Using Machine Learning for Labour Market Intelligence
| Autor: | Mario Mezzanzanica, Mirko Cesarini, Roberto Boselli, Fabio Mercorio |
|---|---|
| Přispěvatelé: | Boselli, R, Cesarini, M, Mercorio, F, Mezzanzanica, M, Ceci, M, Hollmen, J, Todorovski, L, Vens, C, Džeroski, S |
| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry Market intelligence INF/01 - INFORMATICA Governmental application 02 engineering and technology Machine learning computer.software_genre Competitive advantage ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI Field (computer science) 020204 information systems Taxonomy (general) Text classification 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Web usage Artificial intelligence business computer Decision-making models |
| Zdroj: | Machine Learning and Knowledge Discovery in Databases ISBN: 9783319712727 ECML/PKDD (3) |
| DOI: | 10.1007/978-3-319-71273-4_27 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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