2015-10-8Dep. phys., Univ. Fribourg 1 Social Tagging Networks: Structure, Dynamics & Applications Collaborators: Chuang LIU ( 刘闯 ), Yi-Cheng ZHANG ( 张翼成.

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Dep. phys., Univ. Fribourg 1 Social Tagging Networks: Structure, Dynamics & Applications Collaborators: Chuang LIU ( 刘闯 ), Yi-Cheng ZHANG ( 张翼成 ) Tao ZHOU ( 周涛 ) Zi-Ke ZHANG ( 张子柯 ) Department of Physics, University of Fribourg, Switzerland

Dep. phys., Univ. Fribourg Outline Structure and Dynamics of Social Tagging Networks  What is SNT?  Hypergraph Strutures  Dynamics and emergent properties Applications in Personalized Recommendation (PR)  Why Recommendation?  How Tags benefit PR? Conclusions & Discussion

Dep. phys., Univ. Fribourg What is social tagging networks?

Dep. phys., Univ. Fribourg What is social tagging networks?

Dep. phys., Univ. Fribourg From Graph to Hypergraph Bipartite Network [EPL, 90 (2010) 48006] Hyper Network [from wikipedia.org]wikipedia.org Unipartite Network

Dep. phys., Univ. Fribourg Hypergraph structures in STN Zhang and Liu, J. Stat. Mech. (2010) P10005 Hyperedge: basic unit Hyper Network

Dep. phys., Univ. Fribourg Two roles of social tags Zhang and Liu, J. Stat. Mech. (2010) P10005 Roles  Role1: an accessorial tool helping users organize resources: Fig. (a)  Role2: a bridge that connects users and resources: Fig. (b)

Dep. phys., Univ. Fribourg Dynamics and evolution of social tagging networks (1/3) At each time step, a random user can either: Choose an item(resource), and annotate it with a relevant or random tag with probability p (Role 1) or choose a tag, and find a relevant or random item with probability 1-p (Role 2)

Dep. phys., Univ. Fribourg Dynamics of social tagging networks (2/3) HyperDegree Distribution: Clustering Coefficient:

Dep. phys., Univ. Fribourg Dynamics of social tagging networks (3/3) Average Distance:

Dep. phys., Univ. Fribourg How to be personalized?  Social influence  Content-based recommendation  Network-based recommendation Zhang et al. Physica A 389 (2010) 179 Applications in Personalized Recommendation (PR) Why Recommendation? Information overload!

Dep. phys., Univ. Fribourg Method I&II:Tripartite Hybrid(Role 1) Zhang et al. Physica A 389 (2010) 179 Item-user: [Method I, PRE 76 (2007) ] Item-tag: [Method II] Linear Hybrid:

Dep. phys., Univ. Fribourg Method III: Tag-driven(Role 2) Zhang et al. Accepted by EPL Method III:

Dep. phys., Univ. Fribourg Algorithm Performance (1/3)

Dep. phys., Univ. Fribourg Algorithm Performance (2/3)

Dep. phys., Univ. Fribourg Algorithm Performance (3/3)

Dep. phys., Univ. Fribourg Conclusions and Discussion Conclusions  Structure and Dynamics  The roles of social tags  Application in Personalized recommendation Discussion  Recommendation with full hypergraph structure  Multi-scale recommendations (semantic-based)  Recommendation with community structures

Dep. phys., Univ. Fribourg 18 Thank You! Zi-Ke ZHANG