A Recommendation System for People Analytics

Evangelos Katsamakas, Nan Wang

OAI: oai:igi-global.com:280311 • DOI: 10.4018/IJBIR.20210701.oa4

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.

Business Intelligence Business and Management Business Information Systems