Advertising art design computer processing system

Abstract

The invention relates to the technical field of advertisement design, and discloses an advertisement art design computer processing system. The user interaction module is used for receiving advertising art design data input by a user; the data acquisition and preprocessing module is used for acquiring and preprocessing material data of advertising art design; the cloud rendering calculation module is used for performing efficient rendering through distributed calculation resources; and the format recommendation module is used for recommending a proper advertisement format layout to the user based on a machine learning algorithm according to the design requirements and historical design data input by the user. Through the system, the operation effect of advertisement design can be monitored in real time, key indexes such as the popularity degree, the click rate and the conversion rate of advertisements are analyzed through a data feedback function, and the design is optimized and adjusted in time. The function can ensure that the design quality is continuously improved in the advertising process, so that an efficient propagation effect is always kept, and the dynamic change requirement of the market is met.

Description

Technical Field

[0001] The present invention relates to the technical field of advertisement design, in particular to a computer processing system for advertisement art design. Background Art

[0002] With the continuous development of the advertising industry, the demand for advertising design has become increasingly diverse and complex. Traditional advertising design often relies on the creativity and experience of designers and is completed through manual operation and manual adjustment, which is not only time-consuming and laborious, but also has a certain lag in the optimization of design effects and the feedback of effects after advertising.

[0003] In existing advertising art design, designers often need to perform a lot of repetitive work, such as material selection, color matching, layout adjustment, etc., which not only greatly reduces the efficiency of creation, but also makes the optimization of advertising effects a complex and time-consuming task. Although some advertising design software provides templates and automation functions, these software are usually limited to fixed design styles and cannot meet the increasingly personalized and diversified advertising needs.

[0004] With the continuous advancement of data analysis and artificial intelligence technology, more and more advertising designs are beginning to rely on data-driven and intelligent recommendations, especially in the precise delivery and effect optimization of advertising. Analysis based on user behavior and market trends can help advertising designers better understand the needs of the audience, so as to carry out personalized and customized design. The current advertising design system still has certain limitations in these aspects. It is impossible to efficiently combine big data analysis, intelligent recommendation and advertising creative design, resulting in the inability to make dynamic adjustments in real time according to user feedback and market changes during the advertising design process. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a computer processing system for advertising art design, which solves the problems of low efficiency, unstable quality, slow response and difficulty in meeting personalized needs in advertising design.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an advertising art design computer processing system, comprising: A user interaction module, used to receive advertisement art design data input by a user; Data collection and preprocessing module, used to collect and preprocess the material data of advertising art design; Cloud rendering computing module, which performs efficient rendering through distributed computing resources; The layout recommendation module recommends suitable advertising layouts to users based on the design requirements and historical design data input by users and the machine learning algorithm. Evaluation and optimization module, which is used to automatically evaluate the design layout, color matching, fonts, etc., and provide optimization suggestions; The effect feedback module provides real-time feedback on rendering and design effects and provides a preview of the design effects for users to make further adjustments.

[0007] Preferably, the user interaction module includes: Real-time collaboration unit, which supports multiple users to work in parallel on the same design project and synchronize design content in real time; Design data transmission unit, responsible for packaging the user's design data into a unified format; The interactive design unit provides an intuitive design operation interface, allowing users to design advertisements through operations such as dragging and dropping, scaling, and rotating.

[0008] Preferably, the data acquisition and preprocessing module includes: A material acquisition unit collects material data from external sources; The material pre-processing unit processes the collected materials, performs denoising, sharpening, and color enhancement; The data verification and cleaning unit ensures that the collected data is complete, correct, and meets the design requirements.

[0009] Preferably, the cloud rendering computing module includes: The task scheduling unit divides the rendering task into multiple subtasks and assigns them to different nodes based on task priority and the availability of computing resources; Distributed rendering unit, each computing node performs the rendering tasks assigned to it; The rendering result synthesis unit combines the various results of distributed rendering and returns them to the front-end design interface for display.

[0010] Preferably, the layout recommendation module includes: The layout recommendation unit recommends advertising layouts based on the user's design requirements and historical data based on deep learning algorithms; The layout optimization unit optimizes and adjusts the recommended layout in real time to meet the needs of users.

[0011] Preferably, the evaluation and optimization module includes: The quality assessment unit uses computer vision and image processing technology to assess the quality of the advertisement design. The quality assessment includes:

[0012] in, is the design quality score, is the importance weight of each feature, Feature is the value of the corresponding design feature; The optimization suggestion unit gives specific optimization suggestions based on the evaluation results.

[0013] Preferably, the effect feedback module includes: Real-time preview unit, which displays the effect of user-modified advertisements in real time and provides interactive feedback; The feedback analysis unit evaluates the design effect based on the feedback data. The evaluation result is calculated by the following formula:

[0014] in, Rate the overall feedback. Rate a single user's feedback.

[0015] Preferably, the data verification and cleaning unit uses computer vision technology to analyze the clarity, contrast, etc. of the image to ensure that the material meets the design quality requirements, and quantitatively evaluates the quality of the image through the following formula;

[0016] in, Rate image quality, Sharpness and Contrast are the sharpness and contrast scores of the image, is the number of images.

[0017] Preferably, the cloud rendering calculation module uses GPU acceleration technology to improve rendering efficiency using the following formula:

[0018] in, is the total rendering time, is the number of rendering nodes, For Node The computing power of For Node of rendering tasks.

[0019] Preferably, the layout optimization unit calculates the layout optimization index using the following formula according to user feedback:

[0020] in, For the Ratings of user feedback, is the weight of each feedback, is the number of feedbacks.

[0021] The present invention provides a computer processing system for advertising art design. It has the following beneficial effects: 1. The present invention can monitor the running effect of the advertisement design in real time through the system, analyze the key indicators such as the popularity, click-through rate, conversion rate of the advertisement through the data feedback function, and optimize and adjust the design in time. This function can ensure that the design quality of the advertisement is continuously improved during the delivery process, so that it can always maintain an efficient communication effect and meet the dynamic changes in the market.

[0022] 2. The present invention can greatly improve the work efficiency of advertising art design through automated and intelligent processing procedures. The system can automatically generate a variety of design schemes based on the input parameters, reducing manual design time, and quickly screen out the most creative and attractive design schemes through optimization algorithms, so that designers can save a lot of time and energy and focus on creativity and personalized adjustments.

[0023] 3. The present invention uses big data analysis and user behavior prediction to accurately analyze the preferences and needs of the target audience, thereby customizing advertising designs that suit the audience's tastes. Through in-depth analysis of the audience group, the system can provide personalized designs based on different factors such as age, gender, region, and interests, thereby improving the accuracy and effectiveness of advertising and maximizing the dissemination value of advertising. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the system framework of the present invention; Figure 2 is a schematic diagram of a user interaction module of the present invention; Figure 3 It is a schematic diagram of the data acquisition and preprocessing module of the present invention; Figure 4 A schematic diagram of a cloud rendering computing module of the present invention; Figure 5 It is a schematic diagram of the layout recommendation module of the present invention; Figure 6 This is a schematic diagram of the evaluation and optimization module of the present invention; Figure 7 It is a schematic diagram of the effect feedback module of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Please see attached Figure 1-Attached Figure 7 , an embodiment of the present invention provides an advertising art design computer processing system, including: A user interaction module, used to receive advertisement art design data input by a user; Data collection and preprocessing module, used to collect and preprocess the material data of advertising art design; Cloud rendering computing module, which performs efficient rendering through distributed computing resources; The layout recommendation module recommends suitable advertising layouts to users based on the design requirements and historical design data input by users and the machine learning algorithm. Evaluation and optimization module, which is used to automatically evaluate the design layout, color matching, fonts, etc., and provide optimization suggestions; Effect feedback module, real-time feedback on rendering and design effects, providing design effect preview for users to make further adjustments; Specific; The user interaction module is used to receive design requirements and advertising materials input by users and provide users with an intuitive and interactive design interface. This module includes a real-time collaboration unit, a design data transmission unit, and an interactive design unit; In this embodiment, the core function of the real-time collaboration unit is to support multiple users to operate in parallel on the same advertising design project, and to synchronize the design content in real time, thereby ensuring the real-time and bidirectional nature of data exchange. When multiple users are designing, each modification will be instantly synchronized to the interface of other users to ensure the consistency of the design content. In this embodiment, the design data transmission unit is responsible for packaging the user's design data and transmitting it to the subsequent modules. To ensure smooth transmission of data between different devices and platforms, the design data will be uniformly encoded into a standard format.

[0027] In this embodiment, the interactive design unit is the core part of the user interaction module, which is responsible for providing the user with a design operation interface. The unit supports the user to perform intuitive design operations such as dragging and dropping, zooming, rotating, changing colors, adjusting fonts, and editing images through input devices such as a mouse and a touch screen.

[0028] In the interactive design process, all user-operated data will be processed in a digital way, converted and stored through algorithms. When adjusting images, operations such as image scaling and rotation can be described by mathematical formulas. Scaling formula:

[0029] in, are the adjusted coordinates, are the original coordinates, is the scaling factor; Rotation formula:

[0030] in, is the rotation angle, is the original coordinate, is the rotated coordinate. When the user designs, all these operations will be converted into a digital form that the computer can understand through this formula and transmitted to the server through the network for further processing.

[0031] In this embodiment, the data acquisition and preprocessing module acquires the material data required for the advertisement design through an external source, and performs necessary preprocessing on the material data, such as denoising, sharpening, and color enhancement, to ensure that the quality of the material meets the design requirements and can provide a suitable data basis for subsequent layout recommendations and rendering. The efficiency and accuracy of the data acquisition and preprocessing module directly affect the quality of the subsequent design and the overall performance of the system.

[0032] In this embodiment, the main function of the material collection unit is to obtain various materials required for advertising design from external resources, such as images, videos, fonts, color palettes, etc. The unit can collect materials in a variety of ways, including but not limited to obtaining from public databases, open APIs, third-party material platforms or local file systems.

[0033] In this embodiment, the material preprocessing unit performs a series of preprocessing on the collected material to improve the quality of the material and prepare for subsequent design links, including denoising, sharpening, and color enhancement.

[0034] Denoising: Image denoising is to remove random noise in the image through filtering algorithms. Common denoising algorithms include Gaussian filtering, mean filtering, and median filtering. In this embodiment, Gaussian filtering is used to smooth the image to remove the noise generated in the image during the acquisition process. The mathematical expression of Gaussian filtering is:

[0035] in, is the pixel value of the original image, is the pixel value of the image after filtering, is the Gaussian filter kernel, is the radius of the filter; Sharpening: Sharpening is to make the image look clearer by enhancing the edge contrast of the image. Commonly used sharpening methods include Laplacian operator and high-pass filtering. The common formula for the sharpening process is:

[0036] in, is the second-order derivative of the image, is the sharpening coefficient, which controls the intensity of sharpening; Color enhancement: Color enhancement adjusts the color balance of an image to make it more vivid. Common enhancement methods include brightness and contrast adjustment. Specifically, the color of an image can be enhanced by the following formula:

[0037] in, and are contrast gain and brightness adjustment values ​​respectively, is the original pixel value of the image; The task of the data verification and cleaning unit is to ensure that the collected material data is complete, correct, and meets the design requirements. The unit will first check whether the material data is missing or damaged. If so, it will be re-collected from the original source. Secondly, the material data will be formatted to ensure that it meets the input requirements of the design module; For image data, computer vision technology can be used to check its quality to ensure that the image clarity, contrast and other indicators meet the design standards. The quality assessment formula is as follows:

[0038] in, is the image quality score, and is the weight of the corresponding feature, Sharpness and Contrast are the sharpness and contrast scores of the image respectively. Through this weighting method, the system can comprehensively consider various quality indicators of the image. For text materials, the data verification and cleaning unit will check the integrity and format of the text through the text analysis algorithm.

[0039] In this embodiment, efficient rendering of the advertisement design is achieved through distributed computing resources. The cloud rendering computing module can effectively divide and schedule the rendering tasks and process them in parallel on multiple computing nodes, thereby accelerating the rendering process and ensuring high quality and efficiency of the rendering results. In this embodiment, the function of the task scheduling unit is to decompose the rendering task in the advertisement design into multiple subtasks, and reasonably schedule them to different computing nodes according to the task priority and the availability of computing resources. The task scheduling unit performs intelligent scheduling based on the complexity of the design, the time required for rendering, and the current load of each computing node. In order to achieve efficient scheduling, the task scheduling unit adopts a priority-based task allocation strategy; The decomposition of rendering tasks uses image block technology to divide the image to be rendered into multiple small blocks, and each computing node is only responsible for rendering a part of the image. The task scheduling unit performs task scheduling according to the following formula:

[0040] in, is the task time for each computing node, is the total rendering time, is the number of computing nodes involved in rendering. Through this formula, the system can dynamically adjust the task load of each computing node according to the number and load of computing resources; The distributed rendering unit is responsible for executing the rendering subtasks assigned by the task scheduling unit. Each computing node performs local rendering processing on the received subtasks and generates rendering results. During the rendering process, the system will perform rendering according to specific design requirements; During the rendering process, this embodiment uses GPU acceleration technology to accelerate the rendering process. The GPU performs well in processing parallel tasks and can significantly improve the rendering speed. Each computing node will calculate its rendering efficiency according to the following formula:

[0041] in, is the rendering time of a single node, is the computing power of the rendering node, is the load of the node. The formula can be dynamically adjusted according to the actual situation to ensure the efficiency of rendering. After each node completes its rendering task, it uploads the rendering result in the form of data blocks to the cloud storage or result synthesis unit for the next step of processing; The rendering result synthesis unit is responsible for synthesizing the rendering results generated by each distributed computing node and finally generating a complete advertising design image or video. The synthesis process requires seamless splicing of the image parts rendered by each computing node to ensure the coherence and consistency of the results; The mathematical expression of the synthesis process is:

[0042] in, is the final synthesized image. It is Image data generated by a rendering node, To match the number of rendering nodes, the system will reconstruct the image based on the rendering results of each node to ensure that each pixel is correctly merged to achieve high-quality rendering effects.

[0043] In this embodiment, the layout recommendation module recommends suitable advertising layouts to users based on the design requirements and historical design data input by the user. Through intelligent algorithms, the system can automatically analyze the elements of advertising design and provide personalized layout recommendations for advertising design based on user preferences and historical data. Layout recommendations can not only improve design efficiency, but also enhance the aesthetics and visual appeal of advertising design. The main function of the layout recommendation unit is to automatically recommend the most suitable advertising layout for users based on the design requirements, historical design data and characteristics of advertising design elements input by users. The recommendation process is based on deep learning technology, using convolutional neural networks or recurrent neural networks to train historical advertising design data. The model can identify the rules in the design and make recommendations based on the input design requirements. The system uses a deep learning model to analyze and recommend matching ad layouts. The recommendation algorithm of the layout recommendation unit can be represented by the following mathematical model:

[0044] in, Indicates the recommended ad layout. Design requirement data entered by the user, Design data for history, is a recommendation model trained based on deep learning. This formula shows that the layout recommendation is obtained by combining user needs and historical data and using the trained model for reasoning; The function of the layout optimization unit is to optimize the recommended advertising layout in real time to better meet the design needs of users. The optimization content includes adjusting the layout, color matching, font style and other design elements to ensure that the final design not only meets the user's requirements but also achieves the best visual effect. In this embodiment, the layout optimization unit combines the design feedback information and uses the optimization algorithm to dynamically adjust the recommended layout. Through the optimization algorithm, the system can automatically adjust certain elements of the design according to the user's feedback data to improve the effect of the advertising design; The basic principle of the optimization algorithm is to gradually optimize various aspects of the design layout by simulating the evolutionary process in nature. The optimization goal can be described by the following mathematical model:

[0045] in, Indicates the optimized layout design. is the parameter set of the design element, For each design element, is the evaluation function related to the design elements, is the number of design elements. Through this formula, the system can adjust the value of the design elements in multiple iterations to achieve the optimal optimization goal.

[0046] In this embodiment, the evaluation and optimization module automatically evaluates various elements of the advertising design, such as layout, color matching, fonts, etc., and provides optimization suggestions. Through computer vision and image processing technology, the system can quantify the quality of the advertising design, analyze various aspects of the design, identify potential problems and provide specific improvement plans; The function of the quality assessment unit is to analyze multiple features in the ad design, such as layout alignment, color matching, text readability, etc., through computer vision technology, and assign quality scores to these features; In this embodiment, the quality assessment unit uses image processing technology to analyze various elements of the advertisement design. The specific assessment process is as follows: Image clarity evaluation: Use the image sharpness algorithm to measure the clarity of the image. The formula is:

[0047] in, Indicates that the image is at point Sharpness refers to the pixel value at , and the sharpness indicates the sharpness of the image. The higher the sharpness, the clearer the image; Image contrast evaluation: Use contrast algorithms such as the standard deviation method to evaluate the contrast of the image. The formula is:

[0048] in, For the image The value of pixels, is the mean value of the image pixels, is the total number of pixels in the image. The higher the contrast, the richer the image details; Color matching assessment: Use color analysis methods to evaluate whether the colors in the image are harmonious. Color harmony can be achieved through algorithms that calculate color differences and color wheel positions; Text readability assessment: Evaluate the readability of text through factors such as character spacing, font size, and the contrast between background color and text color. This part can use OCR technology for character recognition and then combine contrast algorithms to evaluate the clarity of text; All evaluation results will be comprehensively calculated using the following formula to obtain the overall design quality score: Sharpness Contrast Color Harmony Legibility is the weight of each design feature. Sharpness, Contrast, ColorHarmony, and Legibility represent the clarity, contrast, color harmony, and text readability of the image, respectively. Through this formula, the system can quantitatively evaluate the quality of advertising design; The optimization proposal generation process is based on the following steps: Problem diagnosis: Based on the evaluation results, identify problems with the design. For example, if the image has low contrast, the user may be prompted to adjust the color contrast.

[0049] Optimization path generation: Generate a specific optimization path based on the diagnosis results. The system will provide some automated suggestions and further refine the suggestions based on user feedback.

[0050] Feedback on optimization results: Optimization suggestions are presented to users through the design interface, and users can choose to accept or modify the suggestions. The optimized design will be evaluated again to verify the optimization results; The specific algorithm of the optimization suggestion can be described by the following mathematical model:

[0051] in, For the optimized design, is the parameter set of the design element, is the design quality scoring function. Through this formula, the optimization unit can find the optimal solution in the space of different design parameters to ensure the maximum design quality.

[0052] In this embodiment, the effect feedback module provides real-time feedback on rendering and design effects, provides users with a preview of the design effects, and further adjusts the design based on user feedback. The effect feedback module enables users to see design changes instantly and provides an interactive feedback mechanism to quickly optimize based on real-time effects; The function of the real-time preview unit is to provide users with instant feedback on the changes made during the design process, so that users can view the current effect during the design process and make further adjustments as needed. By integrating front-end rendering technology, this unit can dynamically render the advertising design content and display it to users; In this embodiment; Rendering acceleration: The real-time preview unit ensures real-time rendering of images during the design process by utilizing GPU acceleration technology. This process increases rendering speed and reduces latency through parallel computing, ensuring instant feedback of design content. Real-time design update: When users drag, zoom or rotate the interactive design interface, the system can instantly update the design interface and present the changed design effect to the user through high-speed rendering technology; Design data synchronization: When multiple users are designing the same advertising project at the same time, the real-time preview unit ensures that all participants see the same design content and synchronizes the design data in real time to avoid design conflicts or display delays; Preview and design data consistency: The preview result is consistent with the final rendering effect, ensuring that the preview image seen by the user is highly authentic and accurate; The function of the feedback analysis unit is to analyze the advantages and disadvantages of the current design based on real-time feedback from users and provide corresponding improvement suggestions; In this embodiment, user feedback collection: the system collects user feedback information in real time through the design interface, including user evaluation, clicks, adjustments and other behaviors; Feedback data processing: All collected feedback data will be processed and analyzed and converted into quantifiable evaluation indicators; Feedback score synthesis: Based on the feedback of each design element, the system generates an overall feedback score. The feedback score can be calculated by weighted average, and the specific formula is as follows:

[0053] in, Rate the overall feedback. Represents the feedback scores of color, typography and font design respectively. is the weight of each design feature; Optimization suggestion generation: Based on the overall feedback score, the system will automatically generate optimization suggestions; The effect feedback module in this embodiment can provide timely effect display and adjustment suggestions for advertising design through the organic combination of real-time preview and feedback analysis. By collecting user feedback in real time and generating quantitative scores, the system can accurately evaluate and optimize the design, making the advertising design process more efficient and accurate. This module not only improves the user experience, but also ensures that the quality and effect of advertising design are optimal.

[0054] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An advertising art design computer processing system, characterized in that: include; A user interaction module, used to receive advertisement art design data input by a user; Data collection and preprocessing module, used to collect and preprocess the material data of advertising art design; Cloud rendering computing module, which performs efficient rendering through distributed computing resources; The layout recommendation module recommends suitable advertising layouts to users based on the design requirements and historical design data input by users and the machine learning algorithm. Evaluation and optimization module, which is used to automatically evaluate the design layout, color matching, fonts, etc., and provide optimization suggestions; The effect feedback module provides real-time feedback on rendering and design effects and provides a preview of the design effects for users to make further adjustments.

2. The computer processing system for advertising art design according to claim 1, characterized in that: The user interaction module comprises: Real-time collaboration unit, which supports multiple users to work in parallel on the same design project and synchronize design content in real time; Design data transmission unit, responsible for packaging the user's design data into a unified format; The interactive design unit provides an intuitive design operation interface, allowing users to design advertisements through operations such as dragging and dropping, scaling, and rotating.

3. The computer processing system for advertising art design according to claim 1, characterized in that: The data acquisition and preprocessing module includes: A material acquisition unit collects material data from external sources; The material pre-processing unit processes the collected materials, performs denoising, sharpening, and color enhancement; The data verification and cleaning unit ensures that the collected data is complete, correct, and meets the design requirements.

4. The computer processing system for advertising art design according to claim 1, characterized in that: The cloud rendering computing module includes: The task scheduling unit divides the rendering task into multiple subtasks and assigns them to different nodes based on task priority and the availability of computing resources; Distributed rendering unit, each computing node performs the rendering tasks assigned to it; The rendering result synthesis unit combines the various results of distributed rendering and returns them to the front-end design interface for display.

5. The computer processing system for advertising art design according to claim 1, characterized in that: The layout recommendation module includes: The layout recommendation unit recommends advertising layouts based on the user's design requirements and historical data based on deep learning algorithms; The layout optimization unit optimizes and adjusts the recommended layout in real time to meet the needs of users.

6. The computer processing system for advertising art design according to claim 1, characterized in that: The evaluation and optimization module includes: The quality assessment unit uses computer vision and image processing technology to assess the quality of the advertisement design. The quality assessment includes: in, is the design quality score, is the importance weight of each feature, Feature is the value of the corresponding design feature; The optimization suggestion unit gives specific optimization suggestions based on the evaluation results.

7. The computer processing system for advertising art design according to claim 1, characterized in that: The effect feedback module comprises: Real-time preview unit, which displays the effect of the advertisement modified by the user in real time and provides interactive feedback; The feedback analysis unit evaluates the design effect based on the feedback data. The evaluation result is calculated by the following formula: in, Rate the overall feedback. Rate a single user's feedback.

8. The computer processing system for advertising art design according to claim 3, characterized in that: The data verification and cleaning unit uses computer vision technology to analyze the clarity and contrast of the image to ensure that the material meets the design quality requirements, and quantitatively evaluates the quality of the image through the following formula; in, Rate image quality, Sharpness and Contrast are the sharpness and contrast scores of the image, is the number of images.

9. The computer processing system for advertising art design according to claim 1, characterized in that: The cloud rendering calculation module uses GPU acceleration technology to improve rendering efficiency using the following formula: in, is the total rendering time, is the number of rendering nodes, For Node The computing power of For Node of rendering tasks.

10. The computer processing system for advertising art design according to claim 5, characterized in that: The layout optimization unit calculates the layout optimization index using the following formula based on user feedback; in, For the Ratings of user feedback, is the weight of each feedback, is the number of feedbacks.

Images