Expert Ranking Subsystem for Talent Network Trust Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current talent network management systems lack the ability to effectively assess the trustworthiness of experts and provide personalized recommendations, relying on tabular data and predefined parameters, which limits customization and efficiency in finding suitable experts.
Innovation Solution
A system and method that utilize a processor-based expert ranking measurement subsystem to compute quantitative and qualitative scores for experts, incorporating connection relationships, professional achievements, content, activity, and user preferences, to generate an overall rank and provide personalized expert recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tabular data and predefined parameters are used for talent network management, then the system structure is simple, but the system lacks the ability to effectively assess expert trustworthiness and provide personalized recommendations
Solution Approach 1:
The patent segments the expert assessment into multiple independent components: trust metric (based on connection relationships), talent metric (based on professional achievements), content score (based on provided content), and activity score (based on profile activities). Each component is calculated separately and then integrated to form the overall expert ranking, allowing comprehensive assessment while maintaining manageable system complexity through modular calculation.
2Productivity
If tabular data is used for talent network management, then data storage is simple, but significant human intervention is required and data handling becomes time-consuming
Solution Approach 1:
The system implements self-service by automatically computing trust metrics, talent metrics, content scores, and activity scores without requiring human intervention. The processor-based subsystems automatically retrieve data from the talent network, perform calculations according to predefined algorithms, and generate expert rankings, eliminating the time-consuming manual data handling process.
3Adaptability or versatility
If predefined parameters are used for user selection, then the system is easy to operate, but the system is very limited in terms of providing options and customization
Solution Approach 1:
The patent implements dynamic customization by allowing users to adjust weights for different metric components (trust metric, talent metric, content score, activity score) based on their specific needs. The system dynamically recalculates expert rankings according to user-defined preferences, providing flexible customization while maintaining ease of operation through a user-friendly interface for parameter adjustment.
Data Source
AI summary
A system and method for management of a talent network are disclosed. The system includes an expert ranking measurement subsystem configured to compute a quantitative score of an expert for a user query based on a talent metric and a trust metric, a qualitative score measurement subsystem configured to compute a qualitative score of the expert based on a content score and an activity score, wherein the content score is computed based on a content provided by the expert and the activity score is computed based on an activity occurring on a profile of the expert, an overall expert rank calculation subsystem configured to calculate an overall rank of the expert, wherein the overall expert rank is calculated based on the quantitative score, the qualitative score, predefined weightages assigned to the connection relationship, the plurality of professional achievements, the content score, the activity score, and user preferences.