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dc.contributor.authorLuo, Yongen_US
dc.contributor.authorZhang, Huaizhengen_US
dc.contributor.authorWang, Yongjieen_US
dc.contributor.authorWen, Yonggangen_US
dc.contributor.authorZhang, Xinwenen_US
dc.identifier.citationLuo, Y., Zhang, H., Wang, Y., Wen, Y., & Zhang, X. (2018). ResumeNet : a learning-based framework for automatic resume quality assessment. Proceedings of 2018 IEEE International Conference on Data Mining (ICDM), 307-316. doi:10.1109/icdm.2018.00046en_US
dc.description.abstractRecruitment of appropriate people for certain positions is critical for any companies or organizations. Manually screening to select appropriate candidates from large amounts of resumes can be exhausted and time-consuming. However, there is no public tool that can be directly used for automatic resume quality assessment (RQA). This motivates us to develop a method for automatic RQA. Since there is also no public dataset for model training and evaluation, we build a dataset for RQA by collecting around 10K resumes, which are provided by a private resume management company. By investigating the dataset, we identify some factors or features that could be useful to discriminate good resumes from bad ones, e.g., the consistency between different parts of a resume. Then a neural-network model is designed to predict the quality of each resume, where some text processing techniques are incorporated. To deal with the label deficiency issue in the dataset, we propose several variants of the model by either utilizing the pair/triplet-based loss, or introducing some semi-supervised learning technique to make use of the abundant unlabeled data. Both the presented baseline model and its variants are general and easy to implement. Various popular criteria including the receiver operating characteristic (ROC) curve, F-measure and ranking-based average precision (AP) are adopted for model evaluation. We compare the different variants with our baseline model. Since there is no public algorithm for RQA, we further compare our results with those obtained from a website that can score a resume. Experimental results in terms of different criteria demonstrate effectiveness of the proposed method. We foresee that our approach would transform the way of future human resources management.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleResumeNet : a learning-based framework for automatic resume quality assessmenten_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conference2018 IEEE International Conference on Data Mining (ICDM)en_US
dc.description.versionAccepted versionen_US
dc.subject.keywordsResume Quality Assessmenten_US
dc.subject.keywordsDataset and Featuresen_US
dc.citation.conferencelocationSingapore, Singaporeen_US
dc.description.acknowledgementThis work is supported by Singapore NRF2015ENCGDCR01001-003, administrated via IMDA and NRF2015ENCGBICRD001-012, administrated via BCA.en_US
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