A Content-Based Recommendation Approach Using Semantic User Profile in E-recruitment

摘要

In this paper, we propose a content-based recommendation approach in the domain of e-recruitment to recommend users with job offers that suit the most their profile and learned preferences. In order to present the best offers, we construct a semantic vocabulary of the domain from the job offers corpus and initialize a profile for each user based on his Curriculum Vitae. Our method is enriching the user profiles using triggers and statistical methods following his actions regarding the job offers. The approach we propose presents to the users job offers that are the closest to their learned needs and interests which also can be updated based on his daily actions regarding these offers.