A study of team formation problem : from single to parallel perspectives

Abstract

Given a complex task requiring a specific set of skills, it is useful to form a team of experts who work in a collaborative manner against time and many different costs. Team formation in the setting of social networks is a fundamental problem in many database or web applications. For example, we need to find a suitable team to answer community-based questions and collaborative software development. It is also well-recognized that forming a suitable team in social networks is non-trivial, since the problem involves many cost factors such as communication overhead and load balancing. Although many algorithms have been yet proposed for resolving this problem, most of them are based on very different criteria, and performance metrics, and their performance has not been empirically compared. In this paper, we first compare and contrast all the state-of-the-art team formation algorithms. Next, we propose a benchmark that enables fair comparison amongst these algorithms. The algorithms are compared using datasets which are suitable for evaluating the performance in single machine perspective. We then implement these algorithms using a common platform called the Unified System for Team Formation (USTF) and evaluate their performance using several real datasets. The USTF is a system built on large scale real database and deals with team formation task in distributed manner. We also present a case study that shows the performance of different algorithms in a range of real world cases. We discuss the dynamic case of the team formation problem as well, which could be a future direction of the study of the team formation problem.

Date of Award	2016
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology