Hyred: Hybrid Job Recommendation System

Nowadays people search job opportunities or candidates mainly online, where several websites for this purpose already do exist (LinkedIn, Guru and oDesk, amongst others). This task is especially difficult because of the large number of items to look for and manual compatibility verification. What we propose in this paper is a Hybrid Job Recommendation System that considers the user model (content-based filtering) and social interactions (collaborative filtering) to improve the quality of its recommendations. Our solution is also able to generate adequate teams for a given job opportunity, based not only on the needed competences but also on the social compatibility between their members.