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of one topic to another topic for the same expert, or
2) apply a learned model of one domain expert to an-
other expert. With the help of exploration graphs we
believe it is possible to develop more elaborate algo-
rithms that are able to automate complex search tasks.
The result of this learning goes beyond “learning to
rank”-approaches described earlier, because it has to
take into account the utility of information. That is,
an automated search agent trained with data about
search behavior of human domain experts should sug-
gest only highly relevant and novel information.
7 CONCLUSION
This paper presents a hybrid log-based and observa-
tional approach for modeling search behavior. We
formulate the problem of interpreting search behav-
ior based on browser interaction logs and introduce
the idea of an exploration graph to model transitional
and semantic relationships during search. These in-
terpretations can help to keep valuable information on
how users explore an information space, if reasonable
assumptions about the search behavior can be made.
With the help of a user study we outline how seman-
tic interpretations compare to interpretations without
assumptions. Interpreting a user’s interactions during
search in an exploration graph may be key to various
new investigations, e. g. how users interact in groups
to fulfill a certain research task. Finding meaningful
interpretations becomes a new challenge in the analy-
sis of interaction logs. We also show how leveraging
the data inherent in exploration graphs can be used in
recommender systems to make search tasks in busi-
ness settings more efficient. Since the quality of such
recommendations depends heavily on the quality of
the interpreted exploration graphs it is important to
put a lot of effort into creating meaningful interpre-
tations of search behavior in the first place. We be-
lieve that leveraging data about exploration graphs is
a promising approach to tackle new research direc-
tions and produce highly innovative support systems,
especially for professional searchers.
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