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Membership in social networks and the application in information filtering

Zeng, Wei ; Zeng, An ; Shang, Ming-Sheng ; Zhang, Yi-Cheng

In: The European Physical Journal B, 2013, vol. 86, no. 9, p. 1-7

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    Title
    • Membership in social networks and the application in information filtering
    Author
    • Zeng, Wei. Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, P.R. China
    • Zeng, An. Department of Physics, University of Fribourg, Chemin du Musée 3, 1700, Fribourg, Switzerland
    • Shang, Ming-Sheng. Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, P.R. China
    • Zhang, Yi-Cheng. Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, P.R. China
    Document Type
    • Postprint
    OAI-PMH Identifier
    • oai:doc.rero.ch:312647
    Summary
    During the past few years, users' membership in the online system (i.e. the social groups that online users joined) were widely 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 [T. Zhou, Z. Kuscsik, J.G. Liu, M. Medo, J.R. Wakeling, Y.C. Zhang, Proc. Natl. Acad. Sci. 107, 4511 (2010)], which leads to a further improvement in the overall recommendation performance