Social bookmarking and tagging networks

                                         Marek Lipczak
                          ...
Collaborative Tagging Systems

   Store and share various types of Internet resources
   Content organized by tags
   S...
Delicious – a social bookmarking tool




                                        3
copied from delicious.com
Collaborative Tagging Systems




                                4
Collaborative Tagging Systems




                                5
Collaborative Tagging Systems




                                6
Collaborative Tagging Systems




                                7
Collaborative Tagging Systems




                                8
Collaborative Tagging Systems




                                9
Collaborative Tagging Systems




                                10
“Social Web”

  Social links              Content




                 Metadata



                                      11
“Social Web”

  Social links              Content




                 Metadata



                                      12
“Social Web”

  Social links              Content




                 Metadata



                                      13
The Character of Social Web

   Heterogeneous network of:
       Social relations
       Shared resources (content)
   ...
Similarity between friends

   Experimental observation from Delicious data


              Average similarity over      ...
Personal tags

   Users are inspired by others when they choose bookmarks
       ...but they tag them with personal tags...
On-line social networks




                               17
copied from [Heer05]
Degree distribution in
     on-line social networks




                               number of
                         ...
More power-law distributions

                       Frequency of occurrence in posts
           Tags                     ...
Tag based user profile




                         20
User similarity based on tag profiles




   “Rich” profile for two users
       Are these users similar?

             ...
Social network of... tags




   Relations between tags
   Edge weight represents normalized co-occurrence of terms

   ...
A practical problem:
     Tag recommendation
   Task
       Recommend useful tags while a user is posting a resource


...
A practical problem:
     Tag recommendation
   Task
       Recommend useful tags while a user is posting a resource


...
Why not collaborative filtering?

   Collaborative filtering – standard recommendation approach
       If similar users ...
Hybrid Tag Recommendation System




                                   25
Hybrid Tag Recommendation System

  Social network
      of tags



                       Personal character
            ...
Conclusions


   Social web is
       heterogeneous
       sometimes not social
       sparse


   Hybrid algorithms ...
Social bookmarking and tagging networks

                                         Marek Lipczak
                          ...
References
   [Heer05] Jeffrey Heer and Danah Boyd. Vizster: Visualizing online social networks. In INFOVIS ’05:
    Proc...
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Social Bookmarking and Tagging Networks (by Marek Lipczak & Evangelos Milios)

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Information & Social Networks Symposium 2010
Dalhouise University
SocialMediaLab.ca

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

  1. 1. Social bookmarking and tagging networks Marek Lipczak Evangelos Milios Faculty of Computer Science, Dalhousie University (Canada)
  2. 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. 3. Delicious – a social bookmarking tool 3 copied from delicious.com
  4. 4. Collaborative Tagging Systems 4
  5. 5. Collaborative Tagging Systems 5
  6. 6. Collaborative Tagging Systems 6
  7. 7. Collaborative Tagging Systems 7
  8. 8. Collaborative Tagging Systems 8
  9. 9. Collaborative Tagging Systems 9
  10. 10. Collaborative Tagging Systems 10
  11. 11. “Social Web” Social links Content Metadata 11
  12. 12. “Social Web” Social links Content Metadata 12
  13. 13. “Social Web” Social links Content Metadata 13
  14. 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. 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. 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. 17. On-line social networks 17 copied from [Heer05]
  18. 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. 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. 20. Tag based user profile 20
  21. 21. User similarity based on tag profiles  “Rich” profile for two users  Are these users similar? 21
  22. 22. Social network of... tags  Relations between tags  Edge weight represents normalized co-occurrence of terms 22
  23. 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. 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. 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. 26. Hybrid Tag Recommendation System 25
  27. 27. Hybrid Tag Recommendation System Social network of tags Personal character of tagging 26
  28. 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. 29. Social bookmarking and tagging networks Marek Lipczak Evangelos Milios Faculty of Computer Science, Dalhousie University (Canada)
  30. 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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