Improving the credibility of the extracted position from a vast collection of job offers with machine learning ensemble methods

Paweł Drozda

UWM

Krzysztof Ropiak

University of Warmia and Mazury, Olsztyn

Bartosz A. Nowak

University of Warmia and Mazury, Olsztyn

Arkadiusz Talun

Emplocity S.A.

Maciej Osowski

Emplocity S.A.


Abstract

The main aim of this paper is to evaluate crawlers collecting the job offers from websites. In particular the research is focused on checking the effectiveness of ensemble machine learning methods for the validity of extracted position from the job ads. Moreover, in order to significantly reduce the training time of the algorithms (Random Forests and XGBoost), granularity methods were also tested to significantly reduce the input training dataset. Both methods achieved satisfactory results in accuracy and F1 measures, which exceeded 96%. In addition, granulation reduced the input dataset by more than 99%, and the results obtained were only slightly worse (accuracy between 1% and 5%, F1 between 3% and 8%). Thus, it can be concluded that the considered methods can be used in the evaluation of job web crawlers.


Keywords:

machine learning, web scraping, granularity methods, classification


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Published
2023-09-19

Cited by

Drozda, P., Ropiak, K., Nowak, B., Talun, A., & Osowski, M. (2023). Improving the credibility of the extracted position from a vast collection of job offers with machine learning ensemble methods. Technical Sciences, 26(26), 125–140. https://doi.org/10.31648/ts.9319

Paweł Drozda 
UWM
Krzysztof Ropiak 
University of Warmia and Mazury, Olsztyn
Bartosz A. Nowak 
University of Warmia and Mazury, Olsztyn
Arkadiusz Talun 
Emplocity S.A.
Maciej Osowski 
Emplocity S.A.



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