Personalized GUI Widgets via Machine Learning Segmentation
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Solution Overview
Problem
Existing customization methods for graphical user interfaces (GUIs) are too generic and fail to personalize the interface for specific subsets of users, leading to an unsatisfactory user experience.
Innovation Solution
The system uses a supervised machine learning model to customize a GUI by determining the probability of user preferences for specific widgets based on common user characteristics, such as age range, location, and device type, and updates the model based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generic customization based on location data is used, then implementation simplicity is maintained, but personalization effectiveness deteriorates
Solution Approach 1:
The system changes the parameters used for customization from simple geographic location to multiple user characteristics including age range, device type, and language preference. This allows the system to move from generic location-based customization to more specific user profile-based personalization while maintaining the same technical implementation approach.
Solution Approach 2:
The system segments users into different groups based on multiple characteristics rather than treating all users from a location uniformly. By dividing the user base into smaller segments with shared characteristics, the system can provide more targeted customization without significantly increasing implementation complexity.
2Measurement precision
If supervised machine learning model training is implemented, then personalization accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary action by training the supervised machine learning model in advance using historical user interaction data. This pre-training allows the model to be ready for deployment without requiring complex real-time computation during actual GUI customization, thereby improving personalization accuracy while managing system complexity.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with the GUI are collected and used to generate input-output pairs for retraining the machine learning model. This continuous feedback loop improves personalization accuracy over time while the automated nature of the process helps manage system complexity.
Data Source
AI summary
Embodiments disclosed herein relate generally to a customized or personalized GUL. More specifically, embodiments described herein disclose systems and process for deriving user preferences based upon previous actions of a set of users and using those user preferences to personalize one or more widgets within a GUL.