Method and device for displaying progress bar of user graphical interface in streaming media application program
By combining user data and content data, using deep learning models to customize the progress bar of streaming media apps, the problem of not being able to display progress bars in the prior art is solved, and the user experience is improved.
Patent Information
- Application Number
- CN202411334754.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-24
- Publication Date
- 2025-02-14
AI Technical Summary
The progress bar display of existing streaming apps cannot combine user interests and content characteristics, resulting in the inability to display progress bars that meet user style and preferences, and the user experience is poor.
By obtaining user data and content data in the streaming application, combining preset deep learning models, determine the target progress bar synthesis elements of the target user, and display the target progress bar in real time.
It realizes customizing progress bars based on user interests and content characteristics, improves the user experience of streaming apps, and can target and serve users.
Smart Images
Figure CN119440695A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of computer technology, and in particular to a method and device for displaying a progress bar of a streaming media application. Background Art
[0002] Currently, the progress bar of the user graphical interface of mainstream streaming media apps (Applications) is a customized solution. Most of them display the progress bar in a fixed mode, rather than combining user interests and content to display a progress bar that suits the user's style and preferences. There is no targeted delivery to users, and the user experience of streaming media apps is poor. Summary of the invention
[0003] The present application provides a progress bar display method and device for a streaming media application to solve the problem in the related art that a progress bar that conforms to the user's style and preferences cannot be displayed in combination with the user's interests and content.
[0004] In a first aspect, the present application provides a method for displaying a progress bar of a user graphical interface in a streaming media application, the method comprising:
[0005] Acquire user data and content data in the streaming media application, wherein the user data includes attribute information of all current users in the streaming media application, and the content data includes attribute information of all current contents in the streaming media application;
[0006] For any target user, determine a target progress bar synthesis element according to the user data, content data and a preset deep learning model, wherein the target progress bar synthesis element matches the target user and the content currently played by the target user;
[0007] The target progress bar is displayed in real time based on the target progress bar synthesis element.
[0008] As an optional embodiment, for any target user, determining a target progress bar synthesis element according to the user data, content data and a preset deep learning model includes:
[0009] For any target user, classify the target user and the content currently played by the target user based on the user data and the content data to obtain a user portrait and a content portrait;
[0010] The user portrait and the content portrait are input into the preset deep learning model to obtain a target progress bar synthesis element.
[0011] As an optional embodiment, for any target user, classifying the target user and the content currently played by the target user based on the user data and the content data to obtain a user portrait and a content portrait includes:
[0012] Clustering the user data and content data using a preset clustering algorithm to obtain user attribute labels and content attribute labels respectively;
[0013] For any target user, based on the user attribute label and the content attribute label, the target user and the content currently played by the target user are classified and processed respectively through a preset classification algorithm to obtain a user portrait and a content portrait, wherein the user portrait includes at least one user attribute label, and the content portrait includes at least one content attribute label.
[0014] As an optional embodiment, the step of inputting the user portrait and the content portrait into the preset deep learning model to obtain a target progress bar synthesis element includes:
[0015] Inputting the user portrait and the content portrait into the preset deep learning model to obtain the similarity of the progress bar synthetic element, wherein the similarity of the progress bar synthetic element represents the similarity between the progress bar synthetic element and the user portrait and the content portrait;
[0016] The progress bar synthesis element with the highest similarity is determined as the target progress bar synthesis element.
[0017] As an optional embodiment, the real-time display of the target progress bar based on the target progress bar synthesis element includes:
[0018] Based on the target progress bar synthesis element, a target element material is acquired from a preset element material library, wherein the target element material corresponds to the target progress bar synthesis element, wherein the preset element material library includes a large number of element materials of the progress bar synthesis element;
[0019] Generate a target progress bar according to the target element material;
[0020] The target progress bar is displayed in real time in the current playing content of the target user.
[0021] As an optional embodiment, the real-time display of the target progress bar in the current playing content of the target user includes:
[0022] Obtaining the screen size and operating system type of the playback device of the target user currently playing the content;
[0023] Adjusting the display size and proportion of the target progress bar according to the screen size and the operating system type;
[0024] The target progress bar is displayed in real time based on the adjusted display size and ratio.
[0025] As an optional embodiment, the obtaining of user data and content data in the streaming media application includes:
[0026] Acquire static data and dynamic data for describing attributes of each user from the streaming media application to obtain the user data;
[0027] Descriptive information for describing the attributes of each content is acquired from the streaming media application to obtain the content data.
[0028] As an optional embodiment, the static data used to describe the attributes of each user includes the user's age, gender, location, common language, and device type;
[0029] The dynamic data used to describe the attributes of each user includes viewing history information, preference settings, dwell time in any content, and click-through rate of any content;
[0030] The description information used to describe the attributes of each content includes episode type, topic tag, release time, popularity, video length and color style.
[0031] As an optional embodiment, the method further includes:
[0032] The training data set is input into the target neural network algorithm to train the target neural network algorithm to obtain the preset deep learning model, wherein the training data set includes the correspondence between the user portrait, the content portrait and the progress bar synthesis element.
[0033] In a second aspect, the present application provides a device for displaying a progress bar of a user graphical interface in a streaming media application, the device comprising:
[0034] A data acquisition module, used to acquire user data and content data in the streaming media application, wherein the user data includes attribute information of all current users in the streaming media application, and the content data includes attribute information of all current contents in the streaming media application;
[0035] An element determination module, for determining, for any target user, a target progress bar synthesis element according to the user data, the content data and a preset deep learning model, wherein the target progress bar synthesis element matches the target user and the content currently played by the target user;
[0036] The progress bar display module is used to display the target progress bar in real time based on the target progress bar synthesis element.
[0037] In a third aspect, the present application provides an electronic device, comprising: at least one communication interface; at least one bus connected to the at least one communication interface; at least one processor connected to the at least one bus; at least one memory connected to the at least one bus, wherein the processor is configured to:
[0038] Acquire user data and content data in the streaming media application, wherein the user data includes attribute information of all current users in the streaming media application, and the content data includes attribute information of all current contents in the streaming media application;
[0039] For any target user, determine a target progress bar synthesis element according to the user data, content data and a preset deep learning model, wherein the target progress bar synthesis element matches the target user and the content currently played by the target user;
[0040] The target progress bar is displayed in real time based on the target progress bar synthesis element.
[0041] In a fourth aspect, the present application further provides a computer storage medium storing computer executable instructions, wherein the computer executable instructions are used to execute the method for displaying a progress bar of a user graphical interface in a streaming media application as described in any one of the above items of the present application.
[0042] The above-mentioned technical scheme provided by the embodiment of the present application has the following advantages over the prior art: the method provided by the embodiment of the present application obtains user data and content data in the streaming media application, and combines the deep learning model to determine the target progress bar synthesis element of the target user, forms the target progress bar and displays it in real time, realizes combining user interests and content to display the progress bar that conforms to the user's style and preferences, can carry out targeted delivery to users, and improves the user experience of the streaming media App. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.
[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0045] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.
[0046] Figure 1 One of the flow charts of a method for displaying a progress bar of a user graphical interface in a streaming media application provided in an embodiment of the present application.
[0047] Figure 2 The present invention provides a second flow chart of a method for displaying a progress bar of a user graphical interface in a streaming media application program according to an embodiment of the present application.
[0048] Figure 3 The third flowchart of a method for displaying a progress bar of a user graphical interface in a streaming media application provided in an embodiment of the present application.
[0049] Figure 4 A structural block diagram of a device for displaying a progress bar of a user graphical interface in a streaming media application provided in an embodiment of the present application.
[0050] Figure 5 A structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0051] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0052] The disclosure below provides many different embodiments or examples to realize the different structures of the present application. In order to simplify the disclosure of the present application, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present application. In addition, the present application can repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.
[0053] In order to solve the technical problem that the prior art cannot display a progress bar that conforms to the user's style and preferences in combination with the user's interests and content, the present application provides a progress bar display method and device for a streaming media application, which can display a progress bar that conforms to the user's style and preferences in combination with the user's interests and content, and can perform targeted delivery to users, thereby improving the user experience of the streaming media App.
[0054] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.
[0055] Figure 1 One of the flow charts of a method for displaying a progress bar of a user graphical interface in a streaming media application provided in an embodiment of the present application.
[0056] like Figure 1 As shown, a method for displaying a progress bar of a user graphical interface in a streaming media application may include the following steps:
[0057] Step 101: Obtain user data and content data in a streaming media application.
[0058] In an embodiment of the present application, the streaming media application may be, for example, video playback software (e.g., Youku Video, iQiyi Video, Tencent Video, Migu Video, etc.), which may be installed on a smart carrier device for users to play and browse videos, etc.
[0059] Static data and dynamic data describing the attributes of each user can be obtained from the streaming media application to obtain user data, that is, the user data includes attribute information of all current users in the streaming media application, specifically, for example, static data such as the user's age, gender, region, common language and device type, and user's viewing history information, preference settings, dwell time in any content and click-through rate of any content.
[0060] In addition, descriptive information used to describe the attributes of each content can be obtained from the streaming media application to obtain content data, that is, the content data includes the attribute information of all the current content in the streaming media application, such as episode type, theme tag, release time, popularity, video length and color style.
[0061] Step 102: for any target user, determine the target progress bar synthesis element according to the user data, content data and a preset deep learning model.
[0062] In an embodiment of the present application, a preset deep learning model can be pre-trained. Specifically, a training data set can be input into a target neural network algorithm to train the target neural network algorithm to obtain a preset deep learning model, that is, a neural network deep learning model is used to learn the relationship between user portraits, content portraits, and progress bar synthesis elements by adjusting the number of layers of the neural network, so that after obtaining the user portrait and content portrait of the target user, the preset deep learning model can output a target progress bar synthesis element that matches the user portrait and content portrait. Through this embodiment, using machine learning technology, user preferences and content features can be better matched.
[0063] Step 103: Display the target progress bar in real time based on the target progress bar synthesis element.
[0064] In the embodiment of the present application, the target progress bar synthesis element can be the description information of the progress bar synthesis element material, such as describing different styles, colors, and response actions. For example, the description information of different styles is assumed to be dynamic, modern, retro, static, etc. The corresponding material is determined by these description information, and then the target progress bar is synthesized with these materials, and finally the progress bar is displayed. Therefore, the target progress bar synthesis element can be used to obtain materials to generate the target progress bar. In this way, the obtained target progress bar can be more adapted to user preferences, can be targeted and delivered to users, and improve the user experience of the streaming media app.
[0065] As another optional embodiment of the present application, Figure 2 The second flow chart of a method for displaying a progress bar of a user graphical interface in a streaming media application provided in an embodiment of the present application. Figure 2 As shown, the embodiment of the present application provides another method for displaying a progress bar of a user graphical interface in a streaming media application, which may include the following steps:
[0066] Step 101: Obtain user data and content data in a streaming media application.
[0067] For details, please refer to Figure 1 The detailed contents of step 101 are not repeated here for the sake of brevity.
[0068] Step 1021: for any target user, the target user and the content currently played by the target user are classified based on the user data and the content data to obtain a user portrait and a content portrait.
[0069] The target user is the user for whom the progress bar is to be displayed for the content currently being watched. In this step, the user data and the content data may be clustered by a preset clustering algorithm to obtain user attribute labels and content attribute labels, respectively. For any target user, based on the user attribute labels and content attribute labels, the target user and the content currently played by the target user may be classified by a preset classification algorithm to obtain a user portrait and a content portrait, wherein the user portrait includes at least one user attribute label, and the content portrait includes at least one content attribute label.
[0070] In the embodiments of the present invention, the preset clustering algorithm may be, for example, a K-Means (i.e., K-means) clustering algorithm, a grid-based clustering method (e.g., BIRCH and STING), a sliding window-based clustering method (e.g., Mean-Shift clustering), and the preset classification algorithm may be, for example, a logistic regression, a decision tree, a support vector machine (SVM), a naive Bayes, a K-nearest neighbor algorithm (KNN), a random forest, a gradient boosting tree (GBDT). That is, a clustering algorithm may be used to cluster a large amount of user data and / or content data to obtain a large amount of user attribute labels (e.g., age labels, gender labels, interest labels, etc.) and / or content attribute labels (e.g., style labels, type labels, etc.), and then a classification algorithm may be used to determine the user attribute labels that match the target user.
[0071] For example, if target user A is 25 years old, male, and likes science fiction movies, the classification algorithm is used to classify him and obtain the user portrait: likes science fiction movies, high-tech design, between 20-30 years old, male. Similarly, for the target user's current playing content A (assuming it is "Star Trek"), the classification algorithm is used to classify it and obtain the content portrait: science fiction movies, with a sense of high technology and futuristic style.
[0072] In this embodiment, through big data analysis technologies such as clustering algorithms and classification algorithms, massive amounts of user data and content data can be effectively processed and analyzed, providing a data basis for subsequent user profiling and content profiling.
[0073] Step 1022: Input the user portrait and content portrait into a preset deep learning model to obtain a target progress bar synthesis element.
[0074] After obtaining the user portrait and content portrait through the aforementioned step 1021, a trained deep learning model can be used, for example, to determine the target progress bar synthesis element that matches the target user and the target user's currently playing content. Specifically, the user portrait and the content portrait can be input into a preset deep learning model to obtain the similarity of the progress bar synthesis element. The similarity of the progress bar synthesis element represents the similarity between the progress bar synthesis element and the user portrait and the content portrait, and the progress bar synthesis element with the highest similarity is determined as the target progress bar synthesis element. Among them, the similarity includes, for example, but is not limited to, correlation coefficient, cosine similarity, Euclidean distance, etc.
[0075] Step 103: Display the target progress bar in real time based on the target progress bar synthesis element.
[0076] For details, please refer to Figure 1 The detailed contents of step 103 are not described here for the sake of brevity.
[0077] Figure 3 The third flow chart of a method for displaying a progress bar of a user graphical interface in a streaming media application provided in an embodiment of the present application. Figure 3 As shown, a method for displaying a progress bar of a user graphical interface in a streaming media application provided by another embodiment of the present application may include the following steps:
[0078] Step 101: Obtain user data and content data in a streaming media application.
[0079] For details, please refer to Figure 1 The detailed contents of step 101 are not repeated here for the sake of brevity.
[0080] Step 1021: for any target user, the target user and the content currently played by the target user are classified based on the user data and the content data to obtain a user portrait and a content portrait.
[0081] For details, please refer to Figure 2 The detailed contents of step 1021 are not repeated here for the sake of brevity.
[0082] Step 1022: Input the user portrait and content portrait into a preset deep learning model to obtain a target progress bar synthesis element.
[0083] For details, please refer to Figure 2 The detailed contents of step 1022 are not repeated here for the sake of brevity.
[0084] Step 1031: Acquire target element material from a preset element material library based on the target progress bar synthesis element.
[0085] Step 1032: Generate a target progress bar according to the target element material, and display the target progress bar in real time in the current playing content of the target user.
[0086] In an embodiment of the present application, an element material library can be set in advance. Specifically, element materials (such as graphics, colors, layouts, etc.) of different styles, colors, and response actions can be created, and each element material can be labeled, such as dynamic, modern, retro, static, etc., and then the element materials that have completed the label processing are stored in a specific location to constitute a preset element material library.
[0087] After determining the target progress bar synthesis element, the corresponding material is obtained from the preset element material library according to the target progress bar synthesis element, the target progress bar is generated based on these materials, and then the target progress bar is displayed in real time in the current playback content of the target user. Exemplarily, the target progress bar synthesis element is, for example, the label of the corresponding element material. For a certain user A, the target progress bar synthesis element matching the current playback content is, for example, modern and dynamic. Then, graphics, colors, etc. with labels of modern and dynamic can be obtained from the preset element material library, and corresponding synthetic graphics are composed based on modern and dynamic graphics. Then, the synthetic graphics are rendered with corresponding modern and dynamic colors to obtain the target progress bar.
[0088] Through this embodiment, a target progress bar can be generated in real time, and the target progress bar is generated based on the target progress bar synthesis elements of the user's style and preferences, so that the target progress bar can be more adapted to the user's interests and preferences.
[0089] In the aforementioned Figures 1 to 3 On the basis of the illustrated embodiment, the present application also provides another embodiment, which can adaptively adjust the display of the progress bar in combination with the user's device. Specifically, the display of the progress bar can be adjusted according to the size and / or ratio currently used by the target user. Specifically, the screen size and operating system type of the playback device of the target user's current playback content are obtained, the display size and ratio of the target progress bar are adjusted according to the screen size and operating system type, and the target progress bar is displayed in real time based on the adjusted display size and ratio. Through this embodiment, the display progress bar can respond quickly and run smoothly in different devices, and adapt to the screen sizes and operating systems of various different devices to achieve a seamless user experience.
[0090] In order to enable those skilled in the art to clearly and accurately understand the technical solutions of the embodiments of the present application, the embodiments of the present application are described below by way of examples.
[0091] Assume there are three users: User A, B, and C. The user profiles of the three users are as follows:
[0092] User A: likes science fiction movies and high-tech designs, and is between 20 and 30 years old;
[0093] User B: prefers romantic dramas, likes warm and cute designs, and is between 20 and 30 years old;
[0094] User C: loves historical documentaries, prefers retro and simple designs, and is over 40 years old.
[0095] In addition, three content portraits are obtained as follows:
[0096] "Star Trek": A science fiction film with a high-tech, futuristic style;
[0097] "Love Apartment": a romantic drama with a warm and lovely setting and light-hearted humor;
[0098] "Ancient Civilizations": a historical documentary with a retro background that introduces the history of ancient civilizations.
[0099] Generate a personalized progress bar using a preset deep learning model:
[0100] According to the user portraits of users A, B, and C and the content portraits of the series currently watched by the three users, a pre-trained deep learning model (preset deep learning model) is used to determine the progress bar synthesis elements, and finally generate a suitable progress bar. The specific process includes:
[0101] 1. Analyze users’ viewing history and behavior data to build user portraits;
[0102] 2. Analyze the classification, style, color and theme of the content and build a content portrait;
[0103] 3. Use the preset deep learning model to take the user portrait and content portrait as input to determine the target progress bar synthesis element that meets the characteristics of both;
[0104] 4. According to the target progress bar synthesis element, obtain relevant materials from the element material library and finally generate a progress bar.
[0105] The generation progress bar is as follows:
[0106] When user A is watching Star Trek, the progress bar may have dynamic effects like the starry sky or galaxies in the universe, as well as high-tech lines and icons, which echo his love for science fiction and high technology;
[0107] When user B is watching iPartment, the progress bar may be designed to be more colorful and rounded, with heart or flower decorations and soft colors to match the warm tone of the comedy series;
[0108] When user C is watching "Ancient Civilizations", the progress bar may be designed in sepia tones, bronze, or with mural patterns, reflecting a retro and documentary style.
[0109] The method provided in the embodiment of the present application obtains user data and content data in the streaming media application, and combines with a deep learning model to determine the target user's target progress bar synthesis element, forms a target progress bar and displays it in real time. It can generate an adaptive progress bar in real time based on the user portrait and content portrait, rather than simply selecting from pre-designed templates. Even in the case of users sharing devices, the embodiment of the present application can also identify different users and display personalized progress bars for them, thereby enhancing the experience of multiple users sharing the scene and improving the user experience of the streaming media App.
[0110] Correspondingly, an embodiment of the present application also provides a device for displaying a progress bar of a user graphical interface in a streaming media application.
[0111] Figure 4 This is a structural block diagram of a display device for a user graphical interface progress bar in a streaming media application provided by an embodiment of the present application. Figure 4 As shown, a display device for a progress bar of a user graphical interface in a streaming media application in an embodiment of the present application includes:
[0112] A data acquisition module 201 is used to acquire user data and content data in the streaming media application, wherein the user data includes attribute information of all current users in the streaming media application, and the content data includes attribute information of all current contents in the streaming media application;
[0113] An element determination module 202 is used to determine, for any target user, a target progress bar synthesis element according to the user data, the content data and a preset deep learning model, wherein the target progress bar synthesis element matches the target user and the content currently played by the target user;
[0114] The progress bar display module 203 is used to display the target progress bar in real time based on the target progress bar synthesis element.
[0115] like Figure 5 As shown, an embodiment of the present application provides an electronic device, including a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304.
[0116] Memory 303, used for storing computer programs;
[0117] In one embodiment of the present application, the processor 301 is used to implement a method for displaying a progress bar of a user graphical interface in a streaming media application provided by any one of the aforementioned method embodiments when executing a program stored in the memory 303. The processor 301 is specifically configured as follows:
[0118] Acquire user data and content data in the streaming media application, wherein the user data includes attribute information of all current users in the streaming media application, and the content data includes attribute information of all current contents in the streaming media application;
[0119] For any target user, determine a target progress bar synthesis element according to the user data, content data and a preset deep learning model, wherein the target progress bar synthesis element matches the target user and the content currently played by the target user;
[0120] The target progress bar is displayed in real time based on the target progress bar synthesis element.
[0121] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of a method for displaying a progress bar of a user graphical interface in a streaming media application are implemented as provided in any of the aforementioned method embodiments.
[0122] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0123] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.
[0124] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.
[0125] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.
Claims
1. A method for displaying a progress bar of a user graphical interface in a streaming media application, characterized in that: The method comprises: Acquire user data and content data in the streaming media application, wherein the user data includes attribute information of all current users in the streaming media application, and the content data includes attribute information of all current contents in the streaming media application; For any target user, determine a target progress bar synthesis element according to the user data, content data and a preset deep learning model, wherein the target progress bar synthesis element matches the target user and the content currently played by the target user; The target progress bar is displayed in real time based on the target progress bar synthesis element.
2. The method according to claim 1, characterized in that For any target user, a target progress bar synthesis element is determined according to the user data, content data and a preset deep learning model, including: For any target user, classify the target user and the content currently played by the target user based on the user data and the content data to obtain a user portrait and a content portrait; The user portrait and the content portrait are input into the preset deep learning model to obtain a target progress bar synthesis element.
3. The method according to claim 2, characterized in that For any target user, based on the user data and the content data, the target user and the content currently played by the target user are classified and processed to obtain a user portrait and a content portrait, including: Clustering the user data and content data using a preset clustering algorithm to obtain user attribute labels and content attribute labels respectively; For any target user, based on the user attribute label and the content attribute label, the target user and the content currently played by the target user are classified and processed respectively through a preset classification algorithm to obtain a user portrait and a content portrait, wherein the user portrait includes at least one user attribute label, and the content portrait includes at least one content attribute label.
4. The method according to claim 2, characterized in that: The step of inputting the user portrait and the content portrait into the preset deep learning model to obtain a target progress bar synthesis element includes: Inputting the user portrait and the content portrait into the preset deep learning model to obtain the similarity of the progress bar synthetic element, wherein the similarity of the progress bar synthetic element represents the similarity between the progress bar synthetic element and the user portrait and the content portrait; The progress bar synthesis element with the highest similarity is determined as the target progress bar synthesis element.
5. The method according to claim 2, characterized in that: The real-time display of the target progress bar based on the target progress bar synthesis element includes: Based on the target progress bar synthesis element, a target element material is acquired from a preset element material library, wherein the target element material corresponds to the target progress bar synthesis element, wherein the preset element material library includes a large number of element materials of the progress bar synthesis element; Generate a target progress bar according to the target element material; The target progress bar is displayed in real time in the current playing content of the target user.
6. The method according to claim 5, characterized in that The real-time display of the target progress bar in the current playing content of the target user includes: Obtaining the screen size and operating system type of the playback device of the target user currently playing the content; Adjusting the display size and proportion of the target progress bar according to the screen size and the operating system type; The target progress bar is displayed in real time based on the adjusted display size and ratio.
7. The method according to claim 1, characterized in that The obtaining of user data and content data in the streaming media application comprises: Acquire static data and dynamic data for describing attributes of each user from the streaming media application to obtain the user data; Descriptive information for describing the attributes of each content is obtained from the streaming media application to obtain the content data.
8. The method according to claim 7, characterized in that The static data used to describe the attributes of each user includes the user's age, gender, location, common language, and device type; The dynamic data used to describe the attributes of each user includes viewing history information, preference settings, dwell time in any content, and click-through rate of any content; The description information used to describe the attributes of each content includes episode type, topic tag, release time, popularity, video length and color style.
9. The method according to claim 1, characterized in that: The method further comprises: The training data set is input into the target neural network algorithm to train the target neural network algorithm to obtain the preset deep learning model, wherein the training data set includes the correspondence between the user portrait, the content portrait and the progress bar synthesis element.
10. A display device for a user graphical interface progress bar in a streaming media application, characterized in that: The device comprises: A data acquisition module, used to acquire user data and content data in the streaming media application, wherein the user data includes attribute information of all current users in the streaming media application, and the content data includes attribute information of all current contents in the streaming media application; An element determination module, for determining, for any target user, a target progress bar synthesis element according to the user data, the content data and a preset deep learning model, wherein the target progress bar synthesis element matches the target user and the content currently played by the target user; The progress bar display module is used to display the target progress bar in real time based on the target progress bar synthesis element.