| Titre | Learning Career Progression by Mining Social Media Profiles |
| Type de publication | Conference Paper |
| Année de publication | 2019 |
| Auteurs | Soliman, Z., P. Langlais, and L. Bourg |
| Éditeur | Meurs, M-J., and F. Rudzicz |
| Nom de la conférence | Advances in Artificial Intelligence |
| Éditeur | Springer International Publishing |
| Endroit d'édition | Cham |
| ISBN Number | 978-3-030-18305-9 |
| Résumé | With the popularity of social media, large amounts of data have given us the possibility to learn and build products to optimize certain areas of our existence. In this work, we focus on exploring methods by which we can model the career trajectory of a given candidate, with the help of data mining techniques applied to professional social media data. We first discuss our efforts to normalizing raw data in order to get good enough data for predictive models to be trained. We then report the experiments we conducted. Results show that we can predict job transitions with 67{%} accuracy when looking at the 10 top predictions. |
