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A Job-Seeking Advisor Bot Based in Data Mining

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Computer Aided Systems Theory – EUROCAST 2019 (EUROCAST 2019)

Abstract

Promentor is a solution that advises job seekers how to effectively improve their chances of getting a job in a certain area of interest by focusing on what, at least historically, seems to work best. To this end, Promentor first analyzes previous selection processes, trying to quantitatively evaluate the effective value of the characteristics that the candidates put into play in the selection. With this evaluation, Promentor can estimate the value of a profile of the job seeker who requests advice based on the characteristics that make it up. Promentor then makes a simulation by applying each of the suggestions on the job seeker’s profile exhaustively and evaluating the modified profile. In this way, Promentor identifies which suggestions offer the greatest increase in qualification, and are therefore more recommendable.

Promentor is a module of the employment web portal GetaJob.es, which has been developed in parallel and equipped with specific capabilities for collecting the data required by Promentor.

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References

  1. Sahu, H.B., Sharma, S., Gondhalakar, S.: A Brief Overview on Data Mining Survey (2011)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB 1994, pp. 487–499 (1994)

    Google Scholar 

  3. Al-Otaibi, S.T., Ykhlef, M.: A survey of job recommender systems. Int. J. Phys. Sci. 7(29), 5127–5142 (2012)

    Article  Google Scholar 

  4. Aken, A., Litecky, C., Ahmad, A., Nelson, J.: Mining for computing jobs. IEEE Softw. 27(1), 78–85 (2010)

    Article  Google Scholar 

  5. Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8(1), 53–87 (2004). https://doi.org/10.1023/B:DAMI.0000005258.31418.83

    Article  MathSciNet  Google Scholar 

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Correspondence to A. Quesada-Arencibia .

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Rodríguez-Rodríguez, J.C., de Blasio, G.S., García, C.R., Quesada-Arencibia, A. (2020). A Job-Seeking Advisor Bot Based in Data Mining. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12013. Springer, Cham. https://doi.org/10.1007/978-3-030-45093-9_10

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  • DOI: https://doi.org/10.1007/978-3-030-45093-9_10

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  • Publisher Name: Springer, Cham

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