Slides from a talk at GROUP 2009 conference by Scott Bateman, Michael Muller, & Jill Freyne

1. Personalized Retrieval in
Social Bookmarking
Scott Bateman, University of Saskatchewan
Michael Muller, Center for Social Software, IBM Research
Jill Freyne, CLARITY, University College Dublin

5. pivot browsing to refine the list

6. typed tag filter

7. finding bookmarks
• filters: pivot browsing or typed tag filter
• 59% of filters lead to refinding a bookmark
– refinding: selecting a bookmark that has been
previously visited
– more refinding than discovery

8. bookmark refinding scenario
I need to find that
news article I saw in
Dogear about
collaboration and
social networking in
the workplace.
John

9. John’s target bookmark
-ranked 67,564 of 575,891

10. John’s target bookmark
-ranked 67,564 of 575,891

11. John’s target bookmark
-ranked 67,564 of 575,891
John sees and clicks on collaboration

12. John’s target bookmark:
-ranked 1,254 of 6,931

13. John’s target bookmark:
-ranked 1,254 of 6,931

14. John’s target bookmark:
-ranked 1,254 of 6,931

15. John’s target bookmark:
-ranked 1,254 of 6,931
John sees and clicks on Ryan Jones

16. John’s target bookmark:
-ranked 5th of 121
-presented, 1 of 2 filters

17. new ordering options needed
• list orderings don’t necessarily reflect what is
relevant to a user’s purpose
• move relevant bookmarks to the top of the list
– reduce user effort

18. evaluation of new metrics
• using system logs, identified all query sessions
in a 6 month period where users filtered lists
and selected a bookmark (a target)
– used all session whether refinding or not
• recreated query sessions comparing original
date-based ordering versus new ordering
– positions in result lists for target (rank)
– number of results lists where target was visible

19. wisdom of the crowd
• our initial attempts:
– access histories of all users
– access histories of automatically created
groups – based on cosine sim. of accesses,
tags, or bookmarks

20. personalized ordering metric
selectedij
relevance(useri , bkmk j ) =
∑ selected ij
j

21. John’s target bookmark
-was ranked 4, was 1,254
-presented after 1 filter
Personalized
Personalized

22. results: rank in list
rank

23. results: times presented

24. we also found…
• improved result orderings on all filter types
(by tag, user, or user and tag)
• worked well on profiles of other users ->
suggests refinding?

25. summary
• Personalized orderings based on access
histories provide a simple metric for re-
ordering bookmarks
– improved position in list
– presented after fewer refinement steps

26. future work
• is there a way to incorporate group interaction
histories?

27. thank you
scott.bateman@usask.ca