A Systematic Literature Review of Machine Learning Applications for Team Formation Problems

Abstract

With the development of technology, the variety and number of data held for any process has increased exponentially. By processing and analyzing this data, it is possible to solve many problems. Selection of the most appropriate team member and correct team formation in the activities carried out by the team are the factors that affect the success and result of teamwork. For this reason, the problem of team member selection and team formation has become one of the increasing research topics in recent years. Researchers from different disciplines are trying to develop tools, techniques and methodologies to ensure a successful team building process. Machine Learning (ML) methods have become one of the methods that have started to be used in team formation and team member selection problems in recent years. The successful outcome of this problem depends on the correct collection and processing of data and the selection of appropriate machine learning methods. The aim of this article is to present a systematic literature review of machine learning methods applied in team formation and team member selection problems, and to show which machine learning methods are applied in this field and their performance. Articles on the subject were searched in six scientific databases. In addition to providing fundamental information about ML methods, this review also supports new research efforts on team formation problems.