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http://dx.doi.org/10.21219/jitam.2020.27.6.141

A Design of Content-based Metric Learning Model for HR Matching  

Song, Hee Seok (Department of Global IT Business in Hannam University)
Publication Information
Journal of Information Technology Applications and Management / v.27, no.6, 2020 , pp. 141-151 More about this Journal
Abstract
The job mismatch between job seekers and SMEs is becoming more and more intensifying with the serious difficulties in youth employment. In this study, a bi-directional content-based metric learning model is proposed to recommend suitable jobs for job seekers and suitable job seekers for SMEs, respectively. The proposed model not only enables bi-directional recommendation, but also enables HR matching without relearning for new job seekers and new job offers. As a result of the experiment, the proposed model showed superior performance in terms of precision, recall, and f1 than the existing collaborative filtering model named NCF+GMF. The proposed model is also confirmed that it is an evolutionary model that improves performance as training data increases.
Keywords
Recommender; HR Matching; Content-based Recommendation; Job Recommendation; Metric Learning;
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