Using Machine Learning for Labour Market Intelligence
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.
Cite
Text
Boselli et al. "Using Machine Learning for Labour Market Intelligence." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71273-4_27Markdown
[Boselli et al. "Using Machine Learning for Labour Market Intelligence." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/boselli2017ecmlpkdd-using/) doi:10.1007/978-3-319-71273-4_27BibTeX
@inproceedings{boselli2017ecmlpkdd-using,
title = {{Using Machine Learning for Labour Market Intelligence}},
author = {Boselli, Roberto and Cesarini, Mirko and Mercorio, Fabio and Mezzanzanica, Mario},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {330-342},
doi = {10.1007/978-3-319-71273-4_27},
url = {https://mlanthology.org/ecmlpkdd/2017/boselli2017ecmlpkdd-using/}
}