利用多视图协同教学网络从稀疏交互数据学习工作与简历匹配
Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network
摘要：
随着在线招聘数据的不断增长，职位-简历匹配已经成为一个重要的任务，自动匹配职位与合适的简历。这个任务通常被视为一个有监督的文本匹配问题。当标记数据足够时，监督学习是强大的。然而，在在线招聘平台上，工作-简历交互数据是稀疏和噪声，这影响了工作-简历匹配算法的性能。为了缓解这些问题，在本文中，我们提出了一种新的多视图协同教学网络从稀疏的交互数据的工作简历匹配。我们的网络由两个主要组成部分，即基于文本的匹配模型和基于关系的匹配模型。这两个部分分别从两个不同的角度来把握语义的兼容性，并相互补充。为了解决稀疏和噪声数据的挑战，我们设计了两个具体的策略来结合这两个组件。首先，两个组件共享所学习的参数或表示，使得可以增强每个组件的原始表示。更重要的是，我们采用了一种协同教学机制来减少训练数据中噪声的影响。其核心思想是通过选择更可靠的训练实例，让两个组件相互帮助。这两种策略分别侧重于表示增强和数据增强。与纯基于文本的匹配模型相比，该方法能够从有限甚至稀疏的交互数据中学习更好的数据表示，更能抵抗训练数据中的噪声。实验结果表明，我们的模型是能够优于国家的最先进的方法进行工作简历匹配。
英文摘要：
With the ever-increasing growth of online recruitment data, job-resume matching has become an important task to automatically match jobs with suitable resumes. This task is typically casted as a supervised text matching problem. Supervised learning is powerful when the labeled data is sufficient. However, on online recruitment platforms, job-resume interaction data is sparse and noisy, which affects the performance of job-resume match algorithms. To alleviate these problems, in this paper, we propose a novel multi-view co-teaching network from sparse interaction data for job-resume matching. Our network consists of two major components, namely text-based matching model and relation-based matching model. The two parts capture semantic compatibility in two different views, and complement each other. In order to address the challenges from sparse and noisy data, we design two specific strategies to combine the two components. First, two components share the learned parameters or representations, so that the original representations of each component can be enhanced. More importantly, we adopt a co-teaching mechanism to reduce the influence of noise in training data. The core idea is to let the two components help each other by selecting more reliable training instances. The two strategies focus on representation enhancement and data enhancement, respectively. Compared with pure text-based matching models, the proposed approach is able to learn better data representations from limited or even sparse interaction data, which is more resistible to noise in training data. Experiment results have demonstrated that our model is able to outperform state-of-the-art methods for job-resume matching.
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。
