USTF: A Unified System of Team Formationстатья из журнала
Аннотация: 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. 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. We also present a case study that shows the performance of different algorithms in a range of real world cases. All our experiments are repeatable and the code and the datasets are publicly accessible for further research [40] .
Год издания: 2016
Авторы: Xinyu Wang, Zhou Zhao, Wilfred Ng
Издательство: IEEE Computer Society
Источник: IEEE Transactions on Big Data
Ключевые слова: Mobile Crowdsensing and Crowdsourcing, Expert finding and Q&A systems, Complex Network Analysis Techniques
Открытый доступ: closed
Том: 2
Выпуск: 1
Страницы: 70–84