Relevance Engine for Social Network Connection Prioritization
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Solution Overview
Problem
Social networking services face challenges in helping users determine relevant connections among numerous connection requests, as individuals struggle to identify meaningful connections from a large number of potential contacts, including both members and non-members of the service.
Innovation Solution
A relevance engine is implemented to assess the relevance of individuals by using various signals such as IP address proximity, shared employment history, communication patterns, common connections, and other data points to recommend the most relevant potential connections, sorting and filtering invitations to prioritize those with higher relevance scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually review all connection requests, then they can identify meaningful connections, but it consumes excessive time and effort
Solution Approach 1:
The patent introduces an automated relevance scoring system that acts as an intermediary between connection requests and user review. The system calculates relevance scores based on multiple signals (shared connections, communication patterns, profile similarity) and presents prioritized lists to users, eliminating the need for manual sorting while maintaining accurate identification of meaningful connections.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system. The relevance engine uses algorithms to process connection signals, calculate scores, and generate prioritized lists, substituting human cognitive effort with automated information processing while preserving the ability to identify relevant connections.
2Ease of operation
If the system presents all connection requests equally, then users see all potential connections, but users cannot efficiently identify relevant ones among numerous requests
Solution Approach 1:
The patent applies local quality by differentiating the presentation of connection requests based on their individual relevance characteristics. Instead of uniform treatment, each connection request receives a customized relevance score and positioning in the displayed list based on its specific signals (shared connections, communication history, profile match), allowing users to quickly identify the most relevant connections without losing information about less relevant ones.
3Measurement precision
If the system uses multiple signals to assess relevance, then connection relevance accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the relevance assessment process into distinct signal categories (connection signals, communication signals, profile signals) that are processed independently and then aggregated. This modular segmentation allows the system to handle multiple complex signals through organized sub-routines, improving accuracy while managing computational complexity through structured processing.
Data Source
AI summary
Disclosed in some examples are methods, systems, and machine-readable mediums which provide a relevance engine for determining a relevance of an individual (either a non-member or another member) to another individual (either a non-member or another member). This relevance engine may use signals in the form of data that the social networking service may learn about the individuals to determine how relevant the individuals are to each other.