The Zen of Multidisciplinary Team Recommendation

TL;DR

This paper introduces a framework for building team recommendation systems that balance expertise diversity and social cohesion, demonstrated through a case study on academic teams.

Contribution

It presents a general framework for composing team recommendation subsystems and applies it to a specific case study of academic team formation.

Findings

Framework effectively balances expertise and cohesion metrics

System can personalize team rankings based on user preferences

Case study demonstrates practical applicability in academic contexts

Abstract

In order to accomplish complex tasks, it is often necessary to compose a team consisting of experts with diverse competencies. However, for proper functioning, it is also preferable that a team be socially cohesive. A team recommendation system, which facilitates the search for potential team members can be of great help both for (i) individuals who need to seek out collaborators and (ii) managers who need to build a team for some specific tasks. A decision support system which readily helps summarize such metrics, and possibly rank the teams in a personalized manner according to the end users' preferences, can be a great tool to navigate what would otherwise be an information avalanche. In this work we present a general framework of how to compose such subsystems together to build a composite team recommendation system, and instantiate it for a case study of academic teams.