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2017 | OriginalPaper | Chapter

Using Machine Learning for Labour Market Intelligence

Authors : Roberto Boselli, Mirko Cesarini, Fabio Mercorio, Mario Mezzanzanica

Published in: Machine Learning and Knowledge Discovery in Databases

Publisher: Springer International Publishing

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Abstract

The rapid growth of Web usage for advertising job positions provides a great opportunity for real-time labour market monitoring. This is the aim of Labour Market Intelligence (LMI), a field that is becoming increasingly relevant to EU Labour Market policies design and evaluation. The analysis of Web job vacancies, indeed, represents a competitive advantage to labour market stakeholders with respect to classical survey-based analyses, as it allows for reducing the time-to-market of the analysis by moving towards a fact-based decision making model. In this paper, we present our approach for automatically classifying million Web job vacancies on a standard taxonomy of occupations. We show how this problem has been expressed in terms of text classification via machine learning. Then, we provide details about the classification pipelines we evaluated and implemented, along with the outcomes of the validation activities. Finally, we discuss how machine learning contributed to the LMI needs of the European Organisation that supported the project.

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Title: Using Machine Learning for Labour Market Intelligence
Authors: Roberto Boselli
Mirko Cesarini
Fabio Mercorio
Mario Mezzanzanica
Publisher: Springer International Publishing
Book: Machine Learning and Knowledge Discovery in Databases
Print ISBN: 978-3-319-71272-7

Electronic ISBN: 978-3-319-71273-4

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-71273-4_27

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