Job Posting Structure Classification via Machine Learning
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Solution Overview
Problem
Job postings lack a standardized structure, leading to ill-organized content, making it difficult for job seekers to find relevant information, and existing platforms do not cater to individual preferences, resulting in a less user-friendly job hunting experience.
Innovation Solution
A computer-implemented method and system that classify job posting portions into informative sections using a machine-learned classifier model, enhancing structure and presentation by adding headers, coloring, and reordering sections based on user preferences, and providing personalized job posting snippets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If job postings are displayed as large chunks of plain text without section breakdown, then the posting maintains its original format and structure, but job seekers spend significant time reading to find useful information
Solution Approach 1:
The patent segments job postings into distinct sections (requirements, responsibilities, benefits, company info) using machine learning classification. This divides the large chunk of plain text into organized, labeled portions that job seekers can quickly scan and navigate, directly reducing the time needed to find relevant information while improving overall readability.
2Ease of operation
If job posting information is categorized into sections, then information organization improves, but information may be miscategorized or disjointed due to lack of creator skill
Solution Approach 1:
The patent introduces a machine learning classifier as an intermediary between the job posting creator and the final structured output. This intermediary automatically analyzes the text content and assigns appropriate section labels, eliminating the need for manual categorization by the creator and ensuring consistent, accurate classification regardless of the creator's skill level.
Solution Approach 2:
The patent replaces the manual mechanical process of section categorization (performed by human creators) with an automated computational system. The machine learning model processes the text and determines section assignments algorithmically, substituting human judgment with a scalable, consistent automated classification mechanism.
3Adaptability or versatility
If all job postings are displayed in the same format to all job seekers, then the platform maintains simplicity and consistency, but it does not accommodate individual job seeker preferences
Solution Approach 1:
The patent implements dynamic presentation of job postings by allowing the system to adapt the displayed format and emphasized sections based on individual user preferences. The same job posting can be presented differently to different users or to the same user over time, with the system dynamically adjusting which sections are highlighted or prioritized based on user profile data and preferences.
Data Source
AI summary
The present disclosure provides systems and methods that improve job posting structure and presentation by, for example, classifying portions of job postings into informative sections. As an example, given a job posting, a computing system implementing aspects of the present disclosure can separate the job posting into multiple portions. After separation into portions, the computing system can classify each portion into the most plausible job-posting-specific section. For example, the computing system can include and implement a machine-learned classification model to classify the portions into the sections. Following classification, the computing system can modify the job posting based on the classification of the portions. In particular, the structure and/or presentation of the job posting can be improved based on the classification of the portions into the sections.