n this paper, we propose a multi-objective learning approach for online recruiting. Online recruiting andonline dating are the most known reciprocal recommendation problems. However, the reciprocal recom-mendation has gained little attention in the literature due to the lack of public datasets consisting ofreciprocal preferences of users in a network. We aim to resolve this shortage in our study. Since the sat-isfaction of both candidates and companies is indispensable for successful hiring as opposed to traditionalrecommenders, online recruiting should respect to expectations of all parties and meet their commoninterests as much as possible. For this purpose, we integrated our multi-objective learning approach intovarious state-of-the-art methods, whose success has been proven on similar prediction problems, and weachieved encouraging results. We named and proposed one of the prominent architectures that we’vetested on the problem as a prototype of our multi-objective learning approach however our approachis applicable to any recommender system employing neural networks as its final decision-maker.