Titre: Job Recommandation on Real-World Interaction Data With Heterogeneous Graphs
Abstract: Recruiting changed drastically with the emergence of professional social networks that bring together many people and companies. In this context, we propose a new recommender system based on a recruiting heterogeneous graph. This graph brings together information about a job posting and the personal knowledge graph of the candidates. Our model was tested on a new dataset, and showed that our model outperforms state-of-the-art methods.