User Matching System Proximity Filtering
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Solution Overview
Problem
Existing systems fail to effectively identify and match users who are both geographically nearby and compatible based on their profiles and preferences, limiting the ability to facilitate meaningful interactions.
Innovation Solution
A system and method that collects user registration information, location data, and preference profiles to identify compatible users within a certain distance or proximity to a public meeting space, using compatibility scores and visibility settings to display potential matches, allowing users to interact through messaging or friend requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the system displays all nearby users to maximize potential matches, then the quantity of potential matches increases, but the information overload and user decision difficulty increases
Solution Approach 1:
The system segments the large set of nearby users into smaller compatibility groups based on multiple criteria (distance, age range, interests, activity preferences). This segmentation presents users with manageable subsets of matches rather than overwhelming them with all nearby users at once, resolving the contradiction between quantity and ease of operation.
Solution Approach 2:
The system changes parameters by dynamically adjusting compatibility thresholds and filtering criteria based on user preferences and behavior. By modifying these parameters, the system optimizes the number of displayed matches to be sufficiently large for variety but small enough for easy decision-making, resolving the contradiction between quantity and ease of operation.
2Measurement precision
If the system uses multiple compatibility criteria (distance, interests, preferences) to improve match quality, then the compatibility accuracy increases, but the system complexity increases
Solution Approach 1:
The system segments the complexity by dividing the compatibility assessment into separate modular components: distance calculation module, interest matching module, preference alignment module, etc. Each module handles one criterion independently, then results are combined. This segmentation maintains high compatibility accuracy while managing system complexity through modular design.
Solution Approach 2:
The system implements a universal compatibility calculation framework that can accommodate multiple different criteria (distance, interests, preferences, age, etc.) through a single unified algorithm structure. This multi-functional approach allows the system to maintain high compatibility accuracy across diverse criteria without proportionally increasing system complexity, as the same framework handles all criteria.
3Measurement precision
If the system calculates compatibility scores for all nearby users to ensure accurate matching, then the match quality improves, but the computational time and resources increase
Solution Approach 1:
The system performs preliminary filtering actions before full compatibility calculation. It first identifies nearby users based on simple distance criteria, then pre-filters this set using quickly-checkable criteria (basic profile information, obvious preference mismatches). Only users passing these preliminary filters undergo full compatibility score calculation, reducing computational time while maintaining match quality.
Solution Approach 2:
The system applies partial compatibility calculation in cases where full calculation is unnecessary. For users who clearly meet or fail basic compatibility thresholds, the system uses simplified scoring or binary pass/fail assessment rather than full multi-criteria analysis. This partial action approach maintains adequate match quality while significantly reducing computational resources and time.
Data Source
AI summary
A system and method displays to a user nearby other user's compatible with that user.