Applying different classification techniques in reciprocal job recommender system for considering job candidate preferences

In this paper, a reciprocal job recommendation system, CCRS (Classification-Candidate Reciprocal Recommendation), is proposed. With this proposed system, offering job advertisements in a sequence for candidates that they can get feedback reciprocally by using the user's profile, interaction and preference information is aimed all together. An approach has been used based on the preference information of the candidates to determine the jobs' order in the proposed list and the success of different classification methods has been compared to estimate the feedback rate of the advertisements for the target candidate. CCRS also addresses the cold start problem of new candidates joining the site by providing recommendations based on their profiles. The performance of the proposed method was evaluated by using various performance measurements on an actual data set received from an online recruiting website. Evaluation results show that the proposed method outperforms the compared methods for the top 10 ranked recommendations.