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During the past a few years, users' membership in the online system (i.e. thesocial groups that online users joined) are wildly investigated. Most of theseworks focus on the detection, formulation and growth of online communities. Inthis paper, we study users' membership in a coupled system which containsuser-group and user-object bipartite networks. By linking users' membershipinformation and their object selection, we find that the users who havecollected only a few objects are more likely to be "influenced" by themembership when choosing objects. Moreover, we observe that some users may joinmany online communities though they collected few objects. Based on thesefindings, we design a social diffusion recommendation algorithm which caneffectively solve the user cold-start problem. Finally, we propose apersonalized combination of our method and the hybrid method in [PNAS 107, 4511(2010)], which leads to a further improvement in the overall recommendationperformance.

Membership in social networks and the application in information filtering