A Catfish Effect Based Team Recommendation System

Motivated by the growing complex connectedness of modern society, which is found in many incarnations, especially in a team or a particular network, there has been a coming-together of multiple disciplines in an effort to understand how highly connected teams or networks operate and how to improve their work efficiency. As for teams, due to structural diversity, network heterogeneity and scope difference, team building is rather complicated. In order to build a universal recommendation system, we apply biological phenomena, known as the catfish effect, to built a team recommendation system. By extracting the factors which have a direct influence on team performance and modelling RBF neural network, we can predict the relationship between team performance and factors. We design a CTI(Catfish Identification) algorithm based on RBF i.e., RBF -CTI algorithm, which aims to calculate catfish index CI i.e., the intensity of cat-fish effect of each factor. According to CI, we can easily identify the skill of catf ish, namely the most potential impact factor for the team, as well as the indicator of team recommendation system. We apply the idea of the combination of multi-disciplines and give a universal framework of team recommendation system.