1. 2015-10-8Dep. 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

2. Dep. phys., Univ. Fribourg 2 2015-10-8 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

3. Dep. phys., Univ. Fribourg 3 2015-10-8 What is social tagging networks?

4. Dep. phys., Univ. Fribourg 4 2015-10-8 What is social tagging networks?

5. Dep. phys., Univ. Fribourg 5 2015-10-8 From Graph to Hypergraph Bipartite Network [EPL, 90 (2010) 48006] Hyper Network [from wikipedia.org]wikipedia.org Unipartite Network

6. Dep. phys., Univ. Fribourg 6 2015-10-8 Hypergraph structures in STN Zhang and Liu, J. Stat. Mech. (2010) P10005 Hyperedge: basic unit Hyper Network

7. Dep. phys., Univ. Fribourg 7 2015-10-8 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)

8. Dep. phys., Univ. Fribourg 8 2015-10-8 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)

9. Dep. phys., Univ. Fribourg 9 2015-10-8 Dynamics of social tagging networks (2/3) HyperDegree Distribution: Clustering Coefficient:

10. Dep. phys., Univ. Fribourg 10 2015-10-8 Dynamics of social tagging networks (3/3) Average Distance:

11. Dep. phys., Univ. Fribourg 11 2015-10-8 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!

12. Dep. phys., Univ. Fribourg 12 2015-10-8 Method I&II:Tripartite Hybrid(Role 1) Zhang et al. Physica A 389 (2010) 179 Item-user: [Method I, PRE 76 (2007) 046115] Item-tag: [Method II] Linear Hybrid:

13. Dep. phys., Univ. Fribourg 13 2015-10-8 Method III: Tag-driven(Role 2) Zhang et al. Accepted by EPL Method III:

14. Dep. phys., Univ. Fribourg 14 2015-10-8 Algorithm Performance (1/3)

15. Dep. phys., Univ. Fribourg 15 2015-10-8 Algorithm Performance (2/3)

16. Dep. phys., Univ. Fribourg 16 2015-10-8 Algorithm Performance (3/3)

17. Dep. phys., Univ. Fribourg 17 2015-10-8 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

18. 2015-10-8Dep. phys., Univ. Fribourg 18 Thank You! zhangzike@gmail.com Zi-Ke ZHANG