One exciting development in collaborative search, from Pickens et al., 11, 29 assumes the ranking algorithm should allow users to work at their own pace but be influenced in real time by their teammates’ search activities. The searchers should not step on one another’s proverbial toes; if one person issues a new query, others’ thoughts should not be interrupted.

Pickens et al. 11, 29 addressed this issue by developing an algorithm that combines multiple rounds of queries from multiple searchers during a single search session (see Figure 4), using two criteria for weighting results—both functions of the ranked list of documents returned for a given query. The first variable is “ freshness,” which is higher for documents not yet viewed, while “relevance” is higher for documents closely matching the query. These two factors are combined and continuously updated based on new queries and searcher-specified relevance judgments.

In addition, Pickens et al. 11, 29 assigned different roles to the members of a team. For example, the “ Prospector” is in charge of creating new queries to explore new parts of the information space, and the “Miner” looks at the retrieved results to determine which are relevant. Documents not yet looked at are queued up for the Miner interface according to fresh-ness/relevance weighted scores. The Prospector is shown new query-term suggestions based on how they differ from queries already issued, as well as on the relevance judgments made by the Miner. Each role has its own interface; a third view is used to show continually updating information about the queries that have been issued, the documents that have been marked as relevant, and the system-suggested query terms based on the actions of the users.

Another approach to supporting
real-time search collaboration, de-
scribed by Jetter et al., 16 used a large
work surface and input devices com-
bining physical manual manipula-
tion with virtual markings. The in-
terface was evaluated on a complex
collaborative search task, that of a
group of people selecting a product,
where each member of the group
has different preferences that act
as constraints (such as when choos-
ing a hotel, one needs a heated pool,
another wants one that received at
least four stars of recommendation,
and a third wants the price below a
certain amount). Jetter et al.’s solu-
tion used a combination of faceted
navigation37 and filter-flow visualiza-
tion, 38 showing how many constraints
are met by a set of items, given cer-
tain constraints. The visualization
was displayed on a shared horizontal
workspace, where the controls were
manipulated through physical selec-
tors (see Figure 5). Collaboration was
facilitated by allowing each user to
work privately on a corner of the work-
space, then let the results from each
piece of the query flow into the rest
of the group’s query specification. A
careful usability study by Jetter et al.
found this approach produced results

figure 4. collaborating on a video-search task using technology developed by Pickens et al. 29; each user views a different unique interface, as well as a shared view. the results of one person’s work change the rank ordering of what is seen by the other person.

figure 5. a collaborative search-formulation tool making use of a large table display, physical input devices, and visualization; from Jetter et al. 16

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expression 1: e ANd ((A oR B oR C) ANd d)

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NoveMBeR 2011 | vol. 54 | No. 11 | commuNicatioNs of the acm 63