Job Aggregator Occupation Normalization via ML Classifier
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Solution Overview
Problem
Job aggregators face challenges in collecting, categorizing, and analyzing job listings due to varying job title terminologies across different sources, which hinders indexing, searching, and statistical analysis.
Innovation Solution
A system that generates normalized occupations by translating job titles into standardized terms using a machine learning-based classifier, which maps job title components to a condensed set of normalized occupations based on user search behavior and external data sources like the Bureau of Labor Statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If job titles are collected from multiple different sources, then the quantity and diversity of job listings increases, but the consistency and accuracy of job categorization deteriorates due to varying terminologies
Solution Approach 1:
The patent introduces an occupation normalization engine as an intermediary component that receives job titles from multiple sources and transforms them into standardized occupations. This mediator layer decouples the diversity of input terminologies from the need for consistent categorization, allowing the system to maintain high quantity of job listings while achieving accurate categorization through the normalization process.
Solution Approach 2:
The system changes the parameter of job title representation by transforming varied job title strings into a standardized occupation parameter. The normalization engine analyzes the semantic content of job titles and maps them to a controlled vocabulary of standardized occupations, effectively changing the parameter space from unstructured text to structured categorical data that enables precise categorization.
2Device complexity
If job titles are standardized using traditional methods, then the complexity of the system decreases, but the accuracy of matching similar jobs deteriorates due to inability to handle semantic variations
Solution Approach 1:
The patent replaces traditional mechanical string-matching methods with a machine learning-based normalization engine. Instead of using simple keyword matching or rigid classification rules, the system employs trained classifiers that can understand semantic relationships between different job titles, significantly improving matching accuracy while managing complexity through automated learning processes.
Solution Approach 2:
The normalization engine is trained on historical job title data and user interaction patterns, allowing it to self-improve and adapt to new terminologies automatically. The system learns from accumulated data about how users search for and interact with job listings, continuously refining its normalization accuracy without requiring manual reconfiguration of classification rules.
3Measurement precision
If normalized occupations are generated using machine learning classifiers, then the accuracy of job categorization improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-training the machine learning classifiers on extensive job title datasets before deployment. The normalization engine is pre-loaded with learned relationships between job titles and occupations, allowing it to perform rapid lookups and predictions during actual operation. This preliminary training phase shifts computational burden from runtime to setup time, improving real-time performance.
Solution Approach 2:
The system creates a simplified copy or representation of job titles in the form of standardized occupations that capture the essential semantic meaning. Instead of processing the full complexity of original job titles during matching operations, the system works with normalized occupation codes and standardized terms, reducing computational requirements while preserving categorization accuracy.
4Loss of information
If detailed job title information is maintained, then the loss of information is minimized, but the difficulty of indexing and searching increases due to terminology variations
Solution Approach 1:
The patent segments job title information into multiple layers: the original detailed job title is preserved, while simultaneously extracting and storing normalized occupation components. This segmentation allows the system to maintain the rich information content of original job titles while creating separate indexed structures based on normalized occupations, enabling efficient searching without losing detailed information.
Solution Approach 2:
The normalized occupation serves as a universal identifier that can be used for multiple functions: indexing, searching, categorization, and statistical analysis. This multi-functional standardized representation allows the system to efficiently perform various operations without being hindered by terminology variations in the original job titles, while the original titles remain available for detailed information needs.
Data Source
AI summary
A method and apparatus for generating normalized occupations for job titles at a job aggregation system is described. The method may include receiving a job title having a plurality of words that make up the job title, the job title received as part of a request of a job aggregation system to perform a service. The method may also include translating the plurality of words into standardized terms of the job aggregation system to generate a translated job title. Furthermore, the method may include mapping the translated job title to one of a plurality of normalized occupations of the job aggregation system by a machine learning based classifier of the translated job title, wherein the machine learning based classifier is trained based on user search behavior of users that have searched for jobs at the job aggregation system.