A recommender system for job seeking and recruiting website

In this paper, a hybrid recommender system for job seeking and recruiting websites is presented. The various interaction features designed on the website help the users organize the resources they need as well as express their interest. The hybrid recommender system exploits the job and user profiles and the actions undertaken by users in order to generate personalized recommendations of candidates and jobs. The data collected from the website is modeled using a directed, weighted, and multi-relational graph, and the 3A ranking algorithm is exploited to rank items according to their relevance to the target user. A preliminary evaluation is conducted based on simulated data and production data from a job hunting website in Switzerland.