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Figure 2.3: The Smart Splitting technique ( Morris et al. , 2008 ) adapts personalized search algorithms
to divide a search result set among a group of synchronous searchers according to the similarity of the
vocabulary in each result to the vocabulary in documents stored on each group member's computer. In
this example where two users perform a search on a health topic, one with a technical medical background
and one with a lay background, the results highlighted in red would be sent to the searcher with more
medical knowledge, whereas the articles written for a lay audience (highlighted in purple) would be sent
to her partner.
have sent results containing specialized medical terminology to her doctor's computer, while sending
articles written for a more general audience to Martha.
The strength of group members' relationships can impact the success of their collaboration,
or the level to which they will engage in equal versus unequal roles in such an effort. Morris et al.
( 2010c ) found that people were more likely to answer information requests from members of their
social network whom they felt they had very close relationships with. Techniques such as the one
proposed by Gilbert and Karahalios ( 2009 ) might be used to predict the strength of collaborators'
offline social relationship based on data from social networking tools.
The longevity of a group can also impact the group members' ability to work together.
Longevity refers to whether the relationships among group members are short-term (such as based
on interest in a specific, shared task) or more permanent (such as based on similarities in personal
traits, including sharing a particular hobby or demographic traits). Teevan et al. ( 2009a ) considered
how group profiles might be used rank search results so as to identify the most relevant results to
the group. They found short term groups formed around the information seeking task to particu-
larly benefit from group-profile-based result ranking techniques. The different group relationships
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