Recommendation of job offers using random forests and support vector machines

J. Martinez-Gil, B. Freudenthaler. Recommendation of job offers using random forests and support vector machines. volume 2083, pages 22-27, 3, 2018.

Autoren
  • Jorge Martinez-Gil
  • Bernhard Freudenthaler
BuchProceedings of the Workshops of the EDBT/ICDT 2018 Joint Conference (EDBT/ICDT 2018)
TypIn Konferenzband
VerlagEUR-WS.org
SerieCEUR Workshop Proceedings
Band2083
ISSN1613-0073
Monat3
Jahr2018
Seiten22-27
Abstract

The challenge of automatically recommending job offers to appropriate job seekers is a topic that has attracted many research effort during the last times. However, it is generally assumed that there is a need of more user-friendly filtering methods so that the automated recommendation systems might be more widely used. We present here two methods from the data analytics field being able to disseminate job offers to the right person at the right time, which are based on Random Forest and Support Vector Machines respectively. Both methods try to identify the actual attributes in which users are set when they are attracted to a job offer. Preliminary results in the context of automatic job recommendation suggest that these two methods seem to be promising.