Semantic Job Title Matching via Vector Embeddings
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Solution Overview
Problem
Current systems fail to accurately determine the semantic relationship between job titles across different companies due to varying terminology and keyword-based approaches that do not account for the actual skills associated with roles.
Innovation Solution
A computer-based system utilizing a title data store, training module, and prediction module to calculate the semantic relationship between job titles by generating job-related vectors and adjusting weights through backpropagation, allowing for comparison of job titles and descriptions across companies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If keyword-based approaches are used to match job titles, then the system is simple to implement, but the accuracy of determining semantic relationships between job titles deteriorates due to varying terminology across companies
Solution Approach 1:
The system transforms job title matching from keyword-based string comparison to vector-based semantic comparison. Job titles are converted into numerical vectors that capture their semantic meaning, allowing the system to measure similarity through mathematical operations on these vectors rather than simple keyword matching. This parameter transformation enables accurate comparison of job titles with varying terminology across different companies.
2Adaptability or versatility
If company-specific job titles are used, then the system reflects actual corporate structure, but the ability to compare job titles across companies deteriorates due to lack of standardization
Solution Approach 1:
The system introduces job descriptions and skill sets as intermediary elements between company-specific job titles and cross-company comparison. These intermediaries serve as a common language that translates diverse corporate titling conventions into a standardized framework, enabling reliable comparison while preserving the uniqueness of each company's job title structure.
Solution Approach 2:
The vector representation system serves multiple functions simultaneously: it preserves company-specific job title nuances, enables cross-company comparison, and adapts to different corporate structures. The same mathematical framework works universally across diverse organizations while capturing their unique job title characteristics.
3Measurement precision
If detailed job descriptions and skill sets are analyzed, then the accuracy of job title relationship determination improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The system replaces complex manual analysis of job descriptions with automated vector-based computational methods. Natural language processing algorithms automatically extract and encode skill sets and job description features into numerical vectors, eliminating the need for manual categorization while achieving high accuracy in determining job title relationships.
Data Source
AI summary
In one example, a computer-based system determines a relationship between a first job and a second job at one or more companies, by using a title data store, a training module, and a prediction module, wherein the title data store accepts job-related information characterizing at least one job-related position that includes at least one of title, corporate entity, job description, and job-related interest data. The training module accepts input data from the title data store, calculates or generates a set of coefficients and a set of job-related vectors from the input data, and stores the coefficients into a database. The prediction module may accept: a first set of data including at least one of a first title, a first corporate designation data, a second set of data including at least one of a second title and a second corporate designation data, and the coefficients from the training module; and then a similarity between the first set of data and the second set of data may be calculated.