Dynamic User Profile Matching via Trust Vectors and Proximity
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Solution Overview
Problem
Current platforms fail to effectively match individuals with specific skill sets based on trust and proximity, as they often prioritize price over quality and lack dynamic user profiling and networking capabilities.
Innovation Solution
A computer-implemented method and system that dynamically identifies and presents matching user profiles by analyzing user-selected goals, location, and social networks, ranking profiles based on proximity and trust vectors, and facilitating introductions through shared contacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing platforms prioritize price-based matching, then transaction volume increases, but trust and quality of connections deteriorate
Solution Approach 1:
The system changes the matching parameters from price-based to trust-based by calculating trust vectors that incorporate social proximity (shared contacts), geographic proximity, and goal compatibility. This fundamentally alters the matching criteria from economic efficiency to relational trust, resolving the contradiction between transaction volume and trust quality
Solution Approach 2:
The patent introduces shared contacts as intermediaries who vouch for and introduce users to each other. This intermediary mechanism builds trust through social validation, allowing transactions to occur based on trusted introductions rather than price competition alone
2Stability of the object's composition
If static online resumes are used for networking, then profile information is preserved, but dynamic matching and discovery capabilities are lost
Solution Approach 1:
The system transforms static profiles into dynamic matching entities by continuously calculating trust vectors based on shared contacts, geographic location, and goal compatibility. The matching results are dynamically updated as users add contacts or change locations, enabling real-time adaptation while preserving core profile information
Solution Approach 2:
The system performs preliminary analysis of user profiles, goals, and contact networks to pre-calculate trust vectors and identify potential matches before users actively search. This preliminary action enables proactive matching notifications and reduces the effort users must invest in networking
3Reliability
If friends are asked for referrals manually, then trusted connections are made, but time and social capital are consumed
Solution Approach 1:
The system automates the referral process by having users import their contact lists and allowing the system to automatically identify shared contacts, calculate trust vectors, and generate match recommendations. This self-service approach eliminates the manual time investment while maintaining the trust-based connection quality
Solution Approach 2:
The system provides feedback to users about their trust vectors and match quality based on shared contacts and compatibility metrics. This feedback mechanism helps users understand why certain matches are recommended and allows them to refine their profiles to improve future matching accuracy
Data Source
AI summary
Electronic computer-implemented systems and methods for dynamically identifying and presenting matching user profiles to a user are disclosed. A server system receives, from an electronic device of a user, user profile information that includes at least one user-selected goal, and stores the user profile on a database. Location information as to the geographic location of the user is also received by the server system. The user-selected goal is analyzed to extract entity and keyword samples therefrom, and matching user profiles of other users stored on the database are identified as those that have goals with similar entity and keyword samples therein. The matched user profiles are ranked based on the physical proximity between the user and the other users, and the ranked list is transmitted to the electronic device to be displayed to the user.