1. Milan Vojnovi ć MSRC, Systems and Networking Tagging done by YOU

2. Thanks TagBooster project Dinan Gunawardena James Cruise (U Cambridge) Peter Marbach (U Toronto) Fabian Suchanek (MPI) Product groups O14 Sharepoint Communities Tagspace Officelabs MSR Tagging Summit TagBooster User Study Nick Duffield John Mulgrew Andy Slowey This talk Abi Alex Chris Peter Stephen

3. Social tagging in web2.0

4. Why tag?

5. Tagging: what and why Tag suggestions Conclusion

6. In this talk, well find relations among the following f g x y

7. Discover, filter, share

8. Faceted browsing BBC news Michael Palin BBC radio BBC shop bbc BBC news Michael Palin BBC radio BBC shop bbc palin

9. Tagging vs. traditional classification Traditional classification – Pre-defined vocabulary – Structured – Done by authors/librarians – Non trivial task Social tagging – Use any words – No structure – Done by anyone – Easy

10. Systems with controlled vocabulary

11. Social tagging challenges Vocabulary evolution – Filtering tags, tag suggestions, tagging metaphors – Uncontrolled vocabulary: scalable, mitigate vocabulary problem, but tag noise User interface design – Tagcloud, tag clustering Cold start – Lack of prior knowledge about tags for an object – Participation incentive Scale – More tagging events, easier filtering Making use of tags – Related tags for navigation, expertise tracking, tag meta-data for search, scoped rankings of items, faceted browsing

12. TagBooster User Study Sept-Oct 07 4000+ participants Tagging web pages Questionnaire

13. Tagging done by YOU

14. Analogous to voting music soul london music soul jazz london black artist british singer Feedback !

15. Positive Negative Why suggest tags? Hiding users true preference over tags I picked a suggested tag that now I cant remember I tend to overuse same tags all over again exploit vs. explore Less effort (cognitive, typing) Encourage users to use tags (cold start) Conformance in vocabulary

16. Top Popular: classical suggestion method # sel.tag 174music 110radio 96internet radio 77online radio 49last.fm 40online music 34fm 33streaming music 31streaming 28last fm 22web radio 19scrobbling 18lastfm 12listen 12new music 10mp3 10stream 9streaming radio Suggested tags: radio, music, online radio, internet radio

17. Users generation of tags singer music jazz Black british rehab London soul Set of all tags artist singer music jazz british singer soul Suggested tags artist

18. Simple user model music jazz Black british rehab London soul music jazz british singer soul Suggested tags Set of all tags singer artist singer 1-p p imitationnon imitation riri i riri i

19. Users tag selection affected by tag suggestions Conditional on that the tag was suggested Unconditional Frequency of tag selection 0 0.1 0.2 0.3 0.4 0.5 tag: apollo

20. The imitation rate 0.32 0.310.4 0.34 portion of tag selections not in S; suggestions not made portion of tag selections not in the suggestion set S Boes estimate:

21. Sel.Tag 174music 110radio 96internet radio 77online radio 49last.fm 40online music 34fm 33streaming music 31streaming 28last fm 22web radio 19scrobbling 18lastfm 12listen 12new music 10mp3 10stream 9streaming radio Move-to-Set: simple randomised rule Suggested tags: last.fm, music, online radio, web radio

22. ? Sufficient for Under the user model, for any imitation probability p < 1, the long run frequency of tag selections induces the true popularity ranking Correctness of popularity order

23. Simple update rule Converges to sampling the suggestion set proportional to the product of true rank scores Suggested tags: last.fm, music, online radio, web radio Suggested tags: last.fm, music, radio, web radio radio Suggested tags: last.fm, music, online radio, web radio Same as show most recent item for suggestion set size 1

24. Analogous to exclusion process j i rjrj rjrj

25. Frequency Move-to-Set RankTag 174music 110radio 96internet radio 77online radio 49last.fm 40online music 34fm 33streaming music 31streaming 28last fm 22web radio 19scrobbling 18lastfm 12listen 12new music 10mp3 10stream 9streaming radio Suggested tags: radio, music, online radio, internet radio radio last.fm Rank(radio) remains unchanged (radio suggested) Rank(last.fm) ++ (last.fm NOT suggested)

26. Only sufficiently popular tags eventually suggested frequency of suggesting tag i competing set suggestion set size harmonic mean of r 1,..., r |C| Tag i in the competing set iff:

27. Suggestion methods in action Tag rank i Frequency of tag suggestion TOP FMTS MTS NONE Tag rank i Norm. frequency of tag selection

28. Suggestion methods in action (contd) TOP FMTS MTS NONE

29. How did users appreciate the suggested tags? Web pageMethodThey were confusing They were OK, but not very relevant They were generally helpful engadgetTOP35.00%15.00%50.00% FMTS25.93% 48.15% MTS22.22%25.93%51.85% lastfmTOP22.22%55.56%22.22% FMTS25.00%32.14%42.86% MTS27.59%24.14%48.28% startupTOP39.13%21.74%39.13% FMTS50.00%29.17%20.83% MTS30.30%24.24%45.46% mitTOP21.74%30.44%47.83% FMTS23.08% 53.85% MTS24.24%18.18%57.68%

30. Why did I select these tags? Tags: gadgets technology engadget blog 2 1 I thought these are keywords that I would likely use later to find this item I thought these are categories that best describe the object else

31. Why did I select these tags? (contd) YOU find, search, describe, categorise, identify, remember, organise, classify wikipedia tag (meta data) definition describing the item keyword-based classification search

32. Why did I select these tags (contd)? Semantic analysis of tags, search and content keywords – May 2007 popular Web searches + delicious tags Tags similar to categories Small overlap with search keywords

33. Summary Social tagging poses interesting research challenges – Space for innovation A mix of control theory, user behaviour, information retrieval, interface design Aim at best design of tagging systems to support particular users tasks

34. Sample of research challenges User model? Rate of convergence Asymptotically accurate algorithms Select from the list only (e.g. remote controller/mobile device) What does it mean a tag is relevant? Make suggestions to improve users task (e.g. search, faceted browsing)? Beyond popularity ranking: Ranking across multiple lists Faces project ongoing work Tag to attract

35. Familiarity with tagging Email domainUsers microsoft.com44% hotmail.com15% gmail.com11% other30% Tagging frequencyUsers daily15% weekly25% monthly17% less frequently40% still used infrequently by many