Competences network based on interaction data for recommendation and evaluation aims

Abstract

Companies nowadays face increased product complexity that involves reviewing and optimizing product development business processes. Similarly, other organizations such as research units, distributed enterprises and multi-located organizations, need to set up multidisciplinary projects. It then becomes more complicated to find the right skills when building teams. One solution that can help companies meet this challenge is a data analysis-based system that automatically identifies and recommends skilled people on user request. To meet this need, we propose a benchmark system, based on activity traces analysis, that would help its users in effectively spotting the right skilled people when needed, as well as providing indicators to assess recommendations in terms of accuracy or relevance. In this paper, a general description and the model of this benchmark system are presented. The dataset used for experimental tests is described and reported results are discussed.