Analysis of e-Recruitment Systems and Detecting e-Recruitment Fraud

Reddy, Maxine; Mamatha, T; Balaram, Allam

doi:10.1007/978-981-13-0212-1_43

“…For the purpose of evalauting the latter of the three, we extract text related information from Vidros et al's Fake JobPosting Prediction dataset (Vidros et al, 2017), and from PromptCloud's job dataset (PromptCloud, 2017). Both are high quality datasets listing full descriptions of jobs with large varieties, and versions of both datasets have been used by a plethora of publications (Balachander and Moh, 2018;Kim et al, 2019b;Alghamdi et al, 2019;Mahbub and Pardede, 2018;Reddy et al, 2018). For our purposes, we extract text data from the real job postings in Vidros et al's dataset. We also desire our evaluation set to have exposure to e-commerce applications, medical record information, and fake news articles.…”

Section: Datasetmentioning

confidence: 99%

Conical Classification For Efficient One-Class Topic Determination

Khanna¹

2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute. Dataset Model Accuracy Balanced Accuracy Precision Recall F1 Score Time (s) ECommerce Linear OCSVM 0.900 ± 0.001 0.755 ± 0.003 0.978 ± 0.002 0.512 ± 0.007 0.672 ± 0.006 183.255 ± 3.821 RBF OCSVM 0.899 ± 0.001 0.753 ± 0.004 0.981 ± 0.003 0.508 ± 0.008 0.669 ± 0.007 201.339 ± 3.792 Sigmoid OCSVM 0.899 ± 0.002 0.752 ± 0.005 0.979 ± 0.003 0.508 ± 0.011 0.668 ± 0.009 193.402 ± 7.382 Poly OCSVM 0.885 ± 0.006 0.712 ± 0.002 0.995 ± 0.001 0.424 ± 0.003 0.595 ± 0.003 183.813 ± 2.639 IsolFor 0.199 ± 0.000 0.500 ± 0.000 0.199 ± 0.000 1.000 ± 0.000 0.333 ± 0.000 280.893 ± 12.543 OCRF 0.953 ± 0.000 0.947 ± 0.0176 0.800 ± 0.000 0.898 ± 0.036 0.889 ± 0.000 189.252 ± 244.041 PNB 0.638 ± 0.202 0.762 ± 0.092 0.786 ± 0.203 0.757 ± 0.305 0.687 ± 0.153 111.839 ± 59.581 GloVe CVDD 0.948 ± 0.017 0.906 ± 0.026 0.951 ± 0.015 0.983 ± 0.007 0.967 ± 0.011 188.462 ± 12.773 BERT CVDD 0.951 ± 0.128 0.910 ± 0.013 0.954 ± 0.017 0.987 ± 0.012 0.973 ± 0.028 233.626 ± 23.796 CC 0.988 ± 0.009 0.988 ± 0.007 0.956 ± 0.005 0.988 ± 0.009 0.971 ± 0.002 10.878 ± 0.036 FakeNews Linear OCSVM 0.818 ± 0.039 0.685 ± 0.082 0.819 ± 0.157 0.430 ± 0.232 0.495 ± 0.175 496.206 ± 5.353 RBF OCSVM 0.813 ± 0.033 0.706 ± 0.065 0.772 ± 0.184 0.500 ± 0.229 0.538 ± 0.086 533.529 ± 11.002 Sigmoid OCSVM 0.760 ± 0.046 0.648 ± 0.091 0.757 ± 0.259 0.436 ± 0.347 0.389 ± 0.166 514.913 ± 19.340 Poly OCSVM 0.850 ± 0.014 0.722 ± 0.053 0.846 ± 0.071 0.476 ± 0.128 0.592 ± 0.091 470.485 ± 2.950 IsolFor 0.238 ± 0.000 0.500 ± 0.000 0.238 ± 0.000 1.000 ± 0.000 0.384 ± 0.000 278.308 ± 5.449 OCRF 0.930 ± 0.000 0.955 ± 0.000 0.761 ± 0.000 1.000 ± 0.000 0.864 ± 0.000 197.232 ± 255.352 PNB 0.624 ± 0.223 0.773 ± 0.097 0.900 ± 0.142 0.697 ± 0.283 0.729 ± 0.152 218.296 ± 38.515 GloVe CVDD 0.906 ± 0.003 0.881 ± 0.011 0.938 ± 0.018 0.936 ± 0.022 0.936 ± 0.002 282.293 ± 31.88 BERT CVDD 0.899 ± 0.121 0.878 ± 0.218 0.923 ± 0.342 0.933 ± 0.231 0.927 ± 0.153 322.513 ± 49.659 CC 0.985 ± 0.005 0.985 ± 0.004 0.955 ± 0.002 0.987 ± 0.006 0.969 ± 0.001 13.952 ± 0.026 Jobs Linear OCSVM 0.913 ± 0.001 0.768 ± 0.004 0.971 ± 0.002 0.540 ± 0.009 0.694 ± 0.007 368.263 ± 6.227 RBF OCSVM 0.91...

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“…For the purpose of evalauting the latter of the three, we extract text related information from Vidros et al's Fake JobPosting Prediction dataset (Vidros et al, 2017), and from PromptCloud's job dataset (PromptCloud, 2017). Both are high quality datasets listing full descriptions of jobs with large varieties, and versions of both datasets have been used by a plethora of publications (Balachander and Moh, 2018;Kim et al, 2019b;Alghamdi et al, 2019;Mahbub and Pardede, 2018;Reddy et al, 2018). For our purposes, we extract text data from the real job postings in Vidros et al's dataset.…”

Section: Datasetmentioning

confidence: 99%

Conical Classification For Computationally Efficient One-Class Topic Determination

Khanna¹

2021

Preprint

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As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute.

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“…Even gamification is sometimes employed to entice candidates and speed up the hiring process (Gupta et al, 2016). Currently, human resource management (HRM) systems are widely used by modern firms to manage everyday operations, covering all aspects of human resources, while social media profiles are increasingly being used to screen applicants (Reddy, 2019;Karacsony et al, 2020). Moreover, Hmoud & Laszlo (2019) revealed that due to their time-intensive and laborious nature, the conventional methods of recruiting a candidate are no longer successful.…”

Section: Introductionmentioning

confidence: 99%

Institutional perception of e-recruitment adoption in Nepal: Evidence from structural equation modeling

Dhakal,

Baczar,

Devkota

et al. 2023

Economics &Amp; Sociology

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Awareness of the significance of adapting e-recruitment and applying various management strategies has increased in private corporate organizations. This study aims to investigate the institutional perception of e-recruitment adoption in Kathmandu valley, Nepal by employing the notion of Technology Acceptance Model (TAM) and Self-Disclosure Theory (SDT). Structural Equation Modeling (SEM) and AMOS softwares are used to analyze data gathered from 286 Nepalese private organizations in Kathmandu valley between February 13 and March 10, 2022. The results indicate that private organizations in the Kathmandu valley are wary of completely embracing e-recruitment due to trust and privacy issues. This paper can be seen as an important message to Nepalese Human Resources sector, policymakers, the government and customers. Moreover, it has important theoretical implications explained from the standpoint of TAM and SDT theories. Practically, this is one of the latest attempts to assess and report on the breadth of e-recruitment adoption in Nepal’s private sector.

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Analysis of e-Recruitment Systems and Detecting e-Recruitment Fraud

Cited by 5 publications

References 6 publications

Conical Classification For Efficient One-Class Topic Determination

Conical Classification For Efficient One-Class Topic Determination

Conical Classification For Computationally Efficient One-Class Topic Determination

Institutional perception of e-recruitment adoption in Nepal: Evidence from structural equation modeling

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