GAN-Based Custom GUI Element Generation for User Interest Alignment
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Solution Overview
Problem
Businesses face challenges in creating specific graphical user interface (GUI) elements for numerous products and offerings, as manual generation is time-intensive and costly, resulting in generic elements unrelated to user or vendor interests, limiting their effectiveness in driving desired user behavior.
Innovation Solution
A method that detects user interests through purchase history and digital behaviors, generates a personalized user profile, and uses a generative adversarial network (GAN) to create custom GUI elements tailored to user interests or vendor priorities, replacing generic elements to enhance user engagement and business outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual generation of GUI elements is performed, then customization and relevance to user interests can be achieved, but time consumption and costs increase significantly
Solution Approach 1:
The system automatically generates customized GUI elements by detecting user interests from digital behaviors and purchase history, then using a GAN to create relevant visual elements without manual intervention. This self-service approach eliminates the time-consuming manual creation process while maintaining high customization levels.
Solution Approach 2:
The system dynamically changes parameters of GUI elements based on detected user interests, generating different visual representations tailored to individual user profiles. This allows continuous customization without repeating the full manual design process, reducing time while maintaining adaptability.
2Adaptability or versatility
If manual generation of GUI elements is performed, then quality and relevance can be improved, but costs increase significantly
Solution Approach 1:
The automated system performs the entire GUI element creation process independently by detecting user interests and generating customized elements through machine learning, eliminating the need for expensive manual design services while maintaining high relevance to user interests.
Solution Approach 2:
The system replaces the mechanical process of manual graphic design with an automated GAN-based generation process. This substitution eliminates human labor costs while maintaining or improving the quality and relevance of generated GUI elements through algorithmic optimization.
3Productivity
If generic GUI elements are used, then time and costs are reduced, but effectiveness in driving user behavior decreases
Solution Approach 1:
The system dynamically adjusts parameters of GUI elements based on detected user interests, generating customized visual representations that resonate with individual users. This parameter-based customization maintains high generation efficiency while significantly improving effectiveness in driving desired user behaviors such as clicks and conversions.
Solution Approach 2:
The system transitions from static generic GUI elements to dynamic customized elements that adapt to each user's demonstrated interests. This dynamic approach maintains operational efficiency through automation while substantially improving the adaptability and effectiveness of GUI elements in influencing user behavior.
Data Source
AI summary
An embodiment for generating and modifying graphical user interface elements based on user item interests. The embodiment may detect a user interacting with a connected application. The embodiment may determine item interests of the detected user. The embodiment may generate and store a personalized user profile based on the determined item interests of the user. The embodiment may, in response to detecting a query made by the user, extract from the detected query interest topics, subject matter, and a series of associated key terms. The embodiment may determine a highest-prioritized key term. The embodiment may identify a first GUI element displayed by the connected application based on the detected query. The embodiment may generate, using a generative adversarial network (GAN), a custom GUI element based on the highest-prioritized key term. The embodiment may replace the first GUI element with the custom GUI element.