Dynamic User Interface Personalization via Machine Learning Propensity Models
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Solution Overview
Problem
Existing techniques for generating and presenting user interfaces are often static, rigid, and impersonal, failing to effectively personalize and customize interfaces for users, especially new users with limited data available.
Innovation Solution
The use of contextual information and trained machine learning models, such as propensity and value models, to personalize and customize user interfaces and resources, including booklets, by analyzing user interaction data and providing tailored content and layout based on user-specific metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static and rigid user interface generation techniques are used, then device complexity is reduced, but adaptability and personalization capability deteriorate
Solution Approach 1:
The system segments user interface personalization into modular components: profile data extraction module, propensity model module, value model module, and UI generation module. Each module handles a specific aspect of personalization, allowing the system to achieve high adaptability while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The system performs preliminary actions by pre-collecting user profile data, pre-training propensity and value models, and pre-generating personalized interface configurations. This allows the system to quickly adapt to individual users without requiring complex real-time computations during actual UI rendering.
2Adaptability or versatility
If personalized user interfaces are generated using machine learning models, then adaptability improves, but computational resources increase
Solution Approach 1:
The system applies partial personalization by focusing computational resources on the most impactful UI elements for each user. The propensity and value models identify key personalization opportunities, and the system generates personalized interfaces only for those specific elements rather than over-personalizing the entire interface, thus reducing unnecessary computational overhead.
Solution Approach 2:
The system uses template-based UI generation where personalized interfaces are created as copies or variations of standardized templates. The propensity and value models determine which template parameters to customize for each user, allowing the system to achieve personalization effects without computing entirely unique interfaces from scratch for each user.
3Ease of operation
If contextual information and machine learning models are used for personalization, then user experience improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant contextual information from user data using the profile data extraction module. Instead of processing all available user data, the module selectively extracts key features such as user preferences, behavior patterns, and demographic information that are most predictive of personalization needs, reducing data processing complexity while maintaining personalization quality.
Solution Approach 2:
The propensity and value models serve as intermediaries between raw user data and personalized interface generation. These models process and transform complex user data into simplified predictive scores and recommendations, which then guide the UI generation process. This intermediary layer reduces the complexity of directly processing raw user data for personalization decisions.
Data Source
AI summary
A payment service system receives contextual information regarding an interaction between the payment service and a user device associated with a user. A propensity metric for the user is determined based at least in part on inputting the contextual information into a machine learning (ML) model. Based on the propensity metric, a user interface is dynamically configured to comprise user interface elements arranged in a layout personalized for the user, where a user interface element represents content particular to a service offered by the payment service. Based on receiving an interaction with the user interface element, a booklet is launched corresponding to the service with which the user interface element is associated.