Content rendering method and device and electronic equipment

Abstract

The embodiment of the invention discloses a content rendering method and device and electronic equipment, and relates to the technical field of computer application. The method comprises the following steps: performing semantic structure analysis processing on obtained first content generated based on a first AI model to obtain second content; a layout tree and a drawing layer are created for the second content to obtain rendered third content, and the third content comprises content nodes; on the basis of a second AI model, predicting an interaction behavior of the user on the first content; when it is predicted that the user has the interaction behavior on the first content, generating a corresponding interaction event according to the predicted interaction behavior; and binding the interaction event to a corresponding content node in the third content. Therefore, the problem that a traditional content rendering mode is difficult to deal with increasingly complicated rendering of the content generated by the AI and the intelligent interaction requirement which needs to be met by the AI is solved.

Description

Technical Field

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

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

[0003] Currently, web content rendering mostly relies on the traditional browser rendering pipeline, which displays content through HTML (generating a DOM tree) and CSS (generating a CSSOM tree). In this process, the content to be rendered is primarily defined by static HTML, CSS, and JavaScript. However, with the development of AI technology, an increasing amount of content is generated and recognized using AI (artificial intelligence). For example, articles generated using natural language and tags generated using image recognition are being generated.

[0004] Since AI-generated content is intelligent, dynamic, and highly effective, the above-mentioned content rendering method is difficult to cope with the increasingly complex rendering of content generated by artificial intelligence (AI) and the intelligent interaction needs that AI needs to meet. Summary of the Invention

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

[0006] In a first aspect, an embodiment of the present invention provides a content rendering method, the method comprising: Performing semantic structure analysis on the acquired first content generated based on the first AI model to obtain second content; Creating a layout tree and a drawing layer for the second content to obtain rendered third content, where the third content includes a content node; Predicting, based on the second AI model, a user's interactive behavior with respect to the first content; When it is predicted that the user has an interactive behavior with respect to the first content, generating a corresponding interactive event according to the predicted interactive behavior; Bind the interaction event to a corresponding content node in the third content.

[0007] According to an implementable method of an embodiment of the present application, predicting the user's interactive behavior with respect to the first content based on the second AI model specifically includes: obtaining the user's historical behavior data; and using the second AI model to predict the user's interactive behavior with respect to the first content based on the historical behavior data and the contextual information of the first content.

[0008] According to an implementable manner of an embodiment of the present application, the method further includes: outputting the third content on a display device of the electronic device; when a setting operation is detected on any content node in the third content, calling an interactive event corresponding to the content node to execute an interactive behavior corresponding to the interactive event; and outputting and displaying the fourth content corresponding to the interactive behavior.

[0009] According to an implementable method of an embodiment of the present application, before binding the interactive event to the corresponding content node in the third content, the method also includes: obtaining the correspondence between the interactive event and the binding strategy; if the binding policy corresponding to the corresponding interactive event is to allow binding, binding the interactive event to the corresponding content node in the third content.

[0010] According to an implementable manner of an embodiment of the present 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 called, updating the total number of times the interactive event has not been called within a set time period; when the total number reaches the set number, adjusting the binding policy corresponding to the interactive event to abandon binding; when the total number does not reach the set number, keeping the binding policy corresponding to the interactive event unchanged.

[0011] According to an implementable manner of an embodiment of the present application, the method further includes: performing at least one of the following rendering optimization processing on the third content: resource loading optimization, layout optimization, drawing optimization, and synthesis optimization.

[0012] According to an implementation method of the embodiment of the present application, the first content includes at least text, image, audio or video; Among them, the first content generated based on the first AI model is parsed and processed to obtain the second content with a set semantic structure, specifically including: when the first content is the audio, each frame of audio in the audio is parsed and processed to obtain the second content with the set semantic structure corresponding to each frame of audio; when the first content is the video, each frame of video in the video is parsed and processed to obtain the second content with the set semantic structure corresponding to each frame of video.

[0013] In a second aspect, an embodiment of the present invention provides a content rendering device, the device comprising: a parsing unit, configured to parse the acquired first content generated based on the first AI model to obtain second content having a set semantic structure; a rendering unit, configured to create a layout tree and a drawing layer for the second content to obtain rendered third content, wherein the third content includes content nodes; a prediction unit, configured to predict a user's interaction behavior with respect to the first content based on a second AI model; a generating unit, configured to generate a corresponding interaction event according to the predicted interaction behavior when it is predicted that the user has an interaction behavior with the first content; A binding unit is configured to bind the interaction event to a corresponding content node in the third content.

[0014] According to an implementable method of an embodiment of the present application, the prediction unit is specifically used to: obtain the user's historical behavior data; and use the second AI model to predict the user's interactive 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 the embodiment of the present application, the apparatus further includes: a display unit, configured to output the third content on a display device of the electronic device; a calling unit, configured to, upon detecting that a setting operation occurs on any content node in the third content, call an interaction event corresponding to the content node to execute an interaction behavior corresponding to the interaction event; The display unit is further configured to output and display fourth content corresponding to the interactive behavior.

[0016] According to one possible implementation of the embodiment of the present application, the apparatus further includes: an acquiring unit, configured to acquire a correspondence between an interaction event and a binding strategy before binding the interaction event to a corresponding content node in the third content; The binding unit is specifically configured to: if the binding policy corresponding to the corresponding interaction event is to allow binding, bind the interaction event to the corresponding content node in the third content.

[0017] According to one possible implementation of the embodiment of the present application, the apparatus further includes: a monitoring unit, configured to monitor and process each interactive event bound to the third content; an updating unit, configured 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 policy processing unit is configured to adjust the binding policy 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 policy 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 the embodiment of the present 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 synthesis optimization.

[0019] According to an implementation method of the embodiment of the present application, the first content includes at least text, image, audio or video; Among them, the parsing unit is specifically used to: when the first content is the audio, parse and process each frame of audio in the audio to obtain second content with a set semantic structure corresponding to each frame of audio of the audio; when the first content is the video, parse and process each frame of video in the video to obtain second content with a set semantic structure corresponding to each frame of video of the video.

[0020] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a housing, a processor, a memory, a circuit board, and a power supply circuit, wherein the circuit board is placed inside the space enclosed by the housing, and the processor and the memory are arranged on the circuit board; a power supply circuit for supplying power to various circuits or devices of the above-mentioned 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, and is used to execute the content rendering method described in any implementation method of the above-mentioned first aspect.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the content rendering method described in any implementation of the first aspect.

[0022] An embodiment of the present invention provides a content rendering method, device, electronic device and storage medium, which performs semantic structure analysis on a first content generated based on a first AI model to obtain a second content; creates a layout tree and a drawing layer for the second content to obtain a rendered third content, wherein the third content includes a content node; based on a second AI model, predicts a user's interaction behavior with the first content; when it is predicted that the user has an interaction behavior with the first content, generates a corresponding interaction event based on the predicted interaction behavior; and binds the interaction event to a corresponding content node in the third content.

[0023] In the above scheme, 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 a semantic structure, and a layout tree and a drawing layer are created for the second content to realize the rendering of the second content, which also realizes the efficient rendering of the content generated by the first AI model, thereby solving the problem that the traditional content rendering method is difficult to cope with the rendering of increasingly complex AI-generated content; in addition, in the above scheme, the second AI model can also be used to predict the user's interactive behavior with the first content, and then generate interactive events corresponding to the predicted interactive behavior, and bind the interactive events to the rendered third content. In this way, intelligent interactive event binding can be performed according to the user needs predicted by the second AI model, so as to better provide services to users, meet users' intelligent interaction needs, and improve user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 A flowchart of a content rendering method provided by an embodiment of the present invention; Figure 2 A schematic structural diagram of a content rendering device provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0027] It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0028] The following is combined with Figure 1 , describe in detail the solution provided by the embodiment of the present invention, Figure 1 A flowchart of a content rendering method provided by an embodiment of the present invention. In the embodiment of the present invention, the implementation subject may be an electronic device. The electronic device may be, but is not limited to, a desktop computer, a smart phone, a tablet computer, and a smart TV. Figure 1 As shown, the content rendering method of this embodiment specifically includes the following steps: Step 110: Perform semantic structure analysis on the acquired first content generated based on the first AI model to obtain 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 be able to efficiently output and display it to the user, it is proposed to perform semantic structure analysis on the first content, thereby obtaining second content with a set semantic structure.

[0030] In this embodiment, the first content may be generated based on a content acquisition request entered by a user into the first AI model. Alternatively, the first content may be generated based on a prediction of the user's desired content based on previously generated content. For ease of description, the following example illustrates the generation of the first content based on a content acquisition request entered by a user into the first AI model.

[0031] Specifically, with the development of AI technology, AI functions are becoming increasingly powerful. When a user desires to acquire some knowledge, they can activate a first AI model in an electronic device, and then display a dialog box of the first AI model to the user via the display device of the electronic device. In this way, the user can enter request information (content acquisition request) based on the dialog box, and the first AI model can then generate the above-mentioned first content based on the request information. In addition, this application does not limit the specific structure of the above-mentioned first AI model, and the purpose is to be able to output the first content.

[0032] On this basis, in order to display the first content to the user, this step proposes to perform semantic structural analysis on the first content. Specifically, the first content is converted into structured content, which is roughly obtained through semantic analysis - AI understanding - the first content with a set structure, that is, structured content.

[0033] Optionally, the electronic device may include a built-in content hosting model for implementing step 110. This content hosting model, similar to webcontents in a browser, is primarily used to host and display AI-generated content and dynamically generate a content rendering pipeline for content display. After obtaining the first content, the content hosting model is called to host the first content and the semantic structure of the first content is parsed to obtain the second content.

[0034] Optionally, the first content may include static content and dynamic content, wherein 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; and the dynamic content refers to content with potential user interaction needs. The dynamic content may be, 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 and processed according to step 110; and when the first content is dynamic content such as video, step 110 can be performed according to the following process: when the first content is audio, each frame of audio in the audio is parsed and processed to obtain second content with a set semantic structure corresponding to each frame of audio; when the first content is video, each frame of video in the video is parsed and processed to obtain second content with a set semantic structure corresponding to each frame of video.

[0036] It should be noted that when performing semantic structure analysis on the first content, similar to the generation of the HTML DOM tree, for example, the semantic structure analysis of the first content can be implemented using the currently existing web technology model. To better understand the web technology model, the following text content generated by the first AI model is used as an example for explanation: Plain Text "Text content automatically generated by AI." Using the above web technology model, we can The semantic structure of the above text content is parsed into the following content, where div and p are basic nodes of DOM.

[0037] Plain Text AI automatically generated text content. Optionally, the content generated by the first AI model can adapt to the resolution of the device on which it is located.

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

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

[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. The layout tree is a combination of page structure and style information, containing all content nodes that need to participate in the layout. Accordingly, the third content obtained above also includes content nodes. The drawing layer is a process of elevating content nodes with a specific style in the layout tree created above to an independent layer. This drawing layer indicates which content on the page needs to be drawn separately. In this way, the obtained third content can reduce repeated rendering and improve rendering performance.

[0041] Optionally, the above-mentioned content nodes may include, but are not limited to, z-index nodes, transform nodes, and the like.

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

[0043] Optionally, after executing step 120 and before executing step 130, the method provided in this embodiment may further include the following process: performing at least one of the following rendering optimization processing on the third content: resource loading optimization, layout optimization, drawing optimization, and synthesis 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 processing on the third content. By executing the above-mentioned rendering optimization method, not only the rendering efficiency of the page can be improved, but also the page loading time can be reduced. At the same time, a smooth user experience can be ensured, thereby better coping with the dynamic rendering and display of AI-generated content.

[0045] To better understand the above rendering optimization process, let's take synthesis optimization as an example. Synthesis optimization is mainly used to optimize the layers of the drawing layer, that is, to perform layer synthesis on the layers that can be synthesized. This can reduce the scope of page redrawing. In addition, the pipeline processing of different drawing layers improves the smoothness of page drawing. Similarly, the above layout optimization aims to make the optimized third content have a better layout; the above resource loading optimization aims to make the optimized third content better able to load the required resources; the above drawing optimization is similar to synthesis optimization, and aims to make the optimized third content have more independent layers and better layer effects.

[0046] Step 130: Predict the user's interactive behavior with respect to the first content based on the second AI model.

[0047] In this step, to better meet users' needs for intelligent interaction with AI-generated content and enhance user experience, this embodiment proposes the processing process from steps 130 to 150. Specifically, after generating the first content, a second AI model is invoked to process the first content to predict the user's potential actions in response to the first content, which are referred to as the aforementioned interactive actions. The 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 interactive behavior with the first content based on the historical behavior data and the context information of the first content.

[0049] Specifically, when a user visits a page, they often perform corresponding operations on the content displayed on the page. Based on this, the electronic device can obtain the user's historical behavior data. In addition, users often visit multiple pages in a continuous period of time, and there may be a correlation between the content of each page visited. Based on this, when predicting interactive behavior, it is also possible to consider obtaining the contextual information of the first content to improve the accuracy of the prediction results of the user's interactive behavior with the content. On this basis, after obtaining the first content, when predicting the user's interactive behavior with the first content, the trained second AI model can be used to process the contextual information of the first content and the user's historical behavior data to predict the user's interactive behavior with the first content.

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

[0051] It should be noted that the above-mentioned second AI model is pre-trained, and the training models and training methods corresponding to different outputs may be different. However, the general training process of the model can be: the initial AI model is trained based on a large amount of historical operation data of sample users and the contextual information of the sample content accessed by the sample users, and the parameters of the previously trained AI models are adjusted through the calculation results of the loss function. When 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 an existing neural network model, etc. This embodiment does not specifically limit the specific model of the initial model of the second AI model, and aims to be able to predict accurate interactive behavior.

[0052] In order 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 recorded as the first advertising content, and the third content is recorded 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, which may include but is not limited to: historical browsing data, historical interaction data, historical search data, and historical purchase data, etc. While obtaining the above-mentioned historical behavior data, the contextual information of the first advertising content can also be obtained. In this way, the pre-trained second AI model can be used to predict the user's interactive behavior with the first advertising content, so as to better meet the user's needs and improve user participation.

[0053] It is worth noting that in the advertising recommendation scenario, the above-mentioned historical browsing data at least includes the pages browsed by users on the Internet and the links clicked, so as to demonstrate the user's interests and needs; the above-mentioned historical search data at least includes: the query records made by users through search engines, which reflect the user's short-term needs; the above-mentioned historical interaction data at least includes the user's likes, comments, shares and other data on social platforms, which are used to reflect the user's interests and preferences; the above-mentioned historical purchase data at least includes the user's purchase records, so as 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 to the user and highly relevant can be recommended to the user. For example, if the user has viewed advertisements related to 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 has an interactive behavior with respect to the first content, a corresponding interactive event is generated according to the predicted interactive 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 have 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 probability, the interactive behaviors with a predicted probability higher than a set probability threshold can be selected as the predicted interactive behaviors of the user 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 behavior with the first content.

[0058] On this basis, when multiple interaction behaviors are predicted, a corresponding interaction event can be generated for each interaction behavior.

[0059] To better understand this step, assume that an image generated by the first AI model is shown below: Plain Text { "type": "image", "src": "image1.png", "alt": "AI-generated image" } The second content obtained after semantic structure analysis based on step 110 is as follows: Plain Text When the rendered third content is displayed based on the second content, it is predicted in step 140 that the user has an interactive behavior. For example, if the user may click on the image to view details, the following interactive event is generated and bound: Plain Text document.getElementById('image').addEventListener('click', function(){ / / Display image details showImageDetails('image1.png'); }) The above-mentioned binding interaction event can be understood as follows: finding the content node with the id "image" in the third content, adding a click event and processing action to this content node; when the user clicks the click event corresponding to the image, the logical process of the processing action is executed to display the corresponding content details (the fourth content) to the user.

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

[0061] In this step, after an interaction event is generated, binding is performed to facilitate user interaction and output the desired interactive content. During binding, the interaction event needs to be bound to the rendered third content derived from the first content, as the rendered third content is to be displayed to the user.

[0062] In addition, when executing interactive event binding, in order to achieve accurate binding of interactive events, after creating a layout tree and a drawing layer for the second content obtained by semantically parsing the first content, the third content obtained includes multiple content nodes. Therefore, after predicting each interactive behavior based on the first content, the content block corresponding to the interactive behavior in the first content can be found, and then the content node to which the content block belongs on the third content can be determined. In this way, the interactive event corresponding to the interactive behavior can be bound to the above-mentioned content node to establish an association relationship between the interactive event and the content node. In this way, accurate binding of interactive events and content is achieved, and then the intelligent interaction between users and rendered content is better achieved to better meet the user's intelligent interaction needs.

[0063] In addition, the content rendering method provided by 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 also apply this content rendering method to a personalized advertising recommendation scenario as an example. If the interaction behavior associated with the first advertising content is access to fitness equipment, a corresponding access event for the fitness equipment is obtained. This access event can then be bound to the content node corresponding to the third advertising content. In this way, by binding the access event to the content node, and since the third advertising content is displayed to the user, the user's access to the access event can be satisfied, thereby meeting the user's intelligent interaction needs.

[0065] In implementing the above-mentioned content rendering method, after obtaining the content generated by the first AI model, the content is dynamically subjected to semantic structure analysis to obtain second content with a semantic structure, and a layout tree and a drawing layer are created for the second content to achieve the rendering of the second content. This also achieves efficient handling of the rendering of the content generated by the first AI model, thereby solving the problem that traditional content rendering methods are difficult to cope with the rendering of increasingly complex AI-generated content.

[0066] In addition, in the above scheme, the second AI model can also be used to predict and process the user's interactive behavior with the first content, and then generate interactive events corresponding to the predicted interactive behavior, and bind the interactive events to the rendered third content. In this way, intelligent interactive event binding can be performed according to the user needs predicted by the second AI model, so as to better provide services to users, meet users' intelligent interaction needs, and improve user experience.

[0067] Optionally, based on any of the above embodiments, after obtaining the rendered third content, the content rendering method provided by this embodiment may further include: outputting the third content on a 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 by this embodiment may also include: when a setting operation is detected on 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, after binding an interactive event, the third content can be displayed to the user via a display device. Furthermore, when the user views the third content, they can perform intelligent interactive operations. For example, if a user performs a setting operation on any position in the third content, and that position corresponds to a content node that is bound to an interactive event, the electronic device can invoke the interactive event to perform the interactive behavior corresponding to the interactive event, thereby displaying the fourth content corresponding to the interactive behavior to the user.

[0070] Optionally, the above-mentioned set operation can be, but is not limited to, a click operation, a slide operation, an input operation, a touch operation, a hover operation, etc. The above-mentioned hover operation can be understood as an operation of staying on a certain content node for a set time. It should be noted that the interaction event corresponding to the above-mentioned click operation can be recorded as a click event, recorded as on click; the interaction event corresponding to the above-mentioned hover operation can be recorded as a hover event, recorded as on hover.

[0071] Optionally, the display device may be a display screen of the electronic device itself, or a display screen external to the electronic device, etc.

[0072] In order to better understand this embodiment, the above-mentioned content rendering method is applied to the personalized advertising recommendation scenario as an example for explanation. After the access event of accessing fitness equipment is bound to the content node of the third advertising content, when the third advertising content is output and displayed, the user performs a click operation on the above-mentioned content node based on the third advertising content. The electronic device can then determine that a set operation has occurred on the content node. In this way, the access event can be called to execute the access operation corresponding to the access event, that is, the above-mentioned fitness equipment will be accessed, and the access page of the fitness equipment will be output and displayed to the user, thereby better meeting the user's experience needs and enhancing the user's intelligent interactive experience.

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

[0074] In actual applications, not all interactive events bound to the third content will be triggered by the user. When it is recognized based on the first content that the user may have multiple interactive behaviors, it is necessary to bind the interactive events corresponding to each interactive behavior. If multiple interactive events are bound but the user does not trigger all interactive events, this may result in a waste of binding resources. At the same time, binding multiple interactive events may affect the page loading speed of the third content.

[0075] In view of this, after generating each interactive event and before binding the interactive event, this embodiment proposes to pre-generate a binding strategy for each interactive event. Of course, this binding strategy can be dynamically adjusted. After obtaining the binding strategy for the interactive event, if it is found that the binding strategy allows binding, then the interactive event is bound to the corresponding content node in the third content; if the binding strategy is to abandon binding, then the binding of the interactive event is not performed. In this way, binding resources are saved and the page loading speed is not affected. At the same time, the user's intelligent interactive experience is better satisfied.

[0076] On this basis, the dynamic adjustment method of the above-mentioned binding strategy can be the following process: monitor and process each interactive event bound in the third content; if the interactive event is not called, update the total number of times the interactive event has not been called within the set time period; when the total number reaches the set number, adjust the binding policy corresponding to the interactive event to abandon binding; when the total number does not reach the set number, keep the binding policy corresponding to the interactive event unchanged.

[0077] Specifically, each interaction event in the third content displayed by the electronic device that the user has historically accessed is monitored and processed. During monitoring, each interaction event in the third content within a set time period can be selected. This can avoid the situation where the binding strategy of a certain interaction event remains unchanged and is inconsistent with the user's actual needs. It can also dynamically adjust the binding strategy based on the user's different needs at different times to better meet the user's actual needs. The specific value of the above-mentioned set time period can be set according to actual conditions, and this embodiment does not set its specific value.

[0078] On this basis, when monitoring and processing each interaction event, it is possible to identify whether the interaction event has been called. When it is called, if the current binding policy is to allow binding, the current binding policy remains unchanged; when it is not called, it indicates that the user has no need to access the interaction behavior corresponding to the interaction event this time, so the total number of times the interaction event has not been called within the set time period can be updated, that is, the total number is added by 1 to obtain the updated total number; if it is found that 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 adjusted from the original allowing binding to abandoning binding; and if the total number does not reach the set number, the original binding policy of allowing binding remains unchanged.

[0079] Furthermore, in order to prevent the interactive event corresponding to the binding policy of abandoning binding from being unbound, thereby affecting the user's access experience, this embodiment proposes that, in a possible implementation, a timer can be set to represent the retention time of the binding policy of abandoning binding; when the binding policy of a certain interactive event is adjusted to abandon binding, the timer of the interactive event is started; when the timer is reset, it indicates that the timing time has arrived, and then the binding policy of the interactive event can be adjusted to allow binding, and then dynamically adjusted according to the above-mentioned binding policy adjustment method, thereby solving the problem that the interactive event corresponding to the binding policy of abandoning binding is not bound.

[0080] In addition, in another possible implementation, the user's historical access history can be monitored and processed, and the binding strategy can be adjusted based on the user's historical access history; for example, based on the user's historical access history, it is found that although a certain interactive event is not bound to a third content, the user actively initiates the interactive behavior corresponding to the interactive event, then based on this, the binding strategy of the interactive event is adjusted to allow binding, thereby also realizing dynamic adjustment of the binding strategy.

[0081] In addition to personalized ad recommendation scenarios, the aforementioned content rendering method can also be applied to scenarios such as intelligent web content display and intelligent ad placement. Intelligent ad placement enables dynamic and interactive ad display and a customized ad experience, increasing user click-through rates and engagement.

[0082] Among them, when the above-mentioned dynamic ads are displayed, the ad content can be adjusted according to the user's real-time behavior. For example, after a user clicks on a brand's ad on a social platform, the next ad display may be further optimized based on the user's latest interests. In the above-mentioned interactive ad display scenarios, users can be involved in the ads through mini-games, questionnaires, pop-up interactions, etc., increasing user immersion and participation, and further optimizing the ad content based on user feedback. In the above-mentioned customized advertising experience, for example, personalized offers, coupons, limited-time discounts, etc. can be pushed to attract users to immediately participate in advertising interactions.

[0083] Furthermore, in terms of improving user click-through rates (CTRs), when the content displayed to users using the content rendering method provided by this embodiment is highly consistent with their needs, interests, or current intent, the ads are more likely to attract clicks. This application allows for dynamic adjustment of ad content based on real-time user behavior and interest changes, ensuring that ads appear in front of the right users at the right time and in the right context, thereby increasing CTRs.

[0084] When it comes to increasing user engagement, user engagement doesn't just refer to clicks on ads; it also includes the depth of interaction between users and the ad content. For example, a user might click on an ad to enter an e-commerce website and browse multiple pages. Furthermore, users might fill out surveys within the ad, share their opinions, or enter a sweepstakes. Furthermore, users can share the ad on social media, further spreading the brand's message.

[0085] Figure 2 A schematic diagram of the content rendering device structure provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the content rendering device of this embodiment is applied to an electronic device, and 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 configured to parse the first content generated based on the first AI model to obtain second content having a set semantic structure. A rendering unit 220 is configured to create a layout tree and a drawing layer for the second content to obtain rendered third content, where the third content includes content nodes. A prediction unit 230 is configured to predict a user's interaction behavior with respect to the first content based on a second AI model; The generating unit 240 is configured to generate a corresponding interaction event according to the predicted interaction behavior when it is predicted that the user has an interaction behavior with the first content; The binding unit 250 is configured to bind the interaction event to a corresponding content node in the third content.

[0087] The device of this embodiment can execute the aforementioned method embodiment, and the specific implementation process and technical effects are basically the same, which will not be repeated here.

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

[0089] Optionally, the device further comprises: a display unit (not shown in the figure), configured to output the third content on a display device of the electronic device; a calling unit (not shown in the figure), configured to, upon detecting that a setting operation occurs on any content node in the third content, call an interaction event corresponding to the content node to execute an interaction behavior corresponding to the interaction event; The display unit is further configured to output and display the fourth content corresponding to the interactive behavior.

[0090] Optionally, the device further comprises: an acquiring unit (not shown in the figure), configured to acquire a correspondence between an interaction event and a binding strategy before the binding unit 250 binds the interaction event to a corresponding content node in the third content; The binding unit 250 is specifically configured to bind the interaction event to a corresponding content node in the third content if the binding policy corresponding to the corresponding interaction event is to allow binding.

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

[0092] Optionally, the device further comprises: A rendering optimization unit (not shown in the figure) 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 synthesis optimization.

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

[0094] The device of this embodiment can be used to perform Figure 1 The technical solution of the method embodiment shown has similar implementation principles and technical effects, which will not be repeated here.

[0095] Accordingly, the content rendering device provided by the embodiment of the present invention may also be implemented using another structure. Figure 3 A schematic diagram of the structure of an electronic device embodiment provided by the present invention can implement the present invention Figure 1 The process of the embodiment shown is as follows: Figure 3As shown, the above-mentioned electronic device may include: a shell 31, a processor 32, a memory 33, a circuit board 34 and a power supply circuit 35, wherein the circuit board 34 is placed inside the space enclosed by the shell 31, and the processor 32 and the memory 33 are arranged on the circuit board 34; the power supply circuit 35 is used to supply power to various circuits or devices of the above-mentioned electronic device; the memory 33 is used to store executable program code; the processor 32 runs the program corresponding to the executable program code by reading the executable program code stored in the memory 33, so as to execute the method described in the above-mentioned embodiment.

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

[0097] This electronic device: a device that provides computing services. The electronic device consists of a processor, hard disk, 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, manageability, etc.

[0098] It should be noted that, in this document, terms such as "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

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

[0100] In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

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

[0102] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable media on which the program is printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a 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-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0105] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0106] For the convenience of description, the above device is described as being divided into various units / modules based on their functions. Of course, when implementing the present invention, the functions of each unit / module can be implemented in the same or multiple software and / or hardware.

[0107] From the above description of the embodiments, it is clear that those skilled in the art will clearly understand that the present invention can be implemented using software and a necessary general-purpose hardware platform. Based on this understanding, the technical solution of the present invention, or the portion 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, a magnetic disk, or an optical disk, and includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various embodiments of the present invention, or portions thereof.

Claims

1. A content rendering method, characterized in that: The method comprises: Performing semantic structure analysis on the acquired first content generated based on the first AI model to obtain second content; Creating a layout tree and a drawing layer for the second content to obtain rendered third content, where the third content includes a content node; Predicting, based on the second AI model, a user's interactive behavior with respect to the first content; When it is predicted that the user has an interactive behavior with respect to the first content, generating a corresponding interactive event according to the predicted interactive behavior; Bind the interaction event to a corresponding content node in the third content.

2. The method according to claim 1, characterized in that Predicting the user's interactive behavior with respect to the first content based on the second AI model specifically includes: Obtain user historical behavior data; Based on the historical behavior data and the context information of the first content, the second AI model is used to predict the user's interactive behavior with respect to the first content.

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

4. The method according to claim 1, wherein Before binding the interaction event to the corresponding content node in the third content, the method further includes: Get the correspondence between interaction events and binding strategies; If the binding policy corresponding to the corresponding interaction event is to allow binding, the interaction event is bound to the corresponding content node in the third content.

5. The method according to claim 4, characterized in that After binding the interaction event to the corresponding content node in the third content, the method further includes: Monitoring and processing each interactive event bound to the third content; If the interaction event is not called, the total number of times the interaction event is not called within the set time period is updated; When the total number of times reaches the set number of times, the binding policy corresponding to the interaction event is adjusted to abandon the binding; When the total number of times does not reach the set number of times, the binding strategy corresponding to the interaction event remains unchanged.

6. The method according to claim 1, characterized in that The method further comprises: Perform at least one of the following rendering optimization processes on the third content: Resource loading optimization, layout optimization, drawing optimization, and synthesis optimization.

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

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

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

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

Images