Expert Database Verification Using Social Graph Data
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Solution Overview
Problem
Current online social networks face challenges in accurately identifying and connecting users with verified subject matter experts due to reliance on keyword-based searches, which can lead to false positives and inefficiencies in finding experts with specific skills.
Innovation Solution
An expert-finding algorithm that utilizes member profile data, internal employer data, third-party data, and social graph data to rank and recommend verified experts based on their experience, endorsements, and recommendations, while determining the optimal connection path for user access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If keyword-based search is used to find experts, then search simplicity is maintained, but accuracy and reliability of expert identification deteriorates due to false positives
Solution Approach 1:
The expert verification system segments the evaluation process into multiple independent components: profile data analysis, employer data verification, third-party data validation, and social graph relationship assessment. Each component evaluates specific aspects of expertise claims, and the results are aggregated to produce a comprehensive verification decision, thereby improving reliability while managing complexity through modular architecture
Solution Approach 2:
The patent introduces an intermediary verification system that acts as a mediator between users and expert claims. This system collects and analyzes multiple types of data (profile, employer, third-party, social graph) to validate expertise assertions before presenting experts to users, thereby filtering out false positives while maintaining a manageable search interface
2Reliability
If comprehensive data verification is implemented, then expert verification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting, pre-processing, and pre-organizing multiple types of data (profile data, employer data, third-party data, social graph data) before expert search queries are executed. This pre-prepared data structure enables rapid verification during actual search operations, reducing real-time processing time while maintaining comprehensive verification accuracy
Solution Approach 2:
The patent applies partial verification actions by selectively analyzing different data sources based on the specific expertise query and available data quality. The system can adjust the depth and scope of verification (e.g., checking profile data always, employer data selectively, third-party data when available) to achieve sufficient verification accuracy without unnecessary computational overhead for each specific search case
3Reliability
If multiple data sources are integrated for expert verification, then identification accuracy improves, but system complexity and data management difficulty increase
Solution Approach 1:
The patent implements a universal data integration framework that handles multiple data sources (profile data, employer data, third-party data, social graph data) through a unified processing architecture. This multi-functional system uses common data structures, standardized verification algorithms, and centralized management mechanisms that can accommodate various data types without requiring separate complex subsystems for each data source
Data Source
AI summary
Techniques for generating an expert database and verifying an expert using member data are described. A search request can be received from a device of the user. The search request can include a specific skill associated with the expert. Additionally, profile data can be accessed from a database in the online social network. Additionally, an expert recommendation process can determine an expert from the members of the online social network based on the search request and the profile data of the members. Moreover, social graph data can be accessed from a second database in the online social network. Furthermore, a connection path process can determine the connection path between the user and the expert based on the social graph data. Subsequently, the determined expert and the determined optimal path between the user and the expert can be presented on a display of the device.