The widespread use of online recruitment services has led to an information explosion in the job market. As a result, recruiters have to seek intelligent ways for Person-Job Fit, which is the bridge for adapting the right candidates to the right positions. Existing studies on Person-Job Fit usually focus on measuring the matching degree between talent qualification and job requirements mainly based on the manual inspection of human resource experts, which could be easily misguided by the subjective, incomplete, and inefficient nature of human judgment. To that end, in this article, we propose a novel end-to-end T opic-based A bility-aware P erson- J ob F it N eural N etwork (TAPJFNN) framework, which has a goal of reducing the dependence on manual labor and can provide better interpretability about the fitting results. The key idea is to exploit the rich information available in abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, two hierarchical topic-based ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measure the different contribution of each job experience to a specific ability requirement. In addition, we design a refinement strategy for Person-Job Fit prediction based on historical recruitment records. Furthermore, we introduce how to exploit our TAPJFNN framework for enabling two specific applications in talent recruitment: talent sourcing and job recommendation. Particularly, in the application of job recommendation, a novel training mechanism is designed for addressing the challenge of biased negative labels. Finally, extensive experiments on a large-scale real-world dataset clearly validate the effectiveness and interpretability of the TAPJFNN and its variants compared with several baselines.

中文翻译：

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人才招聘中人事匹配的增强型神经网络方法

在线招聘服务的广泛使用导致了就业市场的信息爆炸。因此，招聘人员必须为人事匹配寻找智能方法，这是使合适的候选人适应合适职位的桥梁。现有的Person-Job Fit研究通常侧重于衡量人才素质与工作要求的匹配程度，主要基于人力资源专家的人工检查，容易被人为判断的主观、不完整和低效性所误导。为此，在本文中，我们提出了一种新颖的端到端吨基于视觉的一种能力感知磷人-ĴobF它ñ欧元ñetwork (TAPJFNN) 框架，其目标是减少对体力劳动的依赖，并可以为拟合结果提供更好的可解释性。关键思想是利用丰富的历史工作申请数据中的丰富信息。具体来说，我们提出了一种基于递归神经网络（RNN）的工作需求和求职者体验的词级语义表示。沿着这条线，设计了两种基于分层主题的能力感知注意策略来衡量工作要求对语义表示的不同重要性，以及衡量每个工作经验对特定能力要求的不同贡献。此外，我们设计了基于历史招聘记录的 Person-Job Fit 预测的细化策略。此外，我们介绍了如何利用我们的 TAPJFNN 框架来实现人才招聘中的两个特定应用：人才采购和工作推荐。特别是在工作推荐的应用中，设计了一种新的训练机制来解决有偏见的负面标签的挑战。最后，在大规模真实世界数据集上的广泛实验清楚地验证了 TAPJFNN 及其变体与几个基线相比的有效性和可解释性。