Group 2009 Bateman Muller Freyne

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

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Group 2009 Bateman Muller Freyne

  1. 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
  2. 2. pivot browsing to refine the list
  3. 3. typed tag filter
  4. 4. 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
  5. 5. bookmark refinding scenario I need to find that news article I saw in Dogear about collaboration and social networking in the workplace. John
  6. 6. John’s target bookmark -ranked 67,564 of 575,891
  7. 7. John’s target bookmark -ranked 67,564 of 575,891
  8. 8. John’s target bookmark -ranked 67,564 of 575,891 John sees and clicks on collaboration
  9. 9. John’s target bookmark: -ranked 1,254 of 6,931
  10. 10. John’s target bookmark: -ranked 1,254 of 6,931
  11. 11. John’s target bookmark: -ranked 1,254 of 6,931
  12. 12. John’s target bookmark: -ranked 1,254 of 6,931 John sees and clicks on Ryan Jones
  13. 13. John’s target bookmark: -ranked 5th of 121 -presented, 1 of 2 filters
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. personalized ordering metric selectedij relevance(useri , bkmk j ) = ∑ selected ij j
  18. 18. John’s target bookmark -was ranked 4, was 1,254 -presented after 1 filter Personalized Personalized
  19. 19. results: rank in list rank
  20. 20. results: times presented
  21. 21. 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?
  22. 22. summary • Personalized orderings based on access histories provide a simple metric for re- ordering bookmarks – improved position in list – presented after fewer refinement steps
  23. 23. future work • is there a way to incorporate group interaction histories?
  24. 24. thank you scott.bateman@usask.ca

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