Information processing method and device, equipment and storage medium

Abstract

The embodiment of the invention relates to an information processing method and device, equipment and a computer readable storage medium. The method provided by the invention comprises the following steps: acquiring reference information associated with a target user; generating row query features and column query features based on the reference information and the object information of the target object; determining a target memory feature from reference memory information based on the row query feature and the column query feature, the reference memory information being constructed based on a plurality of reference users; and based on the target memory feature, determining the association degree between the target user and the target object.

Description

Technical Field

[0001] Example embodiments of the present disclosure generally relate to the field of computers, and more particularly, to an information processing method, apparatus, device, and computer-readable storage medium. Background Art

[0002] With the development of the Internet and multimedia technology, the scale of content on the Internet is getting larger and larger. Some platforms can recommend more matching content to users to reduce the cost of users to obtain content. With the development of computer technology, recommendation systems have evolved from simple rule-based methods to complex collaborative filtering and deep learning models that can process massive amounts of data and capture users' complex preference patterns to improve the accuracy of recommendations. Summary of the invention

[0003] In a first aspect of the present disclosure, an information processing method is provided. The method includes: obtaining reference information associated with a target user; generating row query features and column query features based on the reference information and object information of the target object; determining a target memory feature from reference memory information based on the row query feature and the column query feature, the reference memory information being constructed based on multiple reference users; and determining a degree of association between the target user and the target object based on the target memory feature.

[0004] In a second aspect of the present disclosure, an information processing device is provided. The device includes: an acquisition module configured to acquire reference information associated with a target user; a generation module configured to generate row query features and column query features based on the reference information and object information of the target object; a search module configured to determine a target memory feature from reference memory information based on the row query feature and the column query feature, the reference memory information being constructed based on multiple reference users; and a determination module configured to determine the degree of association between the target user and the target object based on the target memory feature.

[0005] In a third aspect of the present disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory, the at least one memory is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit. When the instructions are executed by the at least one processing unit, the device executes the method of the first aspect.

[0006] In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein computer-executable instructions are stored on the computer-readable storage medium, and the computer-executable instructions can be executed by a processor to implement the method of the first aspect.

[0007] In a fifth aspect of the present disclosure, a computer program product is provided, comprising computer executable instructions, wherein the computer executable instructions implement the method according to the first aspect when executed by a processor.

[0008] It should be understood that the contents described in this content section are not intended to limit the key features or important features of the embodiments of the present disclosure, nor are they 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

[0009] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0010] Figure 1 A schematic diagram showing an example environment in which embodiments of the present disclosure can be implemented;

[0011] Figure 2 A flowchart showing an information processing process according to some embodiments of the present disclosure is shown;

[0012] Figure 3 An example memory system according to some embodiments of the present disclosure is shown;

[0013] Figure 4 A schematic structural block diagram of an information processing device according to some embodiments of the present disclosure is shown; and

[0014] Figure 5 A block diagram of an electronic device capable of implementing various embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0015] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0016] It should be noted that the titles of any sections / subsections provided herein are not restrictive. Various embodiments are described throughout this article, and any type of embodiment may be included under any section / subsection. In addition, the embodiments described in any section / subsection may be combined in any manner with any other embodiments described in the same section / subsection and / or different sections / subsections.

[0017] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may be included below. The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may be included below.

[0018] The embodiments of the present disclosure may involve user data, data acquisition and / or use, etc. These aspects are subject to the corresponding laws, regulations and relevant provisions. In the embodiments of the present disclosure, all data collection, acquisition, processing, processing, forwarding, use, etc. are carried out on the premise that the user knows and confirms. Accordingly, when implementing each embodiment of the present disclosure, the type, scope of use, usage scenario, etc. of the data or information that may be involved should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with the relevant laws and regulations. The specific notification and / or authorization method can vary according to the actual situation and application scenario, and the scope of the present disclosure is not limited in this respect.

[0019] In this specification and the embodiments, if personal information processing is involved, it will be processed on the premise of having a legal basis (such as obtaining the consent of the subject of personal information, or it is necessary to perform a contract, etc.), and will only be processed within the scope of regulations or agreements. If a user refuses to process personal information other than the necessary information for basic functions, it will not affect the user's use of basic functions.

[0020] As briefly mentioned above, with the development of computer technology, recommendation systems have evolved from simple rule-based methods to complex collaborative filtering and deep learning models that can process massive amounts of data and capture users' complex preference patterns to improve the accuracy of recommendations. However, traditional recommendation systems have difficulty capturing the commonalities between similar users, which affects the accuracy of content recommendations.

[0021] The embodiment of the present disclosure proposes a scheme for information processing. The scheme includes: obtaining reference information associated with a target user; generating row query features and column query features based on the reference information and object information of the target object; determining a target memory feature from reference memory information based on the row query feature and the column query feature, the reference memory information being constructed based on multiple reference users; and determining the degree of association between the target user and the target object based on the target memory feature.

[0022] In this way, the embodiment of the present disclosure realizes the rapid location of relevant information in the memory module through the retrieval mechanism based on rows and columns, significantly improving the efficiency of memory acquisition. In addition, by compressing the historical information of multiple reference users into the memory module, the embodiment of the present disclosure can also improve the accuracy of information processing.

[0023] Various example implementations of the solution are described in detail below in conjunction with the accompanying drawings.

[0024] Example Environment

[0025] Figure 1 1 is a schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. Figure 1 As shown, example environment 100 may include electronic device 110 .

[0026] like Figure 1 As described above, the electronic device 110 may acquire reference information 120 associated with the user and object information 130 of the target object. As an example, the reference information may include features for describing the user, and the object information may include features for describing the target object.

[0027] In some embodiments, the user and the target object may be associated with an application associated with the electronic device 110. For example, the target object may include various appropriate types of virtual resources suitable for providing in the application, examples of which may include but are not limited to: media resources, advertising resources, commodity resources, tool resources, etc.

[0028] As will be described in detail below, electronic device 110 may determine a user's association 140 with a target object based on reference information 120 and object information 130. In some embodiments, association 140 may indicate a user's confidence in clicking on a target object presented in an interface of an application.

[0029] In some embodiments, the electronic device 110 may determine whether to recommend the target object to the user based on the association degree 140. For example, when the association degree 140 is greater than a threshold, the electronic device 110 may display the target object to the user through an application. Conversely, the electronic device 110 may determine not to present the target object to the user.

[0030] The electronic device 110 may be an independent physical electronic device, or an electronic device cluster or distributed system composed of multiple physical electronic devices, or a cloud electronic device that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks, and big data and artificial intelligence platforms. The electronic device 110 may include, for example, a computing system / electronic device, such as a mainframe, an edge computing node, a computing device in a cloud environment, and the like.

[0031] It should be understood that the structure and function of the various elements in the environment 100 are described for exemplary purposes only and do not imply any limitation on the scope of the present disclosure.

[0032] Some example embodiments of the present disclosure will be described below with continued reference to the accompanying drawings.

[0033] Example Process

[0034] Figure 2 FIG. 2 is a flowchart of an example process 200 for information processing according to some embodiments of the present disclosure. The process 200 may be implemented, for example, in Figure 1 The electronic device 110 shown in FIG. Figure 1 The process 200 is described below.

[0035] like Figure 2 As shown, in block 210, the electronic device 110 obtains reference information 120 associated with the target user. Figure 1 As introduced, the reference information 120 may be used to describe the target user.

[0036] In block 220 , the electronic device 110 generates a row query feature and a column query feature based on the reference information and the object information of the target object.

[0037] The following will further refer to Figure 3 To describe process 200, Figure 3 An example memory system 300 is shown according to some embodiments of the present disclosure.

[0038] like Figure 3 As shown, the memory system 300 may include an encoding unit 306, which may generate a target query feature M based on the object information 302 and the reference information 304. Q As an example, the encoding unit 306 may be implemented based on a multilayer perceptron (MLP), and the process thereof may be expressed as follows:

[0039] M Q =MLP(Merge(x,u)) (1)

[0040] Merge() may represent a vector addition or concatenation operation, x represents a feature corresponding to the object information 302 , and u represents a feature corresponding to the reference information 304 .

[0041] Furthermore, if Figure 3 As shown, the memory system 300 may also utilize the MLP 308 or the MLP 318 to process the input 316 to generate the row query feature 320M. Q-row and column query features 322M Q-col . This process can be expressed as:

[0042] M Q-row =MLP(M Q ) (2)

[0043] M Q-col =MLP(M Q ) (3)

[0044] Row query feature 320M Q-row and column query features 322M Q-col They may also be collectively referred to as query features 310 .

[0045] Continue to refer Figure 2 In box 230, the electronic device 110 determines a target memory feature from reference memory information based on the row query feature and the column query feature, where the reference memory information is constructed based on multiple reference users.

[0046] Continue to refer Figure 3 , in determining the row query feature 320M Q-row and column query features 322M Q-col After that, the memory system 300 can determine the row query feature 320 and a set of row indexes (also called row keys) M K-row 324 The row similarity S between row .

[0047] This process can be expressed as:

[0048]

[0049] Similarly, the memory system 300 can determine the column query characteristics 322 and a set of column indexes (also referred to as column keys) M K- col 326 column similarity S col .

[0050] This process can be expressed as:

[0051]

[0052] Additionally, the memory system 300 determines multiple similarities associated with multiple memory values ​​in the reference memory information 312 based on the row similarity and the column similarity. Specifically, the process can be expressed as:

[0053] S = Broadcast (S row ,S col ) (6)

[0054] Among them, the Broadcast operation means that the row similarity S row and column similarity S col Extended by broadcast rules

[0055] like Figure 3 As shown, the reference memory information 312 can correspond to the memory value 328, which can be constructed based on multiple reference users associated with the application. The multiple reference users can include but are not limited to the current user. Thus, the reference memory information 312 can compress and store historical information associated with multiple users in the application.

[0056] Furthermore, the memory system 300 may determine the target memory feature based on a set of target memory values ​​among the multiple memory values, and the similarity corresponding to the set of target memory values ​​satisfies a preset condition.

[0057] Specifically, the memory system 300 may determine a group of top K memory values ​​330 from the multiple memory values ​​based on the order of the multiple similarities corresponding to the multiple memory values. As an example, the number of the group of memory values ​​may correspond to a preset number (eg, K).

[0058] Further, the memory system 300 may, for example, perform weighted pooling on the provided K memory values ​​330 to determine an output 314, namely, a target memory feature M O . This process can be expressed as:

[0059] M O =Softmax(S TopK )·M V-TopK (7)

[0060] Among them, S TopK represents the highest K scores, M V-TopK It represents the memory value corresponding to the highest K scores.

[0061] In box 240, the electronic device 110 determines the association degree between the target user and the target object based on the target memory feature.

[0062] In some embodiments, the electronic device 110 may, for example, utilize a prediction model to determine the association between the target user and the target object based on the target memory features provided by the memory system 300. As an example, the prediction model may include a click rate prediction model, which may output the confidence level of the target object generated in the user click interface.

[0063] The process of memory injection will be further described below.

[0064] In some embodiments, the memory system 300 may also dynamically store the compression information in the reference memory information 312. The memory system 300 may update the key-value information of the reference memory information 312 based on a gradient descent method, for example.

[0065] like Figure 3 As shown, in some scenarios, in order to remember the information of the target user and the target object, the memory system 300 can be based on the target memory feature M O and the target query feature M Q The difference between Memory , so that the queried target memory feature M O Should be close to the target query feature M Q As an example, memory loss Memory It can be expressed as:

[0066] Loss Memory =SmoothL1(M O ,M Q ) (8)

[0067] Among them, SmoothL1 represents the smoothed L1 distance.

[0068] Specifically, Figure 3 As shown, the memory system 300 may stop the target query feature M at 332. Q The gradient of , and mask 334 may be applied to it, for example. In addition, memory system 300 may perform memory on output 314 (i.e., target memory feature M O ) Apply mask 338. Further, the memory system 300 can determine a loss value 336 based on the L1 distance between the two, and can update the key-value information of the reference memory information 312 based on the gradient feedback.

[0069] In some embodiments, the memory system 300 may also update the reference memory information 312 based on the interaction result between the user and the target object. Specifically, the training loss Loss of the entire memory system 300 is LMN It can be expressed as:

[0070] Loss LMN=Loss CTR +Loss Memory (9)

[0071] As an example, Loss CTR It can represent the cross entropy loss between the predicted click confidence and the actual interaction result between the user and the target object.

[0072] In some embodiments, in order to deploy the memory system 300 in an online scenario, the embodiments of the present disclosure may deploy a memory parameter server (MPS). As an example, the memory parameter server may adopt a GPU sharding strategy and may store memory values ​​in a distributed manner on the high bandwidth memory HBM of multiple GPUs.

[0073] Furthermore, during the model forward operation, the recommendation model can initiate a request to find the corresponding value based on the memory similarity. In addition, the all2all search can be performed based on the GPU high-speed interconnect technology NVLink. The all2all search means that each GPU will send and receive data to all other GPUs. In addition, the memory system 300 can perform distributed HBM storage and update the memory value through the all2all gradient update.

[0074] In this way, the embodiments of the present disclosure can effectively compress and store user historical behavior information through the memory module. On the one hand, the memory system can enhance the generalization ability of the model for different user behaviors through shared memory blocks in the spatial dimension, and realize interest perception among users. On the other hand, in the time dimension, through the user perception block, LMN can remember the user's long-term interests, thereby improving the accuracy of personalized recommendations.

[0075] Example devices and equipment

[0076] The embodiments of the present disclosure also provide corresponding devices for implementing the above methods or processes. Figure 4 A schematic structural block diagram of an example information processing device 400 according to some embodiments of the present disclosure is shown. The device 400 may be implemented as or included in an electronic device. Each module / component in the device 400 may be implemented by hardware, software, firmware, or any combination thereof.

[0077] like Figure 4As shown, the device 400 includes an acquisition module 410, which is configured to acquire reference information associated with a target user; a generation module 420, which is configured to generate row query features and column query features based on the reference information and object information of the target object; a search module 430, which is configured to determine a target memory feature from reference memory information based on the row query feature and the column query feature, wherein the reference memory information is constructed based on multiple reference users; and a determination module 440, which is configured to determine the association between the target user and the target object based on the target memory feature.

[0078] In some embodiments, the search module 430 is also configured to: determine the row similarity between the row query feature and a set of row indexes of the reference memory information; determine the column similarity between the column query feature and a set of column indexes of the reference memory information; based on the row similarity and the column similarity, determine multiple similarities associated with multiple memory values ​​in the reference memory information; and based on a set of target memory values ​​among the multiple memory values, determine the target memory feature, and the similarity corresponding to the set of target memory values ​​meets a preset condition.

[0079] In some embodiments, the search module 430 is further configured to determine the target memory feature based on weighted pooling of a set of target memory values.

[0080] In some embodiments, the number of a set of target memory values ​​is a preset number.

[0081] In some embodiments, the preset condition is related to the ranking of multiple similarities.

[0082] In some embodiments, the apparatus 400 further comprises an updating module configured to update the reference memory information based on a difference between a target memory feature and a target query feature, wherein the target query feature is determined based on the reference information and the object information.

[0083] In some embodiments, the reference memory information is also updated based on the results of the user's interaction with the target object.

[0084] In some embodiments, the relevance indicates a confidence level that the target user clicked on the target object presented in the interface.

[0085] In some embodiments, the target object includes a virtual resource provided in the application, and a plurality of reference users are associated with the user.

[0086] Figure 5 1 shows a block diagram of an electronic device 500 in which one or more embodiments of the present disclosure may be implemented. It should be understood that Figure 5 The electronic device 500 shown is merely exemplary and should not constitute any limitation on the functionality and scope of the embodiments described herein. Figure 5 The electronic device 500 shown can be used for Figure 1 Information processing system 100 is shown.

[0087] like Figure 5 As shown, the electronic device 500 is in the form of a general electronic device. The components of the electronic device 500 may include, but are not limited to, one or more processors or processing units 510, a memory 520, a storage device 530, one or more communication units 540, one or more input devices 550, and one or more output devices 560. The processing unit 510 may be an actual or virtual processor and is capable of performing various processes according to a program stored in the memory 520. In a multi-processor system, multiple processing units execute computer executable instructions in parallel to improve the parallel processing capability of the electronic device 500.

[0088] The electronic device 500 typically includes a plurality of computer storage media. Such media may be any accessible media that is accessible to the electronic device 500, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 520 may be a volatile memory (e.g., registers, caches, random access memory (RAM)), a non-volatile memory (e.g., a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. The storage device 530 may be a removable or non-removable medium, and may include a machine-readable medium, such as a flash drive, a disk, or any other medium that can be used to store information and / or data and can be accessed within the electronic device 500.

[0089] The electronic device 500 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 5 As shown in , a disk drive for reading or writing from a removable, non-volatile disk (e.g., a "floppy disk") and an optical drive for reading or writing from a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to the bus (not shown) by one or more data media interfaces. The memory 520 may include a computer program product 525 having one or more program modules that are configured to perform various methods or actions of various embodiments of the present disclosure.

[0090] The communication unit 540 implements communication with other electronic devices through a communication medium. Additionally, the functions of the components of the electronic device 500 can be implemented in a single computing cluster or multiple computing machines that can communicate through a communication connection. Therefore, the electronic device 500 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

[0091] The input device 550 may be one or more input devices, such as a mouse, a keyboard, a tracking ball, etc. The output device 560 may be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 500 may also communicate with one or more external devices (not shown) through the communication unit 540 as needed, such as a storage device, a display device, etc., communicate with one or more devices that allow a user to interact with the electronic device 500, or communicate with any device that allows the electronic device 500 to communicate with one or more other electronic devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0092] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein the computer-executable instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above.

[0093] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices, equipment, and computer program products implemented according to the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0094] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0095] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0096] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple implementations of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of a module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some implementations as replacements, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0097] The above descriptions of various implementations of the present disclosure are exemplary, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The selection of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the various implementations disclosed herein.

Claims

1. An information processing method, comprising: Obtain reference information associated with the target user; Generate row query features and column query features based on the reference information and object information of the target object; Determining a target memory feature from reference memory information based on the row query feature and the column query feature, the reference memory information being constructed based on a plurality of reference users; and Based on the target memory feature, the association degree between the target user and the target object is determined.

2. The method according to claim 1, wherein determining the target memory feature from the reference memory information based on the row query feature and the column query feature comprises: Determining row similarity between the row query feature and a set of row indexes of the reference memory information; determining a column similarity between the column query feature and a set of column indexes of the reference memory information; Determining a plurality of similarities associated with a plurality of memory values ​​in the reference memory information based on the row similarity and the column similarity; as well as The target memory feature is determined based on a group of target memory values ​​among the multiple memory values, and the similarity corresponding to the group of target memory values ​​meets a preset condition.

3. The method of claim 2, wherein determining the target memory characteristic based on a set of target memory values ​​among the plurality of memory values ​​comprises: The target memory feature is determined based on weighted pooling of the set of target memory values. The method according to claim 2 , wherein the number of the set of target memory values ​​is a preset number. The method according to claim 2 , wherein the preset condition is related to the ranking of the multiple similarities.

6. The method according to claim 1, further comprising: The reference memory information is updated based on a difference between the target memory feature and a target query feature, wherein the target query feature is determined based on the reference information and the object information.

7. The method according to claim 6, wherein the reference memory information is also updated based on the interaction result between the user and the target object. 8 . The method according to claim 1 , wherein the relevance indicates a confidence level of the target object presented in the target user click interface. 9 . The method according to claim 1 , wherein the target object comprises a virtual resource provided in an application, and the plurality of reference users are associated with the user.

10. An information processing device, comprising: an acquisition module, configured to acquire reference information associated with a target user; A generating module, configured to generate a row query feature and a column query feature based on the reference information and the object information of the target object; A search module configured to determine a target memory feature from reference memory information based on the row query feature and the column query feature, wherein the reference memory information is constructed based on a plurality of reference users; as well as The determination module is configured to determine the association degree between the target user and the target object based on the target memory feature.

11. An electronic device, comprising: at least one processing unit; as well as At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 9 when executed by the at least one processing unit.

12. A computer-readable storage medium having computer-executable instructions stored thereon, wherein the computer-executable instructions are capable of implementing the method according to any one of claims 1 to 9 when executed by a processor.

13. A computer program product comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 9.

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