Autores
Duchanoy Martínez Carlos Alberto
Moreno Armendáriz Marco Antonio
Calvo Castro Francisco Hiram
Hernández Ramos Víctor Eugenio
Título Career path level estimation and skill qualification feedback from textual descriptions
Tipo Revista
Sub-tipo JCR
Descripción Journal of Intelligent & Fuzzy Systems
Resumen LinkedIn is a social medium oriented to professional career handling and networking. In it, users write a textual profile on their experience, and add skill labels in a free format. Users are able to apply for different jobs, but specific feedback on the appropriateness of their application according to their skills is not provided to them. In this work we particularly focus on applicants of the project management branch from information technologies-although the presented methodology could be extended to any area following the same mechanism. Using the information users provide in their profile, it is possible to establish the corresponding level in a predefined Project Manager career path (PM level). 1500+ experiences and skills from 300 profiles were manually tagged to train and test a model to automatically estimate the PM level. In this proposal we were able to perform such prediction with a precision of 98%. Additionally, the proposed model is able to provide feedback to users by offering a guideline of necessary skills to be learned to fulfill the current PM level, or those needed in order to upgrade to the following PM level. This is achieved through the clustering of skill qualification labels. Results of experiments with several clustering algorithms are provided as part of this work.
Observaciones DOI 10.3233/JIFS-179909
Lugar Amsterdam
País Paises Bajos
No. de páginas 2497-2507
Vol. / Cap. v. 39 no. 2
Inicio 2020-03-01
Fin
ISBN/ISSN