Candidate Sourcing via Multi-Attribute Similarity Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current social graph identification and matching systems are inefficient due to reliance on exact email matching, which fails when users have different email addresses across platforms, and do not account for variations in attribute values across social media profiles, leading to poor candidate sourcing results.
Innovation Solution
The Sourcing Abound Candidates Apparatus, Methods, and Systems (Abound) normalize and enrich user profiles across multiple social networks, using data normalization, attributization, complexity reduction, and weighting to identify and match candidate profiles effectively, even with variations in attribute values and email addresses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact email matching is used for profile identification, then matching precision is improved, but adaptability deteriorates when users have different email addresses across platforms
Solution Approach 1:
The system changes the matching parameters from exact email matching to a multi-attribute similarity scoring system. Instead of requiring exact email matches, the system evaluates multiple attributes (name, location, profile text, contact information) and calculates a similarity score, allowing flexible matching across different email addresses while maintaining identification accuracy
Solution Approach 2:
The matching system becomes multi-functional by supporting both exact matching (for high confidence cases) and fuzzy matching (for cross-platform identification). The system can adapt its matching strategy based on the data available, making it universally applicable whether users have consistent or varying email addresses across platforms
2Device complexity
If traditional matching systems are used, then device complexity is reduced, but measurement precision deteriorates due to inability to account for attribute variations
Solution Approach 1:
The matching system is segmented into multiple independent components: data normalization module, attribute weighting module, similarity calculation module, and duplicate detection module. Each component handles a specific aspect of the matching process, making the overall system more precise while maintaining manageable complexity through modular design
Solution Approach 2:
The system introduces intermediate processing steps between raw data and final matching results. Data normalization serves as an intermediary that standardizes input data, and similarity scoring acts as an intermediary that bridges exact matching and fuzzy matching, enabling precise identification without requiring overly complex direct comparison logic
3Loss of information
If profile data is collected from multiple social networks, then information completeness is improved, but device complexity increases due to data heterogeneity
Solution Approach 1:
The system applies local quality by treating different data sources and attribute types with specialized processing rules. Each social network platform and attribute type has its own normalization and weighting parameters, allowing the system to handle data heterogeneity effectively while maintaining information completeness from diverse sources
Solution Approach 2:
Data normalization is performed as a preliminary action before matching operations. By standardizing data formats, schemas, and attribute values in advance, the system reduces the complexity of subsequent processing steps while preserving complete information from multiple sources. This preliminary structuring enables efficient handling of heterogeneous data
4Manufacturing precision
If exact matching criteria are applied, then manufacturing precision is improved, but productivity deteriorates due to missed candidate matches
Solution Approach 1:
The system performs partial matching by evaluating subsets of attributes with different weightings rather than requiring complete exact matches on all fields. This allows the system to identify candidates with high confidence based on key attributes while still considering partial matches on other attributes, thereby improving both accuracy and productivity in candidate sourcing
Data Source
AI summary
The Sourcing Abound Candidates Apparatuses, Methods and Systems (“Abound”) transforms data normalization support request and candidate criteria inputs via Abound components into criteria matching candidate indication outputs. An apparatus for sourcing active and passive jobseekers through jobseeker social media data, comprising a memory and a processor that issues instructions to: extract jobseeker data from a plurality of social media sources. That includes instructions to obtain jobseeker data from at least one of: various social media API's or crawl said social media sources and utilize extracted schemas to analyze said jobseeker data. Thereafter Abound may perform a link resolving and schema merging process to eliminate duplicates from the schemas and transform non-categorical schema data to conform with a master schema standard. Then Abound may reconcile variations in categorical schemas to said master schema standard and load jobseeker data into a master schema. After that, Abound may normalize said jobseeker data to develop initial user profiles and enrich said initial user profile with third party data to form enriched user profiles. Abound then may perform a complexity reduction process on said enriched user profiles to reduce comparisons of said enriched user profiles, evaluate and weight said enriched user profiles; and match said enriched user profiles to source available jobseekers.