Modelling and Predicting Individual Salaries in United Kingdom with Graph Convolutional Network
Modelling and Predicting Individual Salaries in United Kingdom with Graph Convolutional Network
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DOI：
10.1007/978-3-030-14347-3_7
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Long Chen;Yeran Sun;P. Thakuriah
中科院分区：
文献类型：
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作者：
Long Chen;Yeran Sun;P. Thakuriah
招聘网站，如Indeed和Monster，是专门为帮助用户从市场中获取信息而设计的。然而，目前，只有大约一半的英国工作岗位公开显示工资。因此，本研究的目的是对新工作的薪酬进行建模和预测，以提高求职绩效，帮助广大求职者更好地了解他们理想职位的市场价值。为了有效地估计一个给定的工作的工资，我们构建了一个图形数据库的基础上，每个职位的工作档案，并建立一个预测模型，通过机器学习的基础上的元数据特征和关系特征。我们的研究结果表明，这两种类型的功能是条件独立的，他们都是足够的预测。因此，它们可以作为图卷积网络（GCN）（一种半监督学习框架）中的两个视图来利用，除了标记的数据集之外，还可以利用大量未标记的数据来增强工资分类。初步的实验结果表明，GCN优于现有的简单池这两种类型的功能在一起。
Job Posting Sites, such as Indeed and Monster, are specifically designed to help users obtain information from the market. However, at the moment, only approximately half of the UK job postings have a salary publicly displayed. Therefore, the aim of this research is to model and predict the salary of a new job, so as to improve the performance of job search and help a vast amount of job seekers better understand the market worth of their desirable positions. In order to effectively estimate the salary of a given job, we construct a graph database based on job profiles of each posting and build a predictive model through machine learning based on both metadata features and relational features. Our results reveal that these two types of features are conditionally independent and each of them is sufficient for prediction. Therefore they can be exploited as two views in graph convolutional network (GCN), a semi-supervised learning framework, to make use of a large amount of unlabelled data, in addition to the set of labelled ones, for enhanced salary classification. The preliminary experimental results show that GCN outperforms the existing ones that simply pool these two types of features together.