个人-工作匹配：用联合表示学习使合适的人才适应合适的工作
Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning
摘要：
人-职匹配是通过确定工作所需的人才能力，为合适的工作匹配合适的人才的过程。虽然在相关领域已经做出了许多定性的努力，但仍然缺乏定量的方法来衡量人才能力以及工作对人才的要求。为此，在本文中，我们提出了一种新的端到端的数据驱动模型的基础上卷积神经网络（CNN），即人-工作匹配神经网络（PJFNN），匹配人才资格的工作要求。具体地说，PJFNN是一个二分神经网络，它可以有效地从历史工作申请中学习个人-工作适应度的联合表示。特别是，由于设计了一个层次化的表示结构，PJFNN不仅可以估计候选人是否适合工作，但也确定哪些特定的要求项目，在招聘职位是由候选人通过测量相应的潜在表示之间的距离满足。最后，在大规模真实数据集上的大量实验清楚地验证了PJFNN在个人-工作匹配预测方面的性能。此外，我们还提供了有效的数据可视化，以显示PJFNN获得的一些工作和人才基准见解。
英文摘要：
Person-Job Fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have been made in related fields, it still lacks of quantitative ways of measuring talent competencies as well as the job's talent requirements. To this end, in this paper, we propose a novel end-to-end data-driven model based on Convolutional Neural Network (CNN), namely Person-Job Fit Neural Network (PJFNN), for matching a talent qualification to the requirements of a job. To be specific, PJFNN is a bipartite neural network which can effectively learn the joint representation of Person-Job fitness from historical job applications. In particular, due to the design of a hierarchical representation structure, PJFNN can not only estimate whether a candidate fits a job, but also identify which specific requirement items in the job posting are satisfied by the candidate by measuring the distances between corresponding latent representations. Finally, the extensive experiments on a large-scale real-world dataset clearly validate the performance of PJFNN in terms of Person-Job Fit prediction. Also, we provide effective data visualization to show some job and talent benchmark insights obtained by PJFNN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
