Membership in social networks and the application in information filtering
This work addresses the user cold-start problem in social network-based recommendation systems, but it is incremental as it builds on existing hybrid methods.
The paper tackled the user cold-start problem in recommendation systems by analyzing user membership in social groups and object selection patterns, resulting in a social diffusion algorithm that improved recommendation performance.
During the past a few years, users' membership in the online system (i.e. the social groups that online users joined) are wildly investigated. Most of these works focus on the detection, formulation and growth of online communities. In this paper, we study users' membership in a coupled system which contains user-group and user-object bipartite networks. By linking users' membership information and their object selection, we find that the users who have collected only a few objects are more likely to be "influenced" by the membership when choosing objects. Moreover, we observe that some users may join many online communities though they collected few objects. Based on these findings, we design a social diffusion recommendation algorithm which can effectively solve the user cold-start problem. Finally, we propose a personalized combination of our method and the hybrid method in [PNAS 107, 4511 (2010)], which leads to a further improvement in the overall recommendation performance.