A Recommendation System for People Analytics

Nan Wang, Evangelos Katsamakas

Source Title: International Journal of Business Intelligence Research (IJBIR)12(2)

ISSN: 1947-3591|EISSN: 1947-3605|EISBN13: 9781799861812|DOI: 10.4018/IJBIR.20210701.oa4

MLA

Wang, Nan, and Evangelos Katsamakas. "A Recommendation System for People Analytics." IJBIR vol.12, no.2 2021: pp.1-12. http://doi.org/10.4018/IJBIR.20210701.oa4

APA

Wang, N. & Katsamakas, E. (2021). A Recommendation System for People Analytics. International Journal of Business Intelligence Research (IJBIR), 12(2), 1-12. http://doi.org/10.4018/IJBIR.20210701.oa4

Chicago

Wang, Nan, and Evangelos Katsamakas. "A Recommendation System for People Analytics," International Journal of Business Intelligence Research (IJBIR) 12, no.2: 1-12. http://doi.org/10.4018/IJBIR.20210701.oa4

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Abstract

Companies seek to leverage data and people analytics to maximize the business value of their talent. This article proposes a recommendation system for personalized workload assignment in the context of people analytics. The article describes the system, which follows a novel two-level hybrid architecture. We evaluate the system performance in a series of computational experiments and discuss future extensions. Overall, the proposed system could create significant business value as a decision support system that could help managers make better decisions. The article demonstrates how computational and machine learning approaches can complement humans in improving the performance of organizations.