Ranking model training method and device and computer program product

Abstract

The invention provides a sorting model training method and device, a data recommendation method and device, electronic equipment, a storage medium and a computer program product, relates to the technical field of artificial intelligence, in particular to the technical field of deep learning and data recommendation, and can be applied to a data recommendation scene. According to the specific implementation scheme, a training sample set is obtained, each training sample comprises two pieces of resource feature data and a sequence label representing the recommendation sequence between data resources to which the two pieces of resource feature data belong, and the two data resources corresponding to at least part of the training samples in the training sample set are different types of data resources; a machine learning algorithm is adopted, the two pieces of resource feature data serve as input, the sequence labels corresponding to the two pieces of resource feature data serve as expected output, and a sorting model is obtained through training. According to the method and the device, the sorting model is modeled by adopting multiple types of data resources, the recommendation value among the multiple types of data resources is integrally considered, and the accuracy of the sorting model is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, specifically to the field of deep learning and data recommendation technology, and in particular to a training method for a sorting model, a data recommendation method and device, an electronic device, a storage medium, and a computer program product, which can be applied in data recommendation scenarios. Background Art

[0002] In the data resource recommendation scenario, the existing technology determines different types of data resources through different recommendation models, without considering the recommendation value between different types of data resources, resulting in inaccurate recommendation results. Summary of the invention

[0003] The present disclosure provides a training method and device for a ranking model, a data recommendation method and device, an electronic device, a storage medium, and a computer program product.

[0004] According to a first aspect, a method for training a sorting model is provided, comprising: obtaining a training sample set, wherein the training samples in the training sample set include two resource feature data and a sequence label representing a recommended order between data resources to which the two resource feature data respectively belong, and the two data resources corresponding to at least some of the training samples in the training sample set are data resources of different types; adopting a machine learning algorithm, with the two resource feature data as input and the sequence label corresponding to the two resource feature data as the expected output, to train a sorting model.

[0005] According to the second aspect, a data recommendation method is provided, comprising: sorting multiple recalled data resources corresponding to a target user through a pre-trained sorting model to obtain recommended order information of the multiple recalled data resources, wherein the sorting model is trained through any implementation method of the first aspect; generating a recommended list page based on the recommended order information; and outputting the recommended list page to the target user.

[0006] According to a third aspect, a training device for a sorting model is provided, comprising: an acquisition unit, configured to acquire a training sample set, wherein the training samples in the training sample set include two resource feature data and a sequence label representing a recommended order between data resources to which the two resource feature data respectively belong, and the two data resources corresponding to at least some of the training samples in the training sample set are data resources of different types; and a training unit, configured to adopt a machine learning algorithm, with the two resource feature data as input and the sequence label corresponding to the two resource feature data as the expected output, to train a sorting model.

[0007] According to the fourth aspect, a data recommendation device is provided, including: a sorting unit, configured to sort multiple recalled data resources corresponding to a target user through a pre-trained sorting model to obtain recommended order information of the multiple recalled data resources, wherein the sorting model is trained through any implementation method of the third aspect; a generating unit, configured to generate a recommended list page based on the recommended order information; and an output unit, configured to output the recommended list page to the target user.

[0008] According to the fifth aspect, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any implementation of the first aspect or the second aspect.

[0009] According to a sixth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method described in any implementation of the first aspect or the second aspect.

[0010] According to a seventh aspect, a computer program product is provided, comprising: a computer program, which implements the method described in any implementation of the first aspect or the second aspect when executed by a processor.

[0011] According to the technology disclosed in the present invention, a method and device for training a ranking model are provided, in which two data resources corresponding to at least some of the training samples in the training sample set are data resources of different types, and the ranking model is trained with two resource feature data in the training sample as input and sequence labels corresponding to the two resource feature data as expected output, so that the ranking model adopts multiple types of data resources for modeling, considers the recommendation value among multiple types of data resources as a whole, and improves the accuracy of the recommendation sequence information output by the ranking model.

[0012] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0014] Figure 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure may be applied;

[0015] Figure 2 is a flowchart of an embodiment of a method for training a ranking model according to the present disclosure;

[0016] Figure 3 is a schematic diagram of an application scenario of the training method of the sorting model according to this embodiment;

[0017] Figure 4 is a schematic diagram of a sequential label determination process according to this embodiment;

[0018] Figure 5 is a flow chart of an embodiment of a data recommendation method according to the present disclosure;

[0019] Figure 6 is a structural diagram of an embodiment of a training device for a ranking model according to the present disclosure;

[0020] Figure 7 is a structural diagram of an embodiment of a data recommendation device according to the present disclosure;

[0021] Figure 8 It is a schematic diagram of the structure of a computer system suitable for implementing the embodiments of the present disclosure. DETAILED DESCRIPTION

[0022] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0023] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0024] Figure 1 An exemplary architecture 100 is shown to which the ranking model training method and apparatus, and data recommendation method and apparatus of the present disclosure can be applied.

[0025] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The communication connection between the terminal devices 101, 102, 103 constitutes a topological network, and the network 104 is used to provide a medium for the communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0026] Terminal devices 101, 102, 103 may be hardware devices or software that support network connection for data interaction and data processing. When terminal devices 101, 102, 103 are hardware, they may be various electronic devices that support network connection, information acquisition, interaction, display, processing and other functions, including but not limited to smart phones, tablet computers, e-book readers, laptop computers and desktop computers, etc. When terminal devices 101, 102, 103 are software, they may be installed in the electronic devices listed above. They may be implemented as multiple software or software modules for providing distributed services, or they may be implemented as a single software or software module. No specific limitation is made here.

[0027] Server 105 can be a server that provides various services. For example, for the training sample sets provided by terminal devices 101, 102, and 103, a background processing server that uses a machine learning algorithm, takes two resource feature data in the training samples as input, and takes the sequence labels corresponding to the two resource feature data as the expected output, and trains a sorting model. For another example, for recommendation requests sent by terminal devices 101, 102, and 103, a pre-trained sorting model is used to sort multiple recalled data resources corresponding to the target user, and a background processing server that obtains and displays a recommendation list page. As an example, server 105 can be a cloud server.

[0028] It should be noted that the server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules (for example, software or software modules used to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0029] It should also be noted that the training method and data recommendation method of the ranking model provided in the embodiments of the present disclosure are generally executed by a server, but the possibility of being executed by a terminal device, or being executed by a server and a terminal device in cooperation with each other is not excluded. Accordingly, the various parts (such as various units) included in the training device and data recommendation device of the ranking model can be all set in the server, or all set in the terminal device, or can be set in the server and the terminal device respectively.

[0030] It should be understood that Figure 1The number of terminal devices, networks, and servers in the system is only illustrative. Depending on the implementation requirements, there may be any number of terminal devices, networks, and servers. When the electronic device on which the training method of the sorting model and the data recommendation method are running does not need to transmit data with other electronic devices, the system architecture may only include the electronic device (e.g., terminal device or server) on which the training method of the sorting model and the data recommendation method are running.

[0031] Please refer to Figure 2 , Figure 2 A flowchart of a method for training a sorting model provided in an embodiment of the present disclosure. In process 200, the following steps are included:

[0032] Step 201: Obtain a training sample set.

[0033] In this embodiment, the execution subject of the training method of the sorting model (for example, Figure 1 The server in the training sample set can obtain the training sample set remotely or locally through a wired network connection or a wireless network connection. The training samples in the training sample set include two resource feature data and a sequence label representing a recommended sequence between the data resources to which the two resource feature data belong respectively, and the two data resources corresponding to at least some of the training samples in the training sample set are different types of data resources.

[0034] The types of data resources include, but are not limited to, videos, images, graphics, and text. Each training sample in the training sample set includes a resource feature data pair consisting of two resource feature data, and a sequence label representing the recommendation order between the data resources to which the two resource feature data belong (the two data resources can be regarded as a data resource pair). The recommendation order is used to represent the user's interest in the two data resources, and the user is more interested in the data resource that is ranked first in the data resource pair.

[0035] The two data resources corresponding to at least some of the training samples in the training sample set are different types of data resources, and the two data resources corresponding to the training samples refer to the data resources to which the two resource feature data in the training samples belong. For example, the two data resources corresponding to the training samples of a preset proportion in the training sample set are different types of data resources. The preset proportion can be specifically set according to actual conditions and is not limited here.

[0036] It can be understood that the training sample set includes training samples where the corresponding two data resources are data resources of different types and training samples where the corresponding two data resources are data resources of the same type. Taking the data resource types of video and text and graphics as an example, the training sample set includes training samples where the corresponding two data resources are both data resources of the video type, training samples where the corresponding two data resources are both data resources of the dynamic type, and training samples where the corresponding two data resources are respectively video and dynamic types.

[0037] Resource feature data can be obtained by extracting features from data resources. For example, the execution subject can extract features from data resources using preset feature keywords to obtain resource feature data; for another example, the execution subject can extract features from data resources using convolutional neural networks or recurrent neural networks to obtain resource feature data.

[0038] Step 202, adopting a machine learning algorithm, taking two resource feature data as input and taking the sequence labels corresponding to the two resource feature data as the expected output, to train and obtain a sorting model.

[0039] In this embodiment, the execution subject may adopt a machine learning algorithm, and use two resource feature data as input and the sequence labels corresponding to the two resource feature data as the expected output to train and obtain a sorting model.

[0040] As an example, the above execution subject may iteratively perform the following training operations until a preset end condition is reached to obtain a sorting model:

[0041] First, one or more untrained target training samples are determined from the training sample set; then, two resource feature data in the target training sample are input into the current initial sorting model to obtain the actual output; then, the loss between the actual output and the sequential labels in the target training sample is calculated; finally, the parameters of the current initial sorting model are updated according to the loss using a gradient descent algorithm or a gradient descent algorithm to obtain a model corresponding to the next training operation.

[0042] The preset termination conditions include, for example, that the training time exceeds a preset time threshold, the number of training times exceeds a preset number threshold, and the training loss tends to converge.

[0043] Among them, the initial sorting model can adopt neural network models such as convolutional neural networks, recurrent neural networks, deep neural networks, specially designed sorting networks such as RankNet and LambdaRank, and integrated learning models such as the RankBoost model based on AdaBoost and the XGBoost (eXtremeGradientBoosting, extreme gradient boosting) model.

[0044] Continue to see Figure 3 , Figure 3 It is a schematic diagram 300 of an application scenario of the training method of the sorting model according to the present embodiment. The server first obtains a training sample set 301 from a database. Among them, the training samples in the training sample set include two resource feature data and a sequence label representing the recommended order between the data resources to which the two resource feature data respectively belong, and the two data resources corresponding to at least some of the training samples in the training sample set are different types of data resources. Among them, the types of data resources include video types and dynamic types in the form of graphics and text. Then, a machine learning algorithm is used, with the two resource feature data as input and the sequence label corresponding to the two resource feature data as the expected output, to train and obtain a sorting model 302.

[0045] In this embodiment, a training method for a ranking model is provided, in which two data resources corresponding to at least some of the training samples in the training sample set are data resources of different types. The two resource feature data in the training samples are used as input, and the sequence labels corresponding to the two resource feature data are used as the expected output to train the ranking model, so that the ranking model uses multiple types of data resources for modeling, considers the recommendation value between multiple types of data resources as a whole, and improves the accuracy of the recommendation sequence information output by the ranking model.

[0046] In some optional implementations of this embodiment, the resource feature data includes: portrait feature data of users interacting with data resources, resource attribute feature data of data resources, and interaction feature data between users and data resources.

[0047] Among them, the user's portrait feature data includes, for example, gender, age, occupation, geographic location, hobbies and other data. The resource attribute feature data of the data resource includes, for example, the playback time of the data resource, the content type and other data. The interactive feature data between the user and the data resource includes, for example, the user's click, slide and other operation behaviors on the data resource, the viewing time and other data. The user is aware of and authorized to obtain and use the user's portrait feature data, the interactive feature data between the user and the data resource and other user-related information.

[0048] In this implementation, the resource feature data includes feature data of multiple aspects related to the data resources, which improves the comprehensiveness and richness of the resource feature data, helps the sorting model to quickly learn the correspondence between resource feature data and sequence labels during the training process, and can improve the training speed of the model and the accuracy of the recommended sequence information output by the trained sorting model.

[0049] In some optional implementations of this embodiment, the interactive feature data includes interactive feature data between the user and the data resource in the list display page, and interactive feature data between the user and the data resource sequence in the detail display page. The detail display page is a page displayed after the data resource in the list display page is triggered, and the data resource sequence includes the data resource.

[0050] In the data recommendation application, there are a list display page (first-level page) and a detail display page (second-level page). When a user opens the data recommendation application, he enters the list display page, which includes multiple data resources; when a user triggers (for example, clicks) a data resource, he enters the detail display page of the data resource. On the detail display page, the user can view the data resource and display other data resources in the data resource sequence through operations such as sliding.

[0051] In the list display page, the interactive feature data between the user and the data resource, such as whether to click on the data resource and other trigger features; in the detail display page, the interactive feature data between the user and the data resource sequence, such as the viewing time of the data resource in the data resource sequence, the sliding step length and other feature data. Among them, the sliding step length is used to represent the number of data resources played by the user based on the sliding operation.

[0052] In this implementation, the interaction feature data includes the interaction feature data between the user and the data resources in the list display page, and the interaction feature data between the user and the data resource sequence in the details display page, which further improves the comprehensiveness and richness of the resource feature data, and helps to further improve the training speed of the model and the accuracy of the recommendation order information output by the trained sorting model.

[0053] In some optional implementations of this embodiment, the execution subject may obtain the sequence label in the following manner:

[0054] The first step is to combine the user's operation data with the interaction feature data between the two data resources to obtain interaction feature data pairs corresponding to at least two operation dimensions.

[0055] The at least two operation dimensions can be specifically set according to actual conditions. For example, the at least two operation dimensions are key dimensions that affect the recommendation order. Specifically, the at least two operation dimensions include whether to click, whether to slide, sliding step length, and viewing time.

[0056] The two interactive feature data in the interactive feature data pair belong to the same operation dimension. For the click dimension, the two interactive feature data of whether the user clicks on the two data resources can be combined to obtain the interactive feature data pair of the click dimension; referring to the above method, the interactive feature data pairs of the sliding dimension, sliding step dimension and viewing time dimension can be obtained.

[0057] The second step is to obtain the comparison result between two interactive feature data of a single data pair in the interactive feature data pair.

[0058] The third step is to determine the sequence label according to the comparison result.

[0059] As an example, for each interactive feature data pair in multiple interactive feature data, the above-mentioned execution subject can compare the two interactive feature data in the interactive feature data pair to determine the comparison result of the interactive feature data pair. For each comparison result, when the interactive feature data corresponding to data resource A in the two data resources is greater than the interactive feature data corresponding to data resource B, the comparison result is 1; when the interactive feature data corresponding to data resource B in the two data resources is greater than the interactive feature data corresponding to data resource A, the comparison result is -1; when the interactive feature data corresponding to data resource A in the two data resources is equal to the interactive feature data corresponding to data resource B, the comparison result is 0. Then, the comparison results of multiple interactive feature data pairs are summed or weighted summed to determine the order label.

[0060] As another example, the execution subject may determine a target interaction feature data pair from a plurality of interaction feature data pairs according to the degree of importance, and then determine a sequence label according to a comparison result between the interaction feature data in the target interaction feature data pair.

[0061] In this implementation, the sequential labels are determined based on the comparison results between the interactive feature data in the interactive feature data pairs of preset dimensions, thereby improving the accuracy of determining the sequential labels.

[0062] In some optional implementations of this embodiment, the execution subject may perform the second step in the following manner:

[0063] First, the comparison order of the interaction feature data pairs is determined according to the priority of each operation dimension.

[0064] The priorities among multiple operation dimensions can be specifically set according to actual conditions. For example, the priorities among multiple operation dimensions are determined according to the temporal relationship among the interaction characteristics between users and data resources in multiple operation dimensions.

[0065] Continuing with the above-mentioned whether to click dimension, whether to slide dimension, sliding step dimension and viewing time dimension as an example, according to the temporal relationship between click, slide and watch, their priorities from high to low are whether to click dimension, whether to slide dimension, sliding step dimension and viewing time dimension.

[0066] Then, according to the comparison order, the two interactive feature data of a single data pair in the interactive feature data pair are compared in turn to obtain a comparison result.

[0067] In this implementation, the execution subject may perform the third step in the following manner: in response to detecting that the comparison result indicates that two interactive feature data of a single data pair are different, determining a sequence label according to the comparison result.

[0068] Continuing with the example of whether to click dimension, whether to slide dimension, sliding step dimension and viewing time dimension whose priorities are from high to low, the above-mentioned execution entity can compare the two interactive feature data in the interactive feature data pair corresponding to the whether to click dimension. If the two interactive feature data are not equal, the sequence label can be determined; if they are equal, continue to compare the two interactive feature data in the interactive feature data pair corresponding to the whether to slide dimension until the sequence label is determined.

[0069] In this implementation, two interaction feature data in an interaction feature data pair are compared in sequence according to a comparison order determined based on the priorities between multiple operation dimensions, until a sequential label is determined based on the comparison result of one interaction feature data pair among the multiple interaction feature data pairs, thereby improving the efficiency and accuracy of the sequential label determination process.

[0070] Continue to refer Figure 4 , shows a schematic process 400 of an embodiment of a sequential tag determination process according to the present disclosure. In the process 400, the following steps are included:

[0071] Step 401 : Combining the user's operation data with the interaction feature data between two data resources to obtain interaction feature data pairs corresponding to at least two operation dimensions.

[0072] Step 402: Determine the comparison order of the interaction feature data pairs according to the priority of each operation dimension.

[0073] Step 403: Compare two interactive feature data of a single data pair in the interactive feature data pair in sequence according to the comparison order to obtain a comparison result.

[0074] Step 404 , in response to detecting that the comparison result indicates that two interactive feature data of a single data pair are different, determining a sequence label according to the comparison result.

[0075] The process 400 of the method for determining sequential labels in this embodiment specifically illustrates the process of determining sequential labels based on the priorities of multiple operation dimensions, thereby improving the efficiency and accuracy of the process of determining sequential labels.

[0076] Continue to refer Figure 5 , shows a schematic process 500 of an embodiment of a data recommendation method according to the present disclosure. In the process 500, the following steps are included:

[0077] Step 501 , sorting multiple recalled data resources corresponding to target users through a pre-trained sorting model to obtain recommended sequence information of the multiple recalled data resources.

[0078] In this embodiment, the execution subject of the data recommendation method (for example, Figure 1 The server in (a) sorts multiple recalled data resources corresponding to the target user through a pre-trained sorting model to obtain recommended order information of the multiple recalled data resources.

[0079] The sorting model is obtained by training according to the above-mentioned embodiment 200.

[0080] As an example, the above-mentioned execution entity can recall multiple recall data resources that the target user is interested in from the data set through a pre-trained recall model; then, sort the multiple recall data resources through a pre-trained sorting model to obtain recommended order information of the multiple recall data resources.

[0081] Step 502: Generate a recommendation list page based on the recommendation order information.

[0082] In this embodiment, the above-mentioned execution entity can generate a recommendation list page based on the recommendation order information.

[0083] As an example, a preset number of recalled data resources ranked first are determined as target data resources, and a recommendation list page is generated according to the recommendation order information of the multiple target data resources. The preset number can be specifically set according to actual conditions and is not limited here.

[0084] Step 503: output the recommendation list page to the target user.

[0085] In this embodiment, the above execution entity can output a recommendation list page to the target user.

[0086] As an example, the above execution entity may push the recommendation list page to the terminal device of the target user, and display the recommendation list page to the target user through an application deployed in the terminal device.

[0087] In this embodiment, based on modeling using multiple types of data resources, a ranking model that can comprehensively consider the recommendation values ​​among multiple types of data resources recommends data to users, thereby improving the accuracy of data recommendations.

[0088] Continue to refer Figure 6 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a training device for a sorting model. The system embodiment is Figure 2 Corresponding to the method embodiment shown, the system can be specifically applied to various electronic devices.

[0089] like Figure 6 As shown, the training device 600 for the sorting model includes: an acquisition unit 601 is configured to acquire a training sample set, wherein the training samples in the training sample set include two resource feature data and a sequence label representing a recommended order between data resources to which the two resource feature data respectively belong, and the two data resources corresponding to at least some of the training samples in the training sample set are data resources of different types; the training unit 602 is configured to adopt a machine learning algorithm, with the two resource feature data as input and the sequence label corresponding to the two resource feature data as the expected output, to train and obtain the sorting model.

[0090] In some optional implementations of this embodiment, the above-mentioned device also includes: a label determination unit (not shown in the figure), which is configured to: combine the user's operation data with the interaction feature data between two data resources to obtain interaction feature data pairs corresponding one-to-one to at least two operation dimensions; obtain the comparison result between two interaction feature data of a single data pair in the interaction feature data pair; and determine the sequential label according to the comparison result.

[0091] In some optional implementations of this embodiment, the label determination unit is further configured to: determine the comparison order of the interaction feature data pairs according to the priority of each operation dimension; compare the two interaction feature data of a single data pair in the interaction feature data pairs in turn according to the comparison order to obtain a comparison result; in response to detecting that the comparison result indicates that the two interaction feature data of the single data pair are different, determine the sequence label according to the comparison result.

[0092] In some optional implementations of this embodiment, the resource feature data includes: portrait feature data of users interacting with data resources, resource attribute feature data of data resources, and interaction feature data between users and data resources.

[0093] In some optional implementations of this embodiment, the interaction feature data includes: interaction feature data between a user and data resources in a list display page; and interaction feature data between a user and a data resource sequence in a detail display page; wherein the detail display page is a page displayed after the data resources in the list display page are triggered, and the data resource sequence includes data resources.

[0094] In this embodiment, a training device for a sorting model is provided, in which the two data resources corresponding to at least some of the training samples in the training sample set are data resources of different types. The two resource feature data in the training sample are used as input, and the sequence labels corresponding to the two resource feature data are used as expected output to train the sorting model, so that the sorting model uses multiple types of data resources for modeling, considers the recommendation value between multiple types of data resources as a whole, and improves the accuracy of the recommendation sequence information output by the sorting model.

[0095] Continue to refer Figure 7 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a data recommendation device. The system embodiment is Figure 5 Corresponding to the method embodiment shown, the system can be specifically applied to various electronic devices.

[0096] like Figure 7 As shown, the data recommendation device 700 includes: a sorting unit 701 is configured to sort multiple recalled data resources corresponding to the target user through a pre-trained sorting model to obtain recommended order information of the multiple recalled data resources, wherein the sorting model is trained by any implementation method in the above-mentioned embodiment 600; a generating unit 702 is configured to generate a recommended list page based on the recommended order information; and an output unit 703 is configured to output the recommended list page to the target user.

[0097] In this embodiment, based on modeling using multiple types of data resources, a ranking model that can comprehensively consider the recommendation values ​​among multiple types of data resources recommends data to users, thereby improving the accuracy of data recommendations.

[0098] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can implement the training method of the sorting model and the data recommendation method described in any of the above embodiments when executing.

[0099] According to an embodiment of the present disclosure, the present disclosure also provides a readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to implement the training method and data recommendation method of the sorting model described in any of the above embodiments when executed.

[0100] The embodiments of the present disclosure provide a computer program product, which, when executed by a processor, can implement the training method of the sorting model and the data recommendation method described in any of the above embodiments.

[0101] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0102] like Figure 8 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0103] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0104] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as the training method of the sorting model. For example, in some embodiments, the training method of the sorting model may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the training method of the sorting model described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the training method of the sorting model by any other appropriate means (e.g., by means of firmware).

[0105] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, a special purpose computer, or other training device for a programmable sequencing model, so that the program code, when executed by the processor or controller, causes the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0107] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0110] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services; it may also be a server for a distributed system, or a server combined with a blockchain.

[0111] According to the technical solution of the embodiments of the present disclosure, a method for training a ranking model is provided, in which two data resources corresponding to at least part of the training samples in the training sample set are data resources of different types, and the ranking model is trained with two resource feature data in the training samples as input and sequence labels corresponding to the two resource feature data as expected output, so that the ranking model adopts multiple types of data resources for modeling, considers the recommendation value among multiple types of data resources as a whole, and improves the accuracy of the recommendation sequence information output by the ranking model.

[0112] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution provided by this disclosure can be achieved, and this document does not limit this.

[0113] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A training method for a sorting model, comprising: Acquire a training sample set, wherein the training samples in the training sample set include two resource feature data and a sequence label representing a recommended sequence between data resources to which the two resource feature data respectively belong, and the two data resources corresponding to at least some of the training samples in the training sample set are different types of data resources; The sorting model is obtained by training using a machine learning algorithm, with the two resource feature data as input and the sequence labels corresponding to the two resource feature data as expected output.

2. The method according to claim 1, wherein: The method further comprises: Combining the user's operation data with the interaction feature data between the two data resources to obtain interaction feature data pairs corresponding to at least two operation dimensions; Obtaining a comparison result between two interactive feature data of a single data pair in the interactive feature data pair; The sequence label is determined according to the comparison result.

3. The method according to claim 2, wherein: The obtaining of the comparison result between two interactive feature data of a single data pair in the interactive feature data pair includes: Determining a comparison order of the interaction feature data pairs according to the priority of each of the operation dimensions; Comparing two interactive feature data of a single data pair in the interactive feature data pair in sequence according to the comparison order to obtain the comparison result; The determining the sequence label according to the comparison result comprises: In response to detecting that the comparison result indicates that two interaction feature data of a single data pair are different, the sequence label is determined according to the comparison result.

4. The method according to any one of claims 1 to 3, wherein: The resource feature data includes: portrait feature data of a user interacting with the data resource, resource attribute feature data of the data resource, and interaction feature data between the user and the data resource.

5. The method according to claim 4, wherein: The interactive feature data includes: In the list display page, the interaction feature data between the user and the data resource; and In the details display page, the interaction feature data between the user and the data resource sequence; The detail display page is a page displayed after the data resource in the list display page is triggered, and the data resource sequence includes the data resource.

6. A data recommendation method, comprising: Sorting multiple recalled data resources corresponding to target users through a pre-trained sorting model to obtain recommended sequence information of the multiple recalled data resources, wherein the sorting model is obtained by training according to any one of claims 1 to 5; Generate a recommendation list page based on the recommendation order information; The recommendation list page is output to the target user.

7. A training device for a sorting model, comprising: an acquisition unit configured to acquire a training sample set, wherein the training samples in the training sample set include two resource feature data and a sequence label representing a recommended sequence between data resources to which the two resource feature data respectively belong, and the two data resources corresponding to at least some of the training samples in the training sample set are different types of data resources; The training unit is configured to adopt a machine learning algorithm, take the two resource feature data as input, and take the sequence labels corresponding to the two resource feature data as expected output, to train and obtain the sorting model.

8. The device according to claim 7, wherein: Also includes: The label determination unit is configured to: Combine the user's operation data with the interaction feature data between the two data resources to obtain interaction feature data pairs corresponding to at least two operation dimensions; obtain a comparison result between two interaction feature data of a single data pair in the interaction feature data pair; and determine the sequence label according to the comparison result.

9. The device according to claim 8, wherein: The label determination unit is further configured to: Determine the comparison order of the interaction feature data pairs according to the priority of each operation dimension; compare the two interaction feature data of a single data pair in the interaction feature data pairs in turn according to the comparison order to obtain the comparison result; in response to detecting that the comparison result indicates that the two interaction feature data of the single data pair are different, determine the sequence label according to the comparison result.

10. The device according to any one of claims 7 to 9, wherein: The resource feature data includes: portrait feature data of a user interacting with the data resource, resource attribute feature data of the data resource, and interaction feature data between the user and the data resource.

11. The device according to claim 10, wherein: The interactive feature data includes: In the list display page, the interaction feature data between the user and the data resource; and In the details display page, the interaction feature data between the user and the data resource sequence; The detail display page is a page displayed after the data resource in the list display page is triggered, and the data resource sequence includes the data resource.

12. A data recommendation device, comprising: A sorting unit, configured to sort multiple recalled data resources corresponding to target users through a pre-trained sorting model to obtain recommended sequence information of the multiple recalled data resources, wherein the sorting model is obtained by training according to any one of claims 7 to 11; a generating unit, configured to generate a recommendation list page based on the recommendation order information; An output unit is configured to output the recommendation list page to the target user.

13. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

15. A computer program product comprising: A computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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