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2 Technical Sciences / University of Warmia and Mazury in Olsztyn

2 2023

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

Drozda Paweł, Ropiak Krzysztof, Nowak Bartosz A., Talun Arkadiusz, Osowski Maciej

Technical Sciences / University of Warmia and Mazury in Olsztyn

2023

nr 26(1)

125--140

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.

Goal - oriented conversational bot for employment domain

Drozda Paweł, Żmijewski Tomasz, Osowski Maciej, Krasnodębska Aleksandra, Talun Arkadiusz

Technical Sciences / University of Warmia and Mazury in Olsztyn

2023

nr 26(1)

111--123

This paper focuses of the implementation of the goal – oriented chatbot in order to prepare virtual resumes of candidates for job position. In particular the study was devoted to testing the feasibility of using Deep Q Networks (DQN) to prepare an effective chatbot conversation flow with the final system user. The results of the research confirmed that the use of the DQN model in the training of the conversational system allowed to increase the level of success, measured as the acceptance of the resume by the recruiter and the finalization of the conversation with the bot. The success rate increased from 10% to 64% in experimental environment and from 15% to 45% in production environment. Moreover, DQN model allowed the conversation to be shortened by an average of 4 questions from 11 to 7.