Support to the human talent subsystems, selection and recruitment from an expert system. Case study

Main Article Content

Karina Matute-Pinos
Rodolfo Bojorque-Chasi


Human talent management is a key factor in the success of organizations. The inclusion of people with disabilities in the work environment has helped to enhance their qualities and harness their talent. Many of the human talent management systems lack guidelines for the recruitment and selection of a person with a disability, this is why this work shows the study carried out on these two processes indicating the factors that influence the allocation or not of a position, where for each candidate it is considered the level and type of disability, level of education, experience, training among other aspects, focusing on the task of applying supervised learning techniques that enable us to classify a candidate with a disability as suitable or not for a job, and unsupervised learning techniques such as clustering that helps us define hidden patterns in the data, if any. The result obtained from the study presents some classifications techniques and the selection of the most appropriate one for the available dataset. It is not sought to integrate people through their disabilities, but quite the opposite, to integrate people based on the potential of all their abilities.
Abstract 277 | PDF (Español (España)) Downloads 76 PDF Downloads 48 EPUB (Español (España)) Downloads 2


[1] Consejo Nacional de Planificación, Plan Nacional de Desarrollo 2017-2021-Toda una Vida. Secretaría Nacional de Planificación y Desarrollo, Senplades. Quito – Ecuador, 2017. [Online]. Available:
[2] Asamblea Nacional, Ley Orgánica de Discapacidades. Registro Oficial N. 796. República del Ecuador, 2012. [Online]. Available:
[3] H. Jantan, A. Hamdan, and Z. Othman, “Human talent prediction in HRM using C4.5 classification algorithm,” International Journal on Computer Science and Engineering, vol. 2, pp. 2526–2534, 2010. [Online]. Available:
[4] L. Morton, “Talent management value imperatives: Strategies for execution,” in Conference Board, 2005. [Online]. Available:
[5] Ministerio de Relaciones Laborales, Manual de Buenas Prácticas para la inclusión laboral de personas con discapacidad. Dirección de Atención a Grupos Prioritarios. Consejo Nacional de Discapacidades. Ecuador, 2013. [Online]. Available:
[6] Consejo Nacional para la Igualdad de Discapacidades. (2020) Estadísticas de discapacidad. [Online]. Available:
[7] E. Kalugina and S. Shvydun, “An effective personnel selection model,” Procedia Computer Science, vol. 31, pp. 1102–1106, 2014, 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014. [Online]. Available:
[8] C.-F. Chien and L.-F. Chen, “Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry,” Expert Systems with Applications, vol. 34, no. 1, pp. 280–290, 2008. [Online]. Available:
[9] G. Harih and N. Vujica-Herzog, “Towards an expert system for assigning optimal workplaces to workers with disabilities,” in Advances in Social and Occupational Ergonomics, R. H. Goossens and A. Murata, Eds. Cham: Springer International Publishing, 2020, pp. 57–66. [Online]. Available:
[10] P. van Esch, J. S. Black, and J. Ferolie, “Marketing AI recruitment: The next phase in job application and selection,” Computers in Human Behavior, vol. 90, pp. 215–222, 2019. [Online]. Available:
[11] R. Storey Hooper, T. P. Galvin, R. A. Kilmer, and J. Liebowitz, “Use of an expert system in a personnel selection process,” Expert Systems with Applications, vol. 14, no. 4, pp. 425–432, 1998. [Online]. Available:
[12] M. Nussbaum, M. Singer, R. Rosas, M. Castillo, E. Flies, R. Lara, and R. Sommers, “Decision support system for conflict diagnosis in personnel selection,” Information & Management, vol. 36, no. 1, pp. 55–62, 1999. [Online]. Available:
[13] S. M. C. Loureiro, J. Guerreiro, and I. Tussyadiah, “Artificial intelligence in business: State of the art and future research agenda,” Journal of Business Research, vol. 129, pp. 911–926, 2021. [Online]. Available:
[14] H. J. Wilson and P. R. Daugherty, “Collaborative intelligence: Humans and AI are joining forces,” Harvard Business Review, 2018. [Online]. Available:
[15] S. T. Hunter, N. D. Shortland, M. P. Crayne, and G. S. Ligon, “Recruitment and selection in violent extremist organizations: Exploring what industrial and organizational psychology might contribute.” The American psychologist, vol. 72, pp. 242–254, Apr 2017. [Online]. Available:
[16] A. Eckhardt, S. Laumer, C. Maier, and T. Weitzel, “The transformation of people, processes, and it in e-recruiting,” Employee Relations, vol. 36, no. 4, pp. 415–431, Apr. 2021. [Online]. Available:
[17] N. Herbst, S. Becker, S. Kounev, H. Koziolek, M. Maggio, A. Milenkoski, and E. Smirni, Metrics and Benchmarks for Self-aware Computing Systems. Cham: Springer International Publishing, 2017, pp. 437–464. [Online]. Available:
[18] M. J. A. Berry and G. S. Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. John Wiley & Sons, 2004. [Online]. Available:
[19] P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to data mining. Pearson Education, 2013. [Online]. Available:
[20] J. Hill, W. Randolph Ford, and I. G. Farreras, “Real conversations with artificial intelligence: A comparison between human-human online conversations and human-chatbot conversations,” Computers in Human Behavior, vol. 49, pp. 245–250, 2015. [Online]. Available:
[21] A. M. Rahman, A. A. Mamun, and A. Islam, “Programming challenges of chatbot: Current and future prospective,” in 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 2017, pp. 75–78. [Online]. Available:
[22] M. Rodas-Tobar, C. Bernal-Bravo, M. Andrés-Romero, A. Pinos-Figueroa, P. Vidal-Mogrovejo, A. León-Pesántez, V. Robles-Bykbaev, and F. Pesántez-Avilés, “An expert system to support the provisioning of staff with disabilities in industry,” in 2018 IEEE Biennial Congress of Argentina (ARGENCON), 2018, pp. 1–6. [Online]. Available:
[23] R. X. Bojorque Chasi, “Clustering de sistemas de recomendación mediante técnicas de factorization matricial,” Ph.D. dissertation, 2020. [Online]. Available:
[24] J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967. [Online]. Available:
[25] S. Zahra, M. A. Ghazanfar, A. Khalid, M. A. Azam, U. Naeem, and A. Prugel-Bennett, “Novel centroid selection approaches for kmeans-clustering based recommender systems,” Information Sciences, vol. 320, pp. 156–189, 2015. [Online]. Available:
[26] M. A. Syakur, B. K. Khotimah, E. M. S. Rochman, and B. D. Satoto, “Integration k-means clustering method and elbow method for identification of the best customer profile cluster,” in IOP Conference Series: Materials Science and Engineering, vol. 336, 2017. [Online]. Available:
[27] T. Boman, A. Kjellberg, B. Danermark, and E. Boman, “Employment opportunities for persons with different types of disability,” Alter, vol. 9, no. 2, pp. 116–129, 2015. [Online]. Available:
[28] M. A. Espinoza Mina and D. Gallegos Barzola, “Inserción laboral de las personas con discapacidad en Ecuador,” Espacios, vol. 39, no. 51, 2018. [Online]. Available:
[29] M. Reynolds, “Ai coach helps chatbots seem more human,” New Scientist, vol. 235, no. 3135, p. 14, 2017. [Online]. Available: