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.

在本文中，我们提出了一种用于在线招聘的多目标学习方法。在线招聘和在线约会是最著名的互惠推荐问题。然而，由于缺乏由网络中用户的互惠偏好组成的公共数据集，互惠推荐在文献中很少受到关注。我们的目标是在我们的研究中解决这个短缺问题。与传统推荐人相比，招聘成功离不开候选人和公司双方的满意，因此在线招聘应尊重各方的期望，尽可能满足他们的共同利益。为此，我们将我们的多目标学习方法集成到各种最先进的方法中，其成功已在类似的预测问题上得到证明，我们取得了令人鼓舞的成果。我们命名并提出了我们在该问题上测试过的突出架构之一，作为我们多目标学习方法的原型，但是我们的方法适用于任何采用神经网络作为其最终决策者的推荐系统。