arrow
返回

A team-formation algorithm for faultline minimization

delete2019-04-01
delete10
delete
OA
AI
T
Theodoros Lappas *
E
Evimaria Terzi
DOI:10.1016/j.eswa.2018.10.046delete
delete原文链接
delete分享
delete收藏
查看原文
摘要

摘要

En 中文
In recent years, the proliferation of online resumes and the need to evaluate large populations of candidates for on-site and virtual teams have led to a growing interest in automated team-formation. Given a large pool of candidates, the general problem requires the selection of a team of experts to complete a given task. Surprisingly, while ongoing research has studied numerous variations with different constraints, it has overlooked a factor with a well-documented impact on team cohesion and performance: team faultlines. Addressing this gap is challenging, as the available measures for faultlines in existing teams cannot be efficiently applied to faultline optimization. In this work, we meet this challenge with a new measure that can be efficiently used for both faultline measurement and minimization. We then use the measure to solve the problem of automatically partitioning a large population into low-faultline teams. By introducing faultlines to the team-formation literature, our work creates exciting opportunities for algorithmic work on faultline optimization, as well as on work that combines and studies the connection of faultlines with other influential team characteristics. (C) 2018 Elsevier Ltd. All rights reserved.
Keyword:
Teams
Team faultlines
Team formation
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Expert Systems with Applications 封面图
Expert Systems with Applications
IF:
7.5
论文数:
3.0W
被引数:
10.2W

机构

B
boston university
学者数:
3.8W
论文数: 3.2W
被引数: 67
S
Stevens Institute of Technology
学者数:
2.9K
论文数: 2.9K
被引数: 3.2K
引用论文

引用论文

Faultlines and Subgroups: A Meta-Review and Measurement Guide故障线和子组: 元审查和测量指南
err2014-10-08
err102
PREAI
errMeyer, Bertolt; Glenz, Andreas; Antino, Mirko; Rico, Ramon; Gonzalez-Roma, Vicente
err分享
err收藏
学者 查看更多内容