A hybrid recommender system to predict online job offer performance

With the expansion of internet to advertise, the number of potential channels is increasing every day. In the Human Resource domain, recruiters have to choose between hundreds of job search web sites when they post a job offer on the internet. In order to save costs, assessing job board expected performance has become necessary. In this paper, three recommender systems providing job board performance estimation for a given job posting are introduced. This work refers principally to the new item problem, which is still a challenging topic in the literature. The first system (PLS-R) is a content-based approach, while others are hybrid recommendation approaches. Estimation is made on item neighborhood according to a “naive” similarity or a supervised similarity measure. These predictive algorithms are compared through experiments on a real dataset. In this application, supervised similarity-based system overcomes the lacks of other approaches and outperforms them.