Job Position Data Structure Dimensionality Prediction
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Solution Overview
Problem
Position announcements often lack clear indications of desired experience, leading to candidates being presented with positions for which they are under or over qualified, resulting in inefficient searching and resource wastage.
Innovation Solution
The method involves increasing the dimensionality of data structures representing job positions by predicting desired experience using text-based classifiers and experience classifiers, which analyze job postings to identify patterns and relationships, and incorporate predicted experience into searchable fields, allowing for more accurate matching and filtering of job seekers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If position announcements lack clear indication of desired experience, then positions can be posted with minimal specification requirements, but candidates will be presented with positions for which they are under or over qualified
Solution Approach 1:
The system performs preliminary action by automatically predicting desired experience levels and incorporating them into position data structures before candidates search. Text-based classifiers and experience classifiers analyze position information in advance, generate predicted experience requirements, and store them in searchable fields, so that accurate matching information is already prepared when candidates perform their searches.
Solution Approach 2:
The system enables self-service by allowing the position database to automatically enrich itself with predicted experience information without manual intervention. The classifiers autonomously analyze position data, generate predictions, and update data structures, making the system self-improving and reducing the burden on users to provide detailed specifications.
2Measurement precision
If candidates perform multiple searches to find suitable positions, then they can ensure qualified matches, but computing resources and network traffic are wasted
Solution Approach 1:
The system performs preliminary action by pre-computing and storing predicted experience information in position data structures before searches occur. This advance preparation allows the database to quickly return accurate matches on the first search attempt, eliminating the need for candidates to perform multiple iterative searches and thereby conserving computing resources and reducing network traffic.
3Measurement precision
If desired experience is predicted and incorporated into searchable fields, then search accuracy is improved, but data structure complexity increases
Solution Approach 1:
The system applies dimensionality change by adding a new dimension to existing data structures. Instead of fundamentally redesigning the database schema, the patent incorporates predicted experience as an additional searchable field or attribute dimension within the existing position data structure, allowing accurate experience-based filtering while maintaining compatibility with current systems.
Data Source
AI summary
Methods, apparatus, systems, and computer-readable media are provided for increasing dimensionality of data structures associated with filling positions. In some implementations, a prediction of desired experience for a given position to be filled may be incorporated into a searchable field of the data structure. Among other things, increasing the dimensionality of the data structure may facilitate more granular searching of positions and guided creation of new positions to be filled. In some implementations, a predicted desired experience may be used to notify a user posting a new position whether a specified desired experience corresponds to a predicted desired experience.