In this paper, we propose a multi-objective learning approach for online recruiting. Online recruiting and online dating are the most known reciprocal recommendation problems. However, the reciprocal recommendation has gained little attention in the literature due to the lack of public datasets consisting of reciprocal preferences of users in a network. We aim to resolve this shortage in our study. Since the satisfaction of both candidates and companies is indispensable for successful hiring as opposed to traditional recommenders, online recruiting should respect to expectations of all parties and meet their common interests as much as possible. For this purpose, we integrated our multi-objective learning approach into various state-of-the-art methods, whose success has been proven on similar prediction problems, and we achieved encouraging results. We named and proposed one of the prominent architectures that we've tested on the problem as a prototype of our multi-objective learning approach however our approach is applicable to any recommender system employing neural networks as its final decision-maker. (C) 2021 Karabuk University. Publishing services by Elsevier B.V.