Abstract
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.
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Chen, L., Sun, Y., Thakuriah, P. (2020). Modelling and Predicting Individual Salaries in United Kingdom with Graph Convolutional Network. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_7
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DOI: https://doi.org/10.1007/978-3-030-14347-3_7
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