Download - Group 2009 Bateman Muller Freyne
Personalized Retrieval in
Social Bookmarking
Scott Bateman, University of Saskatchewan
Michael Muller, Center for Social Software, IBM Research
Jill Freyne, CLARITY, University College Dublin
pivot browsing to refine the list
typed tag filter
finding bookmarks
• filters: pivot browsing or typed tag filter
• 59% of filters lead to refinding a bookmark
– refinding: selecting a bookmark that has been – refinding: selecting a bookmark that has been
previously visited
– more refinding than discovery
bookmark refinding scenario
I need to find that
news article I saw in
Dogear about
collaboration and collaboration and
social networking in
the workplace.
John
John’s target bookmark
-ranked 67,564 of 575,891
John’s target bookmark
-ranked 67,564 of 575,891
John’s target bookmark
-ranked 67,564 of 575,891
John sees and clicks on collaboration
John’s target bookmark:
-ranked 1,254 of 6,931
John’s target bookmark:
-ranked 1,254 of 6,931
John’s target bookmark:
-ranked 1,254 of 6,931
John’s target bookmark:
-ranked 1,254 of 6,931
John sees and clicks on Ryan Jones
John’s target bookmark:
-ranked 5th of 121
-presented, 1 of 2 filters
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• move relevant bookmarks to the top of the list
– reduce user effort
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– 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
wisdom of the crowd
• our initial attempts:
– access histories of all users
– access histories of automatically created
groups – based on cosine sim. of accesses, groups – based on cosine sim. of accesses,
tags, or bookmarks
personalized ordering metric
∑=
ij
iselected
selectedbkmkuserrelevance ),( j
∑=
j
ij
iselected
bkmkuserrelevance ),( j
Personalized
John’s target bookmark
-was ranked 4, was 1,254
-presented after 1 filter
Personalized
results: rank in list
rank
results: times presented
we also found…
• improved result orderings on all filter types
(by tag, user, or user and tag)
• worked well on profiles of other users -> • worked well on profiles of other users ->
suggests refinding?
summary
• Personalized orderings based on access
histories provide a simple metric for re-
ordering bookmarks
– improved position in list– improved position in list
– presented after fewer refinement steps
future work
• is there a way to incorporate group interaction
histories?
