A Matching Approach Based on Term Clusters for eRecruitment

Bal, Gülşen; Karakas, Askin; Güngör, Tunga; Süzen, Fatmagül; Kara, Kemal Can

doi:10.1007/978-3-319-41561-1_29

Cited by 1 publication

(1 citation statement)

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“…Specifically, the goal of the study was to determine the similarity level between a job advertisement and a developer's profile in the related platform by combining many natural language processing techniques. Bal et al [47] also proposed a matching system for eRecruitment that extracts terms from job advertisements, creates a lexicon of terms, and then uses cosine similarity to match job advertisements and resumes of the candidates. In another related study, Paparrizos et al [48] used machine learning techniques to address the job recommendation problem.…”

Section: Use Of Hr Data With Machine Learningmentioning

confidence: 99%

Human Resources Mining for Examination of R&D Progress and Requirements

Ozcan

Şakar

Suloglu

2021

IEEE Trans. Eng. Manage.

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The amount of job advertisement data is rapidly growing, and this rich dataset is expected to have implications for the employment market, sector trajectories and the education sector. Most significantly, human resources (HR) data has never previously been examined with the lens of tech mining for science and technology analyses. Our study is the first to examine job advertisement data considering research and development (R&D) progress and requirements, and hereafter we refer to this as HR mining. The aim of this study is to use HR mining with the purpose of R&D and human capital intelligence using the job advertisement data of Turkey for the 2015-2017 period. The method of this study follows classification as part of the pre-processing step to determine R&D, engineering and high-tech industry-related job advertisements. Afterwards, we use clustering methods to identify areas where key human capital is required, and investments are made by R&D-oriented companies. The results show that it is possible to identify sector-oriented skill requirements and that the significance of the R&D skills varies. For the case of Turkey, we can clearly show the national human capital and R&D by identifying nine key clusters that indicate R&D progress and directions.

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Section: Use Of Hr Data With Machine Learningmentioning

confidence: 99%