Method and device for presenting user interface, equipment and storage medium

Abstract

According to the embodiment of the invention, the invention provides a user interface presentation method and device, equipment and a storage medium. The method comprises the following steps: acquiring first feature information associated with a target user and second feature information respectively associated with a plurality of user interfaces corresponding to a target page; for the plurality of user interfaces, determining differences between the target user and predetermined interaction indexes corresponding to the plurality of user interfaces based on the first feature information and second feature information associated with the plurality of user interfaces; determining a target user interface for the target user from the plurality of user interfaces based on the difference between the predetermined interaction indexes corresponding to the plurality of interfaces; and in response to determining that the target page is to be provided for the target user, presenting the target page to the target user according to the target user interface. In this way, the presented target page is more personalized.

Description

Technical Field

[0001] Exemplary implementations of the present disclosure generally relate to the field of computers, and in particular, to methods, devices, apparatuses, and computer-readable storage media for user interface presentation. Background Art

[0002] With the rapid development of computer technology, browsing and accessing the Internet has become a part of people's daily life. By browsing various pages, people can obtain various information. In traditional solutions, users can only manually perform simple settings on the presentation of pages, such as changing the background color of the page. However, this simple setting cannot meet the personalized browsing needs of users. For example, some users prefer video channels, some users prefer encyclopedia channels, some users like single-column mode, and some users like multi-column mode. In other words, different users have different requirements for the presentation of pages. Therefore, how to make the presented pages more in line with the personalized needs of users is a technical problem that needs to be solved urgently. Summary of the invention

[0003] In a first aspect of the present disclosure, a method for user interface presentation is provided, comprising: obtaining first characteristic information associated with a target user and second characteristic information respectively associated with a plurality of user interfaces corresponding to a target page; determining, for a plurality of user interfaces, differences between predetermined interaction indicators corresponding to the target user and the plurality of user interfaces based on the first characteristic information and the second characteristic information associated with the plurality of user interfaces; determining a target user interface for the target user from the plurality of user interfaces based on the differences between the predetermined interaction indicators corresponding to the plurality of interfaces; and in response to determining that the target page is to be provided to the target user, presenting the target page to the target user according to the target user interface.

[0004] In a second aspect of the present disclosure, there is provided an apparatus for user interface presentation, comprising: a first acquisition module, configured to: acquire first characteristic information associated with a target user and second characteristic information respectively associated with a plurality of user interfaces corresponding to a target page; a difference determination module, configured to: determine, for a plurality of user interfaces, the difference between predetermined interaction indicators corresponding to the target user and the plurality of user interfaces based on the first characteristic information and the second characteristic information associated with the plurality of user interfaces; a target user interface determination module, configured to: determine a target user interface for the target user from the plurality of user interfaces based on the difference between the predetermined interaction indicators corresponding to the plurality of interfaces; and a target page presentation module, configured to: in response to determining that the target page is to be provided to the target user, present the target page to the target user according to the target user interface.

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

[0006] In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which computer instructions are stored. When the computer instructions are executed by a processor, the processor implements the method according to the first aspect of the present disclosure.

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

[0008] It should be understood that the content described in this content section is not intended to limit the key features or important features of the implementation of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In the following, in conjunction with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages and aspects of various implementations of the present disclosure will become more apparent. In the accompanying drawings, the same or similar figure labels 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 2A shows an example user interface according to some embodiments of the present disclosure;

[0012] Figure 2B A flowchart showing a process for user interface presentation according to some embodiments of the present disclosure is shown;

[0013] Figure 3 A flow chart of model training for user interface presentation according to some embodiments of the present disclosure is shown;

[0014] Figure 4 A flow chart of model training for user interface presentation according to some embodiments of the present disclosure is shown;

[0015] Figure 5 A schematic structural block diagram of an apparatus for generating information according to some embodiments of the present disclosure is shown; and

[0016] Figure 6A block diagram of an electronic device is shown in which one or more embodiments of the present disclosure may be implemented. DETAILED DESCRIPTION

[0017] 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.

[0018] 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 also be included below.

[0019] Herein, unless explicitly stated, executing a step “in response to A” does not mean executing the step immediately after “A” but may include one or more intermediate steps.

[0020] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0021] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0022] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information, so that the user can independently choose whether to provide personal information to software or hardware such as electronic devices, applications, servers or storage media that execute the operations of the technical solution of the present disclosure based on the prompt message.

[0023] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information is sent to the user in a manner such as a pop-up window, in which the prompt information can be presented in text form. In addition, the pop-up window can also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0024] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0025] As used herein, the term "model" can learn the association between the corresponding input and output from the training data, so that after the training is completed, the corresponding output can be generated for a given input. The generation of the model can be based on machine learning technology. Deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs by using multi-layer processing units. A neural network model is an example of a model based on deep learning. In this article, "model" may also be referred to as "machine learning model", "learning model", "machine learning network" or "learning network", and these terms are used interchangeably in this article.

[0026] A "neural network" is a machine learning network based on deep learning. A neural network is capable of processing inputs and providing corresponding outputs, and typically includes an input layer and an output layer and one or more hidden layers between the input layer and the output layer. Neural networks used in deep learning applications typically include many hidden layers, thereby increasing the depth of the network. The layers of a neural network are connected in sequence so that the output of the previous layer is provided as input to the next layer, where the input layer receives the input of the neural network and the output of the output layer serves as the final output of the neural network. Each layer of a neural network includes one or more nodes (also called processing nodes or neurons), each of which processes input from the previous layer.

[0027] Generally, machine learning can be roughly divided into three stages, namely the training stage, the testing stage, and the application stage (also called the inference stage). In the training stage, a given model can be trained using a large amount of training data, and the parameter values ​​are continuously updated iteratively until the model can obtain consistent inferences that meet the expected goals from the training data. Through training, the model can be considered to be able to learn the association between input and output (also called input-to-output mapping) from the training data. The parameter values ​​of the trained model are determined. In the testing stage, the test input is applied to the trained model to test whether the model can provide the correct output, thereby determining the performance of the model. The testing stage can sometimes be integrated into the training stage. In the application or inference stage, the trained model can be used to process the actual model input based on the parameter values ​​obtained through training to determine the corresponding model output.

[0028] As briefly discussed above, with the rapid development of computer technology, browsing and accessing the Internet has become a part of people's daily lives. By browsing various pages, people can obtain various information. In traditional solutions, users can only manually perform simple settings on the presentation of pages, such as changing the background color, etc. However, such simple settings cannot meet the personalized browsing needs of users. For example, some users prefer video channels, some users prefer encyclopedia channels, some users like single-column methods, and some users like multi-column methods. In other words, different users have different requirements for the presentation of pages. Therefore, how to make the presented pages more in line with the personalized needs of users is a technical problem that needs to be solved urgently.

[0029] According to an embodiment of the present disclosure, a method, apparatus, device and storage medium for user interface presentation are provided. The method includes: obtaining first feature information associated with a target user and second feature information respectively associated with multiple user interfaces corresponding to a target page; for multiple user interfaces, determining the difference between the target user and the predetermined interaction indicators corresponding to the multiple user interfaces based on the first feature information and the second feature information associated with the multiple user interfaces; determining a target user interface for the target user from the multiple user interfaces based on the difference between the predetermined interaction indicators corresponding to the multiple interfaces; and in response to determining that a target page is to be provided to the target user, presenting the target page to the target user according to the target user interface. In this way, the presented target page is more personalized.

[0030] It should be understood that the term "using a machine learning model" as used herein refers to using one or more machine learning models. Specifically, a single machine learning model may be used to complete the user interface presentation operation, or multiple machine learning models may be used to collaboratively complete the user interface presentation operation, wherein each machine learning model completes a link in the user interface presentation operation.

[0031] Some example embodiments of the present disclosure will be described below with continued reference to the accompanying drawings.

[0032] Example Environment

[0033] Figure 1 1 is a schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. Figure 1As shown, the example environment 100 may include users 140-1, 140-2 ... 140-M and their terminal devices 110-1, 110-2 ... 110-M, where M is a positive integer greater than or equal to 1. For ease of discussion, the terminal devices 110-1, 110-2 ... 110-M may be individually or collectively referred to as terminal devices 110, and the users 140-1, 140-2 ... 140-M may be individually or collectively referred to as users 140. In the example environment 100, an application 115 is installed in the terminal device 110. The user 140 may interact with the application 115 via the terminal device 110 and / or an attached device of the terminal device 110.

[0034] The application 115 can provide the user 140 with an integration of multiple applications or components. These applications can be used as application modules in the application 115. In some embodiments, the application 115 can be downloaded and installed as an application on the terminal device 110. In some embodiments, the application 115 can also be accessed in other ways, such as through a web page.

[0035] Application 115 may be any appropriate type of application capable of providing media content, examples of which may include but are not limited to: social applications, audio / video applications, media item playback applications, broadcast applications, etc. The embodiments of the present disclosure are not limited in this respect.

[0036] exist Figure 1 In the environment 100, if the application 115 is active, the terminal device 110 can present an interactive page through the application 115. The interactive page can be any appropriate type of page, which can support the user 140 to input any appropriate type of data and present media items of any media item type to the user 140. The interactive interface can include various interfaces that can be provided by the application 115, such as a conversation interface for presenting chat content, an editing interface, a publishing interface, and the like.

[0037] In some embodiments, the terminal device 110 can communicate with the server 130 to provide services for the application 115. The terminal device 110 can be any type of mobile terminal, fixed terminal or portable terminal, including a mobile phone, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a media computer, a multimedia tablet, a handheld computer, a portable game terminal, a VR / AR device, a personal communication system (PCS) device, a personal navigation device, a personal digital assistant (PDA), an audio / video player, a digital camera / camcorder, a positioning device, a television receiver, a radio receiver, an e-book device, a game device, or any combination of the foregoing, including accessories and peripherals of these devices or any combination thereof. In some embodiments, the terminal device 110 can also support any type of interface for the user (such as a "wearable" circuit, etc.).

[0038] The server 130 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server 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 server 130 may include, for example, a computing system / server, such as a mainframe, an edge computing node, a computing device in a cloud environment, etc. The server 130 may provide background services for the application 115 that supports virtual scenes in the terminal device 110.

[0039] A communication connection may be established between the server 130 and the terminal device 110. The communication connection may be established in a wired manner or a wireless manner. The communication connection may include, but is not limited to, a Bluetooth connection, a mobile network connection, a Universal Serial Bus (USB) connection, a Wireless Fidelity (WiFi) connection, etc., and the embodiments of the present disclosure are not limited in this respect. In the embodiments of the present disclosure, the server 130 and the terminal device 110 may implement signaling interaction through the communication connection between the two.

[0040] A machine learning model 120 is deployed in the environment 100. The machine learning model 120 can be deployed on an appropriate electronic device. As an example, the machine learning model 120 can also be deployed on other electronic devices different from the server 130, and the server 130 can, for example, call the machine learning model 120 to perform the corresponding task by way of a service call. In another example, the machine learning model 120 can also be deployed locally on the server 130, for example. In short, the present disclosure is not limited in terms of the deployment location and calling method of the machine learning model 120.

[0041] It should be understood that the structure and function of each element in the environment 100 are described for exemplary purposes only and do not imply any limitation on the scope of the present disclosure.

[0042] Example Process

[0043] For easier understanding, refer to Figure 2A The example user interfaces 201 and 202 according to some embodiments of the present disclosure are shown. It should be understood that the user interfaces 201 and 202 are merely examples, and various page designs may actually exist. The various graphical elements in the page may have different arrangements and different visual representations, one or more of which may be omitted or replaced, and one or more other elements may also exist. The embodiments of the present disclosure are not limited in this respect. The example user interfaces 201 and 202 may be presented at the terminal device 110.

[0044] exist Figure 2A In the example, the home page (an example of the target page) may include multiple channels, namely channels 203-206. When different channels are selected, different styles of home pages may be presented. Specifically, in the example user interface 201, channel 204 is selected, and in the example user interface 202, channel 205 is selected. In actual operation, some users may tend to browse channel 203, another part of users may tend to browse channel 204, another part of users may tend to browse channel 205, and other users may tend to browse 206. It can be seen that if the same home page is presented to all users, the different personalized needs of different users cannot be met. According to the various embodiments of the present disclosure, the different needs of different users can be fully considered to achieve personalized presentation of pages.

[0045] It should be understood that the target page of the present disclosure can be any page to be presented, including but not limited to the home page, recommendation page, video browsing page, etc. The presentation style of a page refers to a combination of a group of visual elements. In operation, the same target page can correspond to multiple user interfaces, where different user interfaces can have different presentation styles, such as user interfaces 201 and 202. It should be understood that the present disclosure is not limited in the specific reference of the page presentation style, including but not limited to the page layout, the content of the page, etc.

[0046] For the convenience of discussion, in the following examples, Figure 2A Specifically, the home page is used as an example of a target page, and user interfaces 201 and 202 are used as examples of multiple user interfaces corresponding to the target page.

[0047] Next, refer to Figure 2B , which shows a schematic diagram of an example process 200B for user interface presentation according to some embodiments of the present disclosure. The process 200B may be implemented at an electronic device, where the electronic device may include Figure 1 One or more of the terminal device 110, server 130 and machine learning model 120.

[0048] As shown in the figure, in box 210, the electronic device obtains first feature information associated with the target user and second feature information respectively associated with multiple user interfaces corresponding to the target page.

[0049] In some embodiments, the first feature information associated with the target user may include, but is not limited to, the target user's personal information, activity, usage time, content type of interest, user preference settings, etc. In other embodiments, the first feature information associated with the target user may also include device information of the target user's terminal device 110, for example, the system type of the terminal device, the software operating version, etc. In some embodiments, the second feature information may include page identification information, layout information, and / or presented visual element information, etc. It should be understood that the above examples of the first feature information and the second feature information are merely exemplary. In other embodiments, the first feature information and the second feature information may include other content. The various embodiments of the present disclosure are not limited in this respect. In operation, a user may click on an application. The homepage of an application may have multiple user interfaces. As discussed below, according to various embodiments of the present disclosure, a target user interface may be determined from multiple user interfaces and presented to the user.

[0050] In block 220, the electronic device determines, for multiple user interfaces, the difference between the predetermined interaction indicators corresponding to the target user and the multiple user interfaces based on the first feature information and the second feature information associated with the multiple user interfaces. In some embodiments, the predetermined interaction indicator includes at least one of the following: the viewing time of the target user on the user interface or the application corresponding to the target page, the use time of the target user on the user interface or the application, and the stay time of the target user on the user interface or the application. The predetermined interaction indicator can objectively reflect the personalized needs of the user to a certain extent.

[0051] In box 230, the electronic device determines a target user interface for the target user from multiple user interfaces based on the difference between the predetermined interaction indicators corresponding to the multiple interfaces. In box 240, in response to determining that the target page is to be provided to the target user, the target page is presented to the target user according to the target user interface. For example, if it is determined that the user prefers user interface 201 based on the difference between the predetermined interaction indicators, user interface 201 is presented when the home page is presented. Conversely, if it is determined that the user prefers user interface 202, user interface 202 is presented when the home page is presented.

[0052] In this way, the same target page can be presented as different user interfaces at different users, thereby meeting the personalized needs of the users.

[0053] Furthermore, in order to make the presented user interface more in line with the user's expectations, according to some embodiments of the present disclosure, at least one machine learning model can be used to determine the differences between the predetermined interaction indicators corresponding to the multiple user interfaces. By utilizing the powerful data processing and analysis capabilities of the machine learning model, the user's needs can be determined more comprehensively and accurately. Figure 3 The present invention describes in detail how to use at least one machine learning model to determine the differences between predetermined interaction indicators corresponding to multiple user interfaces.

[0054] In some embodiments, the electronic device may use at least one trained machine learning model to determine the difference between the target user and the predetermined interaction indicators corresponding to the multiple user interfaces based on the first feature information and the second feature information associated with the multiple user interfaces. Next, the electronic device may determine the difference between the target user and the predetermined interaction indicators corresponding to the multiple user interfaces by aggregating the differences determined by the at least one machine learning model. Figure 3In an example embodiment, at least one machine learning model may include multiple interaction difference prediction models, for example, a first interaction difference prediction model 380, a second interaction difference prediction model 385. It should be understood that in other embodiments, more interaction difference prediction models may be included. In operation, for a certain user, the first feature information associated with the user and the second feature information respectively associated with the multiple user interfaces corresponding to the target page may be respectively input into each of the multiple interaction difference prediction models. Next, the output results of the multiple interaction difference prediction models are aggregated, for example, an average value may be taken, a probability may be calculated, and so on. Next, the target user interface is determined based on the result of the aggregation processing. For example, if the aggregation result shows that the user prefers user interface 201, user interface 201 is presented to the user. By utilizing multiple machine learning models, the accuracy of the differences between the predetermined interaction indicators corresponding to the multiple user interfaces can be improved.

[0055] Next, we will combine Figure 3 Let's further discuss the process of how to train at least one machine learning model. In some embodiments, the electronic device can obtain a set of training samples, each training sample includes first sample feature information associated with a sample user and second sample feature information associated with multiple user interfaces, and interaction indicator labels of multiple user interfaces (including but not limited to viewing time, usage time, retention time, etc.). Next, the electronic device can use a set of training samples to train at least one machine learning model. For example, the electronic device obtains a set of training samples 330, wherein each training sample includes first sample feature information associated with a sample user and second sample feature information associated with multiple user interfaces, and interaction indicator labels of multiple user interfaces. The electronic device can use a set of training samples 330 to train at least one machine learning model.

[0056] exist Figure 3 In the embodiment of the present invention, at least one machine learning model may include a first interaction prediction difference model 380. Next, the training process of the first interaction prediction difference model 380 will be described in detail.

[0057] In operation, the electronic device divides a set of training samples into a first training sample subset and a second training sample subset, each training sample includes first sample feature information associated with a sample user and second sample feature information associated with multiple user interfaces, as well as interaction indicator labels of the multiple user interfaces. Figure 3In the example embodiment, a group of training samples 330 is divided into a first training sample subset 340 and a second training sample subset 350. It should be understood that in other embodiments, a group of training samples can be divided into more subsets. For example, a third training sample subset, a fourth training sample subset, and so on can also be included. As discussed below, each training sample subset can derive an interactive prediction difference model. As an example, when divided into two training sample subsets, two interactive prediction difference models can be ultimately generated. Accordingly, when divided into three training sample subsets, three interactive prediction difference models can be ultimately generated.

[0058] Next, the electronic device can use the first training sample subset to train multiple first indicator prediction models corresponding to multiple user interfaces respectively and use the second training sample subset to train multiple second indicator prediction models corresponding to multiple user interfaces respectively, each first indicator prediction model or each second indicator prediction value is configured to generate a value of a predetermined interaction indicator of the user on the corresponding user interface based on feature information associated with the user and feature information associated with the corresponding user interface. Figure 3 In the example embodiment, the electronic device uses the first training sample subset 340 to train the first indicator prediction models 360-1 and 360-2, and uses the second training sample subset 345 to train the second indicator prediction models 365-1 and 365-2.

[0059] In operation, if there are 10,000 users (a set of training samples 330), 5,000 of them may be randomly pushed to user interface 1, and the other 5,000 users may be randomly pushed to user interface 2. After data collection (e.g., collecting label data, such as browsing time, dwell time, etc.), the data of the 10,000 users may be divided into a first training sample subset 340 and a second training sample subset 345, wherein the first training sample subset 340 may include 2,500 sample data of randomly pushed user interface 1 (i.e., sample subset 350-1) and 2,500 sample data of randomly pushed user interface 2 (i.e., sample subset 350-2). The electronic device can train the first indicator prediction model 360-1 through the 2500 sample data of the randomly pushed user interface 1 included in the first training sample subset 340 (i.e., sample subset 350-1), and the electronic device can train the first indicator prediction model 360-2 through the 2500 sample data of the randomly pushed user interface 2 included in the first training sample subset 340 (i.e., sample subset 350-2). Similarly, the electronic device can train the second indicator prediction model 365-1 through the 2500 sample data of the randomly pushed user interface 1 included in the second training sample subset 345 (i.e., sample subset 355-1), and the electronic device can train the second indicator prediction model 365-2 through the 2500 sample data of the randomly pushed user interface 2 included in the second training sample subset 345 (i.e., sample subset 352-1).

[0060] Next, the electronic device can use multiple first indicator estimation models to determine multiple values ​​of the predetermined interaction indicators of the sample users on multiple user interfaces based on the sample feature information in the second training sample subset. The electronic device can further determine the first difference between the multiple values ​​of the estimated interaction indicators as the first difference label corresponding to each training sample in the second training sample subset. In other words, the electronic device uses the first training sample subset 340 to perform training of the first indicator estimation model, and when performing the estimation, the second training sample subset 345 is input as input data to the trained first indicator estimation model to obtain the estimated interaction value. Figure 3 As shown, the electronic device inputs the second training sample subset 345 into the first indicator prediction model to obtain multiple values ​​of predetermined interaction indicators of sample users of the second training sample subset 345 on the user interfaces A and 2.

[0061] By distinguishing the model training data from the input data during model operation (for example, the training data of the first indicator prediction model is the first training sample subset 340, and the input data during model operation is the second training sample subset 345), the accuracy of the predicted data can be effectively improved.

[0062] Next, the electronic device can train a first interaction difference estimation model based on the second training sample subset and the first difference label corresponding to each training sample, and the first interaction estimation difference model is configured to generate differences between predetermined interaction indicators of the user on multiple user interfaces based on feature information associated with the user and feature information associated with the corresponding user interface. In other words, by inputting the sample feature information of the second training sample subset 345 into the first indicator estimation model, a first interaction difference estimation model training sample 370 (i.e., a first difference) can be obtained. By using the first interaction difference estimation model training sample 370, a first interaction difference estimation model 380 can be finally obtained.

[0063] In some embodiments, at least one machine learning model may further include a second interaction estimation difference model 385. In this case, using a set of training samples to train at least one machine learning model may be further optimized. It should be understood that the training process of the second interaction estimation difference model 385 is substantially similar to the training process of the first interaction estimation difference model 380. Specifically, in some embodiments, the electronic device may use multiple second indicator estimation models to determine multiple values ​​of predetermined interaction indicators of sample users on multiple user interfaces based on sample feature information in the first training sample subset. Next, the electronic device may determine the second difference between the multiple values ​​of the estimated interaction indicators as the second difference label corresponding to each training sample in the first training sample subset. Finally, the electronic device may train a second interaction difference estimation model based on the first training sample subset and the second difference label corresponding to each training sample, and the second interaction estimation difference model is configured to generate the difference between the predetermined interaction indicators of the user on multiple user interfaces based on the feature information associated with the user and the feature information associated with the corresponding user interface. In other words, by inputting the first training sample subset 340 into the second indicator prediction models 365-1 and 365-2, a second interaction difference prediction model training sample 375 (ie, second difference) can be obtained. By using the second interaction difference prediction model training sample 375, a second interaction difference prediction model 385 can be finally obtained.

[0064] As discussed above, a set of training samples can be divided into more sample subsets. In this case, model training (i.e., the first stage) and model prediction (i.e., the second stage) can still use different training sample subsets in a crossover form. For example, a set of training samples can be divided into a first training sample subset, a second training sample subset, a third training sample subset, and a fourth training sample subset. Through the first to fourth training sample subsets, a first indicator estimation model, a second indicator estimation model, a third indicator estimation model, and a fourth indicator estimation model can be obtained respectively. In the model prediction stage, the second training sample subset can be input into the first indicator estimation model to obtain a first interactive difference model training sample, thereby obtaining a first interactive difference estimation model. Similarly, the third training sample subset can be input into the second indicator estimation model to obtain a second interactive difference model training sample, thereby obtaining a second interactive difference estimation model. The fourth training sample subset can be input into the third indicator estimation model to obtain a third interactive difference model training sample, thereby obtaining a third interactive difference estimation model. Finally, the first training sample subset is input into the fourth indicator prediction model to obtain the fourth interactive difference model training sample, thereby obtaining the fourth interactive difference prediction model. It should be understood that when a set of training samples can be divided into other numbers of sample subsets, the method is similar. It is only for the purpose of brevity. This disclosure will not be repeated one by one.

[0065] Alternatively, in some embodiments, at least one machine learning model includes multiple indicator prediction models. In this case, the electronic device can utilize training samples corresponding to multiple user interfaces in a set of training samples to train multiple indicator prediction models corresponding to the multiple user interfaces, each indicator prediction value is configured to generate a value of a predetermined interaction indicator of the user on the corresponding user interface based on feature information associated with the user and feature information associated with the corresponding user interface. Next, the electronic device can utilize multiple indicator prediction models to respectively determine multiple estimated values ​​of the predetermined interaction indicators of the target user corresponding to the multiple user interfaces based on the first feature information and the second feature information, and determine the difference based on comparison of the multiple estimated values. Figure 3 In the embodiment of the present invention, the electronic device can use the first training sample subset to train the first indicator prediction model 360-1 and 360-2. Next, the electronic device can use the first indicator prediction model 360-1 and 360-2 to determine multiple estimated values ​​of the predetermined interaction indicators corresponding to the target user and the multiple user interfaces based on the first feature information and the second feature information. In this way, the training process of the model can be simplified.

[0066] Through the above method, we can make full use of the analysis and processing capabilities of machine learning on massive data, accurately analyze user needs, and display pages that better meet user needs to users.

[0067] Since user needs are changing dynamically, in order to meet the dynamic changes in user needs, according to some embodiments of the present disclosure, the model can be updated dynamically / periodically. Figure 4 To discuss the scheme for user interface presentation according to some embodiments of the present disclosure. Figure 2B similar, Figure 4 The example process shown may also be implemented at an electronic device, where the electronic device may include Figure 1 One or more of the terminal device 110, server 130 and machine learning model 120.

[0068] In block 410-1, the electronic device may collect sample data. In block 420-1, the electronic device may perform model training, such as the above combination Figure 3 Any process discussed about model training. In box 430-1, the performance of the model is tested. If the test results show that the performance of the model is acceptable, the trained model can be officially run (box 440-1). According to some embodiments of the present disclosure, after the model is officially run, the electronic device can continue to collect sample data (box 410-2). In box 420-2, the electronic device also performs model training, as described above in combination with Figure 3 Any process of model training discussed above. In block 430-2, the electronic device may test the performance of the newly trained model. If the test results show that the performance of the model is better than the performance of the running model, the model may be updated (block 440-2). In this way, the personalized needs of users may be dynamically met.

[0069] Example devices, equipment, and media

[0070] The embodiments of the present disclosure also provide corresponding devices for implementing the above methods or processes. Figure 5 FIG. 5 is a schematic structural block diagram of an apparatus 500 for presenting a user interface according to some embodiments of the present disclosure. The apparatus 500 may be implemented as or included in an electronic device, and the electronic device may include Figure 1 The terminal device 110, the server 130 and the machine learning model 120 in the apparatus 500 may be implemented by hardware, software, firmware or any combination thereof.

[0071] like Figure 5As shown, the device 500 includes: a first acquisition module 510, configured to: acquire first characteristic information associated with a target user and second characteristic information respectively associated with a plurality of user interfaces corresponding to a target page; a difference determination module 520, configured to: determine, for a plurality of user interfaces, the difference between predetermined interaction indicators corresponding to the target user and the plurality of user interfaces based on the first characteristic information and the second characteristic information associated with the plurality of user interfaces; a target user interface determination module 530, configured to: determine a target user interface for the target user from a plurality of user interfaces based on the difference between the predetermined interaction indicators corresponding to the plurality of interfaces; and a target page presentation module 640, configured to: in response to determining that a target page is to be provided to the target user, present the target page to the target user according to the target user interface.

[0072] In some embodiments, the predetermined interaction indicator includes at least one of the following: the viewing time of the target user on the user interface or the application corresponding to the target page, the usage time of the target user on the user interface or the application, and the stay time of the target user on the user interface or the application.

[0073] In some embodiments, the difference determination module 520 is further configured to: determine the difference between the predetermined interaction indicators corresponding to the target user and the multiple user interfaces by: utilizing at least one trained machine learning model to determine the difference between the predetermined interaction indicators corresponding to the target user and the multiple user interfaces based on the first feature information and the second feature information associated with the multiple user interfaces; and determine the difference between the predetermined interaction indicators corresponding to the target user and the multiple user interfaces by aggregating the differences determined by the at least one machine learning model.

[0074] In some embodiments, the difference determination module 520 is further configured to: obtain a set of training samples, each training sample including first sample feature information associated with a sample user and second sample feature information associated with multiple user interfaces, as well as interaction indicator labels for each of the multiple user interfaces; and use a set of training samples to train at least one machine learning model.

[0075] In some embodiments, at least one machine learning model includes a first interaction prediction difference model, and the difference determination module 520 is further configured to: use a set of training samples to train at least one machine learning model by: dividing a set of training samples into a first training sample subset and a second training sample subset, each training sample including first sample feature information associated with a sample user and second sample feature information associated with multiple user interfaces, and interaction indicator labels for each of the multiple user interfaces; use the first training sample subset to train multiple first indicator prediction models corresponding to the multiple user interfaces respectively and use the second training sample subset to train multiple second indicator prediction models corresponding to the multiple user interfaces respectively, each first indicator prediction model or each second indicator prediction value is configured to be based on the interaction with the user The method comprises the following steps of: generating a value of a predetermined interaction indicator of a user on a corresponding user interface by using characteristic information associated with the user and characteristic information associated with the corresponding user interface; utilizing multiple first indicator prediction models to respectively determine multiple values ​​of the predetermined interaction indicator of the sample user on multiple user interfaces based on the sample characteristic information in the second training sample subset; determining a first difference between the multiple values ​​of the estimated interaction indicator as a first difference label corresponding to each training sample in the second training sample subset; and training a first interaction difference prediction model based on the second training sample subset and the first difference label corresponding to each training sample, the first interaction prediction difference model being configured to generate a difference between the predetermined interaction indicators of the user on multiple user interfaces based on the characteristic information associated with the user and the characteristic information associated with the corresponding user interface.

[0076] In some embodiments, at least one machine learning model also includes a second interaction prediction difference model, and the difference determination module 520 is further configured to: train at least one machine learning model using a set of training samples by: using multiple second indicator prediction models, based on the sample feature information in the first training sample subset, respectively determine multiple values ​​of predetermined interaction indicators of sample users on multiple user interfaces; determine the second difference between the multiple values ​​of the estimated interaction indicators as the second difference label corresponding to each training sample in the first training sample subset; and train the second interaction difference prediction model based on the first training sample subset and the second difference label corresponding to each training sample, the second interaction prediction difference model being configured to generate the difference between the predetermined interaction indicators of the user on multiple user interfaces based on the feature information associated with the user and the feature information associated with the corresponding user interface.

[0077] In some embodiments, at least one machine learning model includes multiple indicator prediction models, and the difference determination module 520 is further configured to: train at least one machine learning model using a set of training samples by: training multiple indicator prediction models corresponding to multiple user interfaces respectively using training samples in a set of training samples, each indicator prediction value being configured to generate a value of a predetermined interaction indicator of the user on the corresponding user interface based on feature information associated with the user and feature information associated with the corresponding user interface; and determine the difference between the predetermined interaction indicators corresponding to the target user and the multiple user interfaces based on the first feature information and the second feature information associated with the multiple user interfaces by: determining multiple estimated values ​​of the predetermined interaction indicators corresponding to the target user and the multiple user interfaces respectively based on the first feature information and the second feature information using multiple indicator prediction models; and determine the difference based on comparison of the multiple estimated values.

[0078] The units and / or modules included in the device 500 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units and / or modules can be implemented using software and / or firmware, such as machine executable instructions stored on a storage medium. In addition to or as an alternative to machine executable instructions, some or all of the units and / or modules in the device 500 can be implemented at least in part by one or more hardware logic components. As an example and not limitation, exemplary types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0079] It should be understood that one or more steps in the above method can be performed by a suitable electronic device or a combination of electronic devices. Such an electronic device or a combination of electronic devices may include, for example, Figure 1 The terminal device 110 in.

[0080] Figure 6 1 shows a block diagram of an electronic device 600 in which one or more embodiments of the present disclosure may be implemented. It should be understood that Figure 6 The electronic device 600 shown is merely exemplary and should not constitute any limitation on the functionality and scope of the embodiments described herein. Figure 6 The electronic device 600 shown can be used to implement Figure 1 The terminal device 110 or Figure 5 Device 500.

[0081] like Figure 6As shown, the electronic device 600 is in the form of a general electronic device. The components of the electronic device 600 may include, but are not limited to, one or more processors or processing units 610, a memory 620, a storage device 630, one or more communication units 640, one or more input devices 650, and one or more output devices 660. The processing unit 610 may be an actual or virtual processor and is capable of performing various processes according to a program stored in the memory 620. In a multi-processor system, multiple processing units execute computer executable instructions in parallel to improve the parallel processing capability of the electronic device 600.

[0082] The electronic device 600 typically includes a plurality of computer storage media. Such media can be any available media accessible to the electronic device 600, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 620 can 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 630 can be a removable or non-removable medium, and can include a machine-readable medium, such as a flash drive, a disk, or any other medium, which can be used to store information and / or data and can be accessed within the electronic device 600.

[0083] The electronic device 600 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 6 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 620 may include a computer program product 625 having one or more program modules that are configured to perform various methods or actions of various embodiments of the present disclosure.

[0084] The communication unit 640 implements communication with other electronic devices through a communication medium. Additionally, the functions of the components of the electronic device 600 can be implemented with a single computing cluster or multiple computing machines that can communicate through a communication connection. Therefore, the electronic device 600 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.

[0085] The input device 650 may be one or more input devices, such as a mouse, a keyboard, a tracking ball, etc. The output device 660 may be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 600 may also communicate with one or more external devices (not shown) through the communication unit 640 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 600, or communicate with any device that allows the electronic device 600 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).

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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. A method for user interface presentation, comprising: Acquire first characteristic information associated with the target user and second characteristic information respectively associated with a plurality of user interfaces corresponding to the target page; For the multiple user interfaces, determining a difference between predetermined interaction indicators corresponding to the target user and the multiple user interfaces based on the first feature information and second feature information associated with the multiple user interfaces; Determining a target user interface for the target user from the multiple user interfaces based on the difference between the predetermined interaction indicators corresponding to the multiple interfaces; as well as In response to determining that the target page is to be provided to the target user, the target page is presented to the target user according to the target user interface.

2. The method according to claim 1, wherein the predetermined interaction indicator comprises at least one of the following: The viewing time of the target user on the application corresponding to the user interface or the target page, the duration of use of the user interface or the application by the target user, and The length of time that the target user stays on the user interface or the application.

3. The method according to claim 1, wherein determining the difference between the predetermined interaction indicators corresponding to the target user and the plurality of user interfaces comprises: Determine, using at least one trained machine learning model, a difference between predetermined interaction indicators corresponding to the target user and the multiple user interfaces based on the first feature information and second feature information associated with the multiple user interfaces; as well as By aggregating the differences determined by the at least one machine learning model, differences between predetermined interaction indicators corresponding to the target user and the multiple user interfaces are determined.

4. The method of claim 3, wherein the training of the at least one machine learning model comprises: Acquire a set of training samples, each training sample including first sample feature information associated with a sample user and second sample feature information associated with the plurality of user interfaces, and interaction indicator labels of the plurality of user interfaces; and The at least one machine learning model is trained using the set of training samples.

5. The method of claim 4, wherein the at least one machine learning model comprises a first interactive prediction difference model, wherein training the at least one machine learning model using the set of training samples comprises: Dividing a group of training samples into a first training sample subset and a second training sample subset, each training sample including first sample feature information associated with a sample user and second sample feature information associated with the plurality of user interfaces, and interaction indicator labels of the plurality of user interfaces; Using the first training sample subset to train a plurality of first indicator prediction models corresponding to the plurality of user interfaces respectively, and using the second training sample subset to train a plurality of second indicator prediction models corresponding to the plurality of user interfaces respectively, each first indicator prediction model or each second indicator prediction value is configured to generate a value of the predetermined interaction indicator of the user on the corresponding user interface based on feature information associated with the user and feature information associated with the corresponding user interface; Using the multiple first indicator estimation models, based on the sample feature information in the second training sample subset, respectively determine multiple values ​​of the predetermined interaction indicators of the sample users on the multiple user interfaces; Determining a first difference between a plurality of values ​​of the estimated interaction index as a first difference label corresponding to each training sample in the second training sample subset; as well as A first interaction difference prediction model is trained based on the second training sample subset and the first difference label corresponding to each training sample, and the first interaction prediction difference model is configured to generate differences between the predetermined interaction indicators of the user on the multiple user interfaces based on feature information associated with the user and feature information associated with the corresponding user interface.

6. The method according to claim 5, wherein the at least one machine learning model further comprises a second interactive prediction difference model, wherein training the at least one machine learning model using the set of training samples further comprises: Using the multiple second indicator estimation models, based on the sample feature information in the first training sample subset, respectively determine multiple values ​​of the predetermined interaction indicators of the sample users on the multiple user interfaces; Determining a second difference between the multiple values ​​of the estimated interaction index as a second difference label corresponding to each training sample in the first training sample subset; as well as A second interaction difference prediction model is trained based on the first training sample subset and the second difference label corresponding to each training sample, and the second interaction prediction difference model is configured to generate the difference between the predetermined interaction indicators of the user on the multiple user interfaces based on feature information associated with the user and feature information associated with the corresponding user interface.

7. The method of claim 4, wherein the at least one machine learning model comprises a plurality of indicator estimation models, and wherein training the at least one machine learning model using the set of training samples comprises: Using the training samples in the set of training samples corresponding to the multiple user interfaces respectively, training multiple indicator estimation models corresponding to the multiple user interfaces respectively, each indicator estimation value is configured to generate a value of the predetermined interaction indicator of the user on the corresponding user interface based on feature information associated with the user and feature information associated with the corresponding user interface; and Wherein determining the difference between the predetermined interaction indicators corresponding to the target user and the multiple user interfaces based on the first feature information and the second feature information associated with the multiple user interfaces comprises: Using the multiple indicator estimation models, based on the first feature information and the second feature information, respectively determine multiple estimated values ​​of the predetermined interaction indicators corresponding to the target user and the multiple user interfaces; and The difference is determined based on a comparison of the plurality of estimates.

8. An apparatus for user interface presentation, comprising: A first acquisition module is configured to: acquire first feature information associated with a target user and second feature information respectively associated with a plurality of user interfaces corresponding to a target page; a difference determination module configured to: determine, for the multiple user interfaces, a difference between predetermined interaction indicators corresponding to the target user and the multiple user interfaces based on the first feature information and second feature information associated with the multiple user interfaces; A target user interface determination module is configured to: determine a target user interface for the target user from the multiple user interfaces based on the difference between the predetermined interaction indicators corresponding to the multiple interfaces; as well as The target page presenting module is configured to: in response to determining that the target page is to be provided to the target user, present the target page to the target user according to the target user interface.

9. An electronic device, comprising: at least one processor; as well as At least one memory, the at least one memory is coupled to the at least one processor and stores instructions for execution by the at least one processor, the instructions, when executed by the at least one processor, causing the electronic device to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions can be executed by a processor to implement the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, wherein the computer program implements the method according to any one of claims 1 to 7 when executed by a processor.

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