Segmented User List Display for Relevance Prioritization
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Solution Overview
Problem
Conventional methods for displaying lists of messages or users on websites are suboptimal, failing to effectively segregate and prioritize relevant content based on user characteristics, interests, and actions.
Innovation Solution
A system and method that segments lists of users or messages into relevant categories using user characteristics, implicit preferences, common interests, experiences, and popularity scores, with optional time-based filtering and suggestions for users who do not meet initial criteria but match specified preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional display methods are used for lists of messages or users, then the display is simple and straightforward, but the relevance and engagement of displayed content is suboptimal
Solution Approach 1:
The patent segments user lists into multiple categorized segments (e.g., new users, active users, mutual matches, suggested users) based on different criteria such as activity level, compatibility scores, and interaction history. This segmentation allows the system to present highly relevant users in an organized manner without overwhelming the interface, resolving the contradiction between improving content relevance and maintaining system simplicity.
2Productivity
If lists are segmented into multiple categories based on user characteristics and actions, then user engagement increases, but the complexity of list management increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating compatibility scores, activity levels, and segment classifications for users before they are displayed. User profiles are pre-segmented into categories (new, active, mutual matches) based on their historical actions and characteristics. This preliminary processing reduces the complexity of real-time list management while maintaining high user engagement through relevant displays.
3Measurement precision
If relevance is determined using multiple user characteristics and preferences, then the accuracy of matching improves, but the computational requirements increase
Solution Approach 1:
The patent applies local quality by determining relevance differently for different user segments rather than using a uniform complex algorithm for all users. For example, mutual matches are identified based on bidirectional interest, while suggested users are ranked by compatibility scores. This localized approach to relevance determination improves matching accuracy for each segment while reducing overall computational energy consumption by avoiding unnecessary complex calculations for all users equally.
Data Source
AI summary
A system and method displays lists of users or messages in segments, with some segments showing messages or users believed to be more relevant to the user than others on the list.