A new team recommendation model with applications in social network

Nowadays, some users with similar interests or same tasks may form a cooperative team in the social network, where resources and information can be wildly shared. With more and more users joining in the social network, the number of teams is growing up at the same time. How to recommend a team in which a user is interested has become a new and important topic in the social network. The purpose of this paper is to propose a novel model for team recommendation to help users find interesting teams in social network ,which is using collaborative filtering algorithm and trust propagation theory. Formally, we construct team recommendation lists from three steps. Firstly, it computes the team similarity behavior between the user and his/her friends and get the first recommendation team list from it. Secondly, it gets the team recommendation list from the user´s latent friends list. Finally, the third list is got from the hot teams by applied weighting factor theory. We obtain the data from a social network called schol@t (www.scholat.com) to make experimental analysis, which demonstrate that the team recommendation model is effective.