MACHINE LEARNING APPLICATIONS TO IMPROVE ONLINE JOB LISTINGS

MACHINE LEARNING APPLICATIONS TO IMPROVE ONLINE JOB LISTINGS

A system is designed to crawl known job listings web pages and extract the job listing URLs. A machine learning model is trained to recognize job listings and extract relevant information for the job listings. The model can separate multiple job listings on a single page. The machine learning model...

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Bibliographische Detailangaben
Hauptverfasser: KULKARNI, Parshu, KALLEPALLI, Bhanu Kishore, KUMAR, Jainendra, JANAPAREDDY, Venkata, KANNAM, Venkata Rao
Format: Patent
Sprache:eng ; fre ; ger
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Zusammenfassung:A system is designed to crawl known job listings web pages and extract the job listing URLs. A machine learning model is trained to recognize job listings and extract relevant information for the job listings. The model can separate multiple job listings on a single page. The machine learning model can further predict the likelihood of new jobs being added or existing job postings expiring. By using the prediction, the system can subsequently verify that a job expected to expire has expired and remove the same from the results. Similarly, the system can crawl websites with a high likelihood of new job postings without having to crawl the entire internet to maintain an up to date job listing repository.