Title
Vacancy Improver: A Demo towards skill-based debiased Vacancies
Author
de Boer, M.H.T.
Boertjes, E.
Wilmer, M.
Vethman, S.
Adhikari, A.
Tang, J.
Publication year
2021
Abstract
Many vacancy texts do not reach their full potential; vacancies are too generic, too specific, or biased. In this demo paper, we propose a research prototype that helps users to create a better vacancy text using AI techniques in the domain of Labor Market. The proposed vacancy text from the user is analysed using an function classifier, skill extractor, bias detector and skill overlap algorithm. The Competent database consisting of functions, descriptions and skills as well as an annotated set of Dutch vacancy texts are fed to the AI techniques. In a small user evaluation, we show that the prototype has potential to help users in their need to create better vacancy texts. In future work, we aim to test the tool with more participants and improve the different functionalities.
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http://resolver.tudelft.nl/uuid:2edbd8e0-f440-4958-b05e-226c15e9ac4a
TNO identifier
981071
Source
UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and PersonalizationJune 2021
Document type
conference paper