Studo jobs: Enriching data with predicted Job labels

Markus Reiter-Haas, Valentin Slawicek, Emanuel Lacic

Research output: Contribution to journalConference articlepeer-review


In this paper, we present the Studo Jobs platform in which we tackle the problem of automatically assigning labels to new job advertisements. For that purpose we perform an exhaustive comparison study of state-of-the-art classifiers to be used for label prediction in the job domain. Our findings suggest that in most cases an SVM based approach using stochastic gradient descent performs best on the textual content of job advertisements in terms of Accuracy, F1-measure and AUC. Consequently, we plan to use the best performing classifier for each label which is relevant to the Studo Jobs platform in order to automatically enrich the job advertisement data. We believe that our work is of interest for both researchers and practitioners in the area of automatic labeling and enriching text-based data.

Original languageEnglish
JournalCEUR Workshop Proceedings
Publication statusPublished - 1 Jan 2017
Event17. International Conference on Knowledge Technologies and Data-Driven Business: i-KNOW 2017 - Messezentrum Graz, Graz, Austria
Duration: 11 Oct 201712 Oct 2017


  • Comparative study
  • Data enrichment
  • Job platform
  • Label prediction

ASJC Scopus subject areas

  • Computer Science(all)


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