A hybrid recommender system to predict online job offer performance

Special issue: HSDA 2013, Advances in Theory and Applications of High Dimensional and Symbolic Data AnalysisInternational audienceWith the expansion of internet to advertise, the number of potentialchannels is increasing every day. In the Human Resource domain, recruiters haveto choose between hundreds of job search web sites when they post a job offeron the internet. In order to save costs, assessing job board expected performancehas become necessary. In this paper, three recommender systems providing jobboard performance estimation for a given job posting are introduced. This workrefers principally to the new item problem, which is still a challenging topic inthe literature. The first system (PLS-R) is a content-based approach, while othersare hybrid recommendation approaches. Estimation is made on item neighborhoodaccording to a ?naive? similarity or a supervised similarity measure. Thesepredictive algorithms are compared through experiments on a real dataset. In thisapplication, supervised similarity-based system overcomes the lacks of other approachesand outperforms them