User Interface Content Ranking With Adaptive Relevance Weights
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Solution Overview
Problem
Existing information navigation systems on input and display constrained devices face challenges in minimizing user effort for both text-based search and browse-based navigation, particularly in reducing the number of steps and characters required to discover desired information.
Innovation Solution
A user-interface method that learns user navigation and selection behavior over time to personalize content presentation, adjusting relevance weights based on context, time, and location, and decays these weights over time or with user interactions, optimizing the presentation of content items and groups to reduce interaction effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If text-based search is used to find information, then the user can quickly locate specific content, but the user must type multiple characters and navigate through results, increasing input effort
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing relevance weights for content items based on user behavior patterns, navigation history, and selection data. This preliminary processing allows the system to quickly retrieve and present relevant content without requiring the user to type or navigate through large amounts of data, thus reducing both time and input effort.
2Ease of operation
If browse-based navigation is used to find information, then the user can easily navigate through content, but the user must take multiple steps to reach desired information, increasing interaction effort
Solution Approach 1:
The system pre-computes relevance weights for content items based on user navigation patterns and selection behavior, storing this information in advance. When the user needs to find information, the system can immediately present pre-ranked results without requiring the user to browse through multiple levels of content hierarchy, thus reducing both navigation steps and time required.
Solution Approach 2:
The system continuously monitors and learns from user navigation and selection actions, using this feedback to dynamically adjust and refine relevance weights. This feedback mechanism allows the system to adapt to changing user preferences and behaviors, improving the accuracy of content ranking over time and reducing the effort needed to find desired information.
3Measurement precision
If the system learns user behavior over time to personalize content, then the relevance of presented content improves, but the system complexity increases
Solution Approach 1:
The system segments the content into hierarchical groups and assigns relevance weights to both individual content items and groups. This segmentation allows the system to manage complexity by processing and storing relevance information in an organized, modular structure, making it easier to implement and maintain while still achieving high accuracy in content relevance measurement.
Solution Approach 2:
The system uses parameter changes by dynamically adjusting relevance weights based on user behavior patterns, navigation history, and selection data. These parameters are updated over time as the system learns more about user preferences, allowing the system to improve content relevance accuracy without requiring complex reconfiguration or redesign of the underlying system architecture.
Data Source
AI summary
A user-interface method of selecting and presenting a collection of content items based on user navigation and selection actions associated with the content is provided. The method includes associating a relevance weight on a per user basis with content items to indicate a relative measure of likelihood that the user desires the content item. The method includes receiving a user's navigation and selections actions for identifying desired content items, and in response, adjusting the associated relevance weight of the selected content item and group of content items containing the selected item. The method includes, in response to subsequent user input, selecting and presenting a subset of content items and content groups to the user ordered by the adjusted associated relevance weights assigned to the content items and content groups.