are on the same physical device or a cluster. It is also possible that the execution device 210 and the client device 240 are on the same physical device or a cluster.

[0083] Refer to Figure 3, which is a system architecture 300 proposed in an embodiment of the present invention. In this architecture, the execution device 210 is implemented by one or more servers, and optionally cooperates with other computing devices, such as data storage, routers, load balancers and other devices; the execution device 210 can be arranged at one physical site, or distributed at multiple physical sites. The execution device 210 can use the data in the data storage system 250, or call the program code in the data storage system 250 to implement the object recommendation function. Specifically, the information of the object to be recommended is input into the recommendation model, and the recommendation model generates an estimated score for each object to be recommended, and then sorts them in order from high to low according to the estimated score, and recommends the object to be recommended to the user according to the sorting result. For example, the first 10 objects in the sorting result are recommended to the user.

[0084] Among them, the data storage system 250 is used to receive and store the parameters of the recommendation model sent by the training device, as well as the data for storing the recommendation results obtained by the recommendation model. Of course, it may also include the program code (or instructions) required for the normal operation of the storage system 250. The data storage system 250 can be a distributed storage cluster composed of one device or multiple devices deployed outside the execution device 210. In this case, when the execution device 210 needs to use the data on the storage system 250, the storage system 250 can send the data required by the execution device to the execution device 210. Accordingly, the execution device 210 receives and stores (or caches) the data. Of course, the data storage system 250 can also be deployed in the execution device 210. When deployed in the execution device 210, the distributed storage system can include one or more memories. Optionally, when there are multiple memories, different memories are used to store different types of data. For example, the model parameters of the recommendation model generated by the training device and the data of the recommendation results obtained by the recommendation model can be stored in two different memories respectively.

[0085] Users can operate their respective user devices (e.g., local device 301 and local device 302) to interact with execution device 210. Each local device can represent any computing device, such as a personal computer, a computer workstation, a smartphone, a tablet computer, a smart camera, a smart car or other type of cellular phone, a media consumption device, a wearable device, a set-top box, a game console, etc.

[0086] Each user's local device can interact with the execution device 210 through a communication network of any communication mechanism / communication standard. The communication network can be a wide area network, a local area network, a point-to-point connection, etc., or any combination thereof.

[0087] In another implementation, the execution device 210 can be implemented by a local device. For example, the local device 301 can implement the recommendation function of the execution device 210 based on the recommendation model to obtain user feature information and feedback the recommendation results to the user, or provide services to the user of the local device 302.

[0088] Since the embodiments of the present application involve the application of a large number of neural networks, in order to facilitate understanding, the relevant terms and related concepts such as neural networks involved in the embodiments of the present application are first introduced below.

[0089] 1. Click-throughrate (CTR)

[0090] Click probability, also known as click-through rate, refers to the ratio of the number of clicks and exposures of recommended information (for example, recommended items) on a website or application. Click-through rate is usually an important indicator for measuring recommendation systems in recommendation systems.

[0091] 2. Personalized recommendation system

[0092] A personalized recommendation system refers to a system that uses machine learning algorithms to analyze users' historical data (such as the operation information in the embodiments of the present application), predicts new requests based on this data, and provides personalized recommendation results.

[0093] 3. Offline training

[0094] Offline training refers to a module in a personalized recommendation system that iteratively updates the recommendation model parameters according to the machine learning algorithm based on the user's historical data (such as the operation information in the embodiment of the present application) until the set requirements are met.

[0095] 4. Online Inference

[0096] Online prediction refers to predicting the user's preference for recommended items in the current context based on the characteristics of the user, item, and context based on the offline trained model, and predicting the probability of the user selecting the recommended item.

[0097] For example, Figure 4 is a schematic diagram of the recommendation system provided by an embodiment of the present application. As shown in Figure 4, when a user enters the system, a recommendation request will be triggered. The recommendation system will input the request and related information (such as the operation information in the embodiment of the present application) into the recommendation model, and then predict the user's selection rate of items in the system. Furthermore, the items are sorted in descending order according to the predicted selection rate or a function based on the selection rate, that is, the recommendation system can display the items in different positions in sequence as recommendation results for the user. The user browses items in different positions and performs user behaviors, such as browsing, selecting, and downloading. At the same time, the user's actual behavior will be stored in the log as training data, and the parameters of the recommendation model will be continuously updated through the offline training module to improve the prediction effect of the model.

[0098] For example, a user opening an app store on a smart device (e.g., a mobile phone) triggers the app store's recommendation system. The app store's recommendation system predicts the probability of the user downloading each recommended app based on the user's historical behavior logs, such as their download history and selection history, as well as the app store's own characteristics, such as time, location, and other environmental characteristics. Based on the calculated results, the app store's recommendation system can display the candidate apps in descending order of predicted probability, thereby increasing the download probability of the candidate apps.

[0099] For example, an APP with a higher predicted user selection rate may be displayed in a front recommendation position, and an APP with a lower predicted user selection rate may be displayed in a back recommendation position.

[0100] The above-mentioned recommendation model can be a neural network model. The following introduces the relevant terms and concepts of neural networks that may be involved in the embodiments of the present application.

[0101] (1) Neural Network

[0102] A neural network can be composed of neural units. A neural unit can refer to an operation unit that takes xs (i.e., input data) and intercept 1 as input. The output of the operation unit can be:

[0103] Where s = 1, 2, ... n, n is a natural number greater than 1, Ws is the weight of xs, and b is the bias of the neural unit. f is the activation function of the neural unit, which is used to introduce nonlinear characteristics into the neural network to convert the input signal of the neural unit into the output signal. The output signal of the activation function can be used as the input of the next convolutional layer, and the activation function can be a sigmoid function. A neural network is a network formed by connecting multiple single neural units mentioned above, that is, the output of one neural unit can be the input of another neural unit. The input of each neural unit can be connected to the local receptive field of the previous layer to extract the features of the local receptive field. The local receptive field can be an area composed of several neural units.

[0104] (2) Deep Neural Networks

[0105] Deep Neural Network (DNN), also known as multi-layer neural network, can be understood as a neural network with many hidden layers. There is no special metric for "many" here. Based on the position of different layers in DNN, the neural network inside DNN can be divided into three categories: input layer, hidden layer, and output layer. Generally speaking, the first layer is the input layer, the last layer is the output layer, and the layers in between are all hidden layers. The layers are fully connected, that is, any neuron in the i-th layer must be connected to any neuron in the i+1-th layer. Although DNN looks complicated, the work of each layer is actually not complicated. Simply put, it is the following linear relationship expression: in, is the input vector, is the output vector, is the offset vector, W is the weight matrix (also called coefficient), and α() is the activation function. Each layer is just an input vector After such a simple operation, the output vector Since there are many DNN layers, the coefficient W and the offset vector The definition of these parameters in DNN is as follows: Take the coefficient W as an example: Assume that in a three-layer DNN, the linear coefficient from the 4th neuron in the second layer to the 2nd neuron in the third layer is defined as The superscript 3 represents the layer number of the coefficient W, while the subscript corresponds to the output of the third layer index 2 and the input of the second layer index 4. In summary, the coefficient from the kth neuron in the L-1th layer to the jth neuron in the Lth layer is defined as It's important to note that the input layer has no W parameter. In deep neural networks, more hidden layers allow the network to better capture complex real-world situations. Theoretically, a model with more parameters has higher complexity and greater "capacity," meaning it can handle more complex learning tasks. Training a deep neural network is essentially the process of learning the weight matrix, with the ultimate goal of obtaining the weight matrices for all layers of a trained deep neural network (a weight matrix formed by the vectors W across many layers).

[0106] (3) Loss function

[0107] During the training of a deep neural network, because we want the output of the deep neural network to be as close as possible to the desired predicted value, we can compare the current network's predicted value with the desired target value and then update the weight vector of each layer of the neural network based on the difference between the two. (Of course, before the first update, there is usually an initialization process, which pre-configures the parameters for each layer in the deep neural network.) For example, if the network's predicted value is too high, the weight vector is adjusted to make it predict a lower value. This adjustment is continued until the deep neural network can predict the desired target value or a value very close to the desired target value. Therefore, it is necessary to predefine "how to compare the difference between the predicted value and the target value." This is the loss function (or objective function), which is an important equation used to measure the difference between the predicted value and the target value. For example, the loss function output value (loss) indicates a greater difference, so training a deep neural network becomes a process of minimizing this loss.

[0108] (4) Backpropagation algorithm

[0109] The back propagation (BP) algorithm can be used to correct the size of the initial model parameters during training, reducing the model's error loss. Specifically, forward propagation of the input signal to the output generates error loss. This error loss information is then backpropagated to update the parameters in the initial model, thereby converging the error loss. The BP algorithm is a backward propagation movement driven by error loss, aiming to obtain optimal model parameters, such as the weight matrix.

[0110] (5) Machine Learning Systems

[0111] Based on the input data and labels, the parameters of the machine learning model are trained through optimization methods such as gradient descent, and finally the trained model is used to complete the prediction of unknown data.

[0112] (6) Personalized recommendation system

[0113] A system that uses machine learning algorithms to analyze and model users' historical data, predict new user requests, and provide personalized recommendation results.

[0114] (7) Model calibration: By mapping the output of the model, the score distribution of the model output is made as close as possible to the distribution of the true label.

[0115] Accurately estimating user behavior is crucial in recommendation models. For example, when estimating the click-through rate (CTR) of ads, the model needs to accurately estimate the probability of a user clicking on an ad or news item, thereby guiding the platform to perform personalized ranking based on user interests. In typical online ad recommendation systems, the model's estimated scores influence the ranking and bidding mechanisms. Ideally, the score distribution estimated by the recommendation model should align with the probability of actual feedback. For example, if the model estimates an average CTR of 5%, then the expectation is that 5% of ad impressions will result in a click. However, in reality, the model's estimated scores may differ from the actual observed values ​​in online data. This may be due to inherent bias in the model or variations in the data distribution. Overestimation or underestimation of the model's scores can lead to wasted impression opportunities, excessive costs for advertisers, and even online operational failures. Therefore, model calibration methods are often used to post-process the model's estimated scores, ensuring that the score distribution of the recommendation model's estimates aligns with the probability of actual feedback.

[0116] Existing techniques use post-processing to correct the recommendation model's predictions based on recent logs collected online, ensuring that the model's estimated probability distribution aligns with actual user feedback. However, because these existing methods calibrate the model's global output using a single calibration coefficient, the calibration effect is poor.

[0117] In order to solve the above problems, the present application provides a data processing method.

[0118] 5 , which is a schematic diagram of an embodiment of a data processing method provided in an embodiment of the present application. As shown in FIG5 , a data processing method provided in an embodiment of the present application includes:

[0119] 501. Obtain first attribute information of a first user and a first item and at least one calibration feature; the calibration feature indicates at least one of a recommended scenario, an item, or a provider of the item.

[0120] In an embodiment of the present application, the execution entity of step 501 may be a terminal device, which may be a portable mobile device, such as but not limited to a mobile or portable computing device (such as a smart phone), a personal computer, a server computer, a handheld device (such as a tablet) or a laptop device, a multi-processor system, a game console or controller, a microprocessor-based system, a set-top box, a programmable consumer electronic product, a mobile phone, a mobile computing and / or communication device with a wearable or accessory form factor (such as a watch, glasses, a headset or earbuds), a network PC, a minicomputer, a mainframe computer, a distributed computing environment including any of the above systems or devices, and the like.

[0121] In the embodiment of the present application, the execution entity of step 501 may be a server on the cloud side, and the server may receive the user's operation data sent from the terminal device, and then the server may obtain the user's operation data.

[0122] In one possible implementation, when recommending items to a user, it is necessary to calculate a recommendation score for each item recommended to the user. This embodiment of the present application uses the example of calculating a recommendation score for recommending a first item to a first user as an example:

[0123] In a possible implementation, attribute information of the first user and the first item may be obtained.

[0124] Among them, the user's attribute information can be attributes related to the user's preference characteristics, at least one of gender, age, occupation, income, hobbies and education level, among which gender can be male or female, age can be a number between 0-100, occupation can be teacher, programmer, chef, etc., hobbies can be basketball, tennis, running, etc., and education level can be elementary school, junior high school, high school, university, etc.; this application does not limit the specific type of user attribute information.

[0125] Among them, the items can be physical items or virtual items, such as applications (APP), audio and video, web pages, advertisements, and news information. The attribute information of the items can be at least one of the item name, developer, installation package size, category, and praise. Among them, taking the item as an application as an example, the category of the item can be chat, parkour games, office, etc., and the praise degree can be a score, comment, etc. for the item; this application does not limit the specific type of attribute information of the item.

[0126] In one possible implementation, at least one calibration feature may also be acquired. This calibration feature is a feature that affects the recommendation score. For example, the calibration feature may include the item ID, the recommended layout, the item provider's (e.g., advertiser's) ID, the recommendation platform, and the recommendation scenario. The recommendation scenario may be related to context (e.g., time, region, etc.). Different calibration features may result in different data distributions, thus affecting the recommendation score and results.

[0127] Among them, the tasks implemented by the recommendation model in the embodiment of the present application can be, but are not limited to, multiple of the following tasks: purchase behavior prediction, shopping cart behavior prediction, sharing behavior prediction, browsing behavior prediction, completion rate prediction, like prediction, collection prediction, click prediction, and click conversion prediction.

[0128] 502. Obtain a calibration coefficient corresponding to each calibration feature.

[0129] In one possible implementation, a corresponding calibration coefficient can be set in advance for each of the multiple calibration features. When calculating a recommendation score (for example, calculating the recommendation score of the first item for the first user), the calibration features related to the calculation can be obtained. For example, the ID of the first item, the ID of the provider of the first item, the current recommendation layout, and the current recommendation platform can be selected as calibration features. The calibration coefficient corresponding to each calibration feature can be obtained. The calibration coefficient can represent the recommendation score and the actual score (for example, the actual interaction score of the user on the item, such as the actual click-through rate) obtained by the recommendation model when processing the recommendation data related to the calibration feature (for example, including attribute information of the user and the item, contextual information, etc.). Furthermore, the calibration coefficient can be used to adjust the recommendation score output by the subsequent recommendation model, thereby obtaining an accurate recommendation score adapted to different calibration features.

[0130] Next, we will describe how to configure the size of the calibration coefficient corresponding to each calibration feature.

[0131] In one possible implementation, when calculating the calibration coefficient corresponding to a calibration feature, a recommendation sample related to the calibration feature can be obtained. The recommendation sample can include the recommendation score of the recommendation model for recommending items to the user when processing the recommendation data that meets the calibration feature, as well as the actual interaction score of the user on the item.

[0132] For example, taking the target calibration feature as an example, the second recommendation score for recommending the second item to the second user and the actual interaction score of the second user with the second item can be obtained; the second recommendation score is obtained through the recommendation model, and the second attribute information corresponds to the target calibration feature; the target standard feature is one of the at least one calibration feature.

[0133] The calibration coefficient can characterize the deviation between the model output and the actual interaction score. Therefore, in an embodiment of the present application, the first degree of deviation between the actual interaction score and the second recommendation score can be calculated by the calculation method of the binomial confidence interval; the calibration coefficient corresponding to the target calibration feature is generated based on the degree of deviation.

[0134] The confidence interval may be, but is not limited to, a Wilson interval.

[0135] Taking click-through rate prediction as an example, the actual interaction score can be the average click-through rate. For each calibration feature (or feature dimension), the confidence level is calculated using the Wilson interval formula. Based on the number of samples n, the average score of the recommendation model under this calibration feature is calculated. The observed actual average click-through rate p can be substituted into the following formula to calculate the degree of deviation z between the model prediction result and the actual observation result (that is, the first degree of deviation in the embodiment of the present application):

[0136] The value of z can be solved by bisection method or Newton iteration method.

[0137] In one possible implementation, the first degree of deviation can be mapped to obtain a second degree of deviation using a target mapping method; the target mapping method is a monotonically increasing function; a third recommendation score is calculated after the actual interaction score is deviated by the second degree of deviation using a binomial confidence interval calculation method; and a calibration coefficient corresponding to the target calibration feature is obtained based on the difference between the third recommendation score and the second recommendation score. The target mapping method can also be used to reduce the value of the input data, that is, the second degree of deviation is smaller than the first degree of deviation.

[0138] For example, the calculated z can be transformed using the following monotonic transformation function:

[0139] The new deviation degree z′ obtained by the transformation (that is, the second deviation degree in the embodiment of the present application) is further brought into the first formula. Combined with the sample size n and the observed true average click rate p, the calibrated average model score can be obtained. (i means the i-th calibration feature). If The sign in the formula takes a positive value, otherwise it takes a negative value.

[0140] In one possible implementation, the ratio of the third recommendation score to the second recommendation score can be used as the calibration coefficient corresponding to the target calibration feature. That is, the calibration coefficient k under this calibration feature is calculated. iThe corresponding calibration coefficient is the ratio of the average model scores before and after calibration, that is,

[0141] It should be understood that the binomial confidence interval calculation method of the embodiment of the present application can be extended to use other calculation methods in addition to the Wilson interval. Other monotonically increasing bounded functions can also be used as the transformation function of the deviation degree.

[0142] In an embodiment of the present application, the confidence level (i.e., the calibration coefficient) of the positive sample ratio is calculated using the confidence interval of the binomial distribution, and the model is calibrated with different intensities according to the confidence level. Even if the confidence level of the accumulated data in a shorter time window is used, a good calibration effect can be obtained.

[0143] 503. Obtain a first recommendation score for recommending the first item to the first user through a recommendation model based on the first attribute information.

[0144] In a possible implementation, the first attribute information may be processed using a recommendation model to obtain a first recommendation score for recommending the first item to the first user.

[0145] 504. Adjust the first recommendation score according to the calibration coefficient.

[0146] In a possible implementation, after the calibration coefficient is obtained, the first recommendation score may be adjusted according to the calibration coefficient.

[0147] In a possible implementation, after obtaining the multiple calibration coefficients, the multiple calibration coefficients corresponding to the multiple calibration features may be fused to obtain fused calibration coefficients; and the first recommendation score may be adjusted according to the fused calibration coefficients.

[0148] It should be understood that the fusion method in the embodiment of the present application may be a geometric mean, or other mean calculation methods may be used, and the present application is not limited thereto.

[0149] For example, the calibration coefficients under different calibration features are aggregated according to the following formula:

[0150] where w i Represents the geometric mean weight of each calibration feature (the default is 1, which can also be set manually or determined by parameter search). For a specific sample, assuming that the model's score for the sample is y, the calibrated score is

[0151] In an embodiment of the present application, by setting corresponding calibration coefficients for different calibration dimensions and adjusting the output of the model using the calibration coefficients of the calibration dimensions related to the data currently to be recommended, the recommendation effect and accuracy of the model can be improved.

[0152] In addition, the confidence level of the positive sample proportion (that is, the calibration coefficient) is calculated through the confidence interval of the binomial distribution, and the model is calibrated with different intensities according to the size of the confidence level. Even if the confidence level of the accumulated data in a shorter time window is used, a good calibration effect can be obtained.

[0153] Next, an application framework schematic diagram of an embodiment of the present application is introduced with reference to FIG6 , including:

[0154] 101 confidence-based model calibration module and 102 multi-dimensional calibration fusion module.

[0155] In the framework shown in Figure 6, the 101 confidence-based model calibration module considers the confidence of the positive sample proportion calculated by the collected samples through the Wilson confidence interval of the binomial distribution, and maps the degree of deviation between the true observation value and the model estimated distribution according to a mapping function, thereby calibrating the model with different intensities.

[0156] The 102 multi-dimensional calibration fusion module independently calculates the calibration scores of a sample in multiple dimensions and fuses the calibrated scores through weighted geometric averaging. It is a configurable module.

[0157] Next, the beneficial effects of the embodiments of the present application are described in conjunction with experiments:

[0158] The experimental evaluation metrics are the overall deviation between the estimated score and the true statistical value, as well as the deviation in each feature dimension. Experiments were conducted on this dataset, and the results are shown in Table 1.

[0159] Table 1 Experimental results statistics

[0160] As can be seen from Table 1, compared with the baseline, the embodiment of the present application can achieve better results and can consistently reduce the deviation of the model score in each dimension.

[0161] Next, a data processing device provided by an embodiment of the present application will be described from the perspective of a device. Referring to FIG. 7 , FIG. 7 is a schematic diagram of the structure of a data processing device provided by an embodiment of the present application. As shown in FIG. 7 , a data processing device 700 provided by an embodiment of the present application includes:

[0162] An acquisition module 701 is configured to acquire first attribute information of a first user and a first item and at least one calibration feature, wherein the calibration feature indicates at least one of a recommended scene, an item, or an item provider; and acquire a calibration coefficient corresponding to each calibration feature.

[0163] For a detailed introduction to the acquisition module 701 , reference may be made to the description of steps 501 and 502 in the above embodiment, which will not be repeated here.

[0164] The processing module 702 is configured to obtain, based on the first attribute information and through a recommendation model, a first recommendation score for recommending the first item to the first user; and adjust the first recommendation score based on the calibration coefficient.

[0165] The detailed introduction of the processing module 702 can refer to the description of steps 503 and 504 in the above embodiment, which will not be repeated here.

[0166] In a possible implementation, the obtaining module 701 is further configured to:

[0167] Obtaining a second recommendation score for recommending the second item to the second user and an actual interaction score of the second user with the second item, wherein the second recommendation score is obtained by the recommendation model, the second attribute information corresponds to a target calibration feature, and the target standard feature is one of the at least one calibration feature;

[0168] The processing module 702 is also used to

[0169] A first degree of deviation between the actual interaction score and the second recommendation score is calculated using a binomial confidence interval calculation method; and a calibration coefficient corresponding to the target calibration feature is generated based on the degree of deviation.

[0170] In a possible implementation, the processing module 702 is further configured to:

[0171] Mapping the first deviation degree by a target mapping device to obtain a second deviation degree; the target mapping device is a monotonically increasing function;

[0172] Calculating, by a binomial confidence interval calculation device, a third recommendation score obtained after the actual interaction score deviates by the second deviation degree;

[0173] A calibration coefficient corresponding to the target calibration feature is obtained according to a difference between the third recommendation score and the second recommendation score.

[0174] In a possible implementation, the processing module 702 is specifically configured to:

[0175] A ratio of the third recommendation score to the second recommendation score is used as a calibration coefficient corresponding to the target calibration feature.

[0176] In a possible implementation, the confidence interval is a Wilson interval.

[0177] In a possible implementation, the processing module 702 is specifically configured to:

[0178] fusing a plurality of calibration coefficients corresponding to the plurality of calibration features to obtain fused calibration coefficients;

[0179] The first recommendation score is adjusted according to the fused calibration coefficient.

[0180] Next, a terminal device provided in an embodiment of the present application is introduced. Please refer to Figure 8. Figure 8 is a structural schematic diagram of a terminal device provided in an embodiment of the present application. The terminal device 800 can be specifically manifested as a mobile phone, a tablet, a laptop computer, a smart wearable device, etc., which is not limited here. Among them, the terminal device 800 can be used as a training device to implement the function of the data processing method in the embodiment corresponding to Figure 5, or as an execution device to execute the trained model obtained based on the data processing method in the embodiment corresponding to Figure 5. Specifically, the terminal device 800 includes: a receiver 801, a transmitter 802, a processor 803 and a memory 804 (wherein the number of processors 803 in the terminal device 800 can be one or more), wherein the processor 803 may include an application processor 8031 ​​and a communication processor 8032. In some embodiments of the present application, the receiver 801, the transmitter 802, the processor 803 and the memory 804 may be connected via a bus or other means.

[0181] The memory 804 may include a read-only memory and a random access memory, and provides instructions and data to the processor 803. A portion of the memory 804 may also include non-volatile random access memory (NVRAM). The memory 804 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.

[0182] Processor 803 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.

[0183] The method disclosed in the above embodiment of the present application can be applied to the processor 803 or implemented by the processor 803. The processor 803 can be an integrated circuit chip with signal processing capabilities. During the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 803. The above processor 803 can be a general-purpose processor, a digital signal processor (DSP), a microprocessor or a microcontroller, and a vision processor (VPU), a tensor processing unit (TPU) and other processors suitable for AI computing. It can also further include an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The processor 803 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 the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied 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 a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, or a register. The storage medium is located in the memory 804. The processor 803 reads the information in the memory 804 and, in conjunction with its hardware, completes steps 501 to 504 in the above embodiment.

[0184] Receiver 801 can be used to receive input digital or character information and generate signal input related to executing device-related settings and function control. Transmitter 802 can be used to output digital or character information through the first interface. Transmitter 802 can also be used to send instructions to the disk pack through the first interface to modify data in the disk pack. Transmitter 802 can also include a display device such as a display screen.

[0185] The embodiment of the present application also provides a server. Please refer to Figure 9, which is a schematic diagram of the structure of the server provided by the embodiment of the present application. Specifically, the server 900 is implemented by one or more servers. The server 900 may have relatively large differences due to different configurations or performance. It may include one or more central processing units (CPUs) 99 (for example, one or more processors) and memory 932, and one or more storage media 930 (for example, one or more mass storage devices) for storing application programs 942 or data 944. Among them, the memory 932 and storage medium 930 can be temporary storage or permanent storage. The program stored in the storage medium 930 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the training device. Furthermore, the central processing unit 99 can be configured to communicate with the storage medium 930 to execute a series of instruction operations in the storage medium 930 on the server 900.

[0186] The server 900 may also include one or more power supplies 926, one or more wired or wireless network interfaces 950, one or more input and output interfaces 958; or, one or more operating systems 941, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0187] Specifically, the server may serve as a training device to execute steps 501 to 504 in the above embodiment.

[0188] In one possible implementation, the terminal device 800 or the server 900 can serve as a training device to execute steps 501 to 504 in the above embodiment to obtain a trained model, and deploy the trained model to an execution device, which can also be in the form of a terminal device 800 or a server 900. When the execution device executes the trained model, reference can be made to the model feedforward process in the embodiment corresponding to FIG. 5 .

[0189] An embodiment of the present application also provides a computer program product, which, when running on a computer, enables the computer to execute the steps executed by the aforementioned execution device, or enables the computer to execute the steps executed by the aforementioned training device.

[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 executed by the aforementioned execution device, or enables the computer to execute the steps executed by the aforementioned training device.

[0191] The execution device, training device or terminal device provided in the embodiments of the present application can specifically be a chip, and the chip includes: a processing unit and a communication unit, the processing unit can be, for example, a processor, and the communication unit can be, for example, an input / output interface, a pin or a circuit, etc. The processing unit can execute the computer execution instructions stored in the storage unit, so that the chip in the execution device executes the data processing method described in the above embodiment, or so that the chip in the training device executes the data processing method described in the above embodiment. Optionally, the storage unit is a storage unit in the chip, such as a register, a cache, etc. The storage unit can also be a storage unit located outside the chip in 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.

[0192] Specifically, see Figure 10, 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 1000. NPU 1000 is mounted on the host CPU (host CPU) as a coprocessor and is assigned tasks by the host CPU. The core of the NPU is arithmetic circuit 1003, which is controlled by controller 1004 to extract matrix data from memory and perform multiplication operations.

[0193] The NPU 1000 can implement the data processing method provided in the embodiment described in FIG. 5 through the mutual cooperation between various internal components.

[0194] More specifically, in some implementations, the arithmetic circuit 1003 in the NPU 1000 includes multiple processing units (PEs). In some implementations, the arithmetic circuit 1003 is a two-dimensional systolic array. The arithmetic circuit 1003 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 1003 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 1002 and caches it on each PE in the arithmetic circuit. The arithmetic circuit retrieves the data of matrix A from input memory 1001 and performs a matrix operation on matrix B. The partial or final matrix result is stored in accumulator 1008.

[0196] Unified memory 1006 is used to store input and output data. Weight data is directly transferred to weight memory 1002 through the Direct Memory Access Controller (DMAC) 1005. Input data is also transferred to unified memory 1006 through the DMAC.

[0197] BIU stands for Bus Interface Unit, i.e., bus interface unit 1010 , which is used for interaction between the AXI bus, DMAC, and instruction fetch buffer (IFB) 1009 .

[0198] The bus interface unit 1010 (BIU) is used for the instruction fetch memory 1009 to obtain instructions from the external memory, and is also used for the storage unit access controller 1005 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 1006 or transfer weight data to the weight memory 1002 or transfer input data to the input memory 1001.

[0200] The vector calculation unit 1007 includes multiple operation processing units. When necessary, it further processes the output of the operation circuit 1003, 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 1007 can store the processed output vector to the unified memory 1006. For example, the vector calculation unit 1007 can apply a linear function or a nonlinear function to the output of the operation circuit 1003, 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 1007 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 1003, for example, for use in subsequent layers in a neural network.

[0202] An instruction fetch buffer 1009 connected to the controller 1004 is used to store instructions used by the controller 1004;

[0203] Unified memory 1006, input memory 1001, weight memory 1002, and instruction fetch memory 1009 are all on-chip memories. External memories are private to the NPU hardware architecture.

[0204] 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 above program.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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: Acquire first attribute information of a first user and a first object and at least one calibration feature; the calibration feature indicates at least one of a recommended scene, an object, or a provider of the object; Obtaining a calibration coefficient corresponding to each of the calibration features; Obtaining, based on the first attribute information, a first recommendation score for recommending the first item to the first user through a recommendation model; The first recommendation score is adjusted according to the calibration factor.

2. The method according to claim 1, characterized in that The method further comprises: Obtaining a second recommendation score for recommending the second item to the second user and an actual interaction score of the second user on the second item; the second recommendation score is obtained by the recommendation model, the second attribute information corresponds to a target calibration feature; the target standard feature is one of the at least one calibration feature; A first degree of deviation between the actual interaction score and the second recommendation score is calculated by a binomial confidence interval calculation method; and a calibration coefficient corresponding to the target calibration feature is generated according to the degree of deviation.

3. The method according to claim 2, characterized in that The method further comprises: Mapping the first deviation degree by a target mapping method to obtain a second deviation degree; the target mapping method is a monotonically increasing function; Calculate, by using a binomial confidence interval calculation method, a third recommendation score obtained after the actual interaction score deviates by the second deviation degree; A calibration coefficient corresponding to the target calibration feature is obtained according to a difference between the third recommended score and the second recommended score.

4. The method according to claim 2 or 3, characterized in that: The obtaining, according to the difference between the third recommended score and the second recommended score, a calibration coefficient corresponding to the target calibration feature, comprises: A ratio of the third recommended score to the second recommended score is used as a calibration coefficient corresponding to the target calibration feature.

5. The method according to any one of claims 2 to 4, characterized in that: The confidence interval is the Wilson interval.

6. The method according to any one of claims 1 to 5, characterized in that: The first attribute information corresponds to a plurality of calibration features, and the adjusting the first recommendation score according to the calibration coefficient includes: fusing a plurality of calibration coefficients corresponding to the plurality of calibration features to obtain fused calibration coefficients; The first recommendation score is adjusted according to the fused calibration coefficient.

7. A data processing device, characterized in that: The device comprises: An acquisition module, configured to acquire first attribute information of a first user and a first item and at least one calibration feature, wherein the calibration feature indicates at least one of a recommended scene, an item, or a provider of the item; and to acquire a calibration coefficient corresponding to each of the calibration features; A processing module is used to obtain a first recommendation score for recommending the first item to the first user through a recommendation model according to the first attribute information; and adjust the first recommendation score according to the calibration coefficient.

8. The device according to claim 7, characterized in that The acquisition module is further used for: Obtaining a second recommendation score for recommending the second item to the second user and an actual interaction score of the second user on the second item; the second recommendation score is obtained by the recommendation model, the second attribute information corresponds to a target calibration feature; the target standard feature is one of the at least one calibration feature; The processing module is further used for: Calculating a first degree of deviation between the actual interaction score and the second recommendation score by a binomial confidence interval calculation device; The calibration coefficient corresponding to the target calibration feature is generated according to the degree of deviation.

9. The device according to claim 8, characterized in that The processing module is further used for: By means of a target mapping device, the first deviation degree is mapped to obtain a second deviation degree; the target mapping device is a monotonically increasing function; Calculate, by using a binomial confidence interval calculation method, a third recommendation score obtained after the actual interaction score deviates by the second deviation degree; A calibration coefficient corresponding to the target calibration feature is obtained according to a difference between the third recommended score and the second recommended score.

10. The device according to claim 8 or 9, characterized in that The processing module is specifically used for: A ratio of the third recommended score to the second recommended score is used as a calibration coefficient corresponding to the target calibration feature.

11. The device according to any one of claims 8 to 10, characterized in that: The confidence interval is the Wilson interval.

12. The device according to any one of claims 7 to 11, characterized in that The processing module is specifically used for: fusing a plurality of calibration coefficients corresponding to the plurality of calibration features to obtain fused calibration coefficients; The first recommendation score is adjusted according to the fused calibration coefficient.

13. A computing device, characterized in that: The computing device includes a memory and a processor; the memory stores codes, and the processor is configured to obtain the codes and execute the method according to any one of claims 1 to 6.

14. A computer storage medium, characterized in that: The computer storage medium stores one or more instructions, which, when executed by one or more computers, enable the one or more computers to implement the method of any one of claims 1 to 6.

15. A computer program product comprising code, characterized in that When the code is executed, it is used to implement the method according to any one of claims 1 to 6.

16. A chip, comprising a processor, characterized in that: The processor is used to support a data processing device to implement the method according to any one of claims 1 to 6.

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