A content rendering method, apparatus and electronic device

Abstract

This invention discloses a content rendering method, apparatus, and electronic device, relating to the field of computer application technology. The method includes: performing semantic structure parsing on first content generated based on a first AI model to obtain second content; creating a layout tree and a rendering layer for the second content to obtain rendered third content, the third content including content nodes; predicting user interaction behavior on the first content based on a second AI model; generating a corresponding interaction event based on the predicted interaction behavior when user interaction behavior on the first content is predicted; and binding the interaction event to the corresponding content node in the third content. This solves the problem that traditional content rendering methods are unable to handle the rendering of increasingly complex AI-generated content and the intelligent interaction requirements that AI needs to meet.

Description

Technical Field

[0001] This invention relates to the field of computer application technology, and in particular to a content rendering method, apparatus, and electronic device. Background Technology

[0002] Web page content rendering is the process of parsing and displaying the source code of a web page (such as HTML, CSS, JavaScript, etc.) on the screen of the user's terminal device.

[0003] Currently, web page content rendering largely relies on the traditional browser rendering pipeline, which uses HTML (to generate the DOM tree) and CSS (to generate the CSSOM tree) to display content. In this process, the content to be rendered is primarily defined by static HTML, CSS, and JavaScript. However, with the development of AI technology, more and more content is being generated and recognized through AI (Artificial Intelligence). Examples include articles generated from natural language and tag content generated based on image recognition.

[0004] Because AI-generated content is characterized by intelligence, dynamism, and high efficiency, the aforementioned content rendering methods are insufficient to handle the rendering of increasingly complex AI-generated content and the intelligent interaction requirements that AI needs to meet. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a content rendering method, apparatus, electronic device, and storage medium to solve the problem that traditional content rendering methods are unable to cope with the rendering of increasingly complex AI-generated content and the intelligent interaction needs that AI must meet.

[0006] In a first aspect, embodiments of the present invention provide a content rendering method, the method comprising: The first content generated based on the first AI model is subjected to semantic structure parsing to obtain the second content; A layout tree and drawing layer are created for the second content to obtain the rendered third content, which includes content nodes; Based on the second AI model, predict the user's interaction behavior with the first content; When it is predicted that the user will interact with the first content, a corresponding interaction event is generated based on the predicted interaction behavior. The interactive event is bound to the corresponding content node in the third content.

[0007] According to one possible implementation of this application, predicting the user's interaction behavior with the first content based on the second AI model specifically includes: acquiring the user's historical behavior data; and using the second AI model to predict the user's interaction behavior with the first content based on the historical behavior data and the context information of the first content.

[0008] According to one possible implementation of this application, the method further includes: outputting the third content on the display device of the electronic device; when a set operation is detected in any content node of the third content, calling the interactive event corresponding to the content node to execute the interactive behavior corresponding to the interactive event; and outputting and displaying the fourth content corresponding to the interactive behavior.

[0009] According to one possible implementation of this application, before binding the interactive event to the corresponding content node in the third content, the method further includes: obtaining the correspondence between the interactive event and the binding strategy; if the binding strategy corresponding to the corresponding interactive event is to allow binding, then binding the interactive event to the corresponding content node in the third content.

[0010] According to one possible implementation of this application, after binding the interactive event to the corresponding content node in the third content, the method further includes: monitoring and processing each interactive event bound in the third content; if the interactive event is not invoked, updating the total number of times the interactive event has not been invoked within a set time period; when the total number reaches the set number, adjusting the binding strategy corresponding to the interactive event to abandon binding; when the total number does not reach the set number, keeping the binding strategy corresponding to the interactive event unchanged.

[0011] According to one possible implementation of the present application, the method further includes: performing rendering optimization processing on the third content using at least one of the following methods: resource loading optimization, layout optimization, drawing optimization, and compositing optimization.

[0012] According to one possible implementation of an embodiment of this application, the first content includes at least text, images, audio, or video; Specifically, the step of parsing the first content generated based on the first AI model to obtain second content with a set semantic structure includes: when the first content is audio, parsing each frame of the audio to obtain second content with a set semantic structure corresponding to each frame of the audio; when the first content is video, parsing each frame of the video to obtain second content with a set semantic structure corresponding to each frame of the video.

[0013] Secondly, embodiments of the present invention provide a content rendering apparatus, the apparatus comprising: The parsing unit is used to parse the first content generated based on the first AI model to obtain the second content with a set semantic structure. A rendering unit is used to create a layout tree and a drawing layer for the second content to obtain the rendered third content, which includes content nodes; The prediction unit is used to predict the user's interaction behavior with the first content based on the second AI model; The generation unit is used to generate a corresponding interaction event based on the predicted interaction behavior when it is predicted that the user has an interaction behavior with the first content. A binding unit is used to bind the interactive event to the corresponding content node in the third content.

[0014] According to one possible implementation of this application, the prediction unit is specifically used for: acquiring the user's historical behavior data; and using the second AI model to predict the user's interaction behavior with the first content based on the historical behavior data and the context information of the first content.

[0015] According to one possible implementation of an embodiment of this application, the apparatus further includes: A display unit is used to output the third content on the display device of the electronic device; The calling unit is used to call the interaction event corresponding to the content node to execute the interaction behavior corresponding to the interaction event after detecting that any content node in the third content has undergone a set operation; The display unit is also used to output and display the fourth content corresponding to the interactive behavior.

[0016] According to one possible implementation of an embodiment of this application, the apparatus further includes: The acquisition unit is used to acquire the correspondence between the interactive event and the binding strategy before binding the interactive event to the corresponding content node in the third content; The binding unit is specifically used to: if the binding strategy corresponding to the corresponding interactive event is to allow binding, then bind the interactive event to the corresponding content node in the third content.

[0017] According to one possible implementation of an embodiment of this application, the apparatus further includes: The monitoring unit is used to monitor and process each interactive event bound to the third content; An update unit is used to update the total number of times the interaction event has not been called within a set time period if the monitoring unit detects that the interaction event has not been called. The strategy processing unit is configured to adjust the binding strategy corresponding to the interaction event to abandon binding when the total number of times reaches a set number of times; and to keep the binding strategy corresponding to the interaction event unchanged when the total number of times does not reach the set number of times.

[0018] According to one possible implementation of an embodiment of this application, the apparatus further includes: The rendering optimization unit is used to perform at least one of the following rendering optimization processes on the third content: resource loading optimization, layout optimization, drawing optimization, and compositing optimization.

[0019] According to one possible implementation of an embodiment of this application, the first content includes at least text, images, audio, or video; Specifically, the parsing unit is used to: when the first content is the audio, to parse each frame of the audio to obtain second content with a set semantic structure corresponding to each frame of the audio; and when the first content is the video, to parse each frame of the video to obtain second content with a set semantic structure corresponding to each frame of the video.

[0020] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising: a housing, a processor, a memory, a circuit board, and a power supply circuit, wherein the circuit board is disposed within the space enclosed by the housing, and the processor and the memory are disposed on the circuit board; the power supply circuit is used to supply power to various circuits or devices of the electronic device; the memory is used to store executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, for executing the content rendering method described in any implementation of the first aspect.

[0021] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing one or more programs, which can be executed by one or more processors to implement the content rendering method described in any of the implementations of the first aspect.

[0022] This invention provides a content rendering method, apparatus, electronic device, and storage medium. The method involves performing semantic structure parsing on first content generated based on a first AI model to obtain second content; creating a layout tree and a rendering layer for the second content to obtain rendered third content, the third content including content nodes; predicting user interaction behavior on the first content based on a second AI model; generating corresponding interaction events based on the predicted interaction behavior when user interaction behavior on the first content is predicted; and binding the interaction events to corresponding content nodes in the third content.

[0023] In the aforementioned solution, after acquiring the content generated by the first AI model, the semantic structure of the content is dynamically parsed to obtain second content with semantic structure. A layout tree and rendering layer are then created for the second content to achieve rendering. This efficiently handles the rendering of content generated by the first AI model, thus solving the problem that traditional content rendering methods struggle to handle increasingly complex AI-generated content. Furthermore, the solution can also use the second AI model to predict user interaction with the first content, generating corresponding interaction events and binding these events to the rendered third content. This allows for intelligent interaction event binding based on user needs predicted by the second AI model, providing better service, meeting users' intelligent interaction needs, and improving the user experience. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart of a content rendering method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a content rendering device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0027] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0028] The following is in conjunction with the appendix Figure 1 The solution provided by the embodiments of the present invention will be described in detail. Figure 1 This is a flowchart illustrating a content rendering method provided in an embodiment of the present invention. In this embodiment, the implementing entity can be an electronic device. This electronic device can be, but is not limited to, desktop computers, smartphones, tablets, and smart TVs. Figure 1 As shown, the content rendering method in this embodiment specifically includes the following steps: Step 110: Perform semantic structure parsing on the obtained first content generated based on the first AI model to obtain the second content.

[0029] In this step, after the first AI model generates the first content, since the first content is purely semantic content, in order to efficiently output and display it to the user, it is proposed to perform semantic structure parsing processing on the first content to obtain the second content with a set semantic structure.

[0030] In this embodiment, the first content can be generated based on the content request entered by the user into the first AI model; the first content can also be the content that the user wants to obtain, predicted from the previously generated content. For ease of description, the example of the first content being generated based on the content request entered by the user into the first AI model will be used for illustration.

[0031] Specifically, with the development of AI technology, AI is becoming increasingly powerful. When a user wants to acquire certain knowledge, they can activate a first AI model in an electronic device. Then, a dialog box displaying the first AI model is shown to the user through the device's display screen. The user can then input request information (content acquisition request) into this dialog box, and the first AI model can generate the aforementioned first content based on this request information. Furthermore, this application does not limit the specific structure of the first AI model, as long as it can output the first content.

[0032] Based on this, in order to present the first content to the user, this step proposes to perform semantic structure parsing on the first content. Specifically, the first content is converted into structured content, which roughly goes through semantic parsing---AI understanding---first content with a set structure, that is, structured content.

[0033] Optionally, the aforementioned electronic device may have a built-in content-carrying model for implementing step 110. This content-carrying model is similar to webcontents in a browser, primarily used to carry and display AI-generated content, and dynamically generate a content rendering pipeline for content display. After obtaining the first content, the content-carrying model is invoked to carry the first content, and semantic structure parsing processing is performed on the first content to obtain the aforementioned second content.

[0034] Optionally, the first content mentioned above may include static content and dynamic content. Static content refers to content that does not involve interactive logic and is only for users to read or observe. The static content may be, but is not limited to, text, image content, etc. When the first content is image content, it may be content composed of several image contents. The dynamic content mentioned above refers to content with potential user interaction needs. The dynamic content may include, but is not limited to, audio, video, buttons, input boxes or other UI (User Interface) components, etc.

[0035] Based on this, when the first content is static content such as text or image, the structure of the text or image content can be parsed according to step 110; while when the first content is dynamic content such as video, step 110 can be executed according to the following process: when the first content is audio, each frame of the audio is parsed to obtain the second content with a set semantic structure corresponding to each frame of the audio; when the first content is video, each frame of the video is parsed to obtain the second content with a set semantic structure corresponding to each frame of the video.

[0036] It should be noted that the semantic structure parsing of the first content is similar to the generation of the HTML DOM tree. For example, existing web technology models can be used to perform semantic structure parsing of the first content. To better understand web technology models, the following text content generated using the first AI model will be used as an example: Plain Text "AI-generated text content." Using the above web technology model, it is possible to... Based on the semantic structure, the above text content is parsed into the following content, where div and p are basic nodes of the DOM.

[0037] Plain Text AI-generated text content. Optionally, the content generated by the first AI model can adapt to the resolution of the device.

[0038] Step 120: Create a layout tree and drawing layer for the second content to obtain the rendered third content, which includes content nodes.

[0039] In this step, after obtaining the second content, a corresponding layout tree and paint layer can be created based on the semantic structure of the second content, thereby obtaining the rendered third content to achieve the visual presentation of the third content, that is, to display the third content on the display device of the electronic device. Step 120 can be implemented using existing browser web technologies.

[0040] In this embodiment, the layout tree is a rendering structure generated based on the second content, used to describe the page's geometric information, such as the position and size of elements. This layout tree is a combination of page structure and style information, containing all content nodes that need to participate in the layout; correspondingly, the obtained third content also includes content nodes. The drawing layer elevates the content nodes with specific styles in the created layout tree to independent layers. This drawing layer indicates which content on the page needs to be drawn separately, thus reducing redundant rendering and improving rendering performance.

[0041] Optionally, the aforementioned content nodes may include, but are not limited to, z-index nodes, transform nodes, etc.

[0042] Specifically, after obtaining the second content, the content-carrying model can execute the rendering of the second content, that is, dynamically generate content rendering channels (such as the layout tree and drawing layer mentioned above) and display the content.

[0043] Optionally, after performing step 120 and before performing step 130, the method provided in this embodiment may further include the following process: performing rendering optimization processing on the third content using at least one of the following methods: resource loading optimization, layout optimization, drawing optimization, and compositing optimization.

[0044] Specifically, in order to ensure the efficiency and smoothness of content display, this embodiment proposes to perform at least one of the above-mentioned rendering optimization processes on the third content. By performing the above-mentioned rendering optimization methods, not only can the rendering efficiency of the page be improved, but also the page loading time can be reduced, while ensuring a smooth user experience, thereby better coping with the dynamic rendering and display of AI-generated content.

[0045] To better understand the rendering optimization methods described above, let's take compositing optimization as an example. Compositing optimization mainly optimizes the layers of the drawing layer, that is, it performs layer compositing on layers that can be composited. This reduces the scope of page redrawing, and the pipelined processing of different drawing layers improves the smoothness of page rendering. Similarly, the layout optimization mentioned above aims to give the optimized third-party content a better layout; the resource loading optimization mentioned above aims to make the optimized third-party content load the required resources better; the drawing optimization mentioned above is similar to compositing optimization, aiming to give the optimized third-party content more independent layers and better layer effects.

[0046] Step 130: Based on the second AI model, predict the user's interaction behavior with the first content.

[0047] In this step, to better meet users' intelligent interaction needs based on AI-generated content and improve user experience, this embodiment proposes a processing procedure for steps 130 to 150. Specifically, after generating the first content, a second AI model is invoked to process the first content to predict the possible user actions on the first content, denoted as the aforementioned interaction behavior. The aforementioned second AI model can be pre-trained.

[0048] Optionally, in this embodiment, step 130 can be performed according to the following process: obtaining the user's historical behavior data; and using the second AI model to predict the user's interaction behavior with the first content based on the historical behavior data and the context information of the first content.

[0049] Specifically, when users visit a webpage, they often perform corresponding actions on the displayed content. Based on this, electronic devices can acquire users' historical behavior data. Furthermore, users often visit multiple pages within a consecutive period, and the content accessed on these pages may be related. Therefore, when predicting interactive behavior, it's also possible to consider acquiring the contextual information of the first piece of content to improve the accuracy of predicting user interaction behavior. Building upon this foundation, once the first piece of content is acquired, when predicting user interaction behavior on that content, the trained second AI model can process the contextual information of the first piece of content and the user's historical behavior data to predict the user's interaction behavior on that content.

[0050] Specifically, when the second AI model performs the above-mentioned interactive behavior prediction, in one embodiment, it can output the prediction probability of each interactive behavior, and based on each prediction probability, select the interactive behavior with the highest prediction probability as the predicted user's interactive behavior on the first content; in another embodiment, the second AI model outputs classification results, with each classification corresponding to an interactive behavior, and the user's interactive behavior on the first content is predicted based on the classification results of the second AI model.

[0051] It should be noted that the aforementioned second AI model is pre-trained, and the training model and method may differ for different outputs. However, the general training process can be summarized as follows: The initial AI model is trained based on a large amount of historical operation data from sample users and contextual information about the sample content accessed by these users. The parameters of the AI ​​model are then adjusted based on the calculation results of the loss function. Once the model output conditions are met, the trained second AI model is obtained. It is worth noting that the initial model of the second AI model can be any existing neural network model, etc. This embodiment does not specifically limit the type of initial model used for the second AI model, aiming to predict accurate interactive behaviors.

[0052] To better understand this embodiment, the content rendering method can be applied to a personalized advertising recommendation scenario. In this scenario, the first content is designated as the first advertising content, and the third content as the third advertising content. Specifically, after obtaining the rendered third advertising content based on steps 110 and 120, the electronic device can collect and analyze the user's historical behavior data. This historical behavior data may include, but is not limited to, historical browsing data, historical interaction data, historical search data, and historical purchase data, etc. While obtaining the aforementioned historical behavior data, the contextual information of the first advertising content can also be acquired. This allows the use of a pre-trained second AI model to predict the user's interaction behavior with the first advertising content, thereby better meeting the user's needs and increasing user engagement.

[0053] It is worth noting that in advertising recommendation scenarios, the aforementioned historical browsing data should at least include the pages browsed and links clicked by the user on the Internet to demonstrate the user's interests and needs; the aforementioned historical search data should at least include the user's query records through search engines, which reflects the user's short-term needs; the aforementioned historical interaction data should at least include the user's likes, comments, shares, etc. on social media platforms, which reflects the user's interests and preferences; and the aforementioned historical purchase data should at least include the user's purchase records to help the advertising recommendation system understand the user's consumption tendencies.

[0054] After predicting the user's interaction behavior with the first content based on step 130, personalized advertisements that are of interest and highly relevant to the user can be recommended. For example, if the user has viewed advertisements about fitness and health, the electronic device can recommend advertisements for fitness equipment, nutritional supplements, etc.

[0055] Step 140: When it is predicted that the user will interact with the first content, generate a corresponding interaction event based on the predicted interaction behavior.

[0056] In this step, after the second AI model predicts the above-mentioned interactive behavior, in order to facilitate the user's intelligent interaction needs and improve the user experience, it is proposed to generate a matching interactive event based on the predicted interactive behavior, so that the user can trigger the interactive event and then execute the above-mentioned interactive behavior.

[0057] It should be noted that the first content may involve multiple interactive behaviors. Therefore, when predicting the user's possible interactive behaviors with the first content based on the second AI model, the predicted probability of each interactive behavior can be predicted. Then, based on the predicted probabilities, interactive behaviors with predicted probabilities higher than a set probability threshold can be selected as the predicted user interactive behaviors with the first content. If all predicted probabilities are lower than the set probability threshold, it can be concluded that the user has no interactive behaviors with the first content.

[0058] Based on this, when multiple interactive behaviors are predicted, a corresponding interactive event can be generated for each interactive behavior.

[0059] To better understand this step, let's assume we're showing an image generated based on the first AI model, as follows: Plain Text { "type": "image", "src": "image1.png", "alt": "AI-generated image" } The second content obtained after semantic structure parsing in step 110 is shown below: Plain Text When rendering the third content based on the second content shown above, if step 140 predicts that the user may interact with the image, such as clicking on the image to view details, then the following interaction events are generated and bound: Plain Text document.getElementById('image').addEventListener('click', function(){ / / Show image details showImageDetails('image1.png'); }) The above-mentioned binding interaction event can be understood as follows: find the content node with the id "image" in the third content, add a click event and processing action to this content node; when the user clicks the click event corresponding to "image", the logic process of the processing action is executed to display the corresponding content details (fourth content) to the user.

[0060] Step 150: Bind the interactive event to the corresponding content node in the third content.

[0061] In this step, after an interaction event is generated, it is bound to facilitate user interaction and output the required interactive content. During this binding process, since the rendered third-party content is to be displayed to the user, the interaction event needs to be bound to the rendered third-party content obtained based on the first content.

[0062] Furthermore, during the execution of interactive event binding, to achieve accurate binding, after creating a layout tree and rendering layer from the second content obtained through semantic parsing of the first content, the resulting third content includes multiple content nodes. Therefore, after predicting each interactive behavior based on the first content, the corresponding content block in the first content can be found, and then the content node to which the content block belongs in the third content can be determined. In this way, the interactive event corresponding to the interactive behavior can be bound to the aforementioned content node to establish the association between the interactive event and the content node. This achieves accurate binding between interactive events and content, thereby better enabling intelligent interaction between the user and the rendered content, and better meeting the user's intelligent interaction needs.

[0063] Furthermore, the content rendering method provided in this application is also capable of adaptive perception. That is, for the same semantic content input at different times, the returned semantic structure is not exactly the same. It can adapt to changes in the current output environment to reflect the dynamic nature of content rendering.

[0064] To better understand this step, we can illustrate it by applying this content rendering method to a personalized advertising recommendation scenario. If the interaction behavior of the first advertisement content is an access to fitness equipment, then the access event for that fitness equipment can be obtained. This access event can then be bound to the content node corresponding to the third advertisement content. In this way, by binding the access event to the content node, and since the third advertisement content is the content displayed to the user, the user's access to this access event can be satisfied, thus fulfilling the user's intelligent interaction needs.

[0065] In implementing the above content rendering method, after obtaining the content generated by the first AI model, the semantic structure of the content is dynamically parsed to obtain the second content with semantic structure. A layout tree and drawing layer are then created for the second content to achieve the rendering of the second content. This achieves efficient rendering of the content generated by the first AI model, thus solving the problem that traditional content rendering methods are unable to handle the rendering of increasingly complex AI-generated content.

[0066] Furthermore, in the above scheme, a second AI model can be used to predict and process the user's interaction behavior with the first content, thereby generating the interaction events corresponding to the predicted interaction behavior, and binding the interaction events to the rendered third content. Thus, intelligent interaction events can be bound according to the user needs predicted by the second AI model, so as to better provide services to users, meet the user's intelligent interaction needs, and improve the user experience.

[0067] Optionally, based on any of the above embodiments, after obtaining the rendered third content, the content rendering method provided in this embodiment may further include: outputting the third content on the display device of the electronic device.

[0068] Optionally, based on any of the above embodiments, after binding the interactive event to the corresponding content node in the third content, the content rendering method provided in this embodiment may further include: when a set operation is detected in any content node in the third content, calling the interactive event corresponding to the content node to execute the interactive behavior corresponding to the interactive event; and outputting and displaying the fourth content corresponding to the interactive behavior.

[0069] In other embodiments, the third content can be displayed to the user via a display device after an interactive event is bound to it. Then, when the user views the third content, they can perform intelligent interactive operations. For example, if the user performs a setting operation on any location of the third content, and that location corresponds to a content node that is bound to an interactive event, the electronic device can invoke the interactive event to execute the corresponding interactive behavior, thereby displaying the fourth content corresponding to that interactive behavior to the user.

[0070] Optionally, the above-mentioned operations can be, but are not limited to, click operations, swipe operations, input operations, touch operations, hover operations, etc. The hover operation can be understood as an operation where the user stays on a content node for a set time. It should be noted that the interaction event corresponding to the click operation can be recorded as a click event, denoted as `on click`; the interaction event corresponding to the hover operation can be recorded as a hover event, denoted as `on hover`.

[0071] Optionally, the aforementioned display device can be the display screen of the electronic device itself, or it can be an external display screen of the electronic device, etc.

[0072] To better understand this embodiment, we will take the application of the above-described content rendering method to a personalized advertising recommendation scenario as an example. After binding an access event for accessing fitness equipment to the content node of the third advertisement content, when the third advertisement content is displayed, if the user performs a click operation on the content node based on the third advertisement content, the electronic device can determine that the content node has undergone a set operation. In this way, the access event can be invoked to execute the access operation corresponding to the access event, that is, to access the fitness equipment and display the access page of the fitness equipment to the user, thereby better meeting the user's experience needs and improving the user's intelligent interactive experience.

[0073] Furthermore, before performing step 150, the content rendering method provided in this embodiment may further include: obtaining the correspondence between interactive events and binding strategies; if the binding strategy corresponding to the corresponding interactive event is to allow binding, then binding the interactive event to the corresponding content node in the third content.

[0074] In practical applications, not all interactive events bound to the third content will be triggered by the user. When multiple interactive behaviors of the user are identified based on the first content, it is necessary to bind the interactive event corresponding to each interactive behavior. However, if multiple interactive events are bound, but the user does not trigger all of them, it may lead to a waste of binding resources. At the same time, binding multiple interactive events may affect the page loading speed of the third content.

[0075] Therefore, after generating each interactive event but before binding it, this embodiment proposes to pre-generate a binding strategy for each interactive event. This binding strategy can be dynamically adjusted. After obtaining the binding strategy for the interactive event, if the strategy allows binding, then the interactive event is bound to the corresponding content node in the third content; otherwise, if the strategy for abandoning binding, the binding of the interactive event is not executed. This saves binding resources and does not affect page loading speed; at the same time, it better meets the user's intelligent interaction experience.

[0076] Based on this, the dynamic adjustment method of the above binding strategy can be as follows: monitor and process each interactive event bound in the third content; if the interactive event is not invoked, update the total number of times the interactive event has not been invoked within a set time period; when the total number reaches the set number, adjust the binding strategy corresponding to the interactive event to abandon binding; when the total number does not reach the set number, keep the binding strategy corresponding to the interactive event unchanged.

[0077] Specifically, each interactive event displayed on the third-party content of the electronic devices accessed by the user in the past is monitored and processed. During monitoring, each interactive event in the third-party content within a set time period can be selected. This avoids the situation where the binding strategy for a certain interactive event remains unchanged, leading to inconsistencies with the user's actual needs. Moreover, it allows for dynamic adjustment of the binding strategy according to the user's different needs at different times, so as to better meet the user's actual needs. The specific value of the aforementioned set time period can be set according to the actual situation. This embodiment does not set its specific value.

[0078] Based on this, when monitoring and processing each interaction event, it is possible to identify whether the interaction event has been invoked. If it has been invoked, and the current binding policy is "allow binding," then the current binding policy remains unchanged. If it has not been invoked, it indicates that the user has no need to access the interaction behavior corresponding to this interaction event. Therefore, the total number of times the interaction event has not been invoked within the set time period can be updated by incrementing the total number by 1. If the total number reaches the set number, it indicates that the binding policy corresponding to the interaction event needs to be adjusted, that is, the binding policy is changed from "allow binding" to "abandon binding." If the total number does not reach the set number, the original "allow binding" binding policy remains unchanged.

[0079] Furthermore, to prevent the interactive events corresponding to the abandoned binding policy from remaining unbound, thus affecting the user's access experience, this embodiment proposes a possible implementation method: a timer can be set to represent the retention time of the abandoned binding policy; when the binding policy of an interactive event is adjusted to abandoned binding, the timer for that interactive event is started; when the timer is cleared, it indicates that the timeout period has expired, at which point the binding policy of the interactive event can be adjusted to allowed binding, and then dynamically adjusted according to the above binding policy adjustment method. This also solves the problem of the interactive events corresponding to the abandoned binding policy remaining unbound.

[0080] In addition, another possible implementation can monitor and process the user's historical access history, and adjust the binding policy based on the user's historical access history; for example, if the user's historical access history shows that although a certain interaction event is not bound to third-party content, the user actively initiated the interaction behavior corresponding to the interaction event, then the binding policy of the interaction event can be adjusted to allow binding, thereby realizing the dynamic adjustment of the binding policy.

[0081] The content rendering method described above can be applied not only to personalized ad recommendation scenarios but also to intelligent webpage content display and intelligent ad delivery scenarios. When delivering intelligent ads, dynamic ad displays, interactive ad displays, and customized ad experiences can be implemented, improving user click-through rates and user engagement.

[0082] In the aforementioned dynamic ad display scenarios, the ad content can be adjusted based on real-time user behavior. For example, if a user clicks on a brand's ad on a social media platform, the next ad display might be further optimized based on their latest interests. In the interactive ad display scenarios, user participation can be increased through mini-games, surveys, and pop-up interactions, enhancing user immersion and engagement, allowing for further optimization of ad content based on user feedback. In the aforementioned customized ad experiences, personalized offers, coupons, and limited-time discounts can be pushed to attract users to immediately engage with the ad.

[0083] Furthermore, regarding improving ad click-through rates, when the ad content displayed to users using the content rendering method provided in this embodiment closely matches the user's needs, interests, or current intent, the ad is more likely to attract user clicks. Through this application, ad content can be dynamically adjusted based on changes in users' real-time behavior and interests, ensuring that ads appear in front of the right users at the right time and in the right place, thereby improving ad click-through rates.

[0084] In terms of enhancing user engagement, user engagement refers not only to clicking on ads, but also to the depth of user interaction with the ad content. For example, users may enter an e-commerce website by clicking on an ad and browse multiple pages. In addition, users may fill out questionnaires, share personal opinions, or participate in activities such as prize draws within the ad. Furthermore, users can share ads through social media to further spread brand information.

[0085] Figure 2 This is a schematic diagram of the content rendering device structure provided in an embodiment of the present invention, such as... Figure 2 As shown, the content rendering apparatus of this embodiment is applied to an electronic device. The apparatus may include: a parsing unit 210, a rendering unit 220, a prediction unit 230, a generation unit 240, and a binding unit 250.

[0086] The parsing unit 210 is used to parse the first content generated based on the first AI model to obtain the second content with a set semantic structure. The rendering unit 220 is used to create a layout tree and a drawing layer for the second content to obtain the rendered third content, which includes content nodes; The prediction unit 230 is used to predict the user's interaction behavior with the first content based on the second AI model; The generation unit 240 is used to generate a corresponding interaction event based on the predicted interaction behavior when it is predicted that the user has an interaction behavior with the first content. Binding unit 250 is used to bind the interactive event to the corresponding content node in the third content.

[0087] The apparatus in this embodiment can execute the aforementioned method embodiment, and the specific implementation process and technical effects are basically the same, so they will not be described again here.

[0088] Optionally, the prediction unit 230 is specifically used to acquire the user's historical behavior data; and based on the historical behavior data and the context information of the first content, use the second AI model to predict the user's interaction behavior with the first content.

[0089] Optionally, the device further includes: A display unit (not shown in the figure) is used to output the third content on the display device of the electronic device; The calling unit (not shown in the figure) is used to call the interaction event corresponding to the content node to execute the interaction behavior corresponding to the interaction event after detecting that any content node in the third content has a set operation. The aforementioned display unit is also used to output and display the fourth content corresponding to the interactive behavior.

[0090] Optionally, the device further includes: The acquisition unit (not shown in the figure) is used to acquire the correspondence between the interactive event and the binding strategy before the binding unit 250 binds the interactive event to the corresponding content node in the third content. The binding unit 250 is specifically used to bind the interaction event to the corresponding content node in the third content if the binding strategy corresponding to the interaction event is to allow binding.

[0091] Optionally, the device further includes: A monitoring unit (not shown in the figure) is used to monitor and process each interactive event bound in the third content; An update unit (not shown in the figure) is used to update the total number of times the interaction event has not been called within a set time period if the monitoring unit detects that the interaction event has not been called. The strategy processing unit (not shown in the figure) is used to adjust the binding strategy corresponding to the interaction event to abandon binding when the total number of times reaches the set number of times; and to keep the binding strategy corresponding to the interaction event unchanged when the total number of times does not reach the set number of times.

[0092] Optionally, the device further includes: The rendering optimization unit (not shown in the figure) is used to perform rendering optimization processing on the third content in at least one of the following ways: resource loading optimization, layout optimization, drawing optimization, and compositing optimization.

[0093] Optionally, the first content mentioned above includes at least text, images, audio, or video; based on this, the parsing unit 210 is specifically used to: when the first content is the audio, parse each frame of audio in the video to obtain second content with a set semantic structure corresponding to each frame of audio; when the first content is the video, parse each frame of video in the video to obtain second content with a set semantic structure corresponding to each frame of video.

[0094] The apparatus of this embodiment can be used to perform Figure 1 The technical solutions of the method embodiments shown are similar in principle and in effect, and will not be described again here.

[0095] Accordingly, the content rendering apparatus provided in this embodiment of the invention can also be implemented using another structure. Figure 3 This is a schematic diagram of an embodiment of an electronic device provided by the present invention, which can realize the present invention. Figure 1 The process of the illustrated embodiment is as follows: Figure 3As shown, the above-mentioned electronic device may include: a housing 31, a processor 32, a memory 33, a circuit board 34, and a power supply circuit 35, wherein the circuit board 34 is disposed inside the space enclosed by the housing 31, and the processor 32 and the memory 33 are disposed on the circuit board 34; the power supply circuit 35 is used to supply power to the various circuits or devices of the above-mentioned electronic device; the memory 33 is used to store executable program code; the processor 32 runs a program corresponding to the executable program code by reading the executable program code stored in the memory 33, for executing the methods described in the foregoing embodiments.

[0096] For details on the specific execution process of the above steps by processor 32, and the steps further executed by processor 32 through running executable program code, please refer to the present invention. Figure 1 The description of the illustrated embodiments will not be repeated here.

[0097] The electronic device is a device that provides computing services. It consists of a processor, hard drive, memory, system bus, etc. The electronic device is similar to the general computer architecture, but because it needs to provide highly reliable services, it has higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0098] It should be noted that, in this document, terms such as "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0099] The various embodiments in this specification are described in a related manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0100] In particular, the device embodiment is basically similar to the method embodiment, so the description is relatively simple. For relevant details, please refer to the description of the method embodiment.

[0101] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0102] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0103] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof.

[0104] In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0105] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0106] For ease of description, the above apparatus is described by dividing it into various functional units / modules. Of course, in implementing this invention, the functions of each unit / module can be implemented in one or more software and / or hardware.

[0107] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

Claims

1. A content rendering method, characterized by, The method includes: The first content generated based on the first AI model is subjected to semantic structure parsing to obtain the second content; A layout tree and drawing layer are created for the second content to obtain the rendered third content, which includes content nodes; Based on the second AI model, predict the user's interaction behavior with the first content; When it is predicted that the user will interact with the first content, a corresponding interaction event is generated based on the predicted interaction behavior. The interactive event is bound to the corresponding content node in the third content.

2. The method of claim 1, wherein, The prediction of user interaction behavior with the first content based on the second AI model specifically includes: Obtain users' historical behavior data; Based on the historical behavior data and the contextual information of the first content, the second AI model is used to predict the user's interaction behavior with the first content.

3. The method according to claim 1, characterized in that, The method further includes: The third content is output on the display device of the electronic device; When a set operation is detected in any content node of the third content, the corresponding interaction event of the content node is invoked to execute the interaction behavior corresponding to the interaction event. The fourth content corresponding to the interactive behavior will be output and displayed.

4. The method according to claim 1, characterized in that, Before binding the interactive event to the corresponding content node in the third content, the method further includes: Obtain the mapping between interactive events and binding strategies; If the binding policy for the corresponding interactive event is to allow binding, then the interactive event will be bound to the corresponding content node in the third content.

5. The method according to claim 4, characterized in that, After binding the interactive event to the corresponding content node in the third content, the method further includes: Monitor and process each interactive event bound to the third content; If the interaction event is not invoked, update the total number of times the interaction event has not been invoked within the set time period; When the total number of times reaches the set number, the binding strategy corresponding to the interaction event is adjusted to abandon binding; If the total number of times is less than the set number, the binding strategy corresponding to the interaction event remains unchanged.

6. The method according to claim 1, characterized in that, The method further includes: The third content shall undergo at least one of the following rendering optimization processes: Resource loading optimization, layout optimization, rendering optimization, and composition optimization.

7. The method according to claim 1, characterized in that, The first content includes at least text, images, audio, or video; Specifically, the step of parsing the acquired first content generated based on the first AI model to obtain second content with a defined semantic structure includes: When the first content is the audio, each frame of the audio is parsed to obtain the second content with a set semantic structure corresponding to each frame of the audio. When the first content is the video, each frame of the video is parsed to obtain the second content with a set semantic structure corresponding to each frame of the video.

8. A content rendering apparatus, characterized in that, The device includes: The parsing unit is used to parse the first content generated based on the first AI model to obtain the second content with a set semantic structure. A rendering unit is used to create a layout tree and a drawing layer for the second content to obtain the rendered third content, which includes content nodes; The prediction unit is used to predict the user's interaction behavior with the first content based on the second AI model; The generation unit is used to generate a corresponding interaction event based on the predicted interaction behavior when it is predicted that the user has an interaction behavior with the first content. A binding unit is used to bind the interactive event to the corresponding content node in the third content.

9. An electronic device, characterized in that, The electronic device includes: a housing, a processor, a memory, a circuit board, and a power supply circuit, wherein the circuit board is disposed inside the space enclosed by the housing, and the processor and the memory are disposed on the circuit board; the power supply circuit is used to supply power to various circuits or devices of the electronic device; the memory is used to store executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, for executing the content rendering method described in any one of claims 1-7.

10. A computer-readable storage medium storing one or more programs, which can be executed by one or more processors to implement the content rendering method according to any one of claims 1-7.

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