Analytics Personalization Framework for Dynamic UI Adaptation
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Solution Overview
Problem
Users interacting with complex analytics applications often find the initial user interface overwhelming, leading to frustration and reduced engagement, as they struggle to navigate through excessive content and features, resulting in lower retention rates and inefficient use of resources.
Innovation Solution
A method utilizing machine learning models, such as logistical regression, deep, and wide learning, to analyze user interaction data and dynamically modify the user interface by incorporating or removing UI elements based on user interest scores, tailoring the interface to present only relevant content, thereby reducing unnecessary data transmission and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the initial user interface includes all available UI elements and content, then the application provides comprehensive functionality, but users find the interface overwhelming and frustrating to navigate
Solution Approach 1:
The user interface is segmented into multiple pages or sections, with only relevant UI elements displayed on the initial page. Less frequently accessed elements are placed on subsequent pages, reducing the visual complexity and improving navigability while maintaining comprehensive functionality across all pages.
Solution Approach 2:
Different UI elements are selectively displayed based on user characteristics, interaction history, and relevance scores. Each user receives a customized view with locally optimized quality - showing high-priority elements prominently and hiding low-priority elements, rather than applying a uniform display approach to all elements.
2Adaptability or versatility
If the user interface includes all available content and features, then the application is feature-complete, but resource usage increases due to transmitting and processing unnecessary data
Solution Approach 1:
Irrelevant UI elements and content are extracted and removed from the initial user interface display. Only elements with high relevance scores above a threshold are transmitted to and displayed for the user, while all features remain accessible through navigation to other pages or on-demand loading.
Solution Approach 2:
Instead of transmitting and processing all available data, the system applies partial action by selectively transmitting only the subset of data that is most relevant to each user based on their profile and interaction history, reducing resource consumption while maintaining essential functionality.
3Ease of operation
If the user interface is simplified to show only essential elements, then ease of use improves, but the application loses comprehensive functionality
Solution Approach 1:
The user interface is structured as nested pages or sections, where the initial simplified view contains the most essential elements. Additional functionality and UI elements are nested within subsequent pages or accessible through navigation, allowing users to access comprehensive features when needed while maintaining a simple initial view.
Data Source
AI summary
Methods, systems, and computer readable medium for personalizing an analytics user interface. The method includes generating a set of training data from received user interaction data, inputting the set of training data to a machine learning model to train the model, generating a set of user interest scores for the particular user that each indicate a user's interest in accessing information corresponding to a UI element of the application, determining, from the user interest scores, that the user is interested in a particular UI element that was not included in the initial UI and has at least a threshold score, dynamically modifying the initial UI to include the particular UI element, presenting the updated UI, monitoring further user interactions, updating the model based on the further user interactions, and modifying the updated UI based on the updated model.