Recommendation of Job Offers Using Random Forests and Support Vector Machines
Jorge Martinez-Gil, Bernhard Freudenthaler, Thomas Natschlager
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.