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?

thank you

scott.bateman@usask.ca