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Social Tagging Networks: Structure, Dynamics & Applications

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

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Social Tagging Networks: Structure, Dynamics & Applications

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

  2. 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

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

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

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

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

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

  8. 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

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

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

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

  12. Method I&II:Tripartite Hybrid(Role 1) • Item-user: [Method I, PRE 76 (2007) 046115] • Item-tag: [Method II] • Linear Hybrid: Zhang et al. Physica A 389 (2010) 179 Dep. phys., Univ. Fribourg

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

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

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

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

  17. 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 zhangzike@gmail.com Dep. phys., Univ. Fribourg

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