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Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
Social Bookmarking and Tagging Networks (by  Marek Lipczak & Evangelos Milios)
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Social Bookmarking and Tagging Networks (by Marek Lipczak & Evangelos Milios)

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Information & Social Networks Symposium 2010 …

Information & Social Networks Symposium 2010
Dalhouise University
SocialMediaLab.ca

Published in: Technology, Education
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  • 1. Social bookmarking and tagging networks Marek Lipczak Evangelos Milios Faculty of Computer Science, Dalhousie University (Canada)
  • 2. Collaborative Tagging Systems  Store and share various types of Internet resources  Content organized by tags  Social network structure based on “fan” links 2
  • 3. Delicious – a social bookmarking tool 3 copied from delicious.com
  • 4. Collaborative Tagging Systems 4
  • 5. Collaborative Tagging Systems 5
  • 6. Collaborative Tagging Systems 6
  • 7. Collaborative Tagging Systems 7
  • 8. Collaborative Tagging Systems 8
  • 9. Collaborative Tagging Systems 9
  • 10. Collaborative Tagging Systems 10
  • 11. “Social Web” Social links Content Metadata 11
  • 12. “Social Web” Social links Content Metadata 12
  • 13. “Social Web” Social links Content Metadata 13
  • 14. The Character of Social Web  Heterogeneous network of:  Social relations  Shared resources (content)  Metadata information  Two types of links between people:  Explicit links (social network)  Implicit links (based on similarity due to sharing of resources or tags) 14
  • 15. Similarity between friends  Experimental observation from Delicious data Average similarity over Friends Non-friends Cosine similarity (resources) 0.011 0.004 Cosine similarity (tags) 0.081 0.085  Combination of explicit and implicit links  Friends share resources (bookmarks) but not tags  Low overlap between user profiles 15
  • 16. Personal tags  Users are inspired by others when they choose bookmarks  ...but they tag them with personal tags  Not everything in Social Web is social 16
  • 17. On-line social networks 17 copied from [Heer05]
  • 18. Degree distribution in on-line social networks number of friends rank friends (log) number of rank (log) Power-law: few users with large number of friends and “long tail” of users with small number of friends 18 copied from [Heer05]
  • 19. More power-law distributions Frequency of occurrence in posts Tags Resources Users  Power-law is observed everywhere  Combination of information from different sources increases the sparsity problem 19
  • 20. Tag based user profile 20
  • 21. User similarity based on tag profiles  “Rich” profile for two users  Are these users similar? 21
  • 22. Social network of... tags  Relations between tags  Edge weight represents normalized co-occurrence of terms 22
  • 23. A practical problem: Tag recommendation  Task  Recommend useful tags while a user is posting a resource  Why?  Well defined, practical task  Allows to understand the social behaviour of users  tag recommendation prediction modelling 23
  • 24. A practical problem: Tag recommendation  Task  Recommend useful tags while a user is posting a resource  Why?  Well defined, practical task  Allows to understand the social behaviour of users  tag recommendation prediction modelling  Collaborative filtering will not work 24
  • 25. Why not collaborative filtering?  Collaborative filtering – standard recommendation approach  If similar users find it useful, you should find it useful too  Collaborative filtering is not applicable to the long tail of tags and resources 25 copied from amazon.com
  • 26. Hybrid Tag Recommendation System 25
  • 27. Hybrid Tag Recommendation System Social network of tags Personal character of tagging 26
  • 28. Conclusions  Social web is  heterogeneous  sometimes not social  sparse  Hybrid algorithms seem to be the most suitable solution  Our hybrid approach for tag recommendation got first place in two tasks of ECML/PKDD Discovery Challenge 2009 27
  • 29. Social bookmarking and tagging networks Marek Lipczak Evangelos Milios Faculty of Computer Science, Dalhousie University (Canada)
  • 30. References  [Heer05] Jeffrey Heer and Danah Boyd. Vizster: Visualizing online social networks. In INFOVIS ’05: Proceedings of the 2005 IEEE Symposium on Information Visualization, page 5, Washington, DC, USA, 2005. IEEE Computer Society. 30

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